WorldWideScience

Sample records for clinical parameters predicting

  1. Do Urinary Cystine Parameters Predict Clinical Stone Activity?

    Science.gov (United States)

    Friedlander, Justin I; Antonelli, Jodi A; Canvasser, Noah E; Morgan, Monica S C; Mollengarden, Daniel; Best, Sara; Pearle, Margaret S

    2018-02-01

    An accurate urinary predictor of stone recurrence would be clinically advantageous for patients with cystinuria. A proprietary assay (Litholink, Chicago, Illinois) measures cystine capacity as a potentially more reliable estimate of stone forming propensity. The recommended capacity level to prevent stone formation, which is greater than 150 mg/l, has not been directly correlated with clinical stone activity. We investigated the relationship between urinary cystine parameters and clinical stone activity. We prospectively followed 48 patients with cystinuria using 24-hour urine collections and serial imaging, and recorded stone activity. We compared cystine urinary parameters at times of stone activity with those obtained during periods of stone quiescence. We then performed correlation and ROC analysis to evaluate the performance of cystine parameters to predict stone activity. During a median followup of 70.6 months (range 2.2 to 274.6) 85 stone events occurred which could be linked to a recent urine collection. Cystine capacity was significantly greater for quiescent urine than for stone event urine (mean ± SD 48 ± 107 vs -38 ± 163 mg/l, p stone activity (r = -0.29, p r = -0.88, p r = -0.87, p stone quiescence. Decreasing the cutoff to 90 mg/l or greater improved sensitivity to 25.2% while maintaining specificity at 90.9%. Our results suggest that the target for capacity should be lower than previously advised. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  2. Prediction of polycystic ovarian syndrome based on ultrasound findings and clinical parameters.

    Science.gov (United States)

    Moschos, Elysia; Twickler, Diane M

    2015-03-01

    To determine the accuracy of sonographic-diagnosed polycystic ovaries and clinical parameters in predicting polycystic ovarian syndrome. Medical records and ultrasounds of 151 women with sonographically diagnosed polycystic ovaries were reviewed. Sonographic criteria for polycystic ovaries were based on 2003 Rotterdam European Society of Human Reproduction and Embryology/American Society for Reproductive Medicine guidelines: at least one ovary with 12 or more follicles measuring 2-9 mm and/or increased ovarian volume >10 cm(3) . Clinical variables of age, gravidity, ethnicity, body mass index, and sonographic indication were collected. One hundred thirty-five patients had final outcomes (presence/absence of polycystic ovarian syndrome). Polycystic ovarian syndrome was diagnosed if a patient had at least one other of the following two criteria: oligo/chronic anovulation and/or clinical/biochemical hyperandrogenism. A logistic regression model was constructed using stepwise selection to identify variables significantly associated with polycystic ovarian syndrome (p polycystic ovaries and 115 (89.8%) had polycystic ovarian syndrome (p = .009). Lower gravidity, abnormal bleeding, and body mass index >33 were significant in predicting polycystic ovarian syndrome (receiver operating characteristics curve, c = 0.86). Pain decreased the likelihood of polycystic ovarian syndrome. Polycystic ovaries on ultrasound were sensitive in predicting polycystic ovarian syndrome. Ultrasound, combined with clinical parameters, can be used to generate a predictive index for polycystic ovarian syndrome. © 2014 Wiley Periodicals, Inc.

  3. Utility of Clinical Parameters and Multiparametric MRI as Predictive Factors for Differentiating Uterine Sarcoma From Atypical Leiomyoma.

    Science.gov (United States)

    Bi, Qiu; Xiao, Zhibo; Lv, Fajin; Liu, Yao; Zou, Chunxia; Shen, Yiqing

    2018-02-05

    The objective of this study was to find clinical parameters and qualitative and quantitative magnetic resonance imaging (MRI) features for differentiating uterine sarcoma from atypical leiomyoma (ALM) preoperatively and to calculate predictive values for uterine sarcoma. Data from 60 patients with uterine sarcoma and 88 patients with ALM confirmed by surgery and pathology were collected. Clinical parameters, qualitative MRI features, diffusion-weighted imaging with apparent diffusion coefficient values, and quantitative parameters of dynamic contrast-enhanced MRI of these two tumor types were compared. Predictive values for uterine sarcoma were calculated using multivariable logistic regression. Patient clinical manifestations, tumor locations, margins, T2-weighted imaging signals, mean apparent diffusion coefficient values, minimum apparent diffusion coefficient values, and time-signal intensity curves of solid tumor components were obvious significant parameters for distinguishing between uterine sarcoma and ALM (all P Abnormal vaginal bleeding, tumors located mainly in the uterine cavity, ill-defined tumor margins, and mean apparent diffusion coefficient values of uterine sarcoma. When the overall scores of these four predictors were greater than or equal to 7 points, the sensitivity, the specificity, the accuracy, and the positive and negative predictive values were 88.9%, 99.9%, 95.7%, 97.0%, and 95.1%, respectively. The use of clinical parameters and multiparametric MRI as predictive factors was beneficial for diagnosing uterine sarcoma preoperatively. These findings could be helpful for guiding treatment decisions. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  4. Lactate Parameters Predict Clinical Outcomes in Patients with Nonvariceal Upper Gastrointestinal Bleeding.

    Science.gov (United States)

    Lee, Seung Hoon; Min, Yang Won; Bae, Joohwan; Lee, Hyuk; Min, Byung Hoon; Lee, Jun Haeng; Rhee, Poong Lyul; Kim, Jae J

    2017-11-01

    The predictive role of lactate in patients with nonvariceal upper gastrointestinal bleeding (NVUGIB) has been suggested. This study evaluated several lactate parameters in terms of predicting outcomes of bleeding patients and sought to establish a new scoring model by combining lactate parameters and the AIMS65 score. A total of 114 patients with NVUGIB who underwent serum lactate level testing at least twice and endoscopic hemostasis within 24 hours after admission were retrospectively analyzed. The associations between five lactate parameters and clinical outcomes were evaluated and the predictive power of lactate parameter combined AIMS65s (L-AIMS65s) and AIMS56 scoring was compared. The most common cause of bleeding was gastric ulcer (48.2%). Lactate clearance rate (LCR) was associated with 30-day rebleeding (odds ratio [OR], 0.931; 95% confidence interval [CI], 0.872-0.994; P = 0.033). Initial lactate (OR, 1.313; 95% CI, 1.050-1.643; P = 0.017), maximal lactate (OR, 1.277; 95% CI, 1.037-1.573; P = 0.021), and average lactate (OR, 1.535; 95% CI, 1.137-2.072; P = 0.005) levels were associated with 30-day mortality. Initial lactate (OR, 1.213; 95% CI, 1.027-1.432; P = 0.023), maximal lactate (OR, 1.271; 95% CI, 1.074-1.504; P = 0.005), and average lactate (OR, 1.501; 95% CI, 1.150-1.959; P = 0.003) levels were associated with admission over 7 days. Although L-AIMS65s showed the highest area under the curve for prediction of each outcome, differences between L-AIMS65s and AIMS65 did not reach statistical significance. In conclusion, lactate parameters have a prognostic role in patients with NVUGIB. However, they do not increase the predictive power of AIMS65 when combined. © 2017 The Korean Academy of Medical Sciences.

  5. Clinical validation of the LKB model and parameter sets for predicting radiation-induced pneumonitis from breast cancer radiotherapy

    International Nuclear Information System (INIS)

    Tsougos, Ioannis; Mavroidis, Panayiotis; Theodorou, Kyriaki; Rajala, J; Pitkaenen, M A; Holli, K; Ojala, A T; Hyoedynmaa, S; Jaervenpaeae, Ritva; Lind, Bengt K; Kappas, Constantin

    2006-01-01

    The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast radiotherapy where the observed incidence is fairly low. From our previous study based on 150 breast cancer patients, where the fits of dose-volume models to clinical data were estimated (Tsougos et al 2005 Evaluation of dose-response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy Phys. Med. Biol. 50 3535-54), one could get the impression that the relative seriality is significantly better than the LKB NTCP model. However, the estimation of the different NTCP models was based on their goodness-of-fit on clinical data, using various sets of published parameters from other groups, and this fact may provisionally justify the results. Hence, we sought to investigate further the LKB model, by applying different published parameter sets for the very same group of patients, in order to be able to compare the results. It was shown that, depending on the parameter set applied, the LKB model is able to predict the incidence of radiation pneumonitis with acceptable accuracy, especially when implemented on a sub-group of patients (120) receiving D-bar-bar vertical bar EUD higher than 8 Gy. In conclusion, the goodness-of-fit of a certain radiobiological model on a given clinical case is closely related to the selection of the proper scoring criteria and parameter set as well as to the compatibility of the clinical case from which the data were derived. (letter to the editor)

  6. A new model using routinely available clinical parameters to predict significant liver fibrosis in chronic hepatitis B.

    Directory of Open Access Journals (Sweden)

    Wai-Kay Seto

    Full Text Available OBJECTIVE: We developed a predictive model for significant fibrosis in chronic hepatitis B (CHB based on routinely available clinical parameters. METHODS: 237 treatment-naïve CHB patients [58.4% hepatitis B e antigen (HBeAg-positive] who had undergone liver biopsy were randomly divided into two cohorts: training group (n = 108 and validation group (n = 129. Liver histology was assessed for fibrosis. All common demographics, viral serology, viral load and liver biochemistry were analyzed. RESULTS: Based on 12 available clinical parameters (age, sex, HBeAg status, HBV DNA, platelet, albumin, bilirubin, ALT, AST, ALP, GGT and AFP, a model to predict significant liver fibrosis (Ishak fibrosis score ≥3 was derived using the five best parameters (age, ALP, AST, AFP and platelet. Using the formula log(index+1 = 0.025+0.0031(age+0.1483 log(ALP+0.004 log(AST+0.0908 log(AFP+1-0.028 log(platelet, the PAPAS (Platelet/Age/Phosphatase/AFP/AST index predicts significant fibrosis with an area under the receiving operating characteristics (AUROC curve of 0.776 [0.797 for patients with ALT <2×upper limit of normal (ULN] The negative predictive value to exclude significant fibrosis was 88.4%. This predictive power is superior to other non-invasive models using common parameters, including the AST/platelet/GGT/AFP (APGA index, AST/platelet ratio index (APRI, and the FIB-4 index (AUROC of 0.757, 0.708 and 0.723 respectively. Using the PAPAS index, 67.5% of liver biopsies for patients being considered for treatment with ALT <2×ULN could be avoided. CONCLUSION: The PAPAS index can predict and exclude significant fibrosis, and may reduce the need for liver biopsy in CHB patients.

  7. Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

    Science.gov (United States)

    Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

    2013-01-01

    Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

  8. Multidetector-CT angiography in pulmonary embolism - can image parameters predict clinical outcome?

    Energy Technology Data Exchange (ETDEWEB)

    Heyer, Christoph M.; Lemburg, Stefan P.; Nicolas, Volkmar; Roggenland, Daniela [Berufsgenossenschaftliches Universitaetsklinikum Bergmannsheil GmbH, Ruhr-University of Bochum, Institute of Diagnostic Radiology, Interventional Radiology and Nuclear Medicine, Bochum (Germany); Knoop, Heiko [Berufsgenossenschaftliches Universitaetsklinikum Bergmannsheil GmbH, Medical Clinic III - Pneumology, Allergology, and Sleep Medicine, Bochum (Germany); Holland-Letz, Tim [Ruhr-University of Bochum, Department of Medical Informatics, Biometry and Epidemiology, Bochum (Germany)

    2011-09-15

    To assess if pulmonary CT angiography (CTA) can predict outcome in patients with pulmonary embolism (PE). Retrospective analysis of CTA studies of patients with PE and documentation of pulmonary artery (PA)/aorta ratio, right ventricular (RV)/left ventricular (LV) ratio, superior vena cava (SVC) diameter, pulmonary obstruction index (POI), ventricular septal bowing (VSB), venous contrast reflux (VCR), pulmonary infarction and pleural effusion. Furthermore, duration of total hospital stay, necessity for/duration of ICU therapy, necessity for mechanical ventilation and mortality were recorded. Comparison was performed by logistic/linear regression analysis with significance at 5%. 152 patients were investigated. Mean duration of hospital stay was 21 {+-} 24 days. 66 patients were admitted to the ICU; 20 received mechanical ventilation. Mean duration of ICU therapy was 3 {+-} 8 days. Mortality rate was 8%. Significant positive associations of POI, VCR and pulmonary infarction with necessity for ICU therapy were shown. VCR was significantly associated with necessity for mechanical ventilation and duration of ICU treatment. Pleural effusions were significantly associated with duration of total hospital stay whereas the RV/LV ratio correlated with mortality. Selected CTA findings showed significant associations with the clinical course of PE and may thus be used as predictive parameters. (orig.)

  9. Preoperative Biometric Parameters Predict the Vault after ICL Implantation: A Retrospective Clinical Study.

    Science.gov (United States)

    Zheng, Qian-Yin; Xu, Wen; Liang, Guan-Lu; Wu, Jing; Shi, Jun-Ting

    2016-01-01

    To investigate the correlation between the preoperative biometric parameters of the anterior segment and the vault after implantable Collamer lens (ICL) implantation via this retrospective study. Retrospective clinical study. A total of 78 eyes from 41 patients who underwent ICL implantation surgery were included in this study. Preoperative biometric parameters, including white-to-white (WTW) diameter, central corneal thickness, keratometer, pupil diameter, anterior chamber depth, sulcus-to-sulcus diameter, anterior chamber area (ACA) and central curvature radius of the anterior surface of the lens (Lenscur), were measured. Lenscur and ACA were measured with Rhinoceros 5.0 software on the image scanned with ultrasound biomicroscopy (UBM). The vault was assessed by UBM 3 months after surgery. Multiple stepwise regression analysis was employed to identify the variables that were correlated with the vault. The results showed that the vault was correlated with 3 variables: ACA (22.4 ± 4.25 mm2), WTW (11.36 ± 0.29 mm) and Lenscur (9.15 ± 1.21 mm). The regressive equation was: vault (mm) = 1.785 + 0.017 × ACA + 0.051 × Lenscur - 0.203 × WTW. Biometric parameters of the anterior segment (ACA, WTW and Lenscur) can predict the vault after ICL implantation using a new regression equation. © 2016 The Author(s) Published by S. Karger AG, Basel.

  10. Predicting pneumococcal community-acquired pneumonia in the emergency department: evaluation of clinical parameters.

    Science.gov (United States)

    Huijts, S M; Boersma, W G; Grobbee, D E; Gruber, W C; Jansen, K U; Kluytmans, J A J W; Kuipers, B A F; Palmen, F; Pride, M W; Webber, C; Bonten, M J M

    2014-12-01

    The aim of this study was to quantify the value of clinical predictors available in the emergency department (ED) in predicting Streptococcus pneumoniae as the cause of community-acquired pneumonia (CAP). A prospective, observational, cohort study of patients with CAP presenting in the ED was performed. Pneumococcal aetiology of CAP was based on either bacteraemia, or S. pneumoniae being cultured from sputum, or urinary immunochromatographic assay positivity, or positivity of a novel serotype-specific urinary antigen detection test. Multivariate logistic regression was used to identify independent predictors and various cut-off values of probability scores were used to evaluate the usefulness of the model. Three hundred and twenty-eight (31.0%) of 1057 patients with CAP had pneumococcal CAP. Nine independent predictors for pneumococcal pneumonia were identified, but the clinical utility of this prediction model was disappointing, because of low positive predictive values or a small yield. Clinical criteria have insufficient diagnostic capacity to predict pneumococcal CAP. Rapid antigen detection tests are needed to diagnose S. pneumoniae at the time of hospital admission. © 2014 The Authors Clinical Microbiology and Infection © 2014 European Society of Clinical Microbiology and Infectious Diseases.

  11. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    International Nuclear Information System (INIS)

    Hao, Ke; Zhong, Hua; Greenawalt, Danielle; Ferguson, Mark D; Ng, Irene O; Sham, Pak C; Poon, Ronnie T; Molony, Cliona; Schadt, Eric E; Dai, Hongyue; Luk, John M; Lamb, John; Zhang, Chunsheng; Xie, Tao; Wang, Kai; Zhang, Bin; Chudin, Eugene; Lee, Nikki P; Mao, Mao

    2011-01-01

    The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome

  12. Prediction of therapeutic response in steroid-treated pulmonary sarcoidosis. Evaluation of clinical parameters, bronchoalveolar lavage, gallium-67 lung scanning, and serum angiotensin-converting enzyme levels

    International Nuclear Information System (INIS)

    Hollinger, W.M.; Staton, G.W. Jr.; Fajman, W.A.; Gilman, M.J.; Pine, J.R.; Check, I.J.

    1985-01-01

    To find a pretreatment predictor of steroid responsiveness in pulmonary sarcoidosis the authors studied 21 patients before and after steroid treatment by clinical evaluation, pulmonary function tests, bronchoalveolar lavage (BAL), gallium-67 lung scan, and serum angiotensin-converting enzyme (SACE) level. Although clinical score, forced vital capacity (FVC), BAL percent lymphocytes (% lymphs), quantitated gallium-67 lung uptake, and SACE levels all improved with therapy, only the pretreatment BAL % lymphs correlated with the improvement in FVC (r = 0.47, p less than 0.05). Pretreatment BAL % lymphs of greater than or equal to 35% predicted improvement in FVC of 10/11 patients, whereas among 10 patients with BAL % lymphs less than 35%, 5 patients improved and 5 deteriorated. Clinical score, pulmonary function parameters, quantitated gallium-67 lung uptake, and SACE level used alone, in combination with BAL % lymphs or in combination with each other, did not improve this predictive value. The authors conclude that steroid therapy improves a number of clinical and laboratory parameters in sarcoidosis, but only the pretreatment BAL % lymphs are useful in predicting therapeutic responsiveness

  13. Clinical-Radiological Parameters Improve the Prediction of the Thrombolysis Time Window by Both MRI Signal Intensities and DWI-FLAIR Mismatch.

    Science.gov (United States)

    Madai, Vince Istvan; Wood, Carla N; Galinovic, Ivana; Grittner, Ulrike; Piper, Sophie K; Revankar, Gajanan S; Martin, Steve Z; Zaro-Weber, Olivier; Moeller-Hartmann, Walter; von Samson-Himmelstjerna, Federico C; Heiss, Wolf-Dieter; Ebinger, Martin; Fiebach, Jochen B; Sobesky, Jan

    2016-01-01

    With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch. In a retrospective study, patients from 2 centers with proven stroke with onset value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly. In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and DWI-SD (0.91). The best prediction results based on the AUC were found for the final stratified and adjusted models of DWI-SD (0.94) and FLAIR-mean (0.96) and a multivariable DWI-FLAIR model (0.95). The adjusted visual DWI-FLAIR mismatch did not perform in a significantly worse manner (0.89). ADC-rSIs showed fair performance in all models. Quantitative DWI and FLAIR MRI biomarkers as well as the visual DWI-FLAIR mismatch provide excellent prediction of eligibility for thrombolysis in acute stroke, when easily obtainable clinical-radiological parameters are included in the prediction

  14. Clinical parameters predictive of malignancy of thyroid follicular neoplasms

    International Nuclear Information System (INIS)

    Davis, N.L.; Gordon, M.; Germann, E.; Robins, R.E.; McGregor, G.I.

    1991-01-01

    Needle aspiration biopsy is commonly employed in the evaluation of thyroid nodules. Unfortunately, the cytologic finding of a 'follicular neoplasm' does not distinguish between a thyroid adenoma and a follicular cancer. The purpose of this study was to identify clinical parameters that characterize patients with an increased risk of having a thyroid follicular cancer who preoperatively have a 'follicular neoplasm' identified by needle aspiration biopsy. A total of 395 patients initially treated at Vancouver General Hospital and the British Columbia Cancer Agency between the years of 1965 and 1985 were identified and their data were entered into a computer database. Patients with thyroid adenomas were compared to patients with follicular cancer using the chi-square test and Student's t-test. Statistically significant parameters that distinguished patients at risk of having a thyroid cancer (p less than 0.05) included age greater than 50 years, nodule size greater than 3 cm, and a history of neck irradiation. Sex, family history of goiter or neoplasm, alcohol and tobacco use, and use of exogenous estrogen were not significant parameters. Patients can be identified preoperatively to be at an increased risk of having a follicular cancer and accordingly appropriate surgical resection can be planned

  15. Non-invasive clinical parameters for the prediction of urodynamic bladder outlet obstruction: analysis using causal Bayesian networks.

    Directory of Open Access Journals (Sweden)

    Myong Kim

    Full Text Available To identify non-invasive clinical parameters to predict urodynamic bladder outlet obstruction (BOO in patients with benign prostatic hyperplasia (BPH using causal Bayesian networks (CBN.From October 2004 to August 2013, 1,381 eligible BPH patients with complete data were selected for analysis. The following clinical variables were considered: age, total prostate volume (TPV, transition zone volume (TZV, prostate specific antigen (PSA, maximum flow rate (Qmax, and post-void residual volume (PVR on uroflowmetry, and International Prostate Symptom Score (IPSS. Among these variables, the independent predictors of BOO were selected using the CBN model. The predictive performance of the CBN model using the selected variables was verified through a logistic regression (LR model with the same dataset.Mean age, TPV, and IPSS were 6.2 (±7.3, SD years, 48.5 (±25.9 ml, and 17.9 (±7.9, respectively. The mean BOO index was 35.1 (±25.2 and 477 patients (34.5% had urodynamic BOO (BOO index ≥40. By using the CBN model, we identified TPV, Qmax, and PVR as independent predictors of BOO. With these three variables, the BOO prediction accuracy was 73.5%. The LR model showed a similar accuracy (77.0%. However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020.Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

  16. Prediction of DVH parameter changes due to setup errors for breast cancer treatment based on 2D portal dosimetry

    International Nuclear Information System (INIS)

    Nijsten, S. M. J. J. G.; Elmpt, W. J. C. van; Mijnheer, B. J.; Minken, A. W. H.; Persoon, L. C. G. G.; Lambin, P.; Dekker, A. L. A. J.

    2009-01-01

    Electronic portal imaging devices (EPIDs) are increasingly used for portal dosimetry applications. In our department, EPIDs are clinically used for two-dimensional (2D) transit dosimetry. Predicted and measured portal dose images are compared to detect dose delivery errors caused for instance by setup errors or organ motion. The aim of this work is to develop a model to predict dose-volume histogram (DVH) changes due to setup errors during breast cancer treatment using 2D transit dosimetry. First, correlations between DVH parameter changes and 2D gamma parameters are investigated for different simulated setup errors, which are described by a binomial logistic regression model. The model calculates the probability that a DVH parameter changes more than a specific tolerance level and uses several gamma evaluation parameters for the planning target volume (PTV) projection in the EPID plane as input. Second, the predictive model is applied to clinically measured portal images. Predicted DVH parameter changes are compared to calculated DVH parameter changes using the measured setup error resulting from a dosimetric registration procedure. Statistical accuracy is investigated by using receiver operating characteristic (ROC) curves and values for the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Changes in the mean PTV dose larger than 5%, and changes in V 90 and V 95 larger than 10% are accurately predicted based on a set of 2D gamma parameters. Most pronounced changes in the three DVH parameters are found for setup errors in the lateral-medial direction. AUC, sensitivity, specificity, and negative predictive values were between 85% and 100% while the positive predictive values were lower but still higher than 54%. Clinical predictive value is decreased due to the occurrence of patient rotations or breast deformations during treatment, but the overall reliability of the predictive model remains high. Based on our

  17. Factors predictive of abnormal semen parameters in male partners of couples attending the infertility clinic of a tertiary hospital in southwestern Nigeria

    Directory of Open Access Journals (Sweden)

    Peter Aduloju

    2016-12-01

    Full Text Available Background: Infertility is a common gynaecological problem and male factor contributes significantly in the aetiology of infertility. Semen analysis has remained a useful investigation in the search for male factor infertility.Aim: This study assessed the pattern of semen parameters and predictive factors associated with abnormal parameters in male partners of infertile couples attending a Nigerian tertiary hospital.Methods: A descriptive study of infertile couples presenting at the clinic between January 2012and December 2015 was done at Ekiti State University Teaching Hospital, Ado-Ekiti.  Seminal fluid from the male partners were analysed in the laboratory using the WHO 2010 criteria for human semen characteristics. Data was analysed using SPSS 17 and logistic regression analysis was used to determine the predictive factors associated with abnormal semen parameters.Results: A total of 443 men participated in the study and 38.2% had abnormal sperm parameters. Oligozoospermia (34.8% and asthenozoospermia (26.9% are leading single factor abnormality found, astheno-oligozoospermia occurred in 14.2% and oligo-astheno-teratozoospermia in 3.6% of cases. The prevalence of azoospermia was 3.4%. Smoking habit, past infection with mumps and previous groin surgery significantly predicted abnormal semen parameters with p values of 0.025, 0.040 and 0.017 respectively. Positive cultures were recorded in 36.2% of cases and staph aureus was the commonest organism.Conclusion: Male factor abnormalities remain significant contributors to infertility and men should be encouraged through advocacy to participate in investigation of infertility to reduce the level of stigmatization and ostracizing of women with infertility especially in sub-Saharan Africa.

  18. Value of quantitative MRI parameters in predicting and evaluating clinical outcome in conservatively treated patients with chronic midportion Achilles tendinopathy: A prospective study.

    Science.gov (United States)

    Tsehaie, J; Poot, D H J; Oei, E H G; Verhaar, J A N; de Vos, R J

    2017-07-01

    To evaluate whether baseline MRI parameters provide prognostic value for clinical outcome, and to study correlation between MRI parameters and clinical outcome. Observational prospective cohort study. Patients with chronic midportion Achilles tendinopathy were included and performed a 16-week eccentric calf-muscle exercise program. Outcome measurements were the validated Victorian Institute of Sports Assessment-Achilles (VISA-A) questionnaire and MRI parameters at baseline and after 24 weeks. The following MRI parameters were assessed: tendon volume (Volume), tendon maximum cross-sectional area (CSA), tendon maximum anterior-posterior diameter (AP), and signal intensity (SI). Intra-class correlation coefficients (ICCs) and minimum detectable changes (MDCs) for each parameter were established in a reliability analysis. Twenty-five patients were included and complete follow-up was achieved in 20 patients. The average VISA-A scores increased significantly with 12.3 points (27.6%). The reliability was fair-good for all MRI-parameters with ICCs>0.50. Average tendon volume and CSA decreased significantly with 0.28cm 3 (5.2%) and 4.52mm 2 (4.6%) respectively. Other MRI parameters did not change significantly. None of the baseline MRI parameters were univariately associated with VISA-A change after 24 weeks. MRI SI increase over 24 weeks was positively correlated with the VISA-A score improvement (B=0.7, R 2 =0.490, p=0.02). Tendon volume and CSA decreased significantly after 24 weeks of conservative treatment. As these differences were within the MDC limits, they could be a result of a measurement error. Furthermore, MRI parameters at baseline did not predict the change in symptoms, and therefore have no added value in providing a prognosis in daily clinical practice. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  19. Echocardiography and risk prediction in advanced heart failure: incremental value over clinical markers.

    Science.gov (United States)

    Agha, Syed A; Kalogeropoulos, Andreas P; Shih, Jeffrey; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Anarado, Perry; Mangalat, Deepa; Hussain, Imad; Book, Wendy; Laskar, Sonjoy; Smith, Andrew L; Martin, Randolph; Butler, Javed

    2009-09-01

    Incremental value of echocardiography over clinical parameters for outcome prediction in advanced heart failure (HF) is not well established. We evaluated 223 patients with advanced HF receiving optimal therapy (91.9% angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 92.8% beta-blockers, 71.8% biventricular pacemaker, and/or defibrillator use). The Seattle Heart Failure Model (SHFM) was used as the reference clinical risk prediction scheme. The incremental value of echocardiographic parameters for event prediction (death or urgent heart transplantation) was measured by the improvement in fit and discrimination achieved by addition of standard echocardiographic parameters to the SHFM. After a median follow-up of 2.4 years, there were 38 (17.0%) events (35 deaths; 3 urgent transplants). The SHFM had likelihood ratio (LR) chi(2) 32.0 and C statistic 0.756 for event prediction. Left ventricular end-systolic volume, stroke volume, and severe tricuspid regurgitation were independent echocardiographic predictors of events. The addition of these parameters to SHFM improved LR chi(2) to 72.0 and C statistic to 0.866 (P advanced HF.

  20. Clinical Significance of Hemostatic Parameters in the Prediction for Type 2 Diabetes Mellitus and Diabetic Nephropathy

    Directory of Open Access Journals (Sweden)

    Lianlian Pan

    2018-01-01

    Full Text Available It would be important to predict type 2 diabetes mellitus (T2DM and diabetic nephropathy (DN. This study was aimed at evaluating the predicting significance of hemostatic parameters for T2DM and DN. Plasma coagulation and hematologic parameters before treatment were measured in 297 T2DM patients. The risk factors and their predicting power were evaluated. T2DM patients without complications exhibited significantly different activated partial thromboplastin time (aPTT, platelet (PLT, and D-dimer (D-D levels compared with controls (P<0.01. Fibrinogen (FIB, PLT, and D-D increased in DN patients compared with those without complications (P<0.001. Both aPTT and PLT were the independent risk factors for T2DM (OR: 1.320 and 1.211, P<0.01, resp., and FIB and PLT were the independent risk factors for DN (OR: 1.611 and 1.194, P<0.01, resp.. The area under ROC curve (AUC of aPTT and PLT was 0.592 and 0.647, respectively, with low sensitivity in predicting T2DM. AUC of FIB was 0.874 with high sensitivity (85% and specificity (76% for DN, and that of PLT was 0.564, with sensitivity (60% and specificity (89% based on the cutoff values of 3.15 g/L and 245 × 109/L, respectively. This study suggests that hemostatic parameters have a low predicting value for T2DM, whereas fibrinogen is a powerful predictor for DN.

  1. Factors predictive of abnormal semen parameters in male partners of couples attending the infertility clinic of a tertiary hospital in south-western Nigeria

    Directory of Open Access Journals (Sweden)

    Peter Olusola Aduloju

    2016-11-01

    Full Text Available Background: Infertility is a common gynaecological problem and male factor contributes significantly in the aetiology of infertility. Semen analysis has remained a useful investigation in the search for male factor infertility. Aim: This study assessed the pattern of semen parameters and predictive factors associated with abnormal parameters in male partners of infertile couples attending a Nigerian tertiary hospital. Methods: A descriptive study of infertile couples presenting at the clinic between January 2012and December 2015 was done at Ekiti State University Teaching Hospital, Ado-Ekiti. Seminal fluid from the male partners were analysed in the laboratory using the WHO 2010 criteria for human semen characteristics. Data was analysed using SPSS 17 and logistic regression analysis was used to determine the predictive factors associated with abnormal semen parameters. Results: A total of 443 men participated in the study and 38.2% had abnormal sperm parameters. Oligozoospermia (34.8% and asthenozoospermia (26.9% are leading single factor abnormality found, astheno-oligozoospermia occurred in 14.2% and oligo-astheno-teratozoospermia in 3.6% of cases. The prevalence of azoospermia was 3.4%. Smoking habit, past infection with mumps and previous groin surgery significantly predicted abnormal semen parameters with p values of 0.025, 0.040 and 0.017 respectively. Positive cultures were recorded in 36.2% of cases and staph aureus was the commonest organism. Conclusion: Male factor abnormalities remain significant contributors to infertility and men should be encouraged through advocacy to participate in investigation of infertility to reduce the level of stigmatization and ostracizing of women with infertility especially in sub-Saharan Africa.

  2. Systematic review of dose-volume parameters in the prediction of esophagitis in thoracic radiotherapy

    International Nuclear Information System (INIS)

    Rose, Jim; Rodrigues, George; Yaremko, Brian; Lock, Michael; D'Souza, David

    2009-01-01

    Purpose: With dose escalation and increasing use of concurrent chemoradiotherapy, radiation esophagitis (RE) remains a common treatment-limiting acute side effect in the treatment of thoracic malignancies. The advent of 3DCT planning has enabled investigators to study esophageal dose-volume histogram (DVH) parameters as predictors of RE. The purpose of this study was to assess published dosimetric parameters and toxicity data systematically in order to define reproducible predictors of RE, both for potential clinical use, and to provide recommendations for future research in the field. Materials and methods: We performed a systematic literature review of published studies addressing RE in the treatment of lung cancer and thymoma. Our search strategy included a variety of electronic medical databases, textbooks and bibliographies. Both prospective and retrospective clinical studies were included. Information relating to the relationship among measured dosimetric parameters, patient demographics, tumor characteristics, chemotherapy and RE was extracted and analyzed. Results: Eighteen published studies were suitable for analysis. Eleven of these assessed acute RE, while the remainder assessed both acute and chronic RE together. Heterogeneity of esophageal contouring practices, individual differences in information reporting and variability of RE outcome definitions were assessed. Well-described clinical and logistic modeling directly related V 35Gy , V 60Gy and SA 55Gy to clinically significant RE. Conclusions: Several reproducible dosimetric parameters exist in the literature, and these may be potentially relevant in the prediction of RE in the radiotherapy of thoracic malignancies. Further clarification of the predictive relationship between such standardized dosimetric parameters and observed RE outcomes is essential to develop efficient radiation treatment planning in locally advanced NSCLC in the modern concurrent chemotherapy and image-guided IMRT era.

  3. Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements.

    Science.gov (United States)

    Marschollek, M; Nemitz, G; Gietzelt, M; Wolf, K H; Meyer Zu Schwabedissen, H; Haux, R

    2009-08-01

    Falls are among the predominant causes for morbidity and mortality in elderly persons and occur most often in geriatric clinics. Despite several studies that have identified parameters associated with elderly patients' fall risk, prediction models -- e.g., based on geriatric assessment data -- are currently not used on a regular basis. Furthermore, technical aids to objectively assess mobility-associated parameters are currently not used. To assess group differences in clinical as well as common geriatric assessment data and sensory gait measurements between fallers and non-fallers in a geriatric sample, and to derive and compare two prediction models based on assessment data alone (model #1) and added sensory measurement data (model #2). For a sample of n=110 geriatric in-patients (81 women, 29 men) the following fall risk-associated assessments were performed: Timed 'Up & Go' (TUG) test, STRATIFY score and Barthel index. During the TUG test the subjects wore a triaxial accelerometer, and sensory gait parameters were extracted from the data recorded. Group differences between fallers (n=26) and non-fallers (n=84) were compared using Student's t-test. Two classification tree prediction models were computed and compared. Significant differences between the two groups were found for the following parameters: time to complete the TUG test, transfer item (Barthel), recent falls (STRATIFY), pelvic sway while walking and step length. Prediction model #1 (using common assessment data only) showed a sensitivity of 38.5% and a specificity of 97.6%, prediction model #2 (assessment data plus sensory gait parameters) performed with 57.7% and 100%, respectively. Significant differences between fallers and non-fallers among geriatric in-patients can be detected for several assessment subscores as well as parameters recorded by simple accelerometric measurements during a common mobility test. Existing geriatric assessment data may be used for falls prediction on a regular basis

  4. Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters

    International Nuclear Information System (INIS)

    Hao, Ke; Sham, Pak C; Poon, Ronnie TP; Luk, John M; Lee, Nikki PY; Mao, Mao; Zhang, Chunsheng; Ferguson, Mark D; Lamb, John; Dai, Hongyue; Ng, Irene O

    2009-01-01

    Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new

  5. Predicting hospital-acquired infections by scoring system with simple parameters.

    Directory of Open Access Journals (Sweden)

    Ying-Jui Chang

    Full Text Available BACKGROUND: Hospital-acquired infections (HAI are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR and validated by Artificial Neural Networks (ANN simultaneously. METHODOLOGY/PRINCIPAL FINDINGS: A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507 to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447. The scoring system also performed extremely well in the internal (AUC: 0.965 and external (AUC: 0.871 validations. CONCLUSIONS: We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction

  6. Clinical prediction models for bronchopulmonary dysplasia: a systematic review and external validation study

    NARCIS (Netherlands)

    Onland, Wes; Debray, Thomas P.; Laughon, Matthew M.; Miedema, Martijn; Cools, Filip; Askie, Lisa M.; Asselin, Jeanette M.; Calvert, Sandra A.; Courtney, Sherry E.; Dani, Carlo; Durand, David J.; Marlow, Neil; Peacock, Janet L.; Pillow, J. Jane; Soll, Roger F.; Thome, Ulrich H.; Truffert, Patrick; Schreiber, Michael D.; van Reempts, Patrick; Vendettuoli, Valentina; Vento, Giovanni; van Kaam, Anton H.; Moons, Karel G.; Offringa, Martin

    2013-01-01

    Bronchopulmonary dysplasia (BPD) is a common complication of preterm birth. Very different models using clinical parameters at an early postnatal age to predict BPD have been developed with little extensive quantitative validation. The objective of this study is to review and validate clinical

  7. Predictive Clinical Parameters and Glycemic Efficacy of Vildagliptin Treatment in Korean Subjects with Type 2 Diabetes

    Directory of Open Access Journals (Sweden)

    Jin-Sun Chang

    2013-02-01

    Full Text Available BackgroundThe aims of this study are to investigate the glycemic efficacy and predictive parameters of vildagliptin therapy in Korean subjects with type 2 diabetes.MethodsIn this retrospective study, we retrieved data for subjects who were on twice-daily 50 mg vildagliptin for at least 6 months, and classified the subjects into five treatment groups. In three of the groups, we added vildagliptin to their existing medication regimen; in the other two groups, we replaced one of their existing medications with vildagliptin. We then analyzed the changes in glucose parameters and clinical characteristics.ResultsUltimately, 327 subjects were analyzed in this study. Vildagliptin significantly improved hemoglobin A1c (HbA1c levels over 6 months. The changes in HbA1c levels (ΔHbA1c at month 6 were -2.24% (P=0.000, -0.77% (P=0.000, -0.80% (P=0.001, -0.61% (P=0.000, and -0.34% (P=0.025 for groups 1, 2, 3, 4, and 5, respectively, with significance. We also found significant decrements in fasting plasma glucose levels in groups 1, 2, 3, and 4 (P<0.05. Of the variables, initial HbA1c levels (P=0.032 and history of sulfonylurea use (P=0.026 were independently associated with responsiveness to vildagliptin treatment.ConclusionVildagliptin was effective when it was used in subjects with poor glycemic control. It controlled fasting plasma glucose levels as well as sulfonylurea treatment in Korean type 2 diabetic subjects.

  8. Predicting hepatic steatosis and liver fat content in obese children based on biochemical parameters and anthropometry.

    Science.gov (United States)

    Zhang, H-X; Xu, X-Q; Fu, J-F; Lai, C; Chen, X-F

    2015-04-01

    Predictors of quantitative evaluation of hepatic steatosis and liver fat content (LFC) using clinical and laboratory variables available in the general practice in the obese children are poorly identified. To build predictive models of hepatic steatosis and LFC in obese children based on biochemical parameters and anthropometry. Hepatic steatosis and LFC were determined using proton magnetic resonance spectroscopy in 171 obese children aged 5.5-18.0 years. Routine clinical and laboratory parameters were also measured in all subjects. Group analysis, univariable correlation analysis, and multivariate logistic and linear regression analysis were used to develop a liver fat score to identify hepatic steatosis and a liver fat equation to predict LFC in each subject. The predictive model of hepatic steatosis in our participants based on waist circumference and alanine aminotransferase had an area under the receiver operating characteristic curve of 0.959 (95% confidence interval: 0.927-0.990). The optimal cut-off value of 0.525 for determining hepatic steatosis had sensitivity of 93% and specificity of 90%. A liver fat equation was also developed based on the same parameters of hepatic steatosis liver fat score, which would be used to calculate the LFC in each individual. The liver fat score and liver fat equation, consisting of routinely available variables, may help paediatricians to accurately determine hepatic steatosis and LFC in clinical practice, but external validation is needed before it can be employed for this purpose. © 2014 The Authors. Pediatric Obesity © 2014 World Obesity.

  9. Fluid Volume Expansion and Depletion in Hemodialysis Patients Lack Association with Clinical Parameters

    Directory of Open Access Journals (Sweden)

    Sylvia Kalainy

    2015-12-01

    Full Text Available Background: Achievement of normal volume status is crucial in hemodialysis (HD, since both volume expansion and volume contraction have been associated with adverse outcome and events. Objectives: The objectives of this study are to assess the prevalence of fluid volume expansion and depletion and to identify the best clinical parameter or set of parameters that can predict fluid volume expansion in HD patients. Design: This study is cross-sectional. Setting: This study was conducted in three hemodialysis units. Patients: In this study, there are 194 HD patients. Methods: Volume status was assessed by multifrequency bio-impedance spectroscopy (The Body Composition Monitor, Fresenius prior to the mid-week HD session. Results: Of all patients, 48 % ( n = 94 were volume-expanded and 9 % of patients were volume-depleted ( n = 17. Interdialytic weight gain was not different between hypovolemic, normovolemic, and hypervolemic patients. Fifty percent of the volume-expanded patients were hypertensive. Paradoxical hypertension was very common (31 % of all patients; its incidence was not different between patient groups. Intradialytic hypotension was relatively common and was more frequent among hypovolemic patients. Multivariate regression analysis identified only four predictors for volume expansion (edema, lower BMI, higher SBP, and smoking. None of these parameters displayed both a good sensitivity and specificity. Limitations: The volume assessment was performed once. Conclusions: The study indicates that volume expansion is highly prevalent in HD population and could not be identified using clinical parameters alone. No clinical parameters were identified that could reliably predict volume status. This study shows that bio-impedance can assist to determine volume status. Volume status, in turn, is not related to intradialytic weight gain and is unable to explain the high incidence of paradoxical hypertension.

  10. Chairside CAD/CAM materials. Part 3: Cyclic fatigue parameters and lifetime predictions.

    Science.gov (United States)

    Wendler, Michael; Belli, Renan; Valladares, Diana; Petschelt, Anselm; Lohbauer, Ulrich

    2018-06-01

    Chemical and mechanical degradation play a key role on the lifetime of dental restorative materials. Therefore, prediction of their long-term performance in the oral environment should base on fatigue, rather than inert strength data, as commonly observed in the dental material's field. The objective of the present study was to provide mechanistic fatigue parameters of current dental CAD/CAM materials under cyclic biaxial flexure and assess their suitability in predicting clinical fracture behaviors. Eight CAD/CAM materials, including polycrystalline zirconia (IPS e.max ZirCAD), reinforced glasses (Vitablocs Mark II, IPS Empress CAD), glass-ceramics (IPS e.max CAD, Suprinity PC, Celtra Duo), as well as hybrid materials (Enamic, Lava Ultimate) were evaluated. Rectangular plates (12×12×1.2mm 3 ) with highly polished surfaces were prepared and tested in biaxial cyclic fatigue in water until fracture using the Ball-on-Three-Balls (B3B) test. Cyclic fatigue parameters n and A* were obtained from the lifetime data for each material and further used to build SPT diagrams. The latter were used to compare in-vitro with in-vivo fracture distributions for IPS e.max CAD and IPS Empress CAD. Susceptibility to subcritical crack growth under cyclic loading was observed for all materials, being more severe (n≤20) in lithium-based glass-ceramics and Vitablocs Mark II. Strength degradations of 40% up to 60% were predicted after only 1 year of service. Threshold stress intensity factors (K th ) representing the onset of subcritical crack growth (SCG), were estimated to lie in the range of 0.37-0.44 of K Ic for the lithium-based glass-ceramics and Vitablocs Mark II and between 0.51-0.59 of K Ic for the other materials. Failure distributions associated with mechanistic estimations of strength degradation in-vitro showed to be useful in interpreting failure behavior in-vivo. The parameter K th stood out as a better predictor of clinical performance in detriment to the SCG n

  11. Correlation of salivary immunoglobulin A against lipopolysaccharide of Porphyromonas gingivalis with clinical periodontal parameters

    Directory of Open Access Journals (Sweden)

    Pushpa S Pudakalkatti

    2015-01-01

    Full Text Available Background: A major challenge in clinical periodontics is to find a reliable molecular marker of periodontal tissue destruction. Aim: The aim of the present study was to assess, whether any correlation exists between salivary immunoglobulin A (IgA level against lipopolysaccharide of Porphyromonas gingivalis and clinical periodontal parameters (probing depth and clinical attachment loss. Materials and Methods: Totally, 30 patients with chronic periodontitis were included for the study based on clinical examination. Unstimulated saliva was collected from each study subject. Probing depth and clinical attachment loss were recorded in all selected subjects using University of North Carolina-15 periodontal probe. Extraction and purification of lipopolysaccharide were done from the standard strain of P. gingivalis (ATCC 33277. Enzyme linked immunosorbent assay (ELISA was used to detect the level of IgA antibodies against lipopolysaccharide of P. gingivalis in the saliva of each subject by coating wells of ELISA kit with extracted lipopolysaccharide antigen. Statistical Analysis: The correlation between salivary IgA and clinical periodontal parameters was checked using Karl Pearson′s correlation coefficient method and regression analysis. Results: The significant correlation was observed between salivary IgA level and clinical periodontal parameters in chronic periodontitis patients. Conclusion: A significant strong correlation was observed between salivary IgA against lipopolysaccharide of P. gingivalis and clinical periodontal parameters which suggest that salivary IgA level against lipopolysaccharide of P. gingivalis can be used to predict the severity of periodontal destruction in chronic periodontitis patients.

  12. Advancement Flap for Treatment of Complex Cryptoglandular Anal Fistula: Prediction of Therapy Success or Failure Using Anamnestic and Clinical Parameters.

    Science.gov (United States)

    Boenicke, Lars; Karsten, Eduard; Zirngibl, Hubert; Ambe, Peter

    2017-09-01

    Multiple new procedures for treatment of complex anal fistula have been described in the past decades, but an ideal single technique has yet not been identified. Factors that predict the outcome are required to identify the best procedure for each individual patient. The aim of this study was to find those predictors for advancement flap at midterm follow-up. From 2012 to 2015 in a tertiary university clinic, all patients who underwent advancement flap for treatment of complex cryptoglandular fistula were prospectively enrolled. Pre- and postoperatively standardized anamnestic and clinical examinations were performed. Predictive factors for therapy failure were identified using univariate and multivariate analysis. Out of 65 patients, 61 (93%) completed all examinations and were included in the study. Therapy failure after a mean follow-up period of 25 months occurred in total n = 11 patients (18%). There was no significant disturbance of continence among the entire study cohort as shown by the incontinence score (preop 0.34 ± 0.91 pts., postop 0.37 ± 0.97 pts.; p = 0.59). Univariate analysis for risk factors for therapy failure revealed age (p = 0.004), history of surgical abscess drainage (p = 0.04), BMI (p = 0.002), suprasphincteric fistula (p = 0.019) and horseshoe abscess (p = 0.036) as independent parameters for therapy failure. During multivariate analysis, only history of surgical abscess drainage (OR = 8.09, p = 0.048, 95% CI 0.98-64.96), suprasphincteric fistula (OR = 6.83, p = 0.032, 95% CI 1.17-6.83) and BMI (OR = 1.23, p = 0.017, 95% CI 1.03-1.46) were independent parameters for therapy failure. Advancement flap for treatment of complex fistula is effective and has low risk of disturbed continence. BMI, suprasphincteric fistula and history of surgical abscess drainage are predictors for therapy failure.

  13. Which nerve conduction parameters can predict spontaneous electromyographic activity in carpal tunnel syndrome?

    Science.gov (United States)

    Chang, Chia-Wei; Lee, Wei-Ju; Liao, Yi-Chu; Chang, Ming-Hong

    2013-11-01

    We investigate electrodiagnostic markers to determine which parameters are the best predictors of spontaneous electromyographic (EMG) activity in carpal tunnel syndrome (CTS). We enrolled 229 patients with clinically proven and nerve conduction study (NCS)-proven CTS, as well as 100 normal control subjects. All subjects were evaluated using electrodiagnostic techniques, including median distal sensory latencies (DSLs), sensory nerve action potentials (SNAPs), distal motor latencies (DMLs), compound muscle action potentials (CMAPs), forearm median nerve conduction velocities (FMCVs) and wrist-palm motor conduction velocities (W-P MCVs). All CTS patients underwent EMG examination of the abductor pollicis brevis (APB) muscle, and the presence or absence of spontaneous EMG activities was recorded. Normal limits were determined by calculating the means ± 2 standard deviations from the control data. Associations between parameters from the NCS and EMG findings were investigated. In patients with clinically diagnosed CTS, abnormal median CMAP amplitudes were the best predictors of spontaneous activity during EMG examination (p95% (positive predictive rate >95%). If the median CMAP amplitude was higher than the normal limit (>4.9 mV), the rate of no spontaneous EMG activity was >94% (negative predictive rate >94%). An abnormal SNAP amplitude was the second best predictor of spontaneous EMG activity (p<0.001; OR 4.13; 95% CI 2.16-7.90), and an abnormal FMCV was the third best predictor (p=0.01; OR 2.10; 95% CI 1.20-3.67). No other nerve conduction parameters had significant power to predict spontaneous activity upon EMG examination. The CMAP amplitudes of the APB are the most powerful predictors of the occurrence of spontaneous EMG activity. Low CMAP amplitudes are strongly associated with spontaneous activity, whereas high CMAP amplitude are less associated with spontaneous activity, implying that needle EMG examination should be recommended for the detection of

  14. Representative parameter of immunostimulatory ginseng polysaccharide to predict radioprotection

    Energy Technology Data Exchange (ETDEWEB)

    Son, Hyeog Jin; Shim, Ji Young; Ahn, Ji Yeon; Yun, Yeon Sook; Song, Jie Young [Korea Institute of Radiological and Medical Sciences, Seoul (Korea, Republic of)

    2008-09-15

    According to the increase in the use of radiotherapy to cancer patients, many approaches have been tried to develop new agents for the protection of surrounding normal tissues. However, it is still few applied in the clinic as a radioprotector. We aim to find a representative parameter for radioprotection to easily predict the activity of in vivo experiment from the results of in vitro screening. The polysaccharide extracted from Panax ginseng was used in this study because the immunostimulator has been regarded as one of the radioprotective agent category and was already reported having a promising radioprotective activity through the increase of hematopoietic cells and the production of several cytokines. Mitogenic activity, AK cells activity and nitric oxide production were monitored for the in vitro immunological assay, and endogenous Colony-Forming Unit (e-CFU) was measured as in vivo radioprotective parameter. The immunological activity was increased by the galactose contents of ginseng polysaccharide dependently. The result of this study suggests that mitogenic activity of splenocytes demonstrated a good correlation with in vivo radioprotective effect, and may be used as a representative parameter to screen the candidates for radioprotector.

  15. Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea

    KAUST Repository

    Sawlan, Zaid A

    2012-12-01

    Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.

  16. Optimal parameters of the SVM for temperature prediction

    Directory of Open Access Journals (Sweden)

    X. Shi

    2015-05-01

    Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

  17. Prediction of compressibility parameters of the soils using artificial neural network.

    Science.gov (United States)

    Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

    2016-01-01

    The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

  18. The Usefulness of Clinical and Laboratory Parameters for Predicting Severity of Dehydration in Children with Acute Gastroenteritis

    OpenAIRE

    Hoxha, Teuta Faik; Azemi, Mehmedali; Avdiu, Muharrem; Ismaili-jaha, Vlora; Grajqevci, Violeta; Petrela, Ela

    2014-01-01

    ABSTRACT Background: An accurate assessment of the degree of dehydration in infants and children is important for proper decision-making and treatment. This emphasizes the need for laboratory tests to improve the accuracy of clinical assessment of dehydration. The aim of this study was to assess the relationship between clinical and laboratory parameters in the assessment of dehydration. Methods: We evaluated prospectively 200 children aged 1 month to 5 years who presented with diarrhea, vomi...

  19. Parameter transferability within homogeneous regions and comparisons with predictions from a priori parameters in the eastern United States

    Science.gov (United States)

    Chouaib, Wafa; Alila, Younes; Caldwell, Peter V.

    2018-05-01

    The need for predictions of flow time-series persists at ungauged catchments, motivating the research goals of our study. By means of the Sacramento model, this paper explores the use of parameter transfer within homogeneous regions of similar climate and flow characteristics and makes comparisons with predictions from a priori parameters. We assessed the performance using the Nash-Sutcliffe (NS), bias, mean monthly hydrograph and flow duration curve (FDC). The study was conducted on a large dataset of 73 catchments within the eastern US. Two approaches to the parameter transferability were developed and evaluated; (i) the within homogeneous region parameter transfer using one donor catchment specific to each region, (ii) the parameter transfer disregarding the geographical limits of homogeneous regions, where one donor catchment was common to all regions. Comparisons between both parameter transfers enabled to assess the gain in performance from the parameter regionalization and its respective constraints and limitations. The parameter transfer within homogeneous regions outperformed the a priori parameters and led to a decrease in bias and increase in efficiency reaching a median NS of 0.77 and a NS of 0.85 at individual catchments. The use of FDC revealed the effect of bias on the inaccuracy of prediction from parameter transfer. In one specific region, of mountainous and forested catchments, the prediction accuracy of the parameter transfer was less satisfactory and equivalent to a priori parameters. In this region, the parameter transfer from the outsider catchment provided the best performance; less-biased with smaller uncertainty in medium flow percentiles (40%-60%). The large disparity of energy conditions explained the lack of performance from parameter transfer in this region. Besides, the subsurface stormflow is predominant and there is a likelihood of lateral preferential flow, which according to its specific properties further explained the reduced

  20. Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Ahmed M. Al Rajeh

    2016-11-01

    Full Text Available Introduction: The value of monitoring physiological parameters to predict chronic obstructive pulmonary disease (COPD exacerbations is controversial. A few studies have suggested benefit from domiciliary monitoring of vital signs, and/or lung function but there is no existing systematic review. Objectives: To conduct a systematic review of the effectiveness of monitoring physiological parameters to predict COPD exacerbation. Methods: An electronic systematic search compliant with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines was conducted. The search was updated to April 6, 2016. Five databases were examined: Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (Medline, Excerpta Medica dataBASE (Embase, Allied and Complementary Medicine Database (AMED, Cumulative Index of Nursing and Allied Health Literature (CINAHL and the Cochrane clinical trials database. Results: Sixteen articles met the pre-specified inclusion criteria. Fifteen of these articules reported positive results in predicting COPD exacerbation via monitoring of physiological parameters. Nine studies showed a reduction in peripheral oxygen saturation (SpO2% prior to exacerbation onset. Three studies for peak flow, and two studies for respiratory rate reported a significant variation prior to or at exacerbation onset. A particular challenge is accounting for baseline heterogeneity in parameters between patients. Conclusion: There is currently insufficient information on how physiological parameters vary prior to exacerbation to support routine domiciliary monitoring for the prediction of exacerbations in COPD. However, the method remains promising.

  1. Radiation-induced liver disease after stereotactic body radiotherapy for small hepatocellular carcinoma: clinical and dose-volumetric parameters

    International Nuclear Information System (INIS)

    Jung, Jinhong; Choi, Eun Kyung; Kim, Jong Hoon; Yoon, Sang Min; Kim, So Yeon; Cho, Byungchul; Park, Jin-hong; Kim, Su Ssan; Song, Si Yeol; Lee, Sang-wook; Ahn, Seung Do

    2013-01-01

    To investigate the clinical and dose–volumetric parameters that predict the risk of radiation-induced liver disease (RILD) for patients with small, unresectable hepatocellular carcinoma (HCC) treated with stereotactic body radiotherapy (SBRT). Between March 2007 and December 2009, 92 patients with HCC treated with SBRT were reviewed for RILD within 3 months of completing treatment. RILD was evaluated according to the Common Terminology Criteria for Adverse Events, version 3.0. A dose of 10–20 Gy (median, 15 Gy) per fraction was given over 3–4 consecutive days for a total dose of 30–60 Gy (median, 45 Gy). The following clinical and dose–volumetric parameters were examined: age, gender, Child-Pugh class, presence of hepatitis B virus, gross tumor volume, normal liver volume, radiation dose, fraction size, mean dose to the normal liver, and normal liver volumes receiving from < 5 Gy to < 60 Gy (in increments of 5 Gy). Seventeen (18.5%) of the 92 patients developed grade 2 or worse RILD after SBRT (49 patients in grade 1, 11 in grade 2, and 6 in ≥ grade 3). On univariate analysis, Child-Pugh class was identified as a significant clinical parameter, while normal liver volume and normal liver volumes receiving from < 15 Gy to < 60 Gy were the significant dose–volumetric parameters. Upon multivariate analysis, only Child-Pugh class was a significant parameter for predicting grade 2 or worse RILD. The Child-Pugh B cirrhosis was found to have a significantly greater susceptibility to the development of grade 2 or worse RILD after SBRT in patients with small, unresectable HCC. Additional efforts aimed at testing other models to predict the risk of RILD in a large series of HCC patients treated with SBRT are needed

  2. Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices.

    Science.gov (United States)

    Li, Zhigang; Liu, Boying; Yuan, Mengxiong; Zhang, Feifei; Guo, Jiaqiang

    2016-01-01

    Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information.

  3. Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices.

    Directory of Open Access Journals (Sweden)

    Zhigang Li

    Full Text Available Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information.

  4. Histogram analysis of diffusion kurtosis imaging of nasopharyngeal carcinoma: Correlation between quantitative parameters and clinical stage.

    Science.gov (United States)

    Xu, Xiao-Quan; Ma, Gao; Wang, Yan-Jun; Hu, Hao; Su, Guo-Yi; Shi, Hai-Bin; Wu, Fei-Yun

    2017-07-18

    To evaluate the correlation between histogram parameters derived from diffusion-kurtosis (DK) imaging and the clinical stage of nasopharyngeal carcinoma (NPC). High T-stage (T3/4) NPC showed significantly higher Kapp-mean (P = 0.018), Kapp-median (P = 0.029) and Kapp-90th (P = 0.003) than low T-stage (T1/2) NPC. High N-stage NPC (N2/3) showed significantly lower Dapp-mean (P = 0.002), Dapp-median (P = 0.002) and Dapp-10th (P Histogram parameters, including mean, median, 10th, 90th percentiles, skewness and kurtosis of Dapp and Kapp were calculated. Patients were divided into low and high T, N and clinical stage based on American Joint Committee on Cancer (AJCC) staging system. Differences of histogram parameters between low and high T, N and AJCC stages were compared using t test. Multiple receiver operating characteristic (ROC) curves were used to determine and compare the value of significant parameters in predicting high T, N and AJCC stage, respectively. DK imaging-derived parameters correlated well with clinical stage of NPC, therefore could serve as an adjunctive imaging technique for evaluating NPC.

  5. Toward an Efficient Prediction of Solar Flares: Which Parameters, and How?

    Directory of Open Access Journals (Sweden)

    Manolis K. Georgoulis

    2013-11-01

    Full Text Available Solar flare prediction has become a forefront topic in contemporary solar physics, with numerous published methods relying on numerous predictive parameters, that can even be divided into parameter classes. Attempting further insight, we focus on two popular classes of flare-predictive parameters, namely multiscale (i.e., fractal and multifractal and proxy (i.e., morphological parameters, and we complement our analysis with a study of the predictive capability of fundamental physical parameters (i.e., magnetic free energy and relative magnetic helicity. Rather than applying the studied parameters to a comprehensive statistical sample of flaring and non-flaring active regions, that was the subject of our previous studies, the novelty of this work is their application to an exceptionally long and high-cadence time series of the intensely eruptive National Oceanic and Atmospheric Administration (NOAA active region (AR 11158, observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Aiming for a detailed study of the temporal evolution of each parameter, we seek distinctive patterns that could be associated with the four largest flares in the AR in the course of its five-day observing interval. We find that proxy parameters only tend to show preflare impulses that are practical enough to warrant subsequent investigation with sufficient statistics. Combining these findings with previous results, we conclude that: (i carefully constructed, physically intuitive proxy parameters may be our best asset toward an efficient future flare-forecasting; and (ii the time series of promising parameters may be as important as their instantaneous values. Value-based prediction is the only approach followed so far. Our results call for novel signal and/or image processing techniques to efficiently utilize combined amplitude and temporal-profile information to optimize the inferred solar-flare probabilities.

  6. Furosemide/Fludrocortisone Test and Clinical Parameters to Diagnose Incomplete Distal Renal Tubular Acidosis in Kidney Stone Formers.

    Science.gov (United States)

    Dhayat, Nasser A; Gradwell, Michael W; Pathare, Ganesh; Anderegg, Manuel; Schneider, Lisa; Luethi, David; Mattmann, Cedric; Moe, Orson W; Vogt, Bruno; Fuster, Daniel G

    2017-09-07

    Incomplete distal renal tubular acidosis is a well known cause of calcareous nephrolithiasis but the prevalence is unknown, mostly due to lack of accepted diagnostic tests and criteria. The ammonium chloride test is considered as gold standard for the diagnosis of incomplete distal renal tubular acidosis, but the furosemide/fludrocortisone test was recently proposed as an alternative. Because of the lack of rigorous comparative studies, the validity of the furosemide/fludrocortisone test in stone formers remains unknown. In addition, the performance of conventional, nonprovocative parameters in predicting incomplete distal renal tubular acidosis has not been studied. We conducted a prospective study in an unselected cohort of 170 stone formers that underwent sequential ammonium chloride and furosemide/fludrocortisone testing. Using the ammonium chloride test as gold standard, the prevalence of incomplete distal renal tubular acidosis was 8%. Sensitivity and specificity of the furosemide/fludrocortisone test were 77% and 85%, respectively, yielding a positive predictive value of 30% and a negative predictive value of 98%. Testing of several nonprovocative clinical parameters in the prediction of incomplete distal renal tubular acidosis revealed fasting morning urinary pH and plasma potassium as the most discriminative parameters. The combination of a fasting morning urinary threshold pH 3.8 mEq/L yielded a negative predictive value of 98% with a sensitivity of 85% and a specificity of 77% for the diagnosis of incomplete distal renal tubular acidosis. The furosemide/fludrocortisone test can be used for incomplete distal renal tubular acidosis screening in stone formers, but an abnormal furosemide/fludrocortisone test result needs confirmation by ammonium chloride testing. Our data furthermore indicate that incomplete distal renal tubular acidosis can reliably be excluded in stone formers by use of nonprovocative clinical parameters. Copyright © 2017 by the American

  7. Predictive parameters of infectiologic complications in patients after TIPSS

    International Nuclear Information System (INIS)

    Cohnen, M.; Saleh, A.; Moedder, U.; Luethen, R.; Bode, J.; Haeussinger, D.; Daeubener, W.

    2003-01-01

    Aim To define predictive parameters of a complicated clinical course after the TIPSS procedure. Blood cultures were drawn prospectively in 41 patients from a central line and from the portal venous blood before stent placement as well as from the central line 20 min after intervention. C-reactive proteine (CRP) (mg/dl) and white blood cell count (WBC,/μl) on the day of TIPSS-procedure (d0), the first (d1) and seven (d7) days after TIPSS were compared in patients with a complicated clinical course (spontaneous bacterial peritonitis, pneumonia, sepsis; group I) to patients without clinical complications (group II) Group I showed a significant increase in CRP (d0: 1.8±1.0; d1: 3.2±1.5; d7: 4.3±3.2), and white blood cell count (d0: 7700±2600; d1: 10800±2800; d7: 7500±1800) on the first day after TIPSS-procedure in comparison to group II (CRP: d0: 1.6±0.6; d1: 1.8±1.0; d7: 1.9±0.6. WBC: d0: 6900±1500; d1: 8000±1600; d7: 7600±1400).Microbiological analysis showed in 12% skin or oral flora in the last sample. The course of CRP and WBC-count during the first week after TIPSS procedure may indicate patients with a potential risk of a complicated clinical course. (orig.) [de

  8. A prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2, based on simple clinical parameters.

    Science.gov (United States)

    Koeneman, Margot M; van Lint, Freyja H M; van Kuijk, Sander M J; Smits, Luc J M; Kooreman, Loes F S; Kruitwagen, Roy F P M; Kruse, Arnold J

    2017-01-01

    This study aims to develop a prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2 (CIN 2) lesions based on simple clinicopathological parameters. The study was conducted at Maastricht University Medical Center, the Netherlands. The prediction model was developed in a retrospective cohort of 129 women with a histologic diagnosis of CIN 2 who were managed by watchful waiting for 6 to 24months. Five potential predictors for spontaneous regression were selected based on the literature and expert opinion and were analyzed in a multivariable logistic regression model, followed by backward stepwise deletion based on the Wald test. The prediction model was internally validated by the bootstrapping method. Discriminative capacity and accuracy were tested by assessing the area under the receiver operating characteristic curve (AUC) and a calibration plot. Disease regression within 24months was seen in 91 (71%) of 129 patients. A prediction model was developed including the following variables: smoking, Papanicolaou test outcome before the CIN 2 diagnosis, concomitant CIN 1 diagnosis in the same biopsy, and more than 1 biopsy containing CIN 2. Not smoking, Papanicolaou class predictive of disease regression. The AUC was 69.2% (95% confidence interval, 58.5%-79.9%), indicating a moderate discriminative ability of the model. The calibration plot indicated good calibration of the predicted probabilities. This prediction model for spontaneous regression of CIN 2 may aid physicians in the personalized management of these lesions. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Updated climatological model predictions of ionospheric and HF propagation parameters

    International Nuclear Information System (INIS)

    Reilly, M.H.; Rhoads, F.J.; Goodman, J.M.; Singh, M.

    1991-01-01

    The prediction performances of several climatological models, including the ionospheric conductivity and electron density model, RADAR C, and Ionospheric Communications Analysis and Predictions Program, are evaluated for different regions and sunspot number inputs. Particular attention is given to the near-real-time (NRT) predictions associated with single-station updates. It is shown that a dramatic improvement can be obtained by using single-station ionospheric data to update the driving parameters for an ionospheric model for NRT predictions of f(0)F2 and other ionospheric and HF circuit parameters. For middle latitudes, the improvement extends out thousands of kilometers from the update point to points of comparable corrected geomagnetic latitude. 10 refs

  10. Prediction of Radiation Esophagitis in Non–Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels

    Directory of Open Access Journals (Sweden)

    Peter G. Hawkins

    2018-02-01

    Full Text Available Radiation esophagitis (RE is a common adverse event associated with radiotherapy for non–small cell lung cancer (NSCLC. While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc and generalized equivalent uniform dose (gEUD], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc and 0.727 (gEUD. Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning.

  11. Modelling hydrodynamic parameters to predict flow assisted corrosion

    International Nuclear Information System (INIS)

    Poulson, B.; Greenwell, B.; Chexal, B.; Horowitz, J.

    1992-01-01

    During the past 15 years, flow assisted corrosion has been a worldwide problem in the power generating industry. The phenomena is complex and depends on environment, material composition, and hydrodynamic factors. Recently, modeling of flow assisted corrosion has become a subject of great importance. A key part of this effort is modeling the hydrodynamic aspects of this issue. This paper examines which hydrodynamic parameter should be used to correlate the occurrence and rate of flow assisted corrosion with physically meaningful parameters, discusses ways of measuring the relevant hydrodynamic parameter, and describes how the hydrodynamic data is incorporated into the predictive model

  12. Usefulness of Clinical Prediction Rules, D-dimer, and Arterial Blood Gas Analysis to Predict Pulmonary Embolism in Cancer Patients

    Directory of Open Access Journals (Sweden)

    Shazia Awan

    2017-03-01

    Full Text Available Objectives: Pulmonary embolism (PE is seven times more common in cancer patients than non-cancer patients. Since the existing clinical prediction rules (CPRs were validated predominantly in a non-cancer population, we decided to look at the utility of arterial blood gas (ABG analysis and D-dimer in predicting PE in cancer patients. Methods: Electronic medical records were reviewed between December 2005 and November 2010. A total of 177 computed tomography pulmonary angiograms (CTPAs were performed. We selected 104 individuals based on completeness of laboratory and clinical data. Patients were divided into two groups, CTPA positive (patients with PE and CTPA negative (PE excluded. Wells score, Geneva score, and modified Geneva score were calculated for each patient. Primary outcomes of interest were the sensitivities, specificities, positive, and negative predictive values for all three CPRs. Results: Of the total of 104 individuals who had CTPAs, 33 (31.7% were positive for PE and 71 (68.3% were negative. There was no difference in basic demographics between the two groups. Laboratory parameters were compared and partial pressure of oxygen was significantly lower in patients with PE (68.1 mmHg vs. 71 mmHg, p = 0.030. Clinical prediction rules showed good sensitivities (88−100% and negative predictive values (93−100%. An alveolar-arterial (A-a gradient > 20 had 100% sensitivity and negative predictive values. Conclusions: CPRs and a low A-a gradient were useful in excluding PE in cancer patients. There is a need for prospective trials to validate these results.

  13. Predictive value of MR imaging-dependent and non-MR imaging-dependent parameters for recurrence of laryngeal cancer after radiation therapy

    NARCIS (Netherlands)

    Castelijns, J. A.; van den Brekel, M. W.; Smit, E. M.; Tobi, H.; van Wagtendonk, F. W.; Golding, R. P.; Venema, H. W.; van Schaik, C.; Snow, G. B.

    1995-01-01

    To determine the predictive value of several clinical and radiologic parameters for recurrence of laryngeal cancer. Eighty previously untreated patients underwent magnetic resonance (MR) imaging before radiation therapy with curative intent. Tumor volume was calculated from T1-weighted MR images.

  14. Clinical presentation and outcome prediction of clinical, serological, and histopathological classification schemes in ANCA-associated vasculitis with renal involvement.

    Science.gov (United States)

    Córdova-Sánchez, Bertha M; Mejía-Vilet, Juan M; Morales-Buenrostro, Luis E; Loyola-Rodríguez, Georgina; Uribe-Uribe, Norma O; Correa-Rotter, Ricardo

    2016-07-01

    Several classification schemes have been developed for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), with actual debate focusing on their clinical and prognostic performance. Sixty-two patients with renal biopsy-proven AAV from a single center in Mexico City diagnosed between 2004 and 2013 were analyzed and classified under clinical (granulomatosis with polyangiitis [GPA], microscopic polyangiitis [MPA], renal limited vasculitis [RLV]), serological (proteinase 3 anti-neutrophil cytoplasmic antibodies [PR3-ANCA], myeloperoxidase anti-neutrophil cytoplasmic antibodies [MPO-ANCA], ANCA negative), and histopathological (focal, crescenteric, mixed-type, sclerosing) categories. Clinical presentation parameters were compared at baseline between classification groups, and the predictive value of different classification categories for disease and renal remission, relapse, renal, and patient survival was analyzed. Serological classification predicted relapse rate (PR3-ANCA hazard ratio for relapse 2.93, 1.20-7.17, p = 0.019). There were no differences in disease or renal remission, renal, or patient survival between clinical and serological categories. Histopathological classification predicted response to therapy, with a poorer renal remission rate for sclerosing group and those with less than 25 % normal glomeruli; in addition, it adequately delimited 24-month glomerular filtration rate (eGFR) evolution, but it did not predict renal nor patient survival. On multivariate models, renal replacement therapy (RRT) requirement (HR 8.07, CI 1.75-37.4, p = 0.008) and proteinuria (HR 1.49, CI 1.03-2.14, p = 0.034) at presentation predicted renal survival, while age (HR 1.10, CI 1.01-1.21, p = 0.041) and infective events during the induction phase (HR 4.72, 1.01-22.1, p = 0.049) negatively influenced patient survival. At present, ANCA-based serological classification may predict AAV relapses, but neither clinical nor serological

  15. Optimal design criteria - prediction vs. parameter estimation

    Science.gov (United States)

    Waldl, Helmut

    2014-05-01

    G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

  16. Parameter estimation techniques and uncertainty in ground water flow model predictions

    International Nuclear Information System (INIS)

    Zimmerman, D.A.; Davis, P.A.

    1990-01-01

    Quantification of uncertainty in predictions of nuclear waste repository performance is a requirement of Nuclear Regulatory Commission regulations governing the licensing of proposed geologic repositories for high-level radioactive waste disposal. One of the major uncertainties in these predictions is in estimating the ground-water travel time of radionuclides migrating from the repository to the accessible environment. The cause of much of this uncertainty has been attributed to a lack of knowledge about the hydrogeologic properties that control the movement of radionuclides through the aquifers. A major reason for this lack of knowledge is the paucity of data that is typically available for characterizing complex ground-water flow systems. Because of this, considerable effort has been put into developing parameter estimation techniques that infer property values in regions where no measurements exist. Currently, no single technique has been shown to be superior or even consistently conservative with respect to predictions of ground-water travel time. This work was undertaken to compare a number of parameter estimation techniques and to evaluate how differences in the parameter estimates and the estimation errors are reflected in the behavior of the flow model predictions. That is, we wished to determine to what degree uncertainties in flow model predictions may be affected simply by the choice of parameter estimation technique used. 3 refs., 2 figs

  17. Clinical studies of biomarkers in suicide prediction

    OpenAIRE

    Jokinen, Jussi

    2007-01-01

    Suicide is a major clinical problem in psychiatry and suicidal behaviours can be seen as a nosological entity per se. Predicting suicide is difficult due to its low base-rate and the limited specificity of clinical predictors. Prospective biological studies suggest that dysfunctions in the hypothalamo pituitary adrenal (HPA) axis and the serotonergic system have predictive power for suicide in mood disorders. Suicide attempt is the most robust clinical predictor making suici...

  18. Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages.

    Science.gov (United States)

    Choi, Youn-Kyung; Kim, Jinmi; Yamaguchi, Tetsutaro; Maki, Koutaro; Ko, Ching-Chang; Kim, Yong-Il

    2016-01-01

    This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5-18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

  19. A practical approach to parameter estimation applied to model predicting heart rate regulation

    DEFF Research Database (Denmark)

    Olufsen, Mette; Ottesen, Johnny T.

    2013-01-01

    Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities....... Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse. However, it may be possible to estimate...... a subset of model parameters reducing the complexity of the problem. In this study, we compare three methods that allow identification of parameter subsets that can be estimated given a model and a set of data. These methods will be used to estimate patient specific parameters in a model predicting...

  20. Identification and prediction of diabetic sensorimotor polyneuropathy using individual and simple combinations of nerve conduction study parameters.

    Directory of Open Access Journals (Sweden)

    Alanna Weisman

    Full Text Available OBJECTIVE: Evaluation of diabetic sensorimotor polyneuropathy (DSP is hindered by the need for complex nerve conduction study (NCS protocols and lack of predictive biomarkers. We aimed to determine the performance of single and simple combinations of NCS parameters for identification and future prediction of DSP. MATERIALS AND METHODS: 406 participants (61 with type 1 diabetes and 345 with type 2 diabetes with a broad spectrum of neuropathy, from none to severe, underwent NCS to determine presence or absence of DSP for cross-sectional (concurrent validity analysis. The 109 participants without baseline DSP were re-evaluated for its future onset (predictive validity. Performance of NCS parameters was compared by area under the receiver operating characteristic curve (AROC. RESULTS: At baseline there were 246 (60% Prevalent Cases. After 3.9 years mean follow-up, 25 (23% of the 109 Prevalent Controls that were followed became Incident DSP Cases. Threshold values for peroneal conduction velocity and sural amplitude potential best identified Prevalent Cases (AROC 0.90 and 0.83, sensitivity 80 and 83%, specificity 89 and 72%, respectively. Baseline tibial F-wave latency, peroneal conduction velocity and the sum of three lower limb nerve conduction velocities (sural, peroneal, and tibial best predicted 4-year incidence (AROC 0.79, 0.79, and 0.85; sensitivity 79, 70, and 81%; specificity 63, 74 and 77%, respectively. DISCUSSION: Individual NCS parameters or their simple combinations are valid measures for identification and future prediction of DSP. Further research into the predictive roles of tibial F-wave latencies, peroneal conduction velocity, and sum of conduction velocities as markers of incipient nerve injury is needed to risk-stratify individuals for clinical and research protocols.

  1. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty (discussion paper)

    NARCIS (Netherlands)

    Pande, S.; Arkesteijn, L.; Savenije, H.H.G.; Bastidas, L.A.

    2014-01-01

    This paper presents evidence that model prediction uncertainty does not necessarily rise with parameter dimensionality (the number of parameters). Here by prediction we mean future simulation of a variable of interest conditioned on certain future values of input variables. We utilize a relationship

  2. Radiogenomics: predicting clinical normal tissue radiosensitivity

    DEFF Research Database (Denmark)

    Alsner, Jan

    2006-01-01

    Studies on the genetic basis of normal tissue radiosensitivity, or  'radiogenomics', aims at predicting clinical radiosensitivity and optimize treatment from individual genetic profiles. Several studies have now reported links between variations in certain genes related to the biological response...... to radiation injury and risk of normal tissue morbidity in cancer patients treated with radiotherapy. However, after these initial association studies including few genes, we are still far from being able to predict clinical radiosensitivity on an individual level. Recent data from our own studies on risk...

  3. Prediction of interest rate using CKLS model with stochastic parameters

    International Nuclear Information System (INIS)

    Ying, Khor Chia; Hin, Pooi Ah

    2014-01-01

    The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ (j) of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ (j) , we assume that φ (j) depends on φ (j−m) , φ (j−m+1) ,…, φ (j−1) and the interest rate r j+n at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r j+n+1 of the interest rate at the next time point when the value r j+n of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r j+n+d at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters

  4. Prediction of interest rate using CKLS model with stochastic parameters

    Energy Technology Data Exchange (ETDEWEB)

    Ying, Khor Chia [Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor (Malaysia); Hin, Pooi Ah [Sunway University Business School, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang Jaya, Selangor (Malaysia)

    2014-06-19

    The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.

  5. Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

    Science.gov (United States)

    Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

    2018-01-19

    OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

  6. The relationships between preoperative urodynamic parameters and clinical outcomes in urinary stress incontinence

    Directory of Open Access Journals (Sweden)

    Yaşar Bozkurt

    2008-12-01

    Full Text Available The aim of present study was to evaluate the influence of urodynamic parameters on preoperative and postoperative clinical pictures in stress incontinence.Charts of patients, who were operated for stress incontinence using autologous rectus fascia sling between March 1999 and January 2005 in Tepecik Training and Research Hospital Urology Clinic, were evaluated retrospectively.A total of 41 patients were divided into two subgroups as, pure (10 patients and mixed stress incontinence (31 patients groups. Mean age of patients was 50.4 (33-70 years. Fifteen patients had intrinsic sphincter insufficiency (ISI. Mixed incontinence group had lower volume for first sensation and more detrusor overactivity than pure group. ISI did not alter the success of operation. Urodynamically no relationship was found between detrusor pressure and postoperative postvoiding residual urine (P>0.05.In conclusion, urodynamic evaluation before surgery was not related to preoperative and postoperative clinical picture of patients, but first sensation of bladder is only predictive for the success in fascial sling surgery.

  7. Analysis of clinical drug-drug interaction data to predict uncharacterized interaction magnitudes between antiretroviral drugs and co-medications.

    Science.gov (United States)

    Stader, Felix; Kinvig, Hannah; Battegay, Manuel; Khoo, Saye; Owen, Andrew; Siccardi, Marco; Marzolini, Catia

    2018-04-23

    Despite their high potential for drug-drug-interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required.We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes such as CYP3A and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers and inhibitors of CYP3A, to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs.The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and co-medications were successfully quantified meaning 53%, 85% and 98% of the predictions were within 1.25-fold (0.80 - 1.25), 1.5-fold (0.66 - 1.48) and 2-fold (0.66 - 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or conversely minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs.The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs being particularly relevant in a HIV-setting giving the treatments complexity, high DDI risk and limited guidance on the management of DDIs. Copyright © 2018 American Society for Microbiology.

  8. Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

    International Nuclear Information System (INIS)

    Xu Ruirui; Chen Tianlun; Gao Chengfeng

    2006-01-01

    Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

  9. Connecting clinical and actuarial prediction with rule-based methods

    NARCIS (Netherlands)

    Fokkema, M.; Smits, N.; Kelderman, H.; Penninx, B.W.J.H.

    2015-01-01

    Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction

  10. Using ANFIS for selection of more relevant parameters to predict dew point temperature

    International Nuclear Information System (INIS)

    Mohammadi, Kasra; Shamshirband, Shahaboddin; Petković, Dalibor; Yee, Por Lip; Mansor, Zulkefli

    2016-01-01

    Highlights: • ANFIS is used to select the most relevant variables for dew point temperature prediction. • Two cities from the central and south central parts of Iran are selected as case studies. • Influence of 5 parameters on dew point temperature is evaluated. • Appropriate selection of input variables has a notable effect on prediction. • Considering the most relevant combination of 2 parameters would be more suitable. - Abstract: In this research work, for the first time, the adaptive neuro fuzzy inference system (ANFIS) is employed to propose an approach for identifying the most significant parameters for prediction of daily dew point temperature (T_d_e_w). The ANFIS process for variable selection is implemented, which includes a number of ways to recognize the parameters offering favorable predictions. According to the physical factors influencing the dew formation, 8 variables of daily minimum, maximum and average air temperatures (T_m_i_n, T_m_a_x and T_a_v_g), relative humidity (R_h), atmospheric pressure (P), water vapor pressure (V_P), sunshine hour (n) and horizontal global solar radiation (H) are considered to investigate their effects on T_d_e_w. The used data include 7 years daily measured data of two Iranian cities located in the central and south central parts of the country. The results indicate that despite climate difference between the considered case studies, for both stations, V_P is the most influential variable while R_h is the least relevant element. Furthermore, the combination of T_m_i_n and V_P is recognized as the most influential set to predict T_d_e_w. The conducted examinations show that there is a remarkable difference between the errors achieved for most and less relevant input parameters, which highlights the importance of appropriate selection of input parameters. The use of more than two inputs may not be advisable and appropriate; thus, considering the most relevant combination of 2 parameters would be more suitable

  11. [The role of epicardial fat and obesity parameters in the prediction of coronary heart disease].

    Science.gov (United States)

    Prídavková, Dana; Kantárová, Daniela; Lišková, Renáta; Červeň, Peter; Kovář, František; Mokáň, Marián

    2016-04-01

    To assess the relationship of parameters of obesity in relationship to coronary angiography findings with correlation of epicardial fat (EF) thickness in uppermentioned context. There were 80 patients examined (43 males, 37 postmenopausal females) undergoing elective coronary angiography. We examined the regular obesity parameters - BMI, waist circumference (WC), neck circumference (NC), total body fat (TBF), and visceral fat (VF) using bioimpedance. We assessed the echocardiographically measured EF thickness. We added examination of lipidogram, glycaemia, HOMA-IR (insulin resistance index) and AIP (aterogenic index of plasma). The set was divided into group with coronarographically proved stenosis or stenoses (withCS), and a group without finding of quantifiable stenosis or stenoses (withoutCS). The average thickness of EF in withCS group was 6.3 vs 5.6 mm in group withoutCS (p obesity parameters in assessment of pre-clinical stages of coronary atherosclerosis and prediction of risk of coronary heart disease. In adipose parameters, EF thickness was correlated the most by WC. Risk stratification of coronary artery disease is supplemented by increased HOMA-IR and AIP.

  12. Different Vocal Parameters Predict Perceptions of Dominance and Attractiveness.

    Science.gov (United States)

    Hodges-Simeon, Carolyn R; Gaulin, Steven J C; Puts, David A

    2010-12-01

    Low mean fundamental frequency (F(0)) in men's voices has been found to positively influence perceptions of dominance by men and attractiveness by women using standardized speech. Using natural speech obtained during an ecologically valid social interaction, we examined relationships between multiple vocal parameters and dominance and attractiveness judgments. Male voices from an unscripted dating game were judged by men for physical and social dominance and by women in fertile and non-fertile menstrual cycle phases for desirability in short-term and long-term relationships. Five vocal parameters were analyzed: mean F(0) (an acoustic correlate of vocal fold size), F(0) variation, intensity (loudness), utterance duration, and formant dispersion (D(f), an acoustic correlate of vocal tract length). Parallel but separate ratings of speech transcripts served as controls for content. Multiple regression analyses were used to examine the independent contributions of each of the predictors. Physical dominance was predicted by low F(0) variation and physically dominant word content. Social dominance was predicted only by socially dominant word content. Ratings of attractiveness by women were predicted by low mean F(0), low D(f), high intensity, and attractive word content across cycle phase and mating context. Low D(f) was perceived as attractive by fertile-phase women only. We hypothesize that competitors and potential mates may attend more strongly to different components of men's voices because of the different types of information these vocal parameters provide.

  13. Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

    Directory of Open Access Journals (Sweden)

    Wan-Hua Lin

    2013-01-01

    Full Text Available Cardiovascular disease (CVD is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI, and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.

  14. Prediction Model of Interval Grey Numbers with a Real Parameter and Its Application

    Directory of Open Access Journals (Sweden)

    Bo Zeng

    2014-01-01

    Full Text Available Grey prediction models have become common methods which are widely employed to solve the problems with “small examples and poor information.” However, modeling objects of existing grey prediction models are limited to the homogenous data sequences which only contain the same data type. This paper studies the methodology of building prediction models of interval grey numbers that are grey heterogeneous data sequence, with a real parameter. Firstly, the position of the real parameter in an interval grey number sequence is discussed, and the real number is expanded into an interval grey number by adopting the method of grey generation. On this basis, a prediction model of interval grey number with a real parameter is deduced and built. Finally, this novel model is successfully applied to forecast the concentration of organic pollutant DDT in the atmosphere. The analysis and research results in this paper extend the object of grey prediction from homogenous data sequence to grey heterogeneous data sequence. Those research findings are of positive significance in terms of enriching and improving the theory system of grey prediction models.

  15. Factors predictive of abnormal semen parameters in male partners ...

    African Journals Online (AJOL)

    analysis was used to determine the predictive factors associated with abnormal semen parameters. .... for frequency, mean and χ2 with the level of significance set at p<0.05. ... was obtained from each couple participating in the study, following.

  16. Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry.

    Science.gov (United States)

    Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

    2011-01-01

    Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced

  17. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

    Science.gov (United States)

    Pande, S.; Arkesteijn, L.; Savenije, H.; Bastidas, L. A.

    2015-04-01

    This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

  18. Prediction of pork quality parameters by applying fractals and data mining on MRI

    DEFF Research Database (Denmark)

    Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés

    2017-01-01

    This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One...... Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear...... regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate...

  19. Effect of uncertainty parameters on graphene sheets Young's modulus prediction

    International Nuclear Information System (INIS)

    Sahlaoui, Habib; Sidhom Habib; Guedri, Mohamed

    2013-01-01

    Software based on molecular structural mechanics approach (MSMA) and using finite element method (FEM) has been developed to predict the Young's modulus of graphene sheets. Obtained results have been compared to results available in the literature and good agreement has been shown when the same values of uncertainty parameters are used. A sensibility of the models to their uncertainty parameters has been investigated using a stochastic finite element method (SFEM). The different values of the used uncertainty parameters, such as molecular mechanics force field constants k_r and k_θ, thickness (t) of a graphene sheet and length ( L_B) of a carbon carbon bonds, have been collected from the literature. Strong sensibilities of 91% to the thickness and of 21% to the stretching force (k_r) have been shown. The results justify the great difference between Young's modulus predicted values of the graphene sheets and their large disagreement with experimental results.

  20. The predictive value of parameters of clinical presentations for sperm yield in patients with nonobstructive azoospermia receiving microdissection testicular sperm extraction

    Directory of Open Access Journals (Sweden)

    Ming-Hsuan Ku

    2017-12-01

    Conclusion: Clinical presentations or phenotypes can be used as predictive factors for successful sperm retrieval during mTESE in patients with NOA. Hypogonadotropic hypogonadism and cases with UDT history have a higher chance of sperm retrieval. Initial testicular needle biopsy, if available, can provide valuable information about chances of sperm retrieval. Hypospermatogenesis predicts high sperm yield rate, and LMA can have best upgrade results of sperm yield after mTESE.

  1. Clinical implementation of dose-volume histogram predictions for organs-at-risk in IMRT planning

    International Nuclear Information System (INIS)

    Moore, K L; Appenzoller, L M; Tan, J; Michalski, J M; Thorstad, W L; Mutic, S

    2014-01-01

    True quality control (QC) of the planning process requires quantitative assessments of treatment plan quality itself, and QC in IMRT has been stymied by intra-patient anatomical variability and inherently complex three-dimensional dose distributions. In this work we describe the development of an automated system to reduce clinical IMRT planning variability and improve plan quality using mathematical models that predict achievable OAR DVHs based on individual patient anatomy. These models rely on the correlation of expected dose to the minimum distance from a voxel to the PTV surface, whereby a three-parameter probability distribution function (PDF) was used to model iso-distance OAR subvolume dose distributions. DVH models were obtained by fitting the evolution of the PDF with distance. Initial validation on clinical cohorts of 40 prostate and 24 head-and-neck plans demonstrated highly accurate model-based predictions for achievable DVHs in rectum, bladder, and parotid glands. By quantifying the integrated difference between candidate DVHs and predicted DVHs, the models correctly identified plans with under-spared OARs, validated by replanning all cases and correlating any realized improvements against the predicted gains. Clinical implementation of these predictive models was demonstrated in the PINNACLE treatment planning system by use of existing margin expansion utilities and the scripting functionality inherent to the system. To maintain independence from specific planning software, a system was developed in MATLAB to directly process DICOM-RT data. Both model training and patient-specific analyses were demonstrated with significant computational accelerations from parallelization.

  2. Meckel's Diverticulum in Children-Parameters Predicting the Presence of Gastric Heterotopia.

    Science.gov (United States)

    Slívová, Ivana; Vávrová, Zuzana; Tomášková, Hana; Okantey, Okaikor; Penka, Igor; Ihnát, Peter

    2018-05-10

    The presence of gastric ectopic mucosa in Meckel's diverticulum is associated with a higher risk of development of complications. The aim of the present study was to investigate which demographic/clinical parameters predict the presence of gastric heterotopia in Meckel's diverticulum. This was a retrospective cohort study conducted in a single institution (University Hospital Ostrava, Czech republic). All children who underwent laparoscopic/open resection of Meckel's diverticulum within a 20-year study period were included in the study. In total, 88 pediatric patients underwent analysis. The mean age of the children was 4.6 ± 4.73 years; the male-female ratio was approximately 2:1. There were 50 (56.8%) patients with asymptomatic Meckel's diverticulum in our study group. Laparoscopic resection was performed in 24 (27.3%) patients; segmental bowel resection through laparotomy was performed in 13 (14.8%) patients. Gastric heterotopia was found in 39 (44.3%) patients; resection margins of all patients were clear of gastric heterotopia. No correlation was found between the presence of gastric heterotopia and the following parameters: age, gender, maternal age, prematurity, low birth weight, perinatal asphyxia, distance from Bauhin's valve and length of Meckel's diverticulum. The width of the diverticulum base was significantly higher in patients with gastric heterotopia (2.1 ± 0.57 vs. 1.2 ± 0.41 cm; p < 0.001). According to the study outcomes, the width of the diverticulum base seems to be a significant predictive factor associated with the presence of gastric heterotopia in Meckel's diverticulum. The laparoscopic/open resection of asymptomatic MD with a wide base should therefore be recommended.

  3. Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

    Science.gov (United States)

    Hsu, Ling-Yuan; Chen, Tsung-Lin

    2012-01-01

    This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event. PMID:23202231

  4. What Clinical and Laboratory Parameters Distinguish Between ...

    African Journals Online (AJOL)

    Introduction: In developing countries, a large number of patients presenting acutely in renal failure are indeed cases of advanced chronic renal failure. In this study, we compared clinical and laboratory parameters between patients with acute renal failure (ARF) and chronic renal failure (CRF), to identify discriminatory ...

  5. Diagnostic utility of clinical and biochemical parameters in ...

    African Journals Online (AJOL)

    Diagnostic utility of clinical and biochemical parameters in pancreatic head malignancy ... Department of Surgery, Sir Run Run Shaw Hospital College of Medicine, Zhejiang University, ..... technical review on the epidemiology, diagnosis, and.

  6. Correlation of clinical predictions and surgical results in maxillary superior repositioning.

    Science.gov (United States)

    Tabrizi, Reza; Zamiri, Barbad; Kazemi, Hamidreza

    2014-05-01

    This is a prospective study to evaluate the accuracy of clinical predictions related to surgical results in subjects who underwent maxillary superior repositioning without anterior-posterior movement. Surgeons' predictions according to clinical (tooth show at rest and at the maximum smile) and cephalometric evaluation were documented for the amount of maxillary superior repositioning. Overcorrection or undercorrection was documented for every subject 1 year after the operations. Receiver operating characteristic curve test was used to find a cutoff point in prediction errors and to determine positive predictive value (PPV) and negative predictive value. Forty subjects (14 males and 26 females) were studied. Results showed a significant difference between changes in the tooth show at rest and at the maximum smile line before and after surgery. Analysis of the data demonstrated no correlation between the predictive data and the surgical results. The incidence of undercorrection (25%) was more common than overcorrection (7.5%). The cutoff point for errors in predictions was 5 mm for tooth show at rest and 15 mm at the maximum smile. When the amount of the presurgical tooth show at rest was more than 5 mm, 50.5% of clinical predictions did not match the clinical results (PPV), and 75% of clinical predictions showed the same results when the tooth show was less than 5 mm (negative predictive value). When the amount of presurgical tooth shown in the maximum smile line was more than 15 mm, 75% of clinical predictions did not match with clinical results (PPV), and 25% of the predictions had the same results because the tooth show at the maximum smile was lower than 15 mm. Clinical predictions according to the tooth show at rest and at the maximum smile have a poor correlation with clinical results in maxillary superior repositioning for vertical maxillary excess. The risk of errors in predictions increased when the amount of superior repositioning of the maxilla increased

  7. A clinical prediction rule for histological chorioamnionitis in preterm newborns.

    Directory of Open Access Journals (Sweden)

    Jasper V Been

    Full Text Available BACKGROUND: Histological chorioamnionitis (HC is an intrauterine inflammatory process highly associated with preterm birth and adverse neonatal outcome. HC is often clinically silent and diagnosed postnatally by placental histology. Earlier identification could facilitate treatment individualisation to improve outcome in preterm newborns. AIM: Develop a clinical prediction rule at birth for HC and HC with fetal involvement (HCF in preterm newborns. METHODS: Clinical data and placental pathology were obtained from singleton preterm newborns (gestational age ≤ 32.0 weeks born at Erasmus UMC Rotterdam from 2001 to 2003 (derivation cohort; n = 216 or Máxima MC Veldhoven from 2009 to 2010 (validation cohort; n = 206. HC and HCF prediction rules were developed with preference for high sensitivity using clinical variables available at birth. RESULTS: HC and HCF were present in 39% and 24% in the derivation cohort and in 44% and 22% in the validation cohort, respectively. HC was predicted with 87% accuracy, yielding an area under ROC curve of 0.95 (95%CI = 0.92-0.98, a positive predictive value of 80% (95%CI = 74-84%, and a negative predictive value of 93% (95%CI = 88-96%. Corresponding figures for HCF were: accuracy 83%, area under ROC curve 0.92 (95%CI = 0.88-0.96, positive predictive value 59% (95%CI = 52-62%, and negative predictive value 97% (95%CI = 93-99%. External validation expectedly resulted in some loss of test performance, preferentially affecting positive predictive rather than negative predictive values. CONCLUSION: Using a clinical prediction rule composed of clinical variables available at birth, HC and HCF could be predicted with good test characteristics in preterm newborns. Further studies should evaluate the clinical value of these rules to guide early treatment individualisation.

  8. Pulsatility Index as a Diagnostic Parameter of Reciprocating Wall Shear Stress Parameters in Physiological Pulsating Waveforms.

    Directory of Open Access Journals (Sweden)

    Idit Avrahami

    Full Text Available Arterial wall shear stress (WSS parameters are widely used for prediction of the initiation and development of atherosclerosis and arterial pathologies. Traditional clinical evaluation of arterial condition relies on correlations of WSS parameters with average flow rate (Q and heart rate (HR measurements. We show that for pulsating flow waveforms in a straight tube with flow reversals that lead to significant reciprocating WSS, the measurements of HR and Q are not sufficient for prediction of WSS parameters. Therefore, we suggest adding a third quantity-known as the pulsatility index (PI-which is defined as the peak-to-peak flow rate amplitude normalized by Q. We examine several pulsating flow waveforms with and without flow reversals using a simulation of a Womersley model in a straight rigid tube and validate the simulations through experimental study using particle image velocimetry (PIV. The results indicate that clinically relevant WSS parameters such as the percentage of negative WSS (P[%], oscillating shear index (OSI and the ratio of minimum to maximum shear stress rates (min/max, are better predicted when the PI is used in conjunction with HR and Q. Therefore, we propose to use PI as an additional and essential diagnostic quantity for improved predictability of the reciprocating WSS.

  9. Review article. Predicting disease onset in clinically healthy people

    Directory of Open Access Journals (Sweden)

    Zeliger . Harold I.

    2016-06-01

    Full Text Available Virtually all human disease is induced by oxidative stress. Oxidative stress, which is caused by toxic environmental exposure, the presence of disease, lifestyle choices, stress, chronic inflammation or combinations of these, is responsible for most disease. Oxidative stress from all sources is additive and it is the total oxidative stress from all sources that induces the onset of most disease. Oxidative stress leads to lipid peroxidation, which in turn produces Malondialdehyde. Serum malondialdehyde level is an additive parameter resulting from all sources of oxidative stress and, therefore, is a reliable indicator of total oxidative stress which can be used to predict the onset of disease in clinically asymptomatic individuals and to suggest the need for treatment that can prevent much human disease.

  10. The predicted influence of climate change on lesser prairie-chicken reproductive parameters

    Science.gov (United States)

    Grisham, Blake A.; Boal, Clint W.; Haukos, David A.; Davis, D.; Boydston, Kathy K.; Dixon, Charles; Heck, Willard R.

    2013-01-01

    The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Nina events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

  11. [Clinical value of angiogenin in predicting the prognosis of patients with idiopathic pulmonary fibrosis].

    Science.gov (United States)

    Bai, Yanling; Zhu, Haiyan; Sun, Qiyu; Gu, Guozhong; Zhang, Lingyu; Li, Ying; Yang, Baofeng

    2017-09-01

    To explore the relationship between angiogenin-1/2 (Ang-1/2) and clinical parameters of idiopathic pulmonary fibrosis (IPF), and to assess the value of Ang-1/2 in predicting the prognosis of patients with IPF. A retrospective analysis was conducted. Ninety-one patients diagnosed as IPF by high resolution CT (HRCT) and lung biopsy admitted to Daqing Oil Field General Hospital from March 2014 to January 2015 were enrolled. The general data, serum parameters and pulmonary function parameters of all patients were collected. After treatment, all of the 91 patients were followed-up to 2 years. The patients were divided into favorable prognosis group and unfavorable prognosis group according to follow-up results. The differences in all parameters between the two groups were compared. The relationship between Ang-1, Ang-2 and lung function parameters was analyzed by Pearson correlation analysis. Cox proportional hazard regression model was used to evaluate the effect of clinical parameters on the prognosis of patients with IPF. The effect of Ang-2 in predicting prognosis of patients with IPF was analyzed by receiver operating characteristic (ROC) curve. During the 2-year follow-up period, 30 of 91 patients showed a favorable prognosis, and 55 showed an unfavorable prognosis with a poor prognosis rate of 64.71%, and 6 patients withdrew from the study due to loss of follow-up and death. Compared with the favorable prognosis group, Ang-2 level in the unfavorable prognosis group was significantly increased (μg/L: 2.88±1.63 vs. 1.89±1.22, t = 2.909, P = 0.005), but Ang-1 only showed a slight increase (μg/L: 28.70±14.26 vs. 25.62±11.95, t = 1.005, P = 0.318). The results of Pearson correlation analysis showed that Ang-2 level was negatively correlated with forced expiratory volume in 1 second (FVC1) and the percentage of carbon monoxide diffusing capacity accounting for the expected value (DLCO%: r value was -0.227 and -0.206, and P value was 0.147 and 0.253, respectively

  12. Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for Advanced Risk Modeling of Prostate Cancer-Patient-tailored Risk Stratification Can Reduce Unnecessary Biopsies.

    Science.gov (United States)

    Radtke, Jan Philipp; Wiesenfarth, Manuel; Kesch, Claudia; Freitag, Martin T; Alt, Celine D; Celik, Kamil; Distler, Florian; Roth, Wilfried; Wieczorek, Kathrin; Stock, Christian; Duensing, Stefan; Roethke, Matthias C; Teber, Dogu; Schlemmer, Heinz-Peter; Hohenfellner, Markus; Bonekamp, David; Hadaschik, Boris A

    2017-12-01

    Multiparametric magnetic resonance imaging (mpMRI) is gaining widespread acceptance in prostate cancer (PC) diagnosis and improves significant PC (sPC; Gleason score≥3+4) detection. Decision making based on European Randomised Study of Screening for PC (ERSPC) risk-calculator (RC) parameters may overcome prostate-specific antigen (PSA) limitations. We added pre-biopsy mpMRI to ERSPC-RC parameters and developed risk models (RMs) to predict individual sPC risk for biopsy-naïve men and men after previous biopsy. We retrospectively analyzed clinical parameters of 1159 men who underwent mpMRI prior to MRI/transrectal ultrasound fusion biopsy between 2012 and 2015. Multivariate regression analyses were used to determine significant sPC predictors for RM development. The prediction performance was compared with ERSPC-RCs, RCs refitted on our cohort, Prostate Imaging Reporting and Data System (PI-RADS) v1.0, and ERSPC-RC plus PI-RADSv1.0 using receiver-operating characteristics (ROCs). Discrimination and calibration of the RM, as well as net decision and reduction curve analyses were evaluated based on resampling methods. PSA, prostate volume, digital-rectal examination, and PI-RADS were significant sPC predictors and included in the RMs together with age. The ROC area under the curve of the RM for biopsy-naïve men was comparable with ERSPC-RC3 plus PI-RADSv1.0 (0.83 vs 0.84) but larger compared with ERSPC-RC3 (0.81), refitted RC3 (0.80), and PI-RADS (0.76). For postbiopsy men, the novel RM's discrimination (0.81) was higher, compared with PI-RADS (0.78), ERSPC-RC4 (0.66), refitted RC4 (0.76), and ERSPC-RC4 plus PI-RADSv1.0 (0.78). Both RM benefits exceeded those of ERSPC-RCs and PI-RADS in the decision regarding which patient to receive biopsy and enabled the highest reduction rate of unnecessary biopsies. Limitations include a monocentric design and a lack of PI-RADSv2.0. The novel RMs, incorporating clinical parameters and PI-RADS, performed significantly better

  13. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  14. Evaluation of accelerated test parameters for CMOS IC total dose hardness prediction

    International Nuclear Information System (INIS)

    Sogoyan, A.V.; Nikiforov, A.Y.; Chumakov, A.I.

    1999-01-01

    The approach to accelerated test parameters evaluation is presented in order to predict CMOS IC total dose behavior in variable dose-rate environment. The technique is based on the analytical model of MOSFET parameters total dose degradation. The simple way to estimate model parameter is proposed using IC's input-output MOSFET radiation test results. (authors)

  15. Cervical Vertebral Body’s Volume as a New Parameter for Predicting the Skeletal Maturation Stages

    Directory of Open Access Journals (Sweden)

    Youn-Kyung Choi

    2016-01-01

    Full Text Available This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5–18 years of age. We performed Pearson’s correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P<0.05. The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

  16. An oracle: antituberculosis pharmacokinetics-pharmacodynamics, clinical correlation, and clinical trial simulations to predict the future.

    Science.gov (United States)

    Pasipanodya, Jotam; Gumbo, Tawanda

    2011-01-01

    Antimicrobial pharmacokinetic-pharmacodynamic (PK/PD) science and clinical trial simulations have not been adequately applied to the design of doses and dose schedules of antituberculosis regimens because many researchers are skeptical about their clinical applicability. We compared findings of preclinical PK/PD studies of current first-line antituberculosis drugs to findings from several clinical publications that included microbiologic outcome and pharmacokinetic data or had a dose-scheduling design. Without exception, the antimicrobial PK/PD parameters linked to optimal effect were similar in preclinical models and in tuberculosis patients. Thus, exposure-effect relationships derived in the preclinical models can be used in the design of optimal antituberculosis doses, by incorporating population pharmacokinetics of the drugs and MIC distributions in Monte Carlo simulations. When this has been performed, doses and dose schedules of rifampin, isoniazid, pyrazinamide, and moxifloxacin with the potential to shorten antituberculosis therapy have been identified. In addition, different susceptibility breakpoints than those in current use have been identified. These steps outline a more rational approach than that of current methods for designing regimens and predicting outcome so that both new and older antituberculosis agents can shorten therapy duration.

  17. Histopathological Parameters predicting Occult Nodal Metastases in Tongue Carcinoma Cases: An Indian Perspective.

    Science.gov (United States)

    Jacob, Tina Elizabeth; Malathi, N; Rajan, Sharada T; Augustine, Dominic; Manish, N; Patil, Shankargouda

    2016-01-01

    It is a well-established fact that in squamous cell carcinoma cases, the presence of lymph node metastases decreased the 5-year survival rate by 50% and also caused the recurrence of the primary tumor with development of distant metastases. Till date, the predictive factors for occult cervical lymph nodes metastases in cases of tongue squamous cell carcinoma remain inconclusive. Therefore, it is imperative to identify patients who are at the greatest risk for occult cervical metastases. This study was thus performed with the aim to identify various histopathologic parameters of the primary tumor that predict occult nodal metastases. The clinicopathologic features of 56 cases of lateral tongue squamous cell carcinoma with cT1NoMo/cT2NoMo as the stage and without prior radiotherapy or chemotherapy were considered. The surgical excision of primary tumor was followed by elective neck dissection. The glossectomy specimen along with the neck nodes were fixed in formalin and 5 urn thick sections were obtained. The hematoxylin & eosin stained sections were then subjected to microscopic examination. The primary tumor characteristics that were analyzed include tumor grade, invading front, depth of tumor, lymphovascular invasion, perineural invasion and inflammatory response. The nodes were examined for possible metastases using hematoxylin & eosin followed by cytokeratin immunohistochemistry. A total of 12 cases were found with positive occult nodal metastases. On performing univariate analysis, the histopathologic parameters that were found to be statistically significant were lymphovascular invasion (p = 0.004) and perineural invasion (p = 0.003) along with a cut-off depth of infiltration more than 5 mm (p = 0.01). Histopathologic assessment of the primary tumor specimen therefore continues to provide information that is central to guide clinical management, particularly in cases of occult nodal metastases. Clinical significance The study highlights the importance of

  18. Computer simulation for prediction of performance and thermodynamic parameters of high energy materials

    International Nuclear Information System (INIS)

    Muthurajan, H.; Sivabalan, R.; Talawar, M.B.; Asthana, S.N.

    2004-01-01

    A new code viz., Linear Output Thermodynamic User-friendly Software for Energetic Systems (LOTUSES) developed during this work predicts the theoretical performance parameters such as density, detonation factor, velocity of detonation, detonation pressure and thermodynamic properties such as heat of detonation, heat of explosion, volume of explosion gaseous products. The same code also assists in the prediction of possible explosive decomposition products after explosion and power index. The developed code has been validated by calculating the parameters of standard explosives such as TNT, PETN, RDX, and HMX. Theoretically predicated parameters are accurate to the order of ±5% deviation. To the best of our knowledge, no such code is reported in literature which can predict a wide range of characteristics of known/unknown explosives with minimum input parameters. The code can be used to obtain thermochemical and performance parameters of high energy materials (HEMs) with reasonable accuracy. The code has been developed in Visual Basic having enhanced windows environment, and thereby advantages over the conventional codes, written in Fortran. The theoretically predicted HEMs performance can be directly printed as well as stored in text (.txt) or HTML (.htm) or Microsoft Word (.doc) or Adobe Acrobat (.pdf) format in the hard disk. The output can also be copied into the Random Access Memory as clipboard text which can be imported/pasted in other software as in the case of other codes

  19. The usefulness of clinical and laboratory parameters for predicting severity of dehydration in children with acute gastroenteritis.

    Science.gov (United States)

    Hoxha, Teuta Faik; Azemi, Mehmedali; Avdiu, Muharrem; Ismaili-Jaha, Vlora; Grajqevci, Violeta; Petrela, Ela

    2014-10-01

    An accurate assessment of the degree of dehydration in infants and children is important for proper decision-making and treatment. This emphasizes the need for laboratory tests to improve the accuracy of clinical assessment of dehydration. The aim of this study was to assess the relationship between clinical and laboratory parameters in the assessment of dehydration. We evaluated prospectively 200 children aged 1 month to 5 years who presented with diarrhea, vomiting or both. Dehydration assessment was done following a known clinical scheme. We enrolled in the study 200 children (57.5% were male). The mean age was 15.62±9.03 months, with more than half those studied being under 24 months old. Overall, 46.5% (93) had mild dehydration, 34% (68) had moderate dehydration, 5.5% (11) had severe dehydration whereas, 14% (28) had no dehydration. Patients historical clinical variables in all dehydration groups did not differ significantly regarding age, sex, fever, frequency of vomiting, duration of diarrhea and vomiting, while there was a trend toward severe dehydration in children with more frequent diarrhea (p=0.004). Serum urea and creatinine cannot discriminate between mild and moderate dehydration but they showed a good specificity for severe dehydration of 99% and 100% respectively. Serum bicarbonates and base excess decreased significantly with a degree of dehydration and can discriminate between all dehydration groups (Pdehydration status among children presenting with acute gastroenteritis. Serum urea and creatinine were the most specific tests for severe dehydration diagnosis. Historical clinical patterns apart from frequency of diarrhea did not correlate with dehydration status. Further studies are needed to validate our results.

  20. Chest X-rays and associated clinical parameters in pulmonary Tubercolosis cases from the National Tubercolosis Program, Mumbai, India

    Directory of Open Access Journals (Sweden)

    Yatin N. Dholakia

    2012-01-01

    Full Text Available The study was carried out in pulmonary tuberculosis (PTB patients from the local Tuberculosis control programme, Mumbai, India. It examined features of chest X-rays and their correlation with clinical parameters for possible application in suspected multidrug resistant TB (MDRTB and to predict outcome in new and treatment failure PTB cases. X-ray features (infiltrate, cavitation, miliary shadows, pleural effusion, mediastinal lymphadenopathy and extent of lesions were analyzed to identify associations with biological/clinical parameters through univariate and multivariate logistic regression. Failures demonstrated associations between extensive lesions and high glycosylated hemoglobin (GHb levels (P=0.028 and male gender (P=0.03. An association was also detected between cavitation and MDR (P=0.048. In new cases, bilateral cavities were associated with MDR (P=0.018 and male gender (P=0.01, low body mass index with infiltrates (P=0.008, and smoking with cavitation (P=0.0238. Strains belonging to the Manu1 spoligotype were associated with mild lesions (P=0.002. Poor outcome showed borderline significance with extensive lesions at onset (P=0.053. Furthermore, amongst new cases, smoking, the Central Asian Strain (CAS spoligotype and high GHb were associated with cavitation, whereas only CAS spoligotypes and high GHb were associated with extensive lesions. The study highlighted associations between certain clinical parameters and X-ray evidence which support the potential of X-rays to predict TB, MDRTB and poor outcome. The use of Xrays as an additional tool to shorten diagnostic delay and shortlist MDR suspects amongst nonresponders to TB treatment should be explored in a setting with limited resources coping with a high MDR case load such as Mumbai.

  1. Effect of bubble interface parameters on predicted of bubble departure diameter in a narrow channel

    International Nuclear Information System (INIS)

    Xu Jianjun; Xie Tianzhou; Zhou Wenbin; Chen Bingde; Huang Yanping

    2014-01-01

    The predicted model on the bubble departure diameter in a narrow channel is built by analysis of forces acting on the bubble, and effects of bubble interface parameters such as the bubble inclination angle, upstream contact angle, downstream contact angle and bubble contact diameter on predicted bubble departure diameters in a narrow channel are analysed by comparing with the visual experimental data. Based on the above results, the bubble interface parameters as the input parameters used to obtain the bubble departure diameter in a narrow channel are assured, and the bubble departure diameters in a narrow channel are predicted by solving the force equation. The predicted bubble departure diameters are verified by the 58 bubble departure diameters obtained from the vertical and inclined visual experiment, and the predicted results agree with the experimental results. The different forces acting on the bubble are obtained and the effect of thermal parameters in this experiment on bubble departure diameters is analysed. (authors)

  2. Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

    Science.gov (United States)

    Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

    2016-07-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. Reflow Process Parameters Analysis and Reliability Prediction Considering Multiple Characteristic Values

    Directory of Open Access Journals (Sweden)

    Guo Yu

    2016-01-01

    Full Text Available As a major step surface mount technology, reflow process is the key factor affecting the quality of the final product. The setting parameters and characteristic value of temperature curve shows a nonlinear relationship. So parameter impacts on characteristic values are analyzed and the parameters adjustment process based on orthogonal experiment is proposed in the paper. First, setting parameters are determined and the orthogonal test is designed according to production conditions. Then each characteristic value for temperature profile is calculated. Further, multi-index orthogonal experiment is analyzed for acquiring the setting parameters which impacts the PCBA product quality greater. Finally, reliability prediction is carried out considering the main influencing parameters for providing a theoretical basis of parameters adjustment and product quality evaluation in engineering process.

  4. Infection parameters in the sand fly vector that predict transmission of Leishmania major.

    Science.gov (United States)

    Stamper, Lisa W; Patrick, Rachel L; Fay, Michael P; Lawyer, Phillip G; Elnaiem, Dia-Eldin A; Secundino, Nagila; Debrabant, Alain; Sacks, David L; Peters, Nathan C

    2011-08-01

    To identify parameters of Leishmania infection within a population of infected sand flies that reliably predict subsequent transmission to the mammalian host, we sampled groups of infected flies and compared infection intensity and degree of metacyclogenesis with the frequency of transmission. The percentage of parasites within the midgut that were metacyclic promastigotes had the highest correlation with the frequency of transmission. Meta-analysis of multiple transmission experiments allowed us to establish a percent-metacyclic "cutoff" value that predicted transmission competence. Sand fly infections initiated with variable doses of parasites resulted in correspondingly altered percentages of metacyclic promastigotes, resulting in altered transmission frequency and disease severity. Lastly, alteration of sand fly oviposition status and environmental conditions at the time of transmission also influenced transmission frequency. These observations have implications for transmission of Leishmania by the sand fly vector in both the laboratory and in nature, including how the number of organisms acquired by the sand fly from an infection reservoir may influence the clinical outcome of infection following transmission by bite.

  5. Predicting plant biomass accumulation from image-derived parameters

    Science.gov (United States)

    Chen, Dijun; Shi, Rongli; Pape, Jean-Michel; Neumann, Kerstin; Graner, Andreas; Chen, Ming; Klukas, Christian

    2018-01-01

    Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species. PMID:29346559

  6. A proposal of parameter to predict biaxial fatigue life for CF8M cast stainless steels

    International Nuclear Information System (INIS)

    Park, Joong Cheul; Kwon, Jae Do

    2005-01-01

    Biaxial low cycle fatigue test was carried out to predict fatigue life under combined axial-torsional loading condition which is that of in-phase and out-of-phase for CF8M cast stainless steels. Fatemi Socie(FS) parameter which is based on critical plane approach is not only one of methods but also the best method that can predict fatigue life under biaxial loading condition. But the result showed that, biaxial fatigue life prediction by using FS parameter with several different parameters for the CF8M cast stainless steels is not conservative but best results. So in this present research, we proposed new fatigue life prediction parameter considering effective shear stress instead of FS parameter which considers the maximum normal stress acting on maximum shear strain and its effectiveness was verified

  7. Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea

    KAUST Repository

    Sawlan, Zaid A

    2012-01-01

    parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while

  8. Using neural networks for prediction of nuclear parameters

    Energy Technology Data Exchange (ETDEWEB)

    Pereira Filho, Leonidas; Souto, Kelling Cabral, E-mail: leonidasmilenium@hotmail.com, E-mail: kcsouto@bol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ (Brazil); Machado, Marcelo Dornellas, E-mail: dornemd@eletronuclear.gov.br [Eletrobras Termonuclear S.A. (GCN.T/ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear

    2013-07-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  9. Using neural networks for prediction of nuclear parameters

    International Nuclear Information System (INIS)

    Pereira Filho, Leonidas; Souto, Kelling Cabral; Machado, Marcelo Dornellas

    2013-01-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  10. Prediction of betavoltaic battery output parameters based on SEM measurements and Monte Carlo simulation

    International Nuclear Information System (INIS)

    Yakimov, Eugene B.

    2016-01-01

    An approach for a prediction of "6"3Ni-based betavoltaic battery output parameters is described. It consists of multilayer Monte Carlo simulation to obtain the depth dependence of excess carrier generation rate inside the semiconductor converter, a determination of collection probability based on the electron beam induced current measurements, a calculation of current induced in the semiconductor converter by beta-radiation, and SEM measurements of output parameters using the calculated induced current value. Such approach allows to predict the betavoltaic battery parameters and optimize the converter design for any real semiconductor structure and any thickness and specific activity of beta-radiation source. - Highlights: • New procedure for betavoltaic battery output parameters prediction is described. • A depth dependence of beta particle energy deposition for Si and SiC is calculated. • Electron trajectories are assumed isotropic and uniformly started under simulation.

  11. Predictive value of different conventional and non-conventional MRI-parameters for specific domains of cognitive function in multiple sclerosis.

    Science.gov (United States)

    Pinter, Daniela; Khalil, Michael; Pichler, Alexander; Langkammer, Christian; Ropele, Stefan; Marschik, Peter B; Fuchs, Siegrid; Fazekas, Franz; Enzinger, Christian

    2015-01-01

    While many studies correlated cognitive function with changes in brain morphology in multiple sclerosis (MS), few of them used a multi-parametric approach in a single dataset so far. We thus here assessed the predictive value of different conventional and quantitative MRI-parameters both for overall and domain-specific cognitive performance in MS patients from a single center. 69 patients (17 clinically isolated syndrome, 47 relapsing-remitting MS, 5 secondary-progressive MS) underwent the "Brief Repeatable Battery of Neuropsychological Tests" assessing overall cognition, cognitive efficiency and memory function as well as MRI at 3 Tesla to obtain T2-lesion load (T2-LL), normalized brain volume (global brain volume loss), normalized cortical volume (NCV), normalized thalamic volume (NTV), normalized hippocampal volume (NHV), normalized caudate nuclei volume (NCNV), basal ganglia R2* values (iron deposition) and magnetization transfer ratios (MTRs) for cortex and normal appearing brain tissue (NABT). Regression models including clinical, demographic variables and MRI-parameters explained 22-27% of variance of overall cognition, 17-26% of cognitive efficiency and 22-23% of memory. NCV, T2-LL and MTR of NABT were the strongest predictors of overall cognitive function. Cognitive efficiency was best predicted by NCV, T2-LL and iron deposition in the basal ganglia. NTV was the strongest predictor for memory function and NHV was particularly related to memory function. The predictive value of distinct MRI-parameters differs for specific domains of cognitive function, with a greater impact of cortical volume, focal and diffuse white matter abnormalities on overall cognitive function, an additional role of basal ganglia iron deposition on cognitive efficiency, and thalamic and hippocampal volume on memory function. This suggests the usefulness of using multiparametric MRI to assess (micro)structural correlates of different cognitive constructs.

  12. Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry.

    Directory of Open Access Journals (Sweden)

    Jochen Frenzel

    Full Text Available BACKGROUND: Peptide patterns of bronchoalveolar lavage fluid (BALF were assumed to reflect the complex pathology of acute lung injury (ALI/acute respiratory distress syndrome (ARDS better than clinical and inflammatory parameters and may be superior for outcome prediction. METHODOLOGY/PRINCIPAL FINDINGS: A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS. Receiver operating characteristic (ROC analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853. Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART analysis and support vector machine (SVM algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953. Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. CONCLUSIONS/SIGNIFICANCE: MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate

  13. The Usefulness of Clinical and Laboratory Parameters for Predicting Severity of Dehydration in Children with Acute Gastroenteritis

    Science.gov (United States)

    Hoxha, Teuta Faik; Azemi, Mehmedali; Avdiu, Muharrem; Ismaili-jaha, Vlora; Grajqevci, Violeta; Petrela, Ela

    2014-01-01

    ABSTRACT Background: An accurate assessment of the degree of dehydration in infants and children is important for proper decision-making and treatment. This emphasizes the need for laboratory tests to improve the accuracy of clinical assessment of dehydration. The aim of this study was to assess the relationship between clinical and laboratory parameters in the assessment of dehydration. Methods: We evaluated prospectively 200 children aged 1 month to 5 years who presented with diarrhea, vomiting or both. Dehydration assessment was done following a known clinical scheme. Results: We enrolled in the study 200 children (57.5% were male). The mean age was 15.62±9.03 months, with more than half those studied being under 24 months old. Overall, 46.5% (93) had mild dehydration, 34% (68) had moderate dehydration, 5.5% (11) had severe dehydration whereas, 14% (28) had no dehydration. Patients historical clinical variables in all dehydration groups did not differ significantly regarding age, sex, fever, frequency of vomiting, duration of diarrhea and vomiting, while there was a trend toward severe dehydration in children with more frequent diarrhea (p=0.004). Serum urea and creatinine cannot discriminate between mild and moderate dehydration but they showed a good specificity for severe dehydration of 99% and 100% respectively. Serum bicarbonates and base excess decreased significantly with a degree of dehydration and can discriminate between all dehydration groups (P<0.001). Conclusion: Blood gases were useful to diagnose the degree of dehydration status among children presenting with acute gastroenteritis. Serum urea and creatinine were the most specific tests for severe dehydration diagnosis. Historical clinical patterns apart from frequency of diarrhea did not correlate with dehydration status. Further studies are needed to validate our results. PMID:25568559

  14. Bread Affects Clinical Parameters and Induces Gut Microbiome-Associated Personal Glycemic Responses.

    Science.gov (United States)

    Korem, Tal; Zeevi, David; Zmora, Niv; Weissbrod, Omer; Bar, Noam; Lotan-Pompan, Maya; Avnit-Sagi, Tali; Kosower, Noa; Malka, Gal; Rein, Michal; Suez, Jotham; Goldberg, Ben Z; Weinberger, Adina; Levy, Avraham A; Elinav, Eran; Segal, Eran

    2017-06-06

    Bread is consumed daily by billions of people, yet evidence regarding its clinical effects is contradicting. Here, we performed a randomized crossover trial of two 1-week-long dietary interventions comprising consumption of either traditionally made sourdough-leavened whole-grain bread or industrially made white bread. We found no significant differential effects of bread type on multiple clinical parameters. The gut microbiota composition remained person specific throughout this trial and was generally resilient to the intervention. We demonstrate statistically significant interpersonal variability in the glycemic response to different bread types, suggesting that the lack of phenotypic difference between the bread types stems from a person-specific effect. We further show that the type of bread that induces the lower glycemic response in each person can be predicted based solely on microbiome data prior to the intervention. Together, we present marked personalization in both bread metabolism and the gut microbiome, suggesting that understanding dietary effects requires integration of person-specific factors. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

    Directory of Open Access Journals (Sweden)

    Gergely Takács

    2014-01-01

    Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

  16. Development of computer code for determining prediction parameters of radionuclide migration in soil layer

    International Nuclear Information System (INIS)

    Ogawa, Hiromichi; Ohnuki, Toshihiko

    1986-07-01

    A computer code (MIGSTEM-FIT) has been developed to determine the prediction parameters, retardation factor, water flow velocity, dispersion coefficient, etc., of radionuclide migration in soil layer from the concentration distribution of radionuclide in soil layer or in effluent. In this code, the solution of the predicting equation for radionuclide migration is compared with the concentration distribution measured, and the most adequate values of parameter can be determined by the flexible tolerance method. The validity of finite differential method, which was one of the method to solve the predicting equation, was confirmed by comparison with the analytical solution, and also the validity of fitting method was confirmed by the fitting of the concentration distribution calculated from known parameters. From the examination about the error, it was found that the error of the parameter obtained by using this code was smaller than that of the concentration distribution measured. (author)

  17. Cognitive Models of Risky Choice: Parameter Stability and Predictive Accuracy of Prospect Theory

    Science.gov (United States)

    Glockner, Andreas; Pachur, Thorsten

    2012-01-01

    In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are…

  18. Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal 18F-FDG PET Features, Clinical Parameters, and Demographics

    International Nuclear Information System (INIS)

    Zhang, Hao; Tan, Shan; Chen, Wengen; Kligerman, Seth; Kim, Grace; D'Souza, Warren D.; Suntharalingam, Mohan; Lu, Wei

    2014-01-01

    Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent 18 F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV] max , tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than

  19. Clinical implications in the use of the PBC algorithm versus the AAA by comparison of different NTCP models/parameters.

    Science.gov (United States)

    Bufacchi, Antonella; Nardiello, Barbara; Capparella, Roberto; Begnozzi, Luisa

    2013-07-04

    Retrospective analysis of 3D clinical treatment plans to investigate qualitative, possible, clinical consequences of the use of PBC versus AAA. The 3D dose distributions of 80 treatment plans at four different tumour sites, produced using PBC algorithm, were recalculated using AAA and the same number of monitor units provided by PBC and clinically delivered to each patient; the consequences of the difference on the dose-effect relations for normal tissue injury were studied by comparing different NTCP model/parameters extracted from a review of published studies. In this study the AAA dose calculation is considered as benchmark data. The paired Student t-test was used for statistical comparison of all results obtained from the use of the two algorithms. In the prostate plans, the AAA predicted lower NTCP value (NTCPAAA) for the risk of late rectal bleeding for each of the seven combinations of NTCP parameters, the maximum mean decrease was 2.2%. In the head-and-neck treatments, each combination of parameters used for the risk of xerostemia from irradiation of the parotid glands involved lower NTCPAAA, that varied from 12.8% (sd=3.0%) to 57.5% (sd=4.0%), while when the PBC algorithm was used the NTCPPBC's ranging was from 15.2% (sd=2.7%) to 63.8% (sd=3.8%), according the combination of parameters used; the differences were statistically significant. Also NTCPAAA regarding the risk of radiation pneumonitis in the lung treatments was found to be lower than NTCPPBC for each of the eight sets of NTCP parameters; the maximum mean decrease was 4.5%. A mean increase of 4.3% was found when the NTCPAAA was calculated by the parameters evaluated from dose distribution calculated by a convolution-superposition (CS) algorithm. A markedly different pattern was observed for the risk relating to the development of pneumonitis following breast treatments: the AAA predicted higher NTCP value. The mean NTCPAAA varied from 0.2% (sd = 0.1%) to 2.1% (sd = 0.3%), while the mean NTCPPBC

  20. Clinical parameters associated with periodontitis in untreated persons

    NARCIS (Netherlands)

    Lembariti, BS; Van't Hof, MA; Pilot, T; Van Palenstein-Helderman, WH

    The purpose of this study was to investigate the relationship between clinical parameters and periodontitis in a population receiving no regular prophylactic dental care. From a sample of 164 adult rural and urban Tanzanian subjects aged between 30 and 44 years, 16% were identified with

  1. Diagnostic utility of clinical and biochemical parameters in ...

    African Journals Online (AJOL)

    Diagnostic utility of clinical and biochemical parameters in pancreatic head malignancy patients with normal carbohydrate antigen 19-9 levels. Xiaoli Jin1, Yulian Wu2. 1. Department of Surgery, Sir Run Run Shaw Hospital College of Medicine, Zhejiang University, 3 Qingchun. Road East, Hangzhou, Zhejiang Province ...

  2. Combining Physical and Biologic Parameters to Predict Radiation-Induced Lung Toxicity in Patients With Non-Small-Cell Lung Cancer Treated With Definitive Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Stenmark, Matthew H. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Cai Xuwei [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shanghai Cancer Hospital, Fudan University, Shanghai (China); Shedden, Kerby [Department of Biostatistics, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Hayman, James A. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Yuan Shuanghu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shangdong Cancer Hospital, Jinan (China); Ritter, Timothy [Veterans Affairs Medical Center, Ann Arbor, Michigan (United States); Ten Haken, Randall K.; Lawrence, Theodore S. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Kong Fengming, E-mail: fengkong@med.umich.edu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Veterans Affairs Medical Center, Ann Arbor, Michigan (United States)

    2012-10-01

    Purpose: To investigate the plasma dynamics of 5 proinflammatory/fibrogenic cytokines, including interleukin-1beta (IL-1{beta}), IL-6, IL-8, tumor necrosis factor alpha (TNF-{alpha}), and transforming growth factor beta1 (TGF-{beta}1) to ascertain their value in predicting radiation-induced lung toxicity (RILT), both individually and in combination with physical dosimetric parameters. Methods and Materials: Treatments of patients receiving definitive conventionally fractionated radiation therapy (RT) on clinical trial for inoperable stages I-III lung cancer were prospectively evaluated. Circulating cytokine levels were measured prior to and at weeks 2 and 4 during RT. The primary endpoint was symptomatic RILT, defined as grade 2 and higher radiation pneumonitis or symptomatic pulmonary fibrosis. Minimum follow-up was 18 months. Results: Of 58 eligible patients, 10 (17.2%) patients developed RILT. Lower pretreatment IL-8 levels were significantly correlated with development of RILT, while radiation-induced elevations of TGF-ss1 were weakly correlated with RILT. Significant correlations were not found for any of the remaining 3 cytokines or for any clinical or dosimetric parameters. Using receiver operator characteristic curves for predictive risk assessment modeling, we found both individual cytokines and dosimetric parameters were poor independent predictors of RILT. However, combining IL-8, TGF-ss1, and mean lung dose into a single model yielded an improved predictive ability (P<.001) compared to either variable alone. Conclusions: Combining inflammatory cytokines with physical dosimetric factors may provide a more accurate model for RILT prediction. Future study with a larger number of cases and events is needed to validate such findings.

  3. Comparison of statistical and clinical predictions of functional outcome after ischemic stroke.

    Directory of Open Access Journals (Sweden)

    Douglas D Thompson

    Full Text Available To determine whether the predictions of functional outcome after ischemic stroke made at the bedside using a doctor's clinical experience were more or less accurate than the predictions made by clinical prediction models (CPMs.A prospective cohort study of nine hundred and thirty one ischemic stroke patients recruited consecutively at the outpatient, inpatient and emergency departments of the Western General Hospital, Edinburgh between 2002 and 2005. Doctors made informal predictions of six month functional outcome on the Oxford Handicap Scale (OHS. Patients were followed up at six months with a validated postal questionnaire. For each patient we calculated the absolute predicted risk of death or dependence (OHS≥3 using five previously described CPMs. The specificity of a doctor's informal predictions of OHS≥3 at six months was good 0.96 (95% CI: 0.94 to 0.97 and similar to CPMs (range 0.94 to 0.96; however the sensitivity of both informal clinical predictions 0.44 (95% CI: 0.39 to 0.49 and clinical prediction models (range 0.38 to 0.45 was poor. The prediction of the level of disability after stroke was similar for informal clinical predictions (ordinal c-statistic 0.74 with 95% CI 0.72 to 0.76 and CPMs (range 0.69 to 0.75. No patient or clinician characteristic affected the accuracy of informal predictions, though predictions were more accurate in outpatients.CPMs are at least as good as informal clinical predictions in discriminating between good and bad functional outcome after ischemic stroke. The place of these models in clinical practice has yet to be determined.

  4. Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

    Science.gov (United States)

    El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

    2015-04-01

    This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

  5. Predictive Factors of Clinical Response of Infliximab Therapy in Active Nonradiographic Axial Spondyloarthritis Patients

    Directory of Open Access Journals (Sweden)

    Zhiming Lin

    2015-01-01

    Full Text Available Objectives. To evaluate the efficiency and the predictive factors of clinical response of infliximab in active nonradiographic axial spondyloarthritis patients. Methods. Active nonradiographic patients fulfilling ESSG criteria for SpA but not fulfilling modified New York criteria were included. All patients received infliximab treatment for 24 weeks. The primary endpoint was ASAS20 response at weeks 12 and 24. The abilities of baseline parameters and response at week 2 to predict ASAS20 response at weeks 12 and 24 were assessed using ROC curve and logistic regression analysis, respectively. Results. Of 70 axial SpA patients included, the proportions of patients achieving an ASAS20 response at weeks 2, 6, 12, and 24 were 85.7%, 88.6%, 87.1%, and 84.3%, respectively. Baseline MRI sacroiliitis score (AUC = 0.791; P=0.005, CRP (AUC = 0.75; P=0.017, and ASDAS (AUC = 0.778, P=0.007 significantly predicted ASAS20 response at week 12. However, only ASDAS (AUC = 0.696, P=0.040 significantly predicted ASAS20 response at week 24. Achievement of ASAS20 response after the first infliximab infusion was a significant predictor of subsequent ASAS20 response at weeks 12 and 24 (wald χ2=6.87, P=0.009, and wald χ2=5.171, P=0.023. Conclusions. Infliximab shows efficiency in active nonradiographic axial spondyloarthritis patients. ASDAS score and first-dose response could help predicting clinical efficacy of infliximab therapy in these patients.

  6. Are traditional cognitive tests useful in predicting clinical success?

    Science.gov (United States)

    Gray, Sarah A; Deem, Lisa P; Straja, Sorin R

    2002-11-01

    The purpose of this research was to determine the predictive value of the Dental Admission Test (DAT) for clinical success using Ackerman's theory of ability determinants of skilled performance. The Ackerman theory is a valid, reliable schema in the applied psychology literature used to predict complex skill acquisition. Inconsistent stimulus-response skill acquisition depends primarily on determinants of cognitive ability. Consistent information-processing tasks have been described as "automatic," in which stimuli and responses are mapped in a manner that allows for complete certainty once the relationships have been learned. It is theorized that the skills necessary for success in the clinical component of dental schools involve a significant amount of automatic processing demands and, as such, student performance in the clinics should begin to converge as task practice is realized and tasks become more consistent. Subtest scores of the DAT of four classes were correlated with final grades in nine clinical courses. Results showed that the DAT subtest scores played virtually no role with regard to the final clinical grades. Based on this information, the DAT scores were determined to be of no predictive value in clinical achievement.

  7. Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

    Science.gov (United States)

    Doherty, John E.; Hunt, Randall J.; Tonkin, Matthew J.

    2010-01-01

    Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in

  8. Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

    OpenAIRE

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor, Maureen

    2014-01-01

    We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial i...

  9. Predictive significance of standardized uptake value parameters of FDG-PET in patients with non-small cell lung carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Duan, X-Y.; Wang, W.; Li, M.; Li, Y.; Guo, Y-M. [PET-CT Center, The First Affiliated Hospital of Xi' an, Jiaotong University, Xi' an, Shaanxi (China)

    2015-02-03

    {sup 18}F-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is widely used to diagnose and stage non-small cell lung cancer (NSCLC). The aim of this retrospective study was to evaluate the predictive ability of different FDG standardized uptake values (SUVs) in 74 patients with newly diagnosed NSCLC. {sup 18}F-FDG PET/CT scans were performed and different SUV parameters (SUV{sub max}, SUV{sub avg}, SUV{sub T/L}, and SUV{sub T/A}) obtained, and their relationship with clinical characteristics were investigated. Meanwhile, correlation and multiple stepwise regression analyses were performed to determine the primary predictor of SUVs for NSCLC. Age, gender, and tumor size significantly affected SUV parameters. The mean SUVs of squamous cell carcinoma were higher than those of adenocarcinoma. Poorly differentiated tumors exhibited higher SUVs than well-differentiated ones. Further analyses based on the pathologic type revealed that the SUV{sub max}, SUV{sub avg}, and SUV{sub T/L} of poorly differentiated adenocarcinoma tumors were higher than those of moderately or well-differentiated tumors. Among these four SUV parameters, SUV{sub T/L} was the primary predictor for tumor differentiation. However, in adenocarcinoma, SUV{sub max} was the determining factor for tumor differentiation. Our results showed that these four SUV parameters had predictive significance related to NSCLC tumor differentiation; SUV{sub T/L} appeared to be most useful overall, but SUV{sub max} was the best index for adenocarcinoma tumor differentiation.

  10. Incidence and clinical vital parameters in primary ketosis of Murrah buffaloes

    Science.gov (United States)

    Kumar, Ankit; Sindhu, Neelesh; Kumar, Parmod; Kumar, Tarun; Charaya, Gaurav; Surbhi; Jain, V. K.; Sridhar

    2015-01-01

    Aim: The present study was undertaken to ascertain the incidence and clinical vital parameters in cases of primary ketosis in Murrah buffaloes brought to teaching veterinary clinical complex, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar and from adjoining villages of the district Hisar, Haryana, India. Materials and Methods: The investigation was conducted on 24 clinical cases (out of total 145 screened) of primary ketosis. The diagnosis was confirmed on the basis of clinical signs and significantly positive two tests for ketone bodies in urine (Rothera’s and Keto-Diastix strip test). Data collected were statistically analyzed using independent Student’s t-test. Results: Overall incidence of disease in these areas was found to be 16.55% and all the animals were recently parturited (mean: 1.42±0.14 month), on an average in their third lactation (mean: 2.38±0.30) and exhibited clinical signs such as selective anorexia (refusal to feed on concentrate diet), drastic reduction in milk yield (mean: 64.4±5.35%), ketotic odor from urine, breath, and milk and rapid loss of body condition. All the clinical vital parameters in ketotic buffaloes (body temperature, heart rate, respiration rate, and rumen movements) were within normal range. Conclusion: Primary ketosis in Murrah buffaloes was the most common seen in the third lactation, within the first 2 months after parturition with characteristics clinical signs and no variability in vital parameters. The disease has severe effect on the production status of affected animal. PMID:27047203

  11. Incidence and clinical vital parameters in primary ketosis of Murrah buffaloes

    Directory of Open Access Journals (Sweden)

    Ankit Kumar

    2015-09-01

    Full Text Available Aim: The present study was undertaken to ascertain the incidence and clinical vital parameters in cases of primary ketosis in Murrah buffaloes brought to teaching veterinary clinical complex, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar and from adjoining villages of the district Hisar, Haryana, India. Materials and Methods: The investigation was conducted on 24 clinical cases (out of total 145 screened of primary ketosis. The diagnosis was confirmed on the basis of clinical signs and significantly positive two tests for ketone bodies in urine (Rothera’s and Keto-Diastix strip test. Data collected were statistically analyzed using independent Student’s t-test. Results: Overall incidence of disease in these areas was found to be 16.55% and all the animals were recently parturited (mean: 1.42±0.14 month, on an average in their third lactation (mean: 2.38±0.30 and exhibited clinical signs such as selective anorexia (refusal to feed on concentrate diet, drastic reduction in milk yield (mean: 64.4±5.35%, ketotic odor from urine, breath, and milk and rapid loss of body condition. All the clinical vital parameters in ketotic buffaloes (body temperature, heart rate, respiration rate, and rumen movements were within normal range. Conclusion: Primary ketosis in Murrah buffaloes was the most common seen in the third lactation, within the first 2 months after parturition with characteristics clinical signs and no variability in vital parameters. The disease has severe effect on the production status of affected animal.

  12. Application of ann for predicting water quality parameters in the mediterranean sea along gaza-palestine

    International Nuclear Information System (INIS)

    Zaqoot, H.A.; Unar, M.A.

    2008-01-01

    Seawater pollution problems are gaining interest world wide because of their health impacts and other environmental issues. Intense human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. This paper is concerned with the use of ANNs (Artificial Neural Networks) MLP ( Multilayer Perceptron) model for the prediction of pH and EC (Electrical Conductivity) in water quality parameters along Gaza city coast. MLP neural networks are trained and developed with reference to three major oceanographic parameters (water temperature, wind speed and turbidity) to predict the values of pH and EC; these parameters are considered as inputs of the neural network. The data collected comprised of four years and collected from nine locations along Gaza coastline. Results show that the model has high capability and accuracy in predicting both parameters. The network performance has been validated with different data sets and the results show satisfactory performance. Results of the developed model have been compared with multiple regression statistical models and found that MLP predictions are slightly better than the conventional methods. Prediction results prove that the proposed approach is suitable for modeling the water quality in the Mediterranean Sea along Gaza. (author)

  13. Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters

    Directory of Open Access Journals (Sweden)

    Simon van Mourik

    2014-06-01

    Full Text Available Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.

  14. Parameters Online Detection and Model Predictive Control during the Grain Drying Process

    Directory of Open Access Journals (Sweden)

    Lihui Zhang

    2013-01-01

    Full Text Available In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.

  15. What predicts inattention in adolescents? An experience-sampling study comparing chronotype, subjective, and objective sleep parameters.

    Science.gov (United States)

    Hennig, Timo; Krkovic, Katarina; Lincoln, Tania M

    2017-10-01

    Many adolescents sleep insufficiently, which may negatively affect their functioning during the day. To improve sleep interventions, we need a better understanding of the specific sleep-related parameters that predict poor functioning. We investigated to which extent subjective and objective parameters of sleep in the preceding night (state parameters) and the trait variable chronotype predict daytime inattention as an indicator of poor functioning. We conducted an experience-sampling study over one week with 61 adolescents (30 girls, 31 boys; mean age = 15.5 years, standard deviation = 1.1 years). Participants rated their inattention two times each day (morning, afternoon) on a smartphone. Subjective sleep parameters (feeling rested, positive affect upon awakening) were assessed each morning on the smartphone. Objective sleep parameters (total sleep time, sleep efficiency, wake after sleep onset) were assessed with a permanently worn actigraph. Chronotype was assessed with a self-rated questionnaire at baseline. We tested the effect of subjective and objective state parameters of sleep on daytime inattention, using multilevel multiple regressions. Then, we tested whether the putative effect of the trait parameter chronotype on inattention is mediated through state sleep parameters, again using multilevel regressions. We found that short sleep time, but no other state sleep parameter, predicted inattention to a small effect. As expected, the trait parameter chronotype also predicted inattention: morningness was associated with less inattention. However, this association was not mediated by state sleep parameters. Our results indicate that short sleep time causes inattention in adolescents. Extended sleep time might thus alleviate inattention to some extent. However, it cannot alleviate the effect of being an 'owl'. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

    Science.gov (United States)

    Ray, Shonket; Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

    2016-03-01

    This work details a methodology to obtain optimal parameter values for a locally-adaptive texture analysis algorithm that extracts mammographic texture features representative of breast parenchymal complexity for predicting falsepositive (FP) recalls from breast cancer screening with digital mammography. The algorithm has two components: (1) adaptive selection of localized regions of interest (ROIs) and (2) Haralick texture feature extraction via Gray- Level Co-Occurrence Matrices (GLCM). The following parameters were systematically varied: mammographic views used, upper limit of the ROI window size used for adaptive ROI selection, GLCM distance offsets, and gray levels (binning) used for feature extraction. Each iteration per parameter set had logistic regression with stepwise feature selection performed on a clinical screening cohort of 474 non-recalled women and 68 FP recalled women; FP recall prediction was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC) and associations between the extracted features and FP recall were assessed via odds ratios (OR). A default instance of mediolateral (MLO) view, upper ROI size limit of 143.36 mm (2048 pixels2), GLCM distance offset combination range of 0.07 to 0.84 mm (1 to 12 pixels) and 16 GLCM gray levels was set. The highest ROC performance value of AUC=0.77 [95% confidence intervals: 0.71-0.83] was obtained at three specific instances: the default instance, upper ROI window equal to 17.92 mm (256 pixels2), and gray levels set to 128. The texture feature of sum average was chosen as a statistically significant (p<0.05) predictor and associated with higher odds of FP recall for 12 out of 14 total instances.

  17. Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Bambang Wahono

    2015-07-01

    Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.

  18. The Clinical Prediction of Dangerousness.

    Science.gov (United States)

    1985-05-01

    8217 8 ings. Szasz (1963) has argued persuasively that clinical predictions of future dangerous behavior are unfairly focused on the mentally ill...Persons labeled paranoid, Szasz states, are readily commitable, while highly dangerous drunken drivers are not. Indeed, dangerousness such as that...Psychology, 31, 492-494. Szasz , T. (1963). Law, liberty and psychiatry. New York: Macmillan. Taft, R. (1955). The ability to judge people. Psychological

  19. Clinical implications in the use of the PBC algorithm versus the AAA by comparison of different NTCP models/parameters

    International Nuclear Information System (INIS)

    Bufacchi, Antonella; Nardiello, Barbara; Capparella, Roberto; Begnozzi, Luisa

    2013-01-01

    Retrospective analysis of 3D clinical treatment plans to investigate qualitative, possible, clinical consequences of the use of PBC versus AAA. The 3D dose distributions of 80 treatment plans at four different tumour sites, produced using PBC algorithm, were recalculated using AAA and the same number of monitor units provided by PBC and clinically delivered to each patient; the consequences of the difference on the dose-effect relations for normal tissue injury were studied by comparing different NTCP model/parameters extracted from a review of published studies. In this study the AAA dose calculation is considered as benchmark data. The paired Student t-test was used for statistical comparison of all results obtained from the use of the two algorithms. In the prostate plans, the AAA predicted lower NTCP value (NTCP AAA ) for the risk of late rectal bleeding for each of the seven combinations of NTCP parameters, the maximum mean decrease was 2.2%. In the head-and-neck treatments, each combination of parameters used for the risk of xerostemia from irradiation of the parotid glands involved lower NTCP AAA , that varied from 12.8% (sd=3.0%) to 57.5% (sd=4.0%), while when the PBC algorithm was used the NTCP PBC ’s ranging was from 15.2% (sd=2.7%) to 63.8% (sd=3.8%), according the combination of parameters used; the differences were statistically significant. Also NTCP AAA regarding the risk of radiation pneumonitis in the lung treatments was found to be lower than NTCP PBC for each of the eight sets of NTCP parameters; the maximum mean decrease was 4.5%. A mean increase of 4.3% was found when the NTCP AAA was calculated by the parameters evaluated from dose distribution calculated by a convolution-superposition (CS) algorithm. A markedly different pattern was observed for the risk relating to the development of pneumonitis following breast treatments: the AAA predicted higher NTCP value. The mean NTCP AAA varied from 0.2% (sd = 0.1%) to 2.1% (sd = 0.3%), while the

  20. Reference values of clinical chemistry and hematology parameters in rhesus monkeys (Macaca mulatta).

    Science.gov (United States)

    Chen, Younan; Qin, Shengfang; Ding, Yang; Wei, Lingling; Zhang, Jie; Li, Hongxia; Bu, Hong; Lu, Yanrong; Cheng, Jingqiu

    2009-01-01

    Rhesus monkey models are valuable to the studies of human biology. Reference values for clinical chemistry and hematology parameters of rhesus monkeys are required for proper data interpretation. Whole blood was collected from 36 healthy Chinese rhesus monkeys (Macaca mulatta) of either sex, 3 to 5 yr old. Routine chemistry and hematology parameters, and some special coagulation parameters including thromboelastograph and activities of coagulation factors were tested. We presented here the baseline values of clinical chemistry and hematology parameters in normal Chinese rhesus monkeys. These data may provide valuable information for veterinarians and investigators using rhesus monkeys in experimental studies.

  1. Mathematical models to predict rheological parameters of lateritic hydromixtures

    Directory of Open Access Journals (Sweden)

    Gabriel Hernández-Ramírez

    2017-10-01

    Full Text Available The present work had as objective to establish mathematical models that allow the prognosis of the rheological parameters of the lateritic pulp at concentrations of solids from 35% to 48%, temperature of the preheated hydromixture superior to 82 ° C and number of mineral between 3 and 16. Four samples of lateritic pulp were used in the study at different process locations. The results allowed defining that the plastic properties of the lateritic pulp in the conditions of this study conform to the Herschel-Bulkley model for real plastics. In addition, they show that for current operating conditions, even for new situations, UPD mathematical models have a greater ability to predict rheological parameters than least squares mathematical models.

  2. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

    Science.gov (United States)

    Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

    2015-09-15

    The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Three-tiered risk stratification model to predict progression in Barrett's esophagus using epigenetic and clinical features.

    Directory of Open Access Journals (Sweden)

    Fumiaki Sato

    2008-04-01

    Full Text Available Barrett's esophagus predisposes to esophageal adenocarcinoma. However, the value of endoscopic surveillance in Barrett's esophagus has been debated because of the low incidence of esophageal adenocarcinoma in Barrett's esophagus. Moreover, high inter-observer and sampling-dependent variation in the histologic staging of dysplasia make clinical risk assessment problematic. In this study, we developed a 3-tiered risk stratification strategy, based on systematically selected epigenetic and clinical parameters, to improve Barrett's esophagus surveillance efficiency.We defined high-grade dysplasia as endpoint of progression, and Barrett's esophagus progressor patients as Barrett's esophagus patients with either no dysplasia or low-grade dysplasia who later developed high-grade dysplasia or esophageal adenocarcinoma. We analyzed 4 epigenetic and 3 clinical parameters in 118 Barrett's esophagus tissues obtained from 35 progressor and 27 non-progressor Barrett's esophagus patients from Baltimore Veterans Affairs Maryland Health Care Systems and Mayo Clinic. Based on 2-year and 4-year prediction models using linear discriminant analysis (area under the receiver-operator characteristic (ROC curve: 0.8386 and 0.7910, respectively, Barrett's esophagus specimens were stratified into high-risk (HR, intermediate-risk (IR, or low-risk (LR groups. This 3-tiered stratification method retained both the high specificity of the 2-year model and the high sensitivity of the 4-year model. Progression-free survivals differed significantly among the 3 risk groups, with p = 0.0022 (HR vs. IR and p<0.0001 (HR or IR vs. LR. Incremental value analyses demonstrated that the number of methylated genes contributed most influentially to prediction accuracy.This 3-tiered risk stratification strategy has the potential to exert a profound impact on Barrett's esophagus surveillance accuracy and efficiency.

  4. Neurologic dysfunction in patients with rheumatoid arthritis of the cervical spine. Predictive value of clinical, radiographic and MR imaging parameters

    Energy Technology Data Exchange (ETDEWEB)

    Reijnierse, M.; Kroon, H.M.; Holscher, H.C.; Bloem, J.L. [Dept. of Radiology, University Hospital Leiden (Netherlands); Dijkmans, B.A.C.; Breedveld, F.C. [Dept. of Rheumatology, University Hospital Leiden (Netherlands); Hansen, B. [Dept. of Medical Statistics, University Hospital Leiden (Netherlands); Pope, T.L. [Dept. of Diagnostic Radiology, Univ. of South Carolina (United States)

    2001-03-01

    The aim of this study was to evaluate if subjective symptoms, radiographic and especially MR parameters of cervical spine involvement, can predict neurologic dysfunction in patients with severe rheumatoid arthritis (RA). Sequential radiographs, MR imaging, and neurologic examination were performed yearly in 46 consecutive RA patients with symptoms indicative of cervical spine involvement. Radiographic parameters were erosions of the dens or intervertebral joints, disc-space narrowing, horizontal and vertical atlantoaxial subluxation, subluxations below C2, and the diameter of the spinal canal. The MR features evaluated were presence of dens and atlas erosion, brainstem compression, subarachnoid space encroachment, pannus around the dens, abnormal fat body caudal to the clivus, cervicomedullary angle, and distance of the dens to the line of McRae. Muscle weakness was associated with a tenfold increased risk of neurologic dysfunction. Radiographic parameters were not associated. On MR images atlas erosion and a decreased distance of the dens to the line of McRae showed a fivefold increased risk of neurologic dysfunction. Subarachnoid space encroachment was associated with a 12-fold increased risk. Rheumatoid arthritis patients with muscle weakness and subarachnoid space encroachment of the entire cervical spine have a highly increased risk of developing neurologic dysfunction. (orig.)

  5. Neurologic dysfunction in patients with rheumatoid arthritis of the cervical spine. Predictive value of clinical, radiographic and MR imaging parameters

    International Nuclear Information System (INIS)

    Reijnierse, M.; Kroon, H.M.; Holscher, H.C.; Bloem, J.L.; Dijkmans, B.A.C.; Breedveld, F.C.; Hansen, B.; Pope, T.L.

    2001-01-01

    The aim of this study was to evaluate if subjective symptoms, radiographic and especially MR parameters of cervical spine involvement, can predict neurologic dysfunction in patients with severe rheumatoid arthritis (RA). Sequential radiographs, MR imaging, and neurologic examination were performed yearly in 46 consecutive RA patients with symptoms indicative of cervical spine involvement. Radiographic parameters were erosions of the dens or intervertebral joints, disc-space narrowing, horizontal and vertical atlantoaxial subluxation, subluxations below C2, and the diameter of the spinal canal. The MR features evaluated were presence of dens and atlas erosion, brainstem compression, subarachnoid space encroachment, pannus around the dens, abnormal fat body caudal to the clivus, cervicomedullary angle, and distance of the dens to the line of McRae. Muscle weakness was associated with a tenfold increased risk of neurologic dysfunction. Radiographic parameters were not associated. On MR images atlas erosion and a decreased distance of the dens to the line of McRae showed a fivefold increased risk of neurologic dysfunction. Subarachnoid space encroachment was associated with a 12-fold increased risk. Rheumatoid arthritis patients with muscle weakness and subarachnoid space encroachment of the entire cervical spine have a highly increased risk of developing neurologic dysfunction. (orig.)

  6. A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion.

    Science.gov (United States)

    Spiegelhalter, D J; Freedman, L S

    1986-01-01

    The 'textbook' approach to determining sample size in a clinical trial has some fundamental weaknesses which we discuss. We describe a new predictive method which takes account of prior clinical opinion about the treatment difference. The method adopts the point of clinical equivalence (determined by interviewing the clinical participants) as the null hypothesis. Decision rules at the end of the study are based on whether the interval estimate of the treatment difference (classical or Bayesian) includes the null hypothesis. The prior distribution is used to predict the probabilities of making the decisions to use one or other treatment or to reserve final judgement. It is recommended that sample size be chosen to control the predicted probability of the last of these decisions. An example is given from a multi-centre trial of superficial bladder cancer.

  7. Predictive parameters of infectiologic complications in patients after TIPSS; Praediktive Parameter infektiologischer Komplikationen bei Patienten nach TIPSS-Anlage

    Energy Technology Data Exchange (ETDEWEB)

    Cohnen, M.; Saleh, A.; Moedder, U. [Institut fuer Diagnostische Radiologie, Universitaetsklinikum Duesseldorf (Germany); Luethen, R.; Bode, J.; Haeussinger, D. [Klinik fuer Gastroenterologie, Hepatologie und Infektiologie, Universitaetsklinikum Duesseldorf (Germany); Daeubener, W. [Institut fuer Mikrobiologie und Virologie, Universitaetsklinikum Duesseldorf (Germany)

    2003-02-01

    Aim To define predictive parameters of a complicated clinical course after the TIPSS procedure. Blood cultures were drawn prospectively in 41 patients from a central line and from the portal venous blood before stent placement as well as from the central line 20 min after intervention. C-reactive proteine (CRP) (mg/dl) and white blood cell count (WBC,/{mu}l) on the day of TIPSS-procedure (d0), the first (d1) and seven (d7) days after TIPSS were compared in patients with a complicated clinical course (spontaneous bacterial peritonitis, pneumonia, sepsis; group I) to patients without clinical complications (group II) Group I showed a significant increase in CRP (d0: 1.8{+-}1.0; d1: 3.2{+-}1.5; d7: 4.3{+-}3.2), and white blood cell count (d0: 7700{+-}2600; d1: 10800{+-}2800; d7: 7500{+-}1800) on the first day after TIPSS-procedure in comparison to group II (CRP: d0: 1.6{+-}0.6; d1: 1.8{+-}1.0; d7: 1.9{+-}0.6. WBC: d0: 6900{+-}1500; d1: 8000{+-}1600; d7: 7600{+-}1400).Microbiological analysis showed in 12% skin or oral flora in the last sample. The course of CRP and WBC-count during the first week after TIPSS procedure may indicate patients with a potential risk of a complicated clinical course. (orig.) [German] Fragestellung Definition praediktiver Parameter infektiologischer Komplikationen bei Patienten nach TIPSS-Anlage.Methodik Bei 41 Patienten wurden Blutproben prospektiv vor intrahepatischer Stentanlage zentralvenoes und portalvenoes sowie 20 min postinterventionell erneut zentralvenoes entnommen und mikrobiologisch analysiert. C-reaktives Protein (CRP) (mg/dl) und Leukozytenzahl (/{mu}l) wurden am Interventionstag (d0), am 1. (d1) sowie 7 Tage (d7) postinterventionell bestimmt. Patienten mit kompliziertem Verlauf (spontane bakterielle Peritonitis,Pneumonie, Sepsis; Gruppe 1) wurden von Patienten ohne klinische Komplikationen (Gruppe 2) unterschieden.Ergebnisse Gruppe 1 wies einen signifikanten Anstieg des CRP (d0: 1,8{+-}1,0; d1: 3,2{+-}1,5; d7: 4,3{+-}3,2) und

  8. Multi-parametric MRI in cervical cancer. Early prediction of response to concurrent chemoradiotherapy in combination with clinical prognostic factors

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Wei; Chen, Bing; Wang, Ai Jun; Zhao, Jian Guo [The General Hospital of Ningxia Medical University, Department of Radiology, Yinchuan (China); Qiang, Jin Wei [Fudan University, Department of Radiology, Jinshan Hospital, Shanghai (China); Tian, Hai Ping [The General Hospital of Ningxia Medical University, Department of Pathology, Yinchuan (China)

    2018-01-15

    To investigate the prediction of response to concurrent chemoradiotherapy (CCRT) through a combination of pretreatment multi-parametric magnetic resonance imaging (MRI) with clinical prognostic factors (CPF) in cervical cancer patients. Sixty-five patients underwent conventional MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) before CCRT. The patients were divided into non- and residual tumour groups according to post-treatment MRI. Pretreatment MRI parameters and CPF between the two groups were compared and prognostic factors, optimal thresholds, and predictive performance for post-treatment residual tumour occurrence were estimated. The residual group showed a lower maximum slope of increase (MSI{sub L}) and signal enhancement ratio (SER{sub L}) in low-perfusion subregions, a higher apparent diffusion coefficient (ADC) value, and a higher stage than the non-residual tumour group (p < 0.001, p = 0.003, p < 0.001, and p < 0.001, respectively). MSI{sub L} and ADC were independent prognostic factors. The combination of both measures improved the diagnostic performance compared with individual MRI parameters. A further combination of these two factors with CPF exhibited the highest predictive performance. Pretreatment MSI{sub L} and ADC were independent prognostic factors for cervical cancer. The predictive capacity of multi-parametric MRI was superior to individual MRI parameters. The combination of multi-parametric MRI with CPF further improved the predictive performance. (orig.)

  9. Connecting clinical and actuarial prediction with rule-based methods.

    Science.gov (United States)

    Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

    2015-06-01

    Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

  10. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

    Science.gov (United States)

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

  11. Clinical time series prediction: Toward a hierarchical dynamical system framework.

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2015-09-01

    Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Clinical time series prediction: towards a hierarchical dynamical system framework

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2014-01-01

    Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

  13. Baseline 18F-FDG PET image-derived parameters for therapy response prediction in oesophageal cancer

    International Nuclear Information System (INIS)

    Hatt, Mathieu; Visvikis, Dimitris; Cheze-le Rest, Catherine; Pradier, Olivier

    2011-01-01

    The objectives of this study were to investigate the predictive value of tumour measurements on 2-deoxy-2-[ 18 F]fluoro-D-glucose ( 18 F-FDG) positron emission tomography (PET) pretreatment scan regarding therapy response in oesophageal cancer and to evaluate the impact of tumour delineation strategies. Fifty patients with oesophageal cancer treated with concomitant radiochemotherapy between 2004 and 2008 were retrospectively considered and classified as complete, partial or non-responders (including stable and progressive disease) according to Response Evaluation Criteria in Solid Tumors (RECIST). The classification of partial and complete responders was confirmed by biopsy. Tumours were delineated on the 18 F-FDG pretreatment scan using an adaptive threshold and the automatic fuzzy locally adaptive Bayesian (FLAB) methodologies. Several parameters were then extracted: maximum and peak standardized uptake value (SUV), tumour longitudinal length (TL) and volume (TV), SUV mean , and total lesion glycolysis (TLG = TV x SUV mean ). The correlation between each parameter and response was investigated using Kruskal-Wallis tests, and receiver-operating characteristic methodology was used to assess performance of the parameters to differentiate patients. Whereas commonly used parameters such as SUV measurements were not significant predictive factors of the response, parameters related to tumour functional spatial extent (TL, TV, TLG) allowed significant differentiation of all three groups of patients, independently of the delineation strategy, and could identify complete and non-responders with sensitivity above 75% and specificity above 85%. A systematic although not statistically significant trend was observed regarding the hierarchy of the delineation methodologies and the parameters considered, with slightly higher predictive value obtained with FLAB over adaptive thresholding, and TLG over TV and TL. TLG is a promising predictive factor of concomitant

  14. Can we predict uranium bioavailability based on soil parameters? Part 1: Effect of soil parameters on soil solution uranium concentration

    International Nuclear Information System (INIS)

    Vandenhove, H.; Hees, M. van; Wouters, K.; Wannijn, J.

    2007-01-01

    Present study aims to quantify the influence of soil parameters on soil solution uranium concentration for 238 U spiked soils. Eighteen soils collected under pasture were selected such that they covered a wide range for those parameters hypothesised as being potentially important in determining U sorption. Maximum soil solution uranium concentrations were observed at alkaline pH, high inorganic carbon content and low cation exchange capacity, organic matter content, clay content, amorphous Fe and phosphate levels. Except for the significant correlation between the solid-liquid distribution coefficients (K d , L kg -1 ) and the organic matter content (R 2 = 0.70) and amorphous Fe content (R 2 = 0.63), there was no single soil parameter significantly explaining the soil solution uranium concentration (which varied 100-fold). Above pH = 6, log(K d ) was linearly related with pH [log(K d ) = - 1.18 pH + 10.8, R 2 = 0.65]. Multiple linear regression analysis did result in improved predictions of the soil solution uranium concentration but the model was complex. - Uranium solubility in soil can be predicted from organic matter or amorphous iron content and pH or with complex multilinear models considering several soil parameters

  15. Predicting CYP2C19 Catalytic Parameters for Enantioselective Oxidations Using Artificial Neural Networks and a Chirality Code

    Science.gov (United States)

    Hartman, Jessica H.; Cothren, Steven D.; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A.; Miller, Grover P.

    2013-01-01

    Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. PMID:23673224

  16. Physiotherapy clinical educators' perceptions and experiences of clinical prediction rules.

    Science.gov (United States)

    Knox, Grahame M; Snodgrass, Suzanne J; Rivett, Darren A

    2015-12-01

    Clinical prediction rules (CPRs) are widely used in medicine, but their application to physiotherapy practice is more recent and less widespread, and their implementation in physiotherapy clinical education has not been investigated. This study aimed to determine the experiences and perceptions of physiotherapy clinical educators regarding CPRs, and whether they are teaching CPRs to students on clinical placement. Cross-sectional observational survey using a modified Dillman method. Clinical educators (n=211, response rate 81%) supervising physiotherapy students from 10 universities across 5 states and territories in Australia. Half (48%) of respondents had never heard of CPRs, and a further 25% had never used CPRs. Only 27% reported using CPRs, and of these half (51%) were rarely if ever teaching CPRs to students in the clinical setting. However most respondents (81%) believed CPRs assisted in the development of clinical reasoning skills and few (9%) were opposed to teaching CPRs to students. Users of CPRs were more likely to be male (pphysiotherapy (pStudents are unlikely to be learning about CPRs on clinical placement, as few clinical educators use them. Clinical educators will require training in CPRs and assistance in teaching them if students are to better learn about implementing CPRs in physiotherapy clinical practice. Copyright © 2015 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

  17. Predictive event modelling in multicenter clinical trials with waiting time to response.

    Science.gov (United States)

    Anisimov, Vladimir V

    2011-01-01

    A new analytic statistical technique for predictive event modeling in ongoing multicenter clinical trials with waiting time to response is developed. It allows for the predictive mean and predictive bounds for the number of events to be constructed over time, accounting for the newly recruited patients and patients already at risk in the trial, and for different recruitment scenarios. For modeling patient recruitment, an advanced Poisson-gamma model is used, which accounts for the variation in recruitment over time, the variation in recruitment rates between different centers and the opening or closing of some centers in the future. A few models for event appearance allowing for 'recurrence', 'death' and 'lost-to-follow-up' events and using finite Markov chains in continuous time are considered. To predict the number of future events over time for an ongoing trial at some interim time, the parameters of the recruitment and event models are estimated using current data and then the predictive recruitment rates in each center are adjusted using individual data and Bayesian re-estimation. For a typical scenario (continue to recruit during some time interval, then stop recruitment and wait until a particular number of events happens), the closed-form expressions for the predictive mean and predictive bounds of the number of events at any future time point are derived under the assumptions of Markovian behavior of the event progression. The technique is efficiently applied to modeling different scenarios for some ongoing oncology trials. Case studies are considered. Copyright © 2011 John Wiley & Sons, Ltd.

  18. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

    Science.gov (United States)

    Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

    2015-06-25

    Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

  19. Toe Pressures are Superior to Duplex Parameters in Predicting Wound Healing following Toe and Foot Amputations.

    Science.gov (United States)

    Stone, Patrick A; Glomski, Alexis; Thompson, Stephanie N; Adams, Elliott

    2018-01-01

    No criteria, including preamputation vascular diagnostic thresholds, have been established to reliably predict healing versus nonhealing following minor lower extremity amputations. Thus, the goal of our study was to identify clinical factors, including noninvasive vascular laboratory measures, associated with wound healing following toe, forefoot, and midfoot amputations. We retrospectively examined records of patients receiving elective toe, forefoot, or midfoot amputation at our institution over a 5-year span (2010-2015). A total of 333 amputations received noninvasive vascular assessment of the lower extremity preamputation and follow-up at 90 days postamputation. Multivariate binomial logistic regression was used to identify variables predicting wound healing as defined as the absence of reamputation due to wound breakdown. Wound healing occurred in 81% of amputations. A total of 23 (7%) patients required revisions of the foot while 39 (12%) patients required major amputations by 90 days. Chi-squared analysis found that toe pressure at or above the value of 47 mm Hg (P = 0.04), bi/triphasic anterior tibial (P = 0.01), and posterior tibial artery (P = 0.01) waveforms were associated with wound healing. When these diagnostic parameters were examined in the presence of confounders (increasing age, chronic kidney disease, and concomitant revascularization), only toe pressure ≥ 47 mm Hg predicted amputation site healing (odds ratio: 3.1 [95% CI: 1.0-9.4], P = 0.04). Preamputation toe pressures of 47 mm Hg and above are associated with wound healing. No other noninvasive vascular studies predicted wound healing in the presence of confounders. Thus, toe pressures may assist in clinical decision-making and should be routinely obtained preamputation. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Guidelineness of the parameters using integrated test in down syndrome risk prediction

    International Nuclear Information System (INIS)

    Lee, Jin Won; Go, Sung Jin; Kang, Se Sik; Kim, Chang Soo

    2016-01-01

    This study was an evaluation of the significance of each parameter through aimed at pregnant women subjected to screening test(integrated test) in predicting risk of Down syndrome. We retrospectively analysed the correlation of risk of Down's syndrome with Nuchal Translucency(NT) images measured by ultrasound, Pregnancy Associated Plasma Protein A(PAPP-A), alpha-fetoprotein(AFP), unconjugated estriol(uE3), human chorionic gonadotrophin(hCG) and Inhibin A by maternal serum. As a result, a significant correlation with NT, uE3, hCG, Inhibin A is revealed with Down's syndrome risk(P<.001). In ROC analysis, AUC of Inhibin A is analysed as the biggest predictor of Down's syndrome(0.859). And the criterion for cut-off was inhibin A 1.4 MoM(sensitivity 81.8%, specificity 75.9%). In conclusion, Inhibin A was the most useful in parameters to predict Down's syndrome in the integrated test. If we make up for the weakness based on the cut-off value of parameters they will be able to be used as an independent indicator in the risk of Down's syndrome screening

  1. Prediction of extraprostatic extension by prostate specific antigen velocity, endorectal MRI, and biopsy Gleason score in clinically localized prostate cancer

    International Nuclear Information System (INIS)

    Nishimoto, Koshiro; Nakashima, Jun; Hashiguchi, Akinori; Kikuchi, Eiji; Miyajima, Akira; Nakagawa, Ken; Ohigashi, Takashi; Oya, Mototsugu; Murai, Masaru

    2008-01-01

    The objective of this study was to investigate the clinical value of prostate specific antigen velocity (PSAV) in predicting the extraprostatic extension of clinically localized prostate cancer. One hundred and three patients who underwent radical prostatectomy for clinically localized prostate cancer were included in the analysis. The correlation between preoperative parameters, including PSA-based parameters, clinical stage, and histological biopsy findings, and the pathological findings were analyzed. Logistic regression analysis was performed to identify a significant set of independent predictors for the local extent of the disease. Sixty-four (60.2%) patients had organ confined prostate cancer and 39 (39.8%) patients had extraprostatic cancer. The biopsy Gleason score, PSA, PSA density, PSA density of the transition zone, and PSAV were significantly higher in the patients with extraprostatic cancer than in those with organ confined cancer. Multivariate logistic regression analysis indicated that the biopsy Gleason score, endorectal magnetic resonance imaging findings, and PSAV were significant predictors of extraprostatic cancer (P<0.01). Probability curves for extraprostatic cancer were generated using these three preoperative parameters. The combination of PSAV, endorectal magnetic resonance imaging findings, and biopsy Gleason score can provide additional information for selecting appropriate candidates for radical prostatectomy. (author)

  2. Imbalanced target prediction with pattern discovery on clinical data repositories.

    Science.gov (United States)

    Chan, Tak-Ming; Li, Yuxi; Chiau, Choo-Chiap; Zhu, Jane; Jiang, Jie; Huo, Yong

    2017-04-20

    Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies.

  3. Predictions of the marviken subcooled critical mass flux using the critical flow scaling parameters

    Energy Technology Data Exchange (ETDEWEB)

    Park, Choon Kyung; Chun, Se Young; Cho, Seok; Yang, Sun Ku; Chung, Moon Ki [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    A total of 386 critical flow data points from 19 runs of 27 runs in the Marviken Test were selected and compared with the predictions by the correlations based on the critical flow scaling parameters. The results show that the critical mass flux in the very large diameter pipe can be also characterized by two scaling parameters such as discharge coefficient and dimensionless subcooling (C{sub d,ref} and {Delta}{Tau}{sup *} {sub sub}). The agreement between the measured data and the predictions are excellent. 8 refs., 8 figs. 1 tab. (Author)

  4. Predictions of the marviken subcooled critical mass flux using the critical flow scaling parameters

    Energy Technology Data Exchange (ETDEWEB)

    Park, Choon Kyung; Chun, Se Young; Cho, Seok; Yang, Sun Ku; Chung, Moon Ki [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    A total of 386 critical flow data points from 19 runs of 27 runs in the Marviken Test were selected and compared with the predictions by the correlations based on the critical flow scaling parameters. The results show that the critical mass flux in the very large diameter pipe can be also characterized by two scaling parameters such as discharge coefficient and dimensionless subcooling (C{sub d,ref} and {Delta}{Tau}{sup *} {sub sub}). The agreement between the measured data and the predictions are excellent. 8 refs., 8 figs. 1 tab. (Author)

  5. Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

    Directory of Open Access Journals (Sweden)

    Nebot

    2012-04-01

    Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

  6. Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

    DEFF Research Database (Denmark)

    Bechmann, Iben Ellegaard; Jensen, H.S.; Bøknæs, Niels

    1998-01-01

    Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) were...... varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod....... PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised...

  7. What predicts performance during clinical psychology training?

    OpenAIRE

    Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

    2013-01-01

    Objectives While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Design Longitudinal cross-sectional study using prospective and retrospective data. Method Characteristics at application were analysed in relation to a r...

  8. On the Effect of Unit-Cell Parameters in Predicting the Elastic Response of Wood-Plastic Composites

    Directory of Open Access Journals (Sweden)

    Fatemeh Alavi

    2013-01-01

    Full Text Available This paper presents a study on the effect of unit-cell geometrical parameters in predicting elastic properties of a typical wood plastic composite (WPC. The ultimate goal was obtaining the optimal values of representative volume element (RVE parameters to accurately predict the mechanical behavior of the WPC. For each unit cell, defined by a given combination of the above geometrical parameters, finite element simulation in ABAQUS was carried out, and the corresponding stress-strain curve was obtained. A uniaxial test according to ASTM D638-02a type V was performed on the composite specimen. Modulus of elasticity was determined using hyperbolic tangent function, and the results were compared to the sets of finite element analyses. Main effects of RVE parameters and their interactions were demonstrated and discussed, specially regarding the inclusion of two adjacent wood particles within one unit cell of the material. Regression analysis was performed to mathematically model the RVE parameter effects and their interactions over the modulus of elasticity response. The model was finally employed in an optimization analysis to arrive at an optimal set of RVE parameters that minimizes the difference between the predicted and experimental moduli of elasticity.

  9. How Sensitive Are Transdermal Transport Predictions by Microscopic Stratum Corneum Models to Geometric and Transport Parameter Input?

    Science.gov (United States)

    Wen, Jessica; Koo, Soh Myoung; Lape, Nancy

    2018-02-01

    While predictive models of transdermal transport have the potential to reduce human and animal testing, microscopic stratum corneum (SC) model output is highly dependent on idealized SC geometry, transport pathway (transcellular vs. intercellular), and penetrant transport parameters (e.g., compound diffusivity in lipids). Most microscopic models are limited to a simple rectangular brick-and-mortar SC geometry and do not account for variability across delivery sites, hydration levels, and populations. In addition, these models rely on transport parameters obtained from pure theory, parameter fitting to match in vivo experiments, and time-intensive diffusion experiments for each compound. In this work, we develop a microscopic finite element model that allows us to probe model sensitivity to variations in geometry, transport pathway, and hydration level. Given the dearth of experimentally-validated transport data and the wide range in theoretically-predicted transport parameters, we examine the model's response to a variety of transport parameters reported in the literature. Results show that model predictions are strongly dependent on all aforementioned variations, resulting in order-of-magnitude differences in lag times and permeabilities for distinct structure, hydration, and parameter combinations. This work demonstrates that universally predictive models cannot fully succeed without employing experimentally verified transport parameters and individualized SC structures. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  10. Predicting Collateral Status With Magnetic Resonance Perfusion Parameters: Probabilistic Approach With a Tmax-Derived Prediction Model.

    Science.gov (United States)

    Lee, Mi Ji; Son, Jeong Pyo; Kim, Suk Jae; Ryoo, Sookyung; Woo, Sook-Young; Cha, Jihoon; Kim, Gyeong-Moon; Chung, Chin-Sang; Lee, Kwang Ho; Bang, Oh Young

    2015-10-01

    Good collateral flow is an important predictor for favorable responses to recanalization therapy and successful outcomes after acute ischemic stroke. Magnetic resonance perfusion-weighted imaging (MRP) is widely used in patients with stroke. However, it is unclear whether the perfusion parameters and thresholds would predict collateral status. The present study evaluated the relationship between hypoperfusion severity and collateral status to develop a predictive model for good collaterals using MRP parameters. Patients who were eligible for recanalization therapy that underwent both serial diffusion-weighted imaging and serial MRP were enrolled into the study. A collateral flow map derived from MRP source data was generated through automatic postprocessing. Hypoperfusion severity, presented as proportions of every 2-s Tmax strata to the entire hypoperfusion volume (Tmax≥2 s), was compared between patients with good and poor collaterals. Prediction models for good collaterals were developed with each Tmax strata proportion and cerebral blood volumes. Among 66 patients, 53 showed good collaterals based on MRP-based collateral grading. Although no difference was noted in delays within 16 s, more severe Tmax delays (Tmax16-18 s, Tmax18-22 s, Tmax22-24 s, and Tmax>24 s) were associated with poor collaterals. The probability equation model using Tmax strata proportion demonstrated high predictive power in a receiver operating characteristic analysis (area under the curve=0.9303; 95% confidence interval, 0.8682-0.9924). The probability score was negatively correlated with the volume of infarct growth (P=0.030). Collateral status is associated with more severe Tmax delays than previously defined. The present Tmax severity-weighted model can determine good collaterals and subsequent infarct growth. © 2015 American Heart Association, Inc.

  11. A network security situation prediction model based on wavelet neural network with optimized parameters

    Directory of Open Access Journals (Sweden)

    Haibo Zhang

    2016-08-01

    Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

  12. A New Energy-Critical Plane Damage Parameter for Multiaxial Fatigue Life Prediction of Turbine Blades

    Directory of Open Access Journals (Sweden)

    Zheng-Yong Yu

    2017-05-01

    Full Text Available As one of fracture critical components of an aircraft engine, accurate life prediction of a turbine blade to disk attachment is significant for ensuring the engine structural integrity and reliability. Fatigue failure of a turbine blade is often caused under multiaxial cyclic loadings at high temperatures. In this paper, considering different failure types, a new energy-critical plane damage parameter is proposed for multiaxial fatigue life prediction, and no extra fitted material constants will be needed for practical applications. Moreover, three multiaxial models with maximum damage parameters on the critical plane are evaluated under tension-compression and tension-torsion loadings. Experimental data of GH4169 under proportional and non-proportional fatigue loadings and a case study of a turbine disk-blade contact system are introduced for model validation. Results show that model predictions by Wang-Brown (WB and Fatemi-Socie (FS models with maximum damage parameters are conservative and acceptable. For the turbine disk-blade contact system, both of the proposed damage parameters and Smith-Watson-Topper (SWT model show reasonably acceptable correlations with its field number of flight cycles. However, life estimations of the turbine blade reveal that the definition of the maximum damage parameter is not reasonable for the WB model but effective for both the FS and SWT models.

  13. Evolving chemometric models for predicting dynamic process parameters in viscose production

    Energy Technology Data Exchange (ETDEWEB)

    Cernuda, Carlos [Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz (Austria); Lughofer, Edwin, E-mail: edwin.lughofer@jku.at [Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz (Austria); Suppan, Lisbeth [Kompetenzzentrum Holz GmbH, St. Peter-Str. 25, 4021 Linz (Austria); Roeder, Thomas; Schmuck, Roman [Lenzing AG, 4860 Lenzing (Austria); Hintenaus, Peter [Software Research Center, Paris Lodron University Salzburg (Austria); Maerzinger, Wolfgang [i-RED Infrarot Systeme GmbH, Linz (Austria); Kasberger, Juergen [Recendt GmbH, Linz (Austria)

    2012-05-06

    Highlights: Black-Right-Pointing-Pointer Quality assurance of process parameters in viscose production. Black-Right-Pointing-Pointer Automatic prediction of spin-bath concentrations based on FTNIR spectra. Black-Right-Pointing-Pointer Evolving chemometric models for efficiently handling changing system dynamics over time (no time-intensive re-calibration needed). Black-Right-Pointing-Pointer Significant reduction of huge errors produced by statistical state-of-the-art calibration methods. Black-Right-Pointing-Pointer Sufficient flexibility achieved by gradual forgetting mechanisms. - Abstract: In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H{sub 2}SO{sub 4}, Na{sub 2}SO{sub 4} and Z{sub n}SO{sub 4}. During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi-Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual

  14. Stent parameters predict major adverse clinical events and the response to platelet glycoprotein IIb/IIIa blockade: findings of the ESPRIT trial.

    Science.gov (United States)

    Tcheng, James E; Lim, Ing Haan; Srinivasan, Shankar; Jozic, Joseph; Gibson, C Michael; O'Shea, J Conor; Puma, Joseph A; Simon, Daniel I

    2009-02-01

    Only limited data describe relationships between stent parameters (length and diameter), adverse events after percutaneous coronary intervention, and effects of platelet glycoprotein IIb/IIIa blockade by stent parameters. In this post hoc analysis of the 1983 patients receiving a stent in the Enhanced Suppression of the Platelet Glycoprotein IIb/IIIa Receptor with Integrilin Therapy randomized percutaneous coronary intervention trial of eptifibatide versus placebo, rates of the major adverse cardiac event (MACE) end point (death, myocardial infarction, urgent target-vessel revascularization, or thrombotic bailout) at 48 hours and 1 year were correlated with stent parameters and then analyzed by randomization to eptifibatide versus placebo. In the placebo group, MACE increased with number of stents implanted, total stent length (by quartiles of or=30 mm), and total stented vessel area (by quartiles of area or=292 mm(2)). By stent parameters, MACE at 48 hours was reduced in the eptifibatide group at stent lengths of 18 to or=30 mm (OR, 0.43; 95% CI, 0.25 to 0.75; P=0.003), stent diameters of >2.5 to <3.5 mm (OR, 0.56; 95% CI, 0.39 to 0.82; P=0.002), and with 2 stents implanted (OR, 0.39; 95% CI, 0.22 to 0.69; P=0.001). In the placebo group, near-linear relationships were observed between both increasing stent length and increasing stented vessel area and MACE at 48 hours and 1 year (all, P<0.001); these gradients were flattened in the eptifibatide group (P=0.005 for stent length). Stent parameters predict MACE after percutaneous coronary intervention. Glycoprotein IIb/IIIa blockade mitigates much of the hazard of increasing procedural complexity.

  15. Improving weather predictability by including land-surface model parameter uncertainty

    Science.gov (United States)

    Orth, Rene; Dutra, Emanuel; Pappenberger, Florian

    2016-04-01

    The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

  16. MLBCD: a machine learning tool for big clinical data.

    Science.gov (United States)

    Luo, Gang

    2015-01-01

    Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

  17. Baseline {sup 18}F-FDG PET image-derived parameters for therapy response prediction in oesophageal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Hatt, Mathieu; Visvikis, Dimitris; Cheze-le Rest, Catherine [CHU Morvan, LaTIM, INSERM U650, Brest (France); Pradier, Olivier [CHU Morvan, LaTIM, INSERM U650, Brest (France); CHU Morvan, Department of Radiotherapy, Brest (France)

    2011-09-15

    The objectives of this study were to investigate the predictive value of tumour measurements on 2-deoxy-2-[{sup 18}F]fluoro-D-glucose ({sup 18}F-FDG) positron emission tomography (PET) pretreatment scan regarding therapy response in oesophageal cancer and to evaluate the impact of tumour delineation strategies. Fifty patients with oesophageal cancer treated with concomitant radiochemotherapy between 2004 and 2008 were retrospectively considered and classified as complete, partial or non-responders (including stable and progressive disease) according to Response Evaluation Criteria in Solid Tumors (RECIST). The classification of partial and complete responders was confirmed by biopsy. Tumours were delineated on the {sup 18}F-FDG pretreatment scan using an adaptive threshold and the automatic fuzzy locally adaptive Bayesian (FLAB) methodologies. Several parameters were then extracted: maximum and peak standardized uptake value (SUV), tumour longitudinal length (TL) and volume (TV), SUV{sub mean}, and total lesion glycolysis (TLG = TV x SUV{sub mean}). The correlation between each parameter and response was investigated using Kruskal-Wallis tests, and receiver-operating characteristic methodology was used to assess performance of the parameters to differentiate patients. Whereas commonly used parameters such as SUV measurements were not significant predictive factors of the response, parameters related to tumour functional spatial extent (TL, TV, TLG) allowed significant differentiation of all three groups of patients, independently of the delineation strategy, and could identify complete and non-responders with sensitivity above 75% and specificity above 85%. A systematic although not statistically significant trend was observed regarding the hierarchy of the delineation methodologies and the parameters considered, with slightly higher predictive value obtained with FLAB over adaptive thresholding, and TLG over TV and TL. TLG is a promising predictive factor of

  18. Evaluation of the Cerebral State Index in Cats under Isoflurane Anaesthesia: Dose-Effect Relationship and Prediction of Clinical Signs

    Directory of Open Access Journals (Sweden)

    Joana R. Sousa

    2014-01-01

    Full Text Available The performance of the cerebral state index (CSI in reflecting different levels of isoflurane anaesthesia was evaluated in ten cats subjected to four end-tidal isoflurane concentrations (EtIso, each maintained for 15 minutes (0.8%, 1.2%, 1.6%, or 2.0% EtIso. The CSI, hemodynamic data, ocular reflexes, and eye position were recorded for each EtIso concentration. Pharmacodynamic analysis of CSI with EtIso was performed, as well as prediction probability analysis with a clinical scale based on the eye reflexes. The CSI values showed great variability. Between all parameters, burst suppression ratio showed the better fitting with the sigmoidal concentration-effect model (R2=0.93 followed by CSI (R2=0.82 and electromyographic activity (R2=0.79. EtIso was the variable with better prediction of the clinical scale of anaesthesia (prediction probability value of 0.94. Although the CSI values decrease with increasing isoflurane concentrations, the huge variability in CSI values may be a strong limitation for its use in cats and it seems to be no better than EtIso as a predictor of clinical signs.

  19. Guidelineness of the parameters using integrated test in down syndrome risk prediction

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jin Won [Graduate School of Catholic University of Pusan, Busan (Korea, Republic of); Go, Sung Jin; Kang, Se Sik; Kim, Chang Soo [Dept. Radiological Science, College of Health Sciences, Catholic University of Pusan, Busan (Korea, Republic of)

    2016-12-15

    This study was an evaluation of the significance of each parameter through aimed at pregnant women subjected to screening test(integrated test) in predicting risk of Down syndrome. We retrospectively analysed the correlation of risk of Down's syndrome with Nuchal Translucency(NT) images measured by ultrasound, Pregnancy Associated Plasma Protein A(PAPP-A), alpha-fetoprotein(AFP), unconjugated estriol(uE3), human chorionic gonadotrophin(hCG) and Inhibin A by maternal serum. As a result, a significant correlation with NT, uE3, hCG, Inhibin A is revealed with Down's syndrome risk(P<.001). In ROC analysis, AUC of Inhibin A is analysed as the biggest predictor of Down's syndrome(0.859). And the criterion for cut-off was inhibin A 1.4 MoM(sensitivity 81.8%, specificity 75.9%). In conclusion, Inhibin A was the most useful in parameters to predict Down's syndrome in the integrated test. If we make up for the weakness based on the cut-off value of parameters they will be able to be used as an independent indicator in the risk of Down's syndrome screening.

  20. Blood DNA methylation biomarkers predict clinical reactivity in food-sensitized infants.

    Science.gov (United States)

    Martino, David; Dang, Thanh; Sexton-Oates, Alexandra; Prescott, Susan; Tang, Mimi L K; Dharmage, Shyamali; Gurrin, Lyle; Koplin, Jennifer; Ponsonby, Anne-Louise; Allen, Katrina J; Saffery, Richard

    2015-05-01

    The diagnosis of food allergy (FA) can be challenging because approximately half of food-sensitized patients are asymptomatic. Current diagnostic tests are excellent makers of sensitization but poor predictors of clinical reactivity. Thus oral food challenges (OFCs) are required to determine a patient's risk of reactivity. We sought to discover genomic biomarkers of clinical FA with utility for predicting food challenge outcomes. Genome-wide DNA methylation (DNAm) profiling was performed on blood mononuclear cells from volunteers who had undergone objective OFCs, concurrent skin prick tests, and specific IgE tests. Fifty-eight food-sensitized patients (aged 11-15 months) were assessed, half of whom were clinically reactive. Thirteen nonallergic control subjects were also assessed. Reproducibility was assessed in an additional 48 samples by using methylation data from an independent population of patients with clinical FA. Using a supervised learning approach, we discovered a DNAm signature of 96 CpG sites that predict clinical outcomes. Diagnostic scores were derived from these 96 methylation sites, and cutoffs were determined in a sensitivity analysis. Methylation biomarkers outperformed allergen-specific IgE and skin prick tests for predicting OFC outcomes. FA status was correctly predicted in the replication cohort with an accuracy of 79.2%. DNAm biomarkers with clinical utility for predicting food challenge outcomes are readily detectable in blood. The development of this technology in detailed follow-up studies will yield highly innovative diagnostic assays. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

  1. Neonatal Pulmonary MRI of Bronchopulmonary Dysplasia Predicts Short-term Clinical Outcomes.

    Science.gov (United States)

    Higano, Nara S; Spielberg, David R; Fleck, Robert J; Schapiro, Andrew H; Walkup, Laura L; Hahn, Andrew D; Tkach, Jean A; Kingma, Paul S; Merhar, Stephanie L; Fain, Sean B; Woods, Jason C

    2018-05-23

    Bronchopulmonary dysplasia (BPD) is a serious neonatal pulmonary condition associated with premature birth, but the underlying parenchymal disease and trajectory are poorly characterized. The current NICHD/NHLBI definition of BPD severity is based on degree of prematurity and extent of oxygen requirement. However, no clear link exists between initial diagnosis and clinical outcomes. We hypothesized that magnetic resonance imaging (MRI) of structural parenchymal abnormalities will correlate with NICHD-defined BPD disease severity and predict short-term respiratory outcomes. Forty-two neonates (20 severe BPD, 6 moderate, 7 mild, 9 non-BPD controls; 40±3 weeks post-menstrual age) underwent quiet-breathing structural pulmonary MRI (ultrashort echo-time and gradient echo) in a NICU-sited, neonatal-sized 1.5T scanner, without sedation or respiratory support unless already clinically prescribed. Disease severity was scored independently by two radiologists. Mean scores were compared to clinical severity and short-term respiratory outcomes. Outcomes were predicted using univariate and multivariable models including clinical data and scores. MRI scores significantly correlated with severities and predicted respiratory support at NICU discharge (P<0.0001). In multivariable models, MRI scores were by far the strongest predictor of respiratory support duration over clinical data, including birth weight and gestational age. Notably, NICHD severity level was not predictive of discharge support. Quiet-breathing neonatal pulmonary MRI can independently assess structural abnormalities of BPD, describe disease severity, and predict short-term outcomes more accurately than any individual standard clinical measure. Importantly, this non-ionizing technique can be implemented to phenotype disease and has potential to serially assess efficacy of individualized therapies.

  2. Prediction of Marshall Parameters of Modified Bituminous Mixtures Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Sunil Khuntia

    2014-09-01

    Full Text Available This study presents the application of artificial neural networks (ANN and least square support vector machine (LS-SVM for prediction of Marshall parameters obtained from Marshall tests for waste polyethylene (PE modified bituminous mixtures. Waste polyethylene in the form of fibres processed from utilized milk packets has been used to modify the bituminous mixes in order to improve their engineering properties. Marshall tests were carried out on mix specimens with variations in polyethylene and bitumen contents. It has been observed that the addition of waste polyethylene results in the improvement of Marshall characteristics such as stability, flow value and air voids, used to evaluate a bituminous mix. The proposed neural network (NN model uses the quantities of ingredients used for preparation of Marshall specimens such as polyethylene, bitumen and aggregate in order to predict the Marshall stability, flow value and air voids obtained from the tests. Out of two techniques used, the NN based model is found to be compact, reliable and predictable when compared with LS-SVM model. A sensitivity analysis has been performed to identify the importance of the parameters considered.

  3. Derivation of cell population kinetic parameters from clinical statistical data (program RAD3)

    International Nuclear Information System (INIS)

    Cohen, L.

    1978-01-01

    Cellular lethality models generally require up to 6 parameters to simulate a clinical course of fractionated radiation therapy and to derive an estimate of the cellular surviving fraction for a given treatment scheme. These parameters are the mean cellular lethal dose, the extrapolation number, the ratio of sublethal to irreparable events, the regeneration rate, the repopulation limit (cell cycles), and a field-size or tumor-volume factor. A computer program (RAD3) was designed to derive best-fitting values for these parameters in relation to available clinical data based on the assumption that if a number of different fractionation schemes yield similar reactions, the cellular surviving fractions will be about equal in each instance. Parameters were derived for a variety of human tissues from which realistic iso-effect functions could be generated

  4. Identification of optimal soil hydraulic functions and parameters for predicting soil moisture

    Science.gov (United States)

    We examined the accuracy of several commonly used soil hydraulic functions and associated parameters for predicting observed soil moisture data. We used six combined methods formed by three commonly used soil hydraulic functions – i.e., Brooks and Corey (1964) (BC), Campbell (19...

  5. A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment

    Science.gov (United States)

    Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.

    2015-11-01

    This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

  6. The Prediction of Item Parameters Based on Classical Test Theory and Latent Trait Theory

    Science.gov (United States)

    Anil, Duygu

    2008-01-01

    In this study, the prediction power of the item characteristics based on the experts' predictions on conditions try-out practices cannot be applied was examined for item characteristics computed depending on classical test theory and two-parameters logistic model of latent trait theory. The study was carried out on 9914 randomly selected students…

  7. Doppler Ultrasonographic Parameters for Predicting Cerebral Vascular Reserve in Patients with Acute Ischemic Stroke

    International Nuclear Information System (INIS)

    Jung, Han Young; Lee, Hui Joong; Kim, Hye Jung; Kim, Yong Sun; Kang, Duk Sik

    2006-01-01

    We investigated Doppler ultrasonographic (US) parameters of patients with acute stroke to predict the cerebral vascular reserve (CVR) measured by SPECT. We reviewed the flow velocity and cross-sectional area of the circular vessel at the common, external, and internal carotid arteries (ICA) and the vertebral arteries (VA) in 109 acute stroke patients who underwent SPECT. Flow volume (FV) of each artery was calculated as the product of the angle-corrected time averaged flow velocity and cross-sectional area of the circular vessel. Total cerebral FV (TCBFV) was determined as the sum of the FVs of the right and left ICA and VA. We compared the Doppler US parameters between 44 cases of preserved and 65 cases of impaired CVR. In the preserved CVR group, ICA FV, anterior circulating FV (ACFV) and TCBFV were higher than in the impaired CVR group (p < 0.05, independent t-test). In the impaired CVR group, the ROC curves showed ACFV and TCBFV were suitable parameters to predict CVR (p < 0.05). Doppler US was helpful for understanding the hemodynamic state of acute stroke. FV measurement by Doppler US was useful for predicting CVR

  8. Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model

    Directory of Open Access Journals (Sweden)

    Sun Zhangzhen

    2012-08-01

    Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

  9. Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

    Science.gov (United States)

    Kennedy, Curtis E; Turley, James P

    2011-10-24

    Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9

  10. The Meta-Analysis of Clinical Judgment Project: Fifty-Six Years of Accumulated Research on Clinical Versus Statistical Prediction

    Science.gov (United States)

    Aegisdottir, Stefania; White, Michael J.; Spengler, Paul M.; Maugherman, Alan S.; Anderson, Linda A.; Cook, Robert S.; Nichols, Cassandra N.; Lampropoulos, Georgios K.; Walker, Blain S.; Cohen, Genna; Rush, Jeffrey D.

    2006-01-01

    Clinical predictions made by mental health practitioners are compared with those using statistical approaches. Sixty-seven studies were identified from a comprehensive search of 56 years of research; 92 effect sizes were derived from these studies. The overall effect of clinical versus statistical prediction showed a somewhat greater accuracy for…

  11. Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data.

    Science.gov (United States)

    Richardson, Alice; Signor, Ben M; Lidbury, Brett A; Badrick, Tony

    2016-11-01

    Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  12. Theoretical prediction of Grüneisen parameter for SiO_2.TiO_2 bulk metallic glasses

    International Nuclear Information System (INIS)

    Singh, Chandra K.; Pandey, Brijesh K.; Pandey, Anjani K.

    2016-01-01

    The Grüneisen parameter (γ) is very important to decide the limitations for the prediction of thermoelastic properties of bulk metallic glasses. It can be defined in terms of microscopic and macroscopic parameters of the material in which former is based on vibrational frequencies of atoms in the material while later is closely related to its thermodynamic properties. Different formulation and equation of states are used by the pioneer researchers of this field to predict the true sense of Gruneisen parameter for BMG but for SiO_2.TiO_2 very few and insufficient information is available till now. In the present work we have tested the validity of two different isothermal EOS viz. Poirrior-Tarantola EOS and Usual-Tait EOS to predict the true value of Gruneisen parameter for SiO_2.TiO_2 as a function of compression. Using different thermodynamic limitations related to the material constraints and analyzing obtained result it is concluded that the Poirrior-Tarantola EOS gives better numeric values of Grüneisen parameter (γ) for SiO_2.TiO_2 BMG.

  13. Predicting the activity coefficients of free-solvent for concentrated globular protein solutions using independently determined physical parameters.

    Directory of Open Access Journals (Sweden)

    Devin W McBride

    Full Text Available The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations.

  14. Predicting response to epigenetic therapy

    DEFF Research Database (Denmark)

    Treppendahl, Marianne B; Sommer Kristensen, Lasse; Grønbæk, Kirsten

    2014-01-01

    of good pretreatment predictors of response is of great value. Many clinical parameters and molecular targets have been tested in preclinical and clinical studies with varying results, leaving room for optimization. Here we provide an overview of markers that may predict the efficacy of FDA- and EMA...

  15. Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation

    International Nuclear Information System (INIS)

    Gershgorin, B.; Harlim, J.; Majda, A.J.

    2010-01-01

    The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates

  16. An improved method for predicting the evolution of the characteristic parameters of an information system

    Science.gov (United States)

    Dushkin, A. V.; Kasatkina, T. I.; Novoseltsev, V. I.; Ivanov, S. V.

    2018-03-01

    The article proposes a forecasting method that allows, based on the given values of entropy and error level of the first and second kind, to determine the allowable time for forecasting the development of the characteristic parameters of a complex information system. The main feature of the method under consideration is the determination of changes in the characteristic parameters of the development of the information system in the form of the magnitude of the increment in the ratios of its entropy. When a predetermined value of the prediction error ratio is reached, that is, the entropy of the system, the characteristic parameters of the system and the depth of the prediction in time are estimated. The resulting values of the characteristics and will be optimal, since at that moment the system possessed the best ratio of entropy as a measure of the degree of organization and orderliness of the structure of the system. To construct a method for estimating the depth of prediction, it is expedient to use the maximum principle of the value of entropy.

  17. Effect of clinical parameters on the control of myoelectric robotic prosthetic hands.

    Science.gov (United States)

    Atzori, Manfredo; Gijsberts, Arjan; Castellini, Claudio; Caputo, Barbara; Hager, Anne-Gabrielle Mittaz; Elsig, Simone; Giatsidis, Giorgio; Bassetto, Franco; Müller, Henning

    2016-01-01

    Improving the functionality of prosthetic hands with noninvasive techniques is still a challenge. Surface electromyography (sEMG) currently gives limited control capabilities; however, the application of machine learning to the analysis of sEMG signals is promising and has recently been applied in practice, but many questions still remain. In this study, we recorded the sEMG activity of the forearm of 11 male subjects with transradial amputation who were mentally performing 40 hand and wrist movements. The classification performance and the number of independent movements (defined as the subset of movements that could be distinguished with >90% accuracy) were studied in relationship to clinical parameters related to the amputation. The analysis showed that classification accuracy and the number of independent movements increased significantly with phantom limb sensation intensity, remaining forearm percentage, and temporal distance to the amputation. The classification results suggest the possibility of naturally controlling up to 11 movements of a robotic prosthetic hand with almost no training. Knowledge of the relationship between classification accuracy and clinical parameters adds new information regarding the nature of phantom limb pain as well as other clinical parameters, and it can lay the foundations for future "functional amputation" procedures in surgery.

  18. Good Models Gone Bad: Quantifying and Predicting Parameter-Induced Climate Model Simulation Failures

    Science.gov (United States)

    Lucas, D. D.; Klein, R.; Tannahill, J.; Brandon, S.; Covey, C. C.; Domyancic, D.; Ivanova, D. P.

    2012-12-01

    Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Statistical analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation failures of the Parallel Ocean Program (POP2). About 8.5% of our POP2 runs failed for numerical reasons at certain combinations of parameter values. We apply support vector machine (SVM) classification from the fields of pattern recognition and machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. The SVM classifiers readily predict POP2 failures in an independent validation ensemble, and are subsequently used to determine the causes of the failures via a global sensitivity analysis. Four parameters related to ocean mixing and viscosity are identified as the major sources of POP2 failures. Our method can be used to improve the robustness of complex scientific models to parameter perturbations and to better steer UQ ensembles. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).

  19. Clinical predictive score of intracranial hemorrhage in mild traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Yuksen C

    2018-02-01

    Full Text Available Chaiyaporn Yuksen,1 Yuwares Sittichanbuncha,1 Jayanton Patumanond,2 Sombat Muengtaweepongsa,3 Kittisak Sawanyawisuth4,5 1Department of Emergency Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 2Clinical Epidemiology Unit and Clinical Research Center, Faculty of Medicine, Thammasat University, Pathum Thani, 3Department of Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, 4Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, 5Sleep Apnea Research Group, Research Center in Back, Neck, Other Joint Pain and Human Performance (BNOJPH, and Research and Training Center for Enhancing Quality of Life of Working Age People, Khon Kaen University, Khon Kaen, Thailand Background: Mild traumatic brain injury (TBI is a common condition at the Emergency Medicine Department. Head computer tomography (CT scans in mild TBI patients must be properly justified in order to avoid unnecessary exposure to X-rays and to reduce the hospital/transfer costs. This study aimed to evaluate which clinical factors are associated with intracranial hemorrhage in Asian population and to develop a user-friendly predictive model.Methods: The study was conducted retrospectively at the Emergency Medicine Department in Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand. The study period was between September 2013 and August 2016. The inclusion criteria were age >15 years and having received a head CT scan after presenting with mild TBI. Those patients with mild TBI and no symptoms/deterioration after 24 h of clinical observation were excluded. The predictive model and prediction score for intracranial hemorrhage was developed by multivariate logistic regression analysis.Results: During the study period, there were 708 patients who met the study criteria. Of those, 100 patients (14.12% had positive head CT scan results. There were seven independent factors that were

  20. Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function.

    Science.gov (United States)

    Cardenas, Carlos E; McCarroll, Rachel E; Court, Laurence E; Elgohari, Baher A; Elhalawani, Hesham; Fuller, Clifton D; Kamal, Mona J; Meheissen, Mohamed A M; Mohamed, Abdallah S R; Rao, Arvind; Williams, Bowman; Wong, Andrew; Yang, Jinzhong; Aristophanous, Michalis

    2018-06-01

    Automating and standardizing the contouring of clinical target volumes (CTVs) can reduce interphysician variability, which is one of the largest sources of uncertainty in head and neck radiation therapy. In addition to using uniform margin expansions to auto-delineate high-risk CTVs, very little work has been performed to provide patient- and disease-specific high-risk CTVs. The aim of the present study was to develop a deep neural network for the auto-delineation of high-risk CTVs. Fifty-two oropharyngeal cancer patients were selected for the present study. All patients were treated at The University of Texas MD Anderson Cancer Center from January 2006 to August 2010 and had previously contoured gross tumor volumes and CTVs. We developed a deep learning algorithm using deep auto-encoders to identify physician contouring patterns at our institution. These models use distance map information from surrounding anatomic structures and the gross tumor volume as input parameters and conduct voxel-based classification to identify voxels that are part of the high-risk CTV. In addition, we developed a novel probability threshold selection function, based on the Dice similarity coefficient (DSC), to improve the generalization of the predicted volumes. The DSC-based function is implemented during an inner cross-validation loop, and probability thresholds are selected a priori during model parameter optimization. We performed a volumetric comparison between the predicted and manually contoured volumes to assess our model. The predicted volumes had a median DSC value of 0.81 (range 0.62-0.90), median mean surface distance of 2.8 mm (range 1.6-5.5), and median 95th Hausdorff distance of 7.5 mm (range 4.7-17.9) when comparing our predicted high-risk CTVs with the physician manual contours. These predicted high-risk CTVs provided close agreement to the ground-truth compared with current interobserver variability. The predicted contours could be implemented clinically, with only

  1. Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks

    Science.gov (United States)

    Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa

    Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.

  2. Hemorheological and Glycemic Parameters and HDL Cholesterol for the Prediction of Cardiovascular Events

    International Nuclear Information System (INIS)

    Cho, Sung Woo; Kim, Byung Gyu; Kim, Byung Ok; Byun, Young Sup; Goh, Choong Won; Rhee, Kun Joo; Kwon, Hyuck Moon; Lee, Byoung Kwon

    2016-01-01

    Hemorheological and glycemic parameters and high density lipoprotein (HDL) cholesterol are used as biomarkers of atherosclerosis and thrombosis. To investigate the association and clinical relevance of erythrocyte sedimentation rate (ESR), fibrinogen, fasting glucose, glycated hemoglobin (HbA1c), and HDL cholesterol in the prediction of major adverse cardiovascular events (MACE) and coronary heart disease (CHD) in an outpatient population. 708 stable patients who visited the outpatient department were enrolled and followed for a mean period of 28.5 months. Patients were divided into two groups, patients without MACE and patients with MACE, which included cardiac death, acute myocardial infarction, newly diagnosed CHD, and cerebral vascular accident. We compared hemorheological and glycemic parameters and lipid profiles between the groups. Patients with MACE had significantly higher ESR, fibrinogen, fasting glucose, and HbA1c, while lower HDL cholesterol compared with patients without MACE. High ESR and fibrinogen and low HDL cholesterol significantly increased the risk of MACE in multivariate regression analysis. In patients with MACE, high fibrinogen and HbA1c levels increased the risk of multivessel CHD. Furthermore, ESR and fibrinogen were significantly positively correlated with HbA1c and negatively correlated with HDL cholesterol, however not correlated with fasting glucose. Hemorheological abnormalities, poor glycemic control, and low HDL cholesterol are correlated with each other and could serve as simple and useful surrogate markers and predictors for MACE and CHD in outpatients

  3. Hemorheological and Glycemic Parameters and HDL Cholesterol for the Prediction of Cardiovascular Events

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sung Woo [Division of Cardiology - Department of Internal Medicine - Sanggye Paik Hospital, Inje University College of Medicine, Seoul (Korea, Republic of); Division of Cardiology - Department of Medicine - Samsung Medical Center, Seoul (Korea, Republic of); Kim, Byung Gyu; Kim, Byung Ok; Byun, Young Sup; Goh, Choong Won; Rhee, Kun Joo [Division of Cardiology - Department of Internal Medicine - Sanggye Paik Hospital, Inje University College of Medicine, Seoul (Korea, Republic of); Kwon, Hyuck Moon; Lee, Byoung Kwon, E-mail: cardiobk@yuhs.ac [Division of Cardiology - Department of Internal Medicine - Gangnam Severance Hospital - Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2016-01-15

    Hemorheological and glycemic parameters and high density lipoprotein (HDL) cholesterol are used as biomarkers of atherosclerosis and thrombosis. To investigate the association and clinical relevance of erythrocyte sedimentation rate (ESR), fibrinogen, fasting glucose, glycated hemoglobin (HbA1c), and HDL cholesterol in the prediction of major adverse cardiovascular events (MACE) and coronary heart disease (CHD) in an outpatient population. 708 stable patients who visited the outpatient department were enrolled and followed for a mean period of 28.5 months. Patients were divided into two groups, patients without MACE and patients with MACE, which included cardiac death, acute myocardial infarction, newly diagnosed CHD, and cerebral vascular accident. We compared hemorheological and glycemic parameters and lipid profiles between the groups. Patients with MACE had significantly higher ESR, fibrinogen, fasting glucose, and HbA1c, while lower HDL cholesterol compared with patients without MACE. High ESR and fibrinogen and low HDL cholesterol significantly increased the risk of MACE in multivariate regression analysis. In patients with MACE, high fibrinogen and HbA1c levels increased the risk of multivessel CHD. Furthermore, ESR and fibrinogen were significantly positively correlated with HbA1c and negatively correlated with HDL cholesterol, however not correlated with fasting glucose. Hemorheological abnormalities, poor glycemic control, and low HDL cholesterol are correlated with each other and could serve as simple and useful surrogate markers and predictors for MACE and CHD in outpatients.

  4. Comparative prediction of nonepileptic events using MMPI-2 clinical scales, Harris Lingoes subscales, and restructured clinical scales.

    Science.gov (United States)

    Yamout, Karim Z; Heinrichs, Robin J; Baade, Lyle E; Soetaert, Dana K; Liow, Kore K

    2017-03-01

    The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a psychological testing tool used to measure psychological and personality constructs. The MMPI-2 has proven helpful in identifying individuals with nonepileptic events/nonepileptic seizures. However, the MMPI-2 has had some updates that enhanced its original scales. The aim of this article was to test the utility of updated MMPI-2 scales in predicting the likelihood of non-epileptic seizures in individuals admitted to an EEG video monitoring unit. We compared sensitivity, specificity, and likelihood ratios of traditional MMPI-2 Clinical Scales against more homogenous MMPI-2 Harris-Lingoes subscales and the newer Restructured Clinical (RC) scales. Our results showed that the Restructured Scales did not show significant improvement over the original Clinical scales. However, one Harris-Lingoes subscale (HL4 of Clinical Scale 3) did show improved predictive utility over the original Clinical scales as well as over the newer Restructured Clinical scales. Our study suggests that the predictive utility of the MMPI-2 can be improved using already existing scales. This is particularly useful for those practitioners who are not invested in switching over to the newly developed MMPI-2 Restructured Form (MMPI-2 RF). Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Analysis Of Clinical, Haematological And Biochemical Parameters In Patients With Infectious Mononucleosis

    Directory of Open Access Journals (Sweden)

    Canović Petar

    2015-12-01

    Full Text Available Primary infection with Epstein-Barr virus (EBV usually occurs in early childhood and often does not present clinical symptoms. More than 90% of adults are infected with this virus. A primary infection that occurs in adolescence or adulthood is usually clinically presented as infectious mononucleosis with a triad of symptoms: fever, lymphadenopathy and pharyngitis. Our retrospective study included 51 patients with a median age of 17 (9-23 years and serologically confirmed infectious mononucleosis. All patients with infectious mononucleosis were treated at the Clinic for Infectious Diseases at the Clinical Center in Kragujevac during 2013. We analysed the clinical, haematological and laboratory parameters of patients. The aspartate-aminotransferase levels were increased in 40 patients, with a mean value of 116.24 (±93.22; the alanine-aminotransferase levels were increased in 44 patients, with a mean value of 189.24 (±196.69. Lymphadenopathy was the most common clinical feature upon admission in 49 patients (96%; 38 patients (74.5% had splenomegaly, and 20 (39% had hepatomegaly. Twenty-six patients (51% had leukocytosis with lymphocytosis, while 15 (75% of the 20 who had a normal leukocyte count also had lymphocytosis. In the present study, we updated the clinical, haematological and laboratory parameters, which may lead to the establishment of an accurate diagnosis and promote further treatment of the patients.

  6. Comparison of clinical utility between diaphragm excursion and thickening change using ultrasonography to predict extubation success

    Science.gov (United States)

    Yoo, Jung-Wan; Lee, Seung Jun; Lee, Jong Deog; Kim, Ho Cheol

    2018-01-01

    Background/Aims Both diaphragmatic excursion and change in muscle thickening are measured using ultrasonography (US) to assess diaphragm function and mechanical ventilation weaning outcomes. However, which parameter can better predict successful extubation remains to be determined. The aim of this study was to compare the clinical utility of these two diaphragmatic parameters to predict extubation success. Methods This study included patients subjected to extubation trial in the medical or surgical intensive care unit of a university-affiliated hospital from May 2015 through February 2016. Diaphragm excursion and percent of thickening change (Δtdi%) were measured using US within 24 hours before extubation. Results Sixty patients were included, and 78.3% (47/60) of these patients were successfully extubated, whereas 21.7% (13/60) were not. The median degree of excursion was greater in patients with extubation success than in those with extubation failure (1.65 cm vs. 0.8 cm, p success had a greater Δtdi% than those with extubation failure (42.1% vs. 22.5%, p = 0.03). The areas under the receiver operating curve for excursion and Δtdi% were 0.836 (95% confidence interval [CI], 0.717 to 0.919) and 0.698 (95% CI, 0.566 to 0.810), respectively (p = 0.017). Conclusions Diaphragm excursion seems more accurate than a change in the diaphragm thickness to predict extubation success. PMID:29050461

  7. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    Science.gov (United States)

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  8. Mid-Treatment Sleep Duration Predicts Clinically Significant Knee Osteoarthritis Pain reduction at 6 months: Effects From a Behavioral Sleep Medicine Clinical Trial.

    Science.gov (United States)

    Salwen, Jessica K; Smith, Michael T; Finan, Patrick H

    2017-02-01

    To determine the relative influence of sleep continuity (sleep efficiency, sleep onset latency, total sleep time [TST], and wake after sleep onset) on clinical pain outcomes within a trial of cognitive behavioral therapy for insomnia (CBT-I) for patients with comorbid knee osteoarthritis and insomnia. Secondary analyses were performed on data from 74 patients with comorbid insomnia and knee osteoarthritis who completed a randomized clinical trial of 8-session multicomponent CBT-I versus an active behavioral desensitization control condition (BD), including a 6-month follow-up assessment. Data used herein include daily diaries of sleep parameters, actigraphy data, and self-report questionnaires administered at specific time points. Patients who reported at least 30% improvement in self-reported pain from baseline to 6-month follow-up were considered responders (N = 31). Pain responders and nonresponders did not differ significantly at baseline across any sleep continuity measures. At mid-treatment, only TST predicted pain response via t tests and logistic regression, whereas other measures of sleep continuity were nonsignificant. Recursive partitioning analyses identified a minimum cut-point of 382 min of TST achieved at mid-treatment in order to best predict pain improvements 6-month posttreatment. Actigraphy results followed the same pattern as daily diary-based results. Clinically significant pain reductions in response to both CBT-I and BD were optimally predicted by achieving approximately 6.5 hr sleep duration by mid-treatment. Thus, tailoring interventions to increase TST early in treatment may be an effective strategy to promote long-term pain reductions. More comprehensive research on components of behavioral sleep medicine treatments that contribute to pain response is warranted. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  9. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  10. Debris-flows scale predictions based on basin spatial parameters calculated from Remote Sensing images in Wenchuan earthquake area

    International Nuclear Information System (INIS)

    Zhang, Huaizhen; Chi, Tianhe; Liu, Tianyue; Wang, Wei; Yang, Lina; Zhao, Yuan; Shao, Jing; Yao, Xiaojing; Fan, Jianrong

    2014-01-01

    Debris flow is a common hazard in the Wenchuan earthquake area. Collapse and Landslide Regions (CLR), caused by earthquakes, could be located from Remote Sensing images. CLR are the direct material source regions for debris flow. The Spatial Distribution of Collapse and Landslide Regions (SDCLR) strongly impact debris-flow formation. In order to depict SDCLR, we referred to Strahler's Hypsometric analysis method and developed 3 functional models to depict SDCLR quantitatively. These models mainly depict SDCLR relative to altitude, basin mouth and main gullies of debris flow. We used the integral of functions as the spatial parameters of SDCLR and these parameters were employed during the process of debris-flows scale predictions. Grouping-occurring debris-flows triggered by the rainstorm, which occurred on September 24th 2008 in Beichuan County, Sichuan province China, were selected to build the empirical equations for debris-flows scale predictions. Given the existing data, only debris-flows runout zone parameters (Max. runout distance L and Lateral width B) were estimated in this paper. The results indicate that the predicted results were more accurate when the spatial parameters were used. Accordingly, we suggest spatial parameters of SDCLR should be considered in the process of debris-flows scale prediction and proposed several strategies to prevent debris flow in the future

  11. [Value of sepsis single-disease manage system in predicting mortality in patients with sepsis].

    Science.gov (United States)

    Chen, J; Wang, L H; Ouyang, B; Chen, M Y; Wu, J F; Liu, Y J; Liu, Z M; Guan, X D

    2018-04-03

    Objective: To observe the effect of sepsis single-disease manage system on the improvement of sepsis treatment and the value in predicting mortality in patients with sepsis. Methods: A retrospective study was conducted. Patients with sepsis admitted to the Department of Surgical Intensive Care Unit of Sun Yat-Sen University First Affiliated Hospital from September 22, 2013 to May 5, 2015 were enrolled in this study. Sepsis single-disease manage system (Rui Xin clinical data manage system, China data, China) was used to monitor 25 clinical quality parameters, consisting of timeliness, normalization and outcome parameters. Based on whether these quality parameters could be completed or not, the clinical practice was evaluated by the system. The unachieved quality parameter was defined as suspicious parameters, and these suspicious parameters were used to predict mortality of patients with receiver operating characteristic curve (ROC). Results: A total of 1 220 patients with sepsis were enrolled, included 805 males and 415 females. The mean age was (59±17) years, and acute physiology and chronic health evaluation (APACHE Ⅱ) scores was 19±8. The area under ROC curve of total suspicious numbers for predicting 28-day mortality was 0.70; when the suspicious parameters number was more than 6, the sensitivity was 68.0% and the specificity was 61.0% for predicting 28-day mortality. In addition, the area under ROC curve of outcome suspicious number for predicting 28-day mortality was 0.89; when the suspicious outcome parameters numbers was more than 1, the sensitivity was 88.0% and the specificity was 78.0% for predicting 28-day mortality. Moreover, the area under ROC curve of total suspicious number for predicting 90-day mortality was 0.73; when the total suspicious parameters number was more than 7, the sensitivity was 60.0% and the specificity was 74.0% for predicting 90-day mortality. Finally, the area under ROC curve of outcome suspicious numbers for predicting 90

  12. Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

    International Nuclear Information System (INIS)

    Fendler, Wolfgang Peter; Ilhan, Harun; Paprottka, Philipp M.; Jakobs, Tobias F.; Heinemann, Volker; Bartenstein, Peter; Haug, Alexander R.; Khalaf, Feras; Ezziddin, Samer; Hacker, Marcus

    2015-01-01

    Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

  13. Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Fendler, Wolfgang Peter [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Klinik und Poliklinik fuer Nuklearmedizin, Munich (Germany); Ilhan, Harun [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Paprottka, Philipp M. [Ludwig-Maximilians-University of Munich, Department of Clinical Radiology, Munich (Germany); Jakobs, Tobias F. [Hospital Barmherzige Brueder, Department of Diagnostic and Interventional Radiology, Munich (Germany); Heinemann, Volker [Ludwig-Maximilians-University of Munich, Department of Internal Medicine III, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Bartenstein, Peter; Haug, Alexander R. [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Khalaf, Feras [University Hospital Bonn, Department of Nuclear Medicine, Bonn (Germany); Ezziddin, Samer [Saarland University Medical Center, Department of Nuclear Medicine, Homburg (Germany); Hacker, Marcus [Vienna General Hospital, Department of Nuclear Medicine, Vienna (Austria)

    2015-09-15

    Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

  14. Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.

    Science.gov (United States)

    Erdoğan, Onur; Aydin Son, Yeşim

    2014-01-01

    Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.

  15. Genetic parameters of blood β-hydroxybutyrate predicted from milk infrared spectra and clinical ketosis, and their associations with milk production traits in Norwegian Red cows.

    Science.gov (United States)

    Belay, T K; Svendsen, M; Kowalski, Z M; Ådnøy, T

    2017-08-01

    The aim of this study was to estimate genetic parameters for blood β-hydroxybutyrate (BHB) predicted from milk spectra and for clinical ketosis (KET), and to examine genetic association of blood BHB with KET and milk production traits (milk, fat, protein, and lactose yields, and milk fat, protein, and lactose contents). Data on milk traits, KET, and milk spectra were obtained from the Norwegian Dairy Herd Recording System with legal permission from TINE SA (Ås, Norway), the Norwegian Dairy Association that manages the central database. Data recorded up to 120 d after calving were considered. Blood BHB was predicted from milk spectra using a calibration model developed based on milk spectra and blood BHB measured in Polish dairy cows. The predicted blood BHB was grouped based on days in milk into 4 groups and each group was considered as a trait. The milk components for test-day milk samples were obtained by Fourier transform mid-infrared spectrometer with previously developed calibration equations from Foss (Hillerød, Denmark). Veterinarian-recorded KET data within 15 d before calving to 120 d after calving were used. Data were analyzed using univariate or bivariate linear animal models. Heritability estimates for predicted blood BHB at different stages of lactation were moderate, ranging from 0.250 to 0.365. Heritability estimate for KET from univariate analysis was 0.078, and the corresponding average estimate from bivariate analysis with BHB or milk production traits was 0.002. Genetic correlations between BHB traits were higher for adjacent lactation intervals and decreased as intervals were further apart. Predicted blood BHB at first test day was moderately genetically correlated with KET (0.469) and milk traits (ranged from -0.367 with protein content to 0.277 with milk yield), except for milk fat content from across lactation stages that had near zero genetic correlation with BHB (0.033). These genetic correlations indicate that a lower BHB is genetically

  16. Dynamic Contrast-Enhanced MRI of Cervical Cancers: Temporal Percentile Screening of Contrast Enhancement Identifies Parameters for Prediction of Chemoradioresistance

    International Nuclear Information System (INIS)

    Andersen, Erlend K.F.; Hole, Knut Håkon; Lund, Kjersti V.; Sundfør, Kolbein; Kristensen, Gunnar B.; Lyng, Heidi; Malinen, Eirik

    2012-01-01

    Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test, resulting in p value and relative risk maps of all percentile–time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile–time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee. Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile–time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile–time interval of nRSI was associated with progression-free survival. Conclusions: The percentile–time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer.

  17. High-throughput cardiac safety evaluation and multi-parameter arrhythmia profiling of cardiomyocytes using microelectrode arrays

    Energy Technology Data Exchange (ETDEWEB)

    Gilchrist, Kristin H., E-mail: kgilchrist@rti.org; Lewis, Gregory F.; Gay, Elaine A.; Sellgren, Katelyn L.; Grego, Sonia

    2015-10-15

    Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak for field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5 minute recordings at multiple time points (0.5, 1, 2 and 4 h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. - Highlights: • Six parameters describing arrhythmia were defined and measured for known compounds. • Software for efficient parameter extraction from large MEA data sets was developed. • The proposed cellular parameter set is predictive of clinical drug proarrhythmia.

  18. Accuracy of clinical prediction rules in peptic ulcer perforation: an observational study

    DEFF Research Database (Denmark)

    Buck, David Levarett; Vester-Andersen, Morten; Møller, Morten Hylander

    2012-01-01

    Abstract Objective. The aim of the present study was to compare the ability of four clinical prediction rules to predict adverse outcome in perforated peptic ulcer (PPU): the Boey score, the American Society of Anesthesiologists (ASA) score, the Acute Physiology and Chronic Health Evaluation...... and breastfeeding women, non-surgically treated patients, patients with malignant ulcers, and patients with perforation of other organs were excluded. Primary outcome measure: 30-day mortality rate. Statistical analysis: the ability of four clinical prediction rules to distinguish survivors from non...

  19. A Personalized Approach to Biological Therapy Using Prediction of Clinical Response Based on MRP8/14 Serum Complex Levels in Rheumatoid Arthritis Patients.

    Directory of Open Access Journals (Sweden)

    S C Nair

    Full Text Available Measurement of MRP8/14 serum levels has shown potential in predicting clinical response to different biological agents in rheumatoid arthritis (RA. We aimed to develop a treatment algorithm based on a prediction score using MRP8/14 measurements and clinical parameters predictive for response to different biological agents.Baseline serum levels of MRP8/14 were measured in 170 patients starting treatment with infliximab, adalimumab or rituximab. We used logistic regression analysis to develop a predictive score for clinical response at 16 weeks. MRP8/14 levels along with clinical variables at baseline were investigated. We also investigated how the predictive effect of MRP8/14 was modified by drug type. A treatment algorithm was developed based on categorizing the expected response per drug type as high, intermediate or low for each patient and optimal treatment was defined. Finally, we present the utility of using this treatment algorithm in clinical practice.The probability of response increased with higher baseline MRP8/14 complex levels (OR = 1.39, differentially between the TNF-blockers and rituximab (OR of interaction term = 0.78, and also increased with higher DAS28 at baseline (OR = 1.28. Rheumatoid factor positivity, functional disability (a higher HAQ, and previous use of a TNF-inhibitor decreased the probability of response. Based on the treatment algorithm 80 patients would have been recommended for anti-TNF treatment, 8 for rituximab, 13 for another biological treatment (other than TNFi or rituximab and for 69 no recommendation was made. The predicted response rates matched the observed response in the cohort well. On group level the predicted response based on the algorithm resulted in a modest 10% higher response rate in our cohort with much higher differences in response probability in individual patients treated contrary to treatment recommendation.Prediction of response using MRP8/14 levels along with clinical predictors has

  20. An improved robust model predictive control for linear parameter-varying input-output models

    NARCIS (Netherlands)

    Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.

    2018-01-01

    This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal

  1. Improved time series prediction with a new method for selection of model parameters

    International Nuclear Information System (INIS)

    Jade, A M; Jayaraman, V K; Kulkarni, B D

    2006-01-01

    A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

  2. Reliable prediction of clinical outcome in patients with chronic HCV infection and compensated advanced hepatic fibrosis: a validated model using objective and readily available clinical parameters.

    Science.gov (United States)

    van der Meer, Adriaan J; Hansen, Bettina E; Fattovich, Giovanna; Feld, Jordan J; Wedemeyer, Heiner; Dufour, Jean-François; Lammert, Frank; Duarte-Rojo, Andres; Manns, Michael P; Ieluzzi, Donatella; Zeuzem, Stefan; Hofmann, W Peter; de Knegt, Robert J; Veldt, Bart J; Janssen, Harry L A

    2015-02-01

    Reliable tools to predict long-term outcome among patients with well compensated advanced liver disease due to chronic HCV infection are lacking. Risk scores for mortality and for cirrhosis-related complications were constructed with Cox regression analysis in a derivation cohort and evaluated in a validation cohort, both including patients with chronic HCV infection and advanced fibrosis. In the derivation cohort, 100/405 patients died during a median 8.1 (IQR 5.7-11.1) years of follow-up. Multivariate Cox analyses showed age (HR=1.06, 95% CI 1.04 to 1.09, pstatistic=0.78, 95% CI 0.72 to 0.83). In the validation cohort, 58/296 patients with cirrhosis died during a median of 6.6 (IQR 4.4-9.0) years. Among patients with estimated 5-year mortality risks 10%, the observed 5-year mortality rates in the derivation cohort and validation cohort were 0.9% (95% CI 0.0 to 2.7) and 2.6% (95% CI 0.0 to 6.1), 8.1% (95% CI 1.8 to 14.4) and 8.0% (95% CI 1.3 to 14.7), 21.8% (95% CI 13.2 to 30.4) and 20.9% (95% CI 13.6 to 28.1), respectively (C statistic in validation cohort = 0.76, 95% CI 0.69 to 0.83). The risk score for cirrhosis-related complications also incorporated HCV genotype (C statistic = 0.80, 95% CI 0.76 to 0.83 in the derivation cohort; and 0.74, 95% CI 0.68 to 0.79 in the validation cohort). Prognosis of patients with chronic HCV infection and compensated advanced liver disease can be accurately assessed with risk scores including readily available objective clinical parameters. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  3. Development of Health Parameter Model for Risk Prediction of CVD Using SVM

    Directory of Open Access Journals (Sweden)

    P. Unnikrishnan

    2016-01-01

    Full Text Available Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD. The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.

  4. Volumetric PET/CT parameters predict local response of head and neck squamous cell carcinoma to chemoradiotherapy

    International Nuclear Information System (INIS)

    Hanamoto, Atsushi; Tatsumi, Mitsuaki; Takenaka, Yukinori; Hamasaki, Toshimitsu; Yasui, Toshimichi; Nakahara, Susumu; Yamamoto, Yoshifumi; Seo, Yuji; Isohashi, Fumiaki; Ogawa, Kazuhiko; Hatazawa, Jun; Inohara, Hidenori

    2014-01-01

    It is not well established whether pretreatment 18 F-FDG PET/CT can predict local response of head and neck squamous cell carcinoma (HNSCC) to chemoradiotherapy (CRT). We examined 118 patients: 11 with nasopharyngeal cancer (NPC), 30 with oropharyngeal cancer (OPC), and 77 with laryngohypopharyngeal cancer (LHC) who had completed CRT. PET/CT parameters of primary tumor, including metabolic tumor volume (MTV), total lesion glycolysis (TLG), and maximum and mean standardized uptake value (SUV max and SUV mean ), were correlated with local response, according to primary site and human papillomavirus (HPV) status. Receiver-operating characteristic analyses were made to access predictive values of the PET/CT parameters, while logistic regression analyses were used to identify independent predictors. Area under the curve (AUC) of the PET/CT parameters ranged from 0.53 to 0.63 in NPC and from 0.50 to 0.54 in OPC. HPV-negative OPC showed AUC ranging from 0.51 to 0.58, while all of HPV-positive OPCs showed complete response. In contrast, AUC ranged from 0.71 to 0.90 in LHC. Moreover, AUCs of MTV and TLG were significantly higher than those of SUV max and SUV mean (P < 0.01). After multivariate analysis, high MTV >25.0 mL and high TLG >144.8 g remained as independent, significant predictors of incomplete response compared with low MTV (odds ratio [OR], 13.4; 95% confidence interval [CI], 2.5–72.9; P = 0.003) and low TLG (OR, 12.8; 95% CI, 2.4–67.9; P = 0.003), respectively. In conclusion, predictive efficacy of pretreatment 18 F-FDG PET/CT varies with different primary sites and chosen parameters. Local response of LHC is highly predictable by volume-based PET/CT parameters

  5. Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages

    OpenAIRE

    Choi, Youn-Kyung; Kim, Jinmi; Yamaguchi, Tetsutaro; Maki, Koutaro; Ko, Ching-Chang; Kim, Yong-Il

    2016-01-01

    This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies ...

  6. Influence of occlusal loading on peri-implant clinical parameters. A pilot study.

    Science.gov (United States)

    Pellicer-Chover, Hilario; Viña-Almunia, José; Romero-Millán, Javier; Peñarrocha-Oltra, David; García-Mira, Berta; Peñarrocha-Diago, María

    2014-05-01

    To investigate the relation between occlusal loading and peri-implant clinical parameters (probing depth, bleeding on probing, gingival retraction, width of keratinized mucosa, and crevicular fluid volume) in patients with implant-supported complete fixed prostheses in both arches. This clinical study took place at the University of Valencia (Spain) dental clinic. It included patients attending the clinic for regular check-ups during at least 12 months after rehabilitation of both arches with implant-supported complete fixed ceramo-metallic prostheses. One study implant and one control implant were established for each patient using the T-Scan®III computerized system (Tesco, South Boston, USA). The maxillary implant closest to the point of maximum occlusal loading was taken as the study implant and the farthest (with least loading) as the control. Occlusal forces were registered with the T-Scan® III and then occlusal adjustment was performed to distribute occlusal forces correctly. Peri-implant clinical parameters were analyzed in both implants before and two and twelve months after occlusal adjustment. Before occlusal adjustment, study group implants presented a higher mean volume of crevicular fluid (51.3 ± 7.4 UP) than the control group (25.8 ± 5.5 UP), with statistically significant difference. Two months after occlusal adjustment, there were no significant differences between groups (24.6 ± 3.8 UP and 26 ± 4.5 UP respectively) (p=0.977). After twelve months, no significant differences were found between groups (24.4 ± 11.1 UP and 22.5 ± 8.9 UP respectively) (p=0.323). For the other clinical parameters, no significant differences were identified between study and control implants at any of the study times (p>0.05). Study group implants receiving higher occlusal loading presented significantly higher volumes of crevicular fluid than control implants. Crevicular fluid volumes were similar in both groups two and twelve months after occlusal adjustment.

  7. Clinical gestalt and the prediction of massive transfusion after trauma.

    Science.gov (United States)

    Pommerening, Matthew J; Goodman, Michael D; Holcomb, John B; Wade, Charles E; Fox, Erin E; Del Junco, Deborah J; Brasel, Karen J; Bulger, Eileen M; Cohen, Mitch J; Alarcon, Louis H; Schreiber, Martin A; Myers, John G; Phelan, Herb A; Muskat, Peter; Rahbar, Mohammad; Cotton, Bryan A

    2015-05-01

    Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesised that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable. Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma centres in patients who survived ≥ 30 min after admission and received ≥ 1 unit of RBC within 6h of arrival. Subjects who received ≥ 10 units within 24h of admission were classified as MT patients. Trauma surgeons were asked the clinical gestalt question "Is the patient likely to be massively transfused?" 10 min after the patients arrival. The performance of clinical gestalt to predict MT was assessed using chi-square tests and ROC analysis to compare gestalt to previously described scoring systems. Of the 1245 patients enrolled, 966 met inclusion criteria and 221 (23%) patients received MT. 415 (43%) were predicted to have a MT and 551(57%) were predicted to not have MT. Patients predicted to have MT were younger, more often sustained penetrating trauma, had higher ISS scores, higher heart rates, and lower systolic blood pressures (all pGestalt sensitivity was 65.6% and specificity was 63.8%. PPV and NPV were 34.9% and 86.2% respectively. Data from this large multicenter trial demonstrates that predicting the need for MT continues to be a challenge. Because of the increased mortality associated with delayed therapy, a more reliable algorithm is needed to identify and treat these severely injured patients earlier. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. The combination of kinetic and flow cytometric semen parameters as a tool to predict fertility in cryopreserved bull semen.

    Science.gov (United States)

    Gliozzi, T M; Turri, F; Manes, S; Cassinelli, C; Pizzi, F

    2017-11-01

    Within recent years, there has been growing interest in the prediction of bull fertility through in vitro assessment of semen quality. A model for fertility prediction based on early evaluation of semen quality parameters, to exclude sires with potentially low fertility from breeding programs, would therefore be useful. The aim of the present study was to identify the most suitable parameters that would provide reliable prediction of fertility. Frozen semen from 18 Italian Holstein-Friesian proven bulls was analyzed using computer-assisted semen analysis (CASA) (motility and kinetic parameters) and flow cytometry (FCM) (viability, acrosomal integrity, mitochondrial function, lipid peroxidation, plasma membrane stability and DNA integrity). Bulls were divided into two groups (low and high fertility) based on the estimated relative conception rate (ERCR). Significant differences were found between fertility groups for total motility, active cells, straightness, linearity, viability and percentage of DNA fragmented sperm. Correlations were observed between ERCR and some kinetic parameters, and membrane instability and some DNA integrity indicators. In order to define a model with high relation between semen quality parameters and ERCR, backward stepwise multiple regression analysis was applied. Thus, we obtained a prediction model that explained almost half (R 2=0.47, P<0.05) of the variation in the conception rate and included nine variables: five kinetic parameters measured by CASA (total motility, active cells, beat cross frequency, curvilinear velocity and amplitude of lateral head displacement) and four parameters related to DNA integrity evaluated by FCM (degree of chromatin structure abnormality Alpha-T, extent of chromatin structure abnormality (Alpha-T standard deviation), percentage of DNA fragmented sperm and percentage of sperm with high green fluorescence representative of immature cells). A significant relationship (R 2=0.84, P<0.05) was observed between

  9. Laboratory mechanical parameters of composite resins and their relation to fractures and wear in clinical trials-A systematic review.

    Science.gov (United States)

    Heintze, Siegward D; Ilie, Nicoleta; Hickel, Reinhard; Reis, Alessandra; Loguercio, Alessandro; Rousson, Valentin

    2017-03-01

    To evaluate a range of mechanical parameters of composite resins and compare the data to the frequency of fractures and wear in clinical studies. Based on a search of PubMed and SCOPUS, clinical studies on posterior composite restorations were investigated with regard to bias by two independent reviewers using Cochrane Collaboration's tool for assessing risk of bias in randomized trials. The target variables were chipping and/or fracture, loss of anatomical form (wear) and a combination of both (summary clinical index). These outcomes were modelled by time and material in a linear mixed effect model including random study and experiment effects. The laboratory data from one test institute were used: flexural strength, flexural modulus, compressive strength, and fracture toughness (all after 24-h storage in distilled water). For some materials flexural strength data after aging in water/saliva/ethanol were available. Besides calculating correlations between clinical and laboratory outcomes, we explored whether a model including a laboratory predictor dichotomized at a cut-off value better predicted a clinical outcome than a linear model. A total of 74 clinical experiments from 45 studies were included involving 31 materials for which laboratory data were also available. A weak positive correlation between fracture toughness and clinical fractures was found (Spearman rho=0.34, p=0.11) in addition to a moderate and statistically significant correlation between flexural strength and clinical wear (Spearman rho=0.46, p=0.01). When excluding those studies with "high" risk of bias (n=18), the correlations were generally weaker with no statistically significant correlation. For aging in ethanol, a very strong correlation was found between flexural strength decrease and clinical index, but this finding was based on only 7 materials (Spearman rho=0.96, p=0.0001). Prediction was not consistently improved with cutoff values. Correlations between clinical and laboratory

  10. Improving the Prediction of Prostate Cancer Overall Survival by Supplementing Readily Available Clinical Data with Gene Expression Levels of IGFBP3 and F3 in Formalin-Fixed Paraffin Embedded Core Needle Biopsy Material.

    Directory of Open Access Journals (Sweden)

    Zhuochun Peng

    Full Text Available A previously reported expression signature of three genes (IGFBP3, F3 and VGLL3 was shown to have potential prognostic value in estimating overall and cancer-specific survivals at diagnosis of prostate cancer in a pilot cohort study using freshly frozen Fine Needle Aspiration (FNA samples.We carried out a new cohort study with 241 prostate cancer patients diagnosed from 2004-2007 with a follow-up exceeding 6 years in order to verify the prognostic value of gene expression signature in formalin fixed paraffin embedded (FFPE prostate core needle biopsy tissue samples. The cohort consisted of four patient groups with different survival times and death causes. A four multiplex one-step RT-qPCR test kit, designed and optimized for measuring the expression signature in FFPE core needle biopsy samples, was used. In archive FFPE biopsy samples the expression differences of two genes (IGFBP3 and F3 were measured. The survival time predictions using the current clinical parameters only, such as age at diagnosis, Gleason score, PSA value and tumor stage, and clinical parameters supplemented with the expression levels of IGFBP3 and F3, were compared.When combined with currently used clinical parameters, the gene expression levels of IGFBP3 and F3 are improving the prediction of survival time as compared to using clinical parameters alone.The assessment of IGFBP3 and F3 gene expression levels in FFPE prostate cancer tissue would provide an improved survival prediction for prostate cancer patients at the time of diagnosis.

  11. PREDICTIVE ACCURACY OF TRANSCEREBELLAR DIAMETER IN COMPARISON WITH OTHER FOETAL BIOMETRIC PARAMETERS FOR GESTATIONAL AGE ESTIMATION AMONG PREGNANT NIGERIAN WOMEN.

    Science.gov (United States)

    Adeyekun, A A; Orji, M O

    2014-04-01

    To compare the predictive accuracy of foetal trans-cerebellar diameter (TCD) with those of other biometric parameters in the estimation of gestational age (GA). A cross-sectional study. The University of Benin Teaching Hospital, Nigeria. Four hundred and fifty healthy singleton pregnant women, between 14-42 weeks gestation. Trans-cerebellar diameter (TCD), biparietal diameter (BPD), femur length (FL), abdominal circumference (AC) values across the gestational age range studied. Correlation and predictive values of TCD compared to those of other biometric parameters. The range of values for TCD was 11.9 - 59.7mm (mean = 34.2 ± 14.1mm). TCD correlated more significantly with menstrual age compared with other biometric parameters (r = 0.984, p = 0.000). TCD had a higher predictive accuracy of 96.9% ± 12 days), BPD (93.8% ± 14.1 days). AC (92.7% ± 15.3 days). TCD has a stronger predictive accuracy for gestational age compared to other routinely used foetal biometric parameters among Nigerian Africans.

  12. Prognostic value of metabolic parameters and clinical impact of {sup 18}F-fluorocholine PET/CT in biochemical recurrent prostate cancer

    Energy Technology Data Exchange (ETDEWEB)

    Colombie, M.; Bailly, C.; Rusu, D.; Rousseau, N. [Institut de Cancerologie de l' Ouest Rene Gauducheau, Nuclear Medicine, 44805 Nantes-St Herblain Cedex (France); Campion, L. [ICO Cancer Center, Statistics, Saint-Herblain (France); Nantes University, Nantes-Angers Cancer Research Center, INSERM U892-CNRS UMR 6299, Nantes (France); Rousseau, T. [Urologic Clinic Nantes-Atlantis, Saint Herblain (France); Mathieu, C. [University Hospital, Nuclear Medicine, Nantes (France); Ferrer, L. [ICO Cancer Center, Physics, Saint-Herblain (France); Kraeber-Bodere, F. [Institut de Cancerologie de l' Ouest Rene Gauducheau, Nuclear Medicine, 44805 Nantes-St Herblain Cedex (France); University Hospital, Nuclear Medicine, Nantes (France); Nantes University, Nantes-Angers Cancer Research Center, INSERM U892-CNRS UMR 6299, Nantes (France); Rousseau, C. [Institut de Cancerologie de l' Ouest Rene Gauducheau, Nuclear Medicine, 44805 Nantes-St Herblain Cedex (France); Nantes University, Nantes-Angers Cancer Research Center, INSERM U892-CNRS UMR 6299, Nantes (France)

    2015-11-15

    To evaluate the therapeutic impact of {sup 18}F-fluorocholine (FCH) PET/CT in biochemical recurrent prostate cancer (PC) and to investigate the value of quantitative FCH PET/CT parameters in predicting progression-free survival (PFS). This retrospective study included 172 consecutive patients with PC who underwent FCH PET/CT for biochemical recurrence. Mean rising PSA was 10.7 ± 35.0 ng/ml. Patients with positive FCH PET were classified into three groups: those with uptake only in the prostatic bed, those with locoregional disease, and those with distant metastases. Referring physicians were asked to indicate the hypothetical therapeutic strategy with and without the FCH PET/CT results. Clinical variables and PET parameters including SUVmax, SUVpeak, SUVmean, total lesion choline kinase activity (TLCKA) and standardized added metabolic activity (SAM) were recorded and a multivariate analysis was performed to determine the factors independently predicting PFS. In 137 of the 172 patients, the FCH PET/CT scan was positive, and of these, 29.9 % (41/137) had prostatic recurrence, 42.3 % (58/137) had pelvic lymph node recurrence with or without prostatic recurrence, and 27.7 % (38/137) had distant metastases. The FCH PET/CT result led to a change in treatment plan in 43.6 % (75/172) of the 172 patients. Treatment was changed in 49.6 % (68/137) of those with a positive FCH PET/CT scan and in 20 % (7/35) of those with a negative FCH PET/CT scan. After a median follow-up of 29.3 months (95 % CI 18.9 - 45.9 months), according to multivariate analysis age <70 years, SAM ≥23 and SUVmean ≥3 were parameters independently predicting PFS. A nomogram constructed using the three parameters showed 49 months of PFS in patients with the best scores (0 or 1) and only 11 months in patients with a poor score (score 3). This study indicates that a positive FCH PET result in PC patients with biochemical recurrence predicts a shorter PFS and confirms the major impact of the FCH PET

  13. Pretreatment data is highly predictive of liver chemistry signals in clinical trials.

    Science.gov (United States)

    Cai, Zhaohui; Bresell, Anders; Steinberg, Mark H; Silberg, Debra G; Furlong, Stephen T

    2012-01-01

    The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy's law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.

  14. Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech.

    Science.gov (United States)

    Whitwell, Jennifer L; Weigand, Stephen D; Duffy, Joseph R; Strand, Edythe A; Machulda, Mary M; Senjem, Matthew L; Gunter, Jeffrey L; Lowe, Val J; Jack, Clifford R; Josephs, Keith A

    2016-01-01

    Beta-amyloid (Aβ) deposition can be observed in primary progressive aphasia (PPA) and progressive apraxia of speech (PAOS). While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified) and PAOS (n = 42) subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+) status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+) status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+) status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified PPA subjects.

  15. Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech

    Directory of Open Access Journals (Sweden)

    Jennifer L. Whitwell

    2016-01-01

    Full Text Available Beta-amyloid (Aβ deposition can be observed in primary progressive aphasia (PPA and progressive apraxia of speech (PAOS. While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified and PAOS (n = 42 subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+ status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+ status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+ status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified

  16. Automated interpretable computational biology in the clinic: a framework to predict disease severity and stratify patients from clinical data

    Directory of Open Access Journals (Sweden)

    Soumya Banerjee

    2017-10-01

    Full Text Available We outline an automated computational and machine learning framework that predicts disease severity and stratifies patients. We apply our framework to available clinical data. Our algorithm automatically generates insights and predicts disease severity with minimal operator intervention. The computational framework presented here can be used to stratify patients, predict disease severity and propose novel biomarkers for disease. Insights from machine learning algorithms coupled with clinical data may help guide therapy, personalize treatment and help clinicians understand the change in disease over time. Computational techniques like these can be used in translational medicine in close collaboration with clinicians and healthcare providers. Our models are also interpretable, allowing clinicians with minimal machine learning experience to engage in model building. This work is a step towards automated machine learning in the clinic.

  17. THE EFFICACY OF ANGLE-MATCHED ISOKINETIC KNEE FLEXOR AND EXTENSOR STRENGTH PARAMETERS IN PREDICTING AGILITY TEST PERFORMANCE.

    Science.gov (United States)

    Greig, Matt; Naylor, James

    2017-10-01

    Agility is a fundamental performance element in many sports, but poses a high risk of injury. Hierarchical modelling has shown that eccentric hamstring strength is the primary determinant of agility performance. The purpose of this study was to investigate the relationship between knee flexor and extensor strength parameters and a battery of agility tests. Controlled laboratory study. Nineteen recreational intermittent games players completed an agility battery and isokinetic testing of the eccentric knee flexors (eccH) and concentric knee extensors (conQ) at 60, 180 and 300°·s -1 . Peak torque and the angle at which peak torque occurred were calculated for eccH and conQ at each speed. Dynamic control ratios (eccH:conQ) and fast:slow ratios (300:60) were calculated using peak torque values, and again using angle-matched data, for eccH and conQ. The agility test battery differentiated linear vs directional changes and prescriptive vs reactive tasks. Linear regression showed that eccH parameters were generally a better predictor of agility performance than conQ parameters. Stepwise regression showed that only angle-matched strength ratios contributed to the prediction of each agility test. Trdaitionally calculated strength ratios using peak torque values failed to predict performance. Angle-matched strength parameters were able to account for 80% of the variation in T-test performance, 70% of deceleration distance, 55% of 10m sprint performance, and 44% of reactive change of direction speed. Traditionally calculated strength ratios failed to predict agility performance, whereas angle-matched strength ratios had better predictive ability and featured in a predictive stepwise model for each agility task. 2c.

  18. Some uses of predictive probability of success in clinical drug development

    Directory of Open Access Journals (Sweden)

    Mauro Gasparini

    2013-03-01

    Full Text Available Predictive probability of success is a (subjective Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations imposed in the world of pharmaceutical development.Within a single trial, predictive probability of success can be identified with expected power, i.e. the evaluation of the success probability of the trial. Success means, for example, obtaining a significant result of a standard superiority test.Across trials, predictive probability of success can be the probability of a successful completion of an entire part of clinical development, for example a successful phase III development in the presence of phase II data.Calculations of predictive probability of success in the presence of normal data with known variance will be illustrated, both for within-trial and across-trial predictions.

  19. Model structural uncertainty quantification and hydrologic parameter and prediction error analysis using airborne electromagnetic data

    DEFF Research Database (Denmark)

    Minsley, B. J.; Christensen, Nikolaj Kruse; Christensen, Steen

    Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure...... is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne...... electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential...

  20. Clinical and functional criteria for predicting asthma in infants

    Directory of Open Access Journals (Sweden)

    Yu. L. Mizemitskiy

    2015-01-01

    Full Text Available Objective: to determine clinical and functional criteria for predicting asthma in children who have sustained acute obstructive bronchitis in infancy. Subjects and methods. A total of 125 infants aged 2 to 36 months who had experienced 1 -2 episodes of acute obstructive bronchitis and treated at hospital were examined when bronchial obstruction syndrome was being relieved. In addition to physical examination, functional studies (computerized bronchophonography and heart rate variability assessment were used. Immunological examination included determination of the serum levels of immunoglobulin E and interleuMn-17A. The infants who had sustained acute obstructive bronchitis were followed up for 12-36 months. Results. The infants who had sustained acute obstructive bronchitis in the presence of mild perinatal CNS damage caused by hypoxia were typified by high respiratory morbidity; early-onset bronchial obstruction; long-term bronchial obstruction relief; high incidence of grade 2 respiratory failure in acute obstructive bronchitis. These patients developed asthma more often than twice and repeated episodes of bronchial obstruction. ROC analysis was used to elaborate clinical and functional criteria for predicting the development of asthma in infants. Conclusion. The proposed additional clinical and functional criteria characterizing external respiratory dysfunction and autonomic homeostatic changes contribute to the early diagnosis of asthma and substantially increase the validity of prediction of its development in children younger than 3 years, which is of great importance for goal-oriented preventive measures.

  1. Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.

    Science.gov (United States)

    Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

    2017-05-01

    Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P  sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  2. Adaptability and Prediction of Anticipatory Muscular Activity Parameters to Different Movements in the Sitting Position.

    Science.gov (United States)

    Chikh, Soufien; Watelain, Eric; Faupin, Arnaud; Pinti, Antonio; Jarraya, Mohamed; Garnier, Cyril

    2016-08-01

    Voluntary movement often causes postural perturbation that requires an anticipatory postural adjustment to minimize perturbation and increase the efficiency and coordination during execution. This systematic review focuses specifically on the relationship between the parameters of anticipatory muscular activities and movement finality in sitting position among adults, to study the adaptability and predictability of anticipatory muscular activities parameters to different movements and conditions in sitting position in adults. A systematic literature search was performed using PubMed, Science Direct, Web of Science, Springer-Link, Engineering Village, and EbscoHost. Inclusion and exclusion criteria were applied to retain the most rigorous and specific studies, yielding 76 articles, Seventeen articles were excluded at first reading, and after the application of inclusion and exclusion criteria, 23 were retained. In a sitting position, central nervous system activity precedes movement by diverse anticipatory muscular activities and shows the ability to adapt anticipatory muscular activity parameters to the movement direction, postural stability, or charge weight. In addition, these parameters could be adapted to the speed of execution, as found for the standing position. Parameters of anticipatory muscular activities (duration, order, and amplitude of muscle contractions constituting the anticipatory muscular activity) could be used as a predictive indicator of forthcoming movement. In addition, this systematic review may improve methodology in empirical studies and assistive technology for people with disabilities. © The Author(s) 2016.

  3. On Better Estimating and Normalizing the Relationship between Clinical Parameters: Comparing Respiratory Modulations in the Photoplethysmogram and Blood Pressure Signal (DPOP versus PPV

    Directory of Open Access Journals (Sweden)

    Paul S. Addison

    2015-01-01

    Full Text Available DPOP (ΔPOP or Delta-POP is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method. We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements.

  4. On better estimating and normalizing the relationship between clinical parameters: comparing respiratory modulations in the photoplethysmogram and blood pressure signal (DPOP versus PPV).

    Science.gov (United States)

    Addison, Paul S; Wang, Rui; Uribe, Alberto A; Bergese, Sergio D

    2015-01-01

    DPOP (ΔPOP or Delta-POP) is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method). We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N) is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements.

  5. Tumor microenvironment in head and neck squamous cell carcinomas: predictive value and clinical relevance of hypoxic markers. A review.

    Science.gov (United States)

    Hoogsteen, Ilse J; Marres, Henri A M; Bussink, Johan; van der Kogel, Albert J; Kaanders, Johannes H A M

    2007-06-01

    Hypoxia and tumor cell proliferation are important factors determining the treatment response of squamous cell carcinomas of the head and neck. Successful approaches have been developed to counteract these resistance mechanisms although usually at the cost of increased short- and long-term side effects. To provide the best attainable quality of life for individual patients and the head and neck cancer patient population as a whole, it is of increasing importance that tools be developed that allow a better selection of patients for these intensified treatments. A literature review was performed with special focus on the predictive value and clinical relevance of endogenous hypoxia-related markers. New methods for qualitative and quantitative assessment of functional microenvironmental parameters such as hypoxia, proliferation, and vasculature have identified several candidate markers for future use in predictive assays. Hypoxia-related markers include hypoxia inducible factor (HIF)-1alpha, carbonic anhydrase IX, glucose transporters, erythropoietin receptor, osteopontin, and others. Although several of these markers and combinations of markers are associated with treatment outcome, their clinical value as predictive factors remains to be established. A number of markers and marker profiles have emerged that may have potential as a predictive assay. Validation of these candidate assays requires testing in prospective trials comparing standard treatment against experimental treatments targeting the related microregional constituent. (c) 2007 Wiley Periodicals, Inc. Head Neck, 2007.

  6. Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

    Directory of Open Access Journals (Sweden)

    Cobaugh Christian W

    2004-08-01

    Full Text Available Abstract Background A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. Results The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. Conclusion Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.

  7. Pharmacokinetic parameters derived from dynamic contrast enhanced MRI of cervical cancers predict chemoradiotherapy outcome

    International Nuclear Information System (INIS)

    Andersen, Erlend K.F.; Hole, Knut Håkon; Lund, Kjersti V.; Sundfør, Kolbein; Kristensen, Gunnar B.; Lyng, Heidi; Malinen, Eirik

    2013-01-01

    Purpose: To assess the prognostic value of pharmacokinetic parameters derived from pre-chemoradiotherapy dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of cervical cancer patients. Materials and methods: Seventy-eight patients with locally advanced cervical cancer underwent DCE-MRI with Gd-DTPA before chemoradiotherapy. The pharmacokinetic Brix and Tofts models were fitted to contrast enhancement curves in all tumor voxels, providing histograms of several pharmacokinetic parameters (Brix: A Brix , k ep , k el , Tofts: K trans , ν e ). A percentile screening approach including log-rank survival tests was undertaken to identify the clinically most relevant part of the intratumoral parameter distribution. Clinical endpoints were progression-free survival (PFS) and locoregional control (LRC). Multivariate analysis including FIGO stage and tumor volume was used to assess the prognostic significance of the imaging parameters. Results: A Brix , k el , and K trans were significantly (P e was significantly positively correlated with PFS only. k ep showed no association with any endpoint. A Brix was positively correlated with K trans and ν e , and showed the strongest association with endpoint in the log-rank testing. k el and K trans were independent prognostic factors in multivariate analysis with LRC as endpoint. Conclusions: Parameters estimated by pharmacokinetic analysis of DCE-MR images obtained prior to chemoradiotherapy may be used for identifying patients at risk of treatment failure

  8. Safety analysis methodology with assessment of the impact of the prediction errors of relevant parameters

    International Nuclear Information System (INIS)

    Galia, A.V.

    2011-01-01

    The best estimate plus uncertainty approach (BEAU) requires the use of extensive resources and therefore it is usually applied for cases in which the available safety margin obtained with a conservative methodology can be questioned. Outside the BEAU methodology, there is not a clear approach on how to deal with the issue of considering the uncertainties resulting from prediction errors in the safety analyses performed for licensing submissions. However, the regulatory document RD-310 mentions that the analysis method shall account for uncertainties in the analysis data and models. A possible approach is presented, that is simple and reasonable, representing just the author's views, to take into account the impact of prediction errors and other uncertainties when performing safety analysis in line with regulatory requirements. The approach proposes taking into account the prediction error of relevant parameters. Relevant parameters would be those plant parameters that are surveyed and are used to initiate the action of a mitigating system or those that are representative of the most challenging phenomena for the integrity of a fission barrier. Examples of the application of the methodology are presented involving a comparison between the results with the new approach and a best estimate calculation during the blowdown phase for two small breaks in a generic CANDU 6 station. The calculations are performed with the CATHENA computer code. (author)

  9. Pressure Ulcers in Adults: Prediction and Prevention. Clinical Practice Guideline Number 3.

    Science.gov (United States)

    Agency for Health Care Policy and Research (DHHS/PHS), Rockville, MD.

    This package includes a clinical practice guideline, quick reference guide for clinicians, and patient's guide to predicting and preventing pressure ulcers in adults. The clinical practice guideline includes the following: overview of the incidence and prevalence of pressure ulcers; clinical practice guideline (introduction, risk assessment tools…

  10. Semen molecular and cellular features: these parameters can reliably predict subsequent ART outcome in a goat model

    Directory of Open Access Journals (Sweden)

    Mereu Paolo

    2009-11-01

    Full Text Available Abstract Currently, the assessment of sperm function in a raw or processed semen sample is not able to reliably predict sperm ability to withstand freezing and thawing procedures and in vivo fertility and/or assisted reproductive biotechnologies (ART outcome. The aim of the present study was to investigate which parameters among a battery of analyses could predict subsequent spermatozoa in vitro fertilization ability and hence blastocyst output in a goat model. Ejaculates were obtained by artificial vagina from 3 adult goats (Capra hircus aged 2 years (A, B and C. In order to assess the predictive value of viability, computer assisted sperm analyzer (CASA motility parameters and ATP intracellular concentration before and after thawing and of DNA integrity after thawing on subsequent embryo output after an in vitro fertility test, a logistic regression analysis was used. Individual differences in semen parameters were evident for semen viability after thawing and DNA integrity. Results of IVF test showed that spermatozoa collected from A and B lead to higher cleavage rates (0

  11. The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression

    Directory of Open Access Journals (Sweden)

    Chunxiao Zhang

    2012-01-01

    Full Text Available The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

  12. Can We Predict Patient Wait Time?

    Science.gov (United States)

    Pianykh, Oleg S; Rosenthal, Daniel I

    2015-10-01

    The importance of patient wait-time management and predictability can hardly be overestimated: For most hospitals, it is the patient queues that drive and define every bit of clinical workflow. The objective of this work was to study the predictability of patient wait time and identify its most influential predictors. To solve this problem, we developed a comprehensive list of 25 wait-related parameters, suggested in earlier work and observed in our own experiments. All parameters were chosen as derivable from a typical Hospital Information System dataset. The parameters were fed into several time-predicting models, and the best parameter subsets, discovered through exhaustive model search, were applied to a large sample of actual patient wait data. We were able to discover the most efficient wait-time prediction factors and models, such as the line-size models introduced in this work. Moreover, these models proved to be equally accurate and computationally efficient. Finally, the selected models were implemented in our patient waiting areas, displaying predicted wait times on the monitors located at the front desks. The limitations of these models are also discussed. Optimal regression models based on wait-line sizes can provide accurate and efficient predictions for patient wait time. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  13. An investigation of engine performance parameters and artificial intelligent emission prediction of hydrogen powered car

    International Nuclear Information System (INIS)

    Ho, Tien; Karri, Vishy; Lim, Daniel; Barret, Danny

    2008-01-01

    With the depletion of fossil fuel resources and the potential consequences of climate change due to fossil fuel use, much effort has been put into the search for alternative fuels for transportation. Although there are several potential alternative fuels, which have low impact on the environment, none of these fuels have the ability to be used as the sole 'fuel of the future'. One fuel which is likely to become a part of the over all solution to the transportation fuel dilemma is hydrogen. In this paper, The Toyota Corolla four cylinder, 1.8 l engine running on petrol is systematically converted to run on hydrogen. Several ancillary instruments for measuring various engine operating parameters and emissions are fitted to appraise the performance of the hydrogen car. The effect of hydrogen as a fuel compares with gasoline on engine operating parameters and effect of engine operating parameters on emission characteristics is discussed. Based on the experimental setup, a suite of neural network models were tested to accurately predict the effect of major engine operating conditions on the hydrogen car emissions. Predictions were found to be ±4% to the experimental values. This work provided better understanding of the effect of engine process parameters on emissions. (author)

  14. TU-C-12A-09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo-Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics

    International Nuclear Information System (INIS)

    Zhang, H; Chen, W; Kligerman, S; D’Souza, W; Suntharalingam, M; Lu, W; Tan, S; Kim, G

    2014-01-01

    Purpose: To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo-radiotherapy (CRT) in patients with locally advanced esophageal cancer. Methods: This study included 20 patients who underwent tri-modality therapy (CRT + surgery) and had 18 F-FDG PET/CT scans before initiation of CRT and 4-6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross-validations to avoid model over-fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model. Conclusion: The SVM model using all features including

  15. Clinical significance of sleep bruxism on several occlusal and functional parameters.

    Science.gov (United States)

    Ommerborn, Michelle A; Giraki, Maria; Schneider, Christine; Fuck, Lars Michael; Zimmer, Stefan; Franz, Matthias; Raab, Wolfgang Hans-michael; Schaefer, Ralf

    2010-10-01

    The aim of this study was to evaluate the association between various functional and occlusal parameters and sleep bruxism. Thirty-nine (39) sleep bruxism patients and 30 controls participated in this investigation. The assessment of sleep bruxism was performed using the Bruxcore Bruxism-Monitoring Device (BBMD) combined with a new computer-based analyzing method. Sixteen functional and/or occlusal parameters were recorded. With a mean slide of 0.95 mm in the sleep bruxism group and a mean slide of 0.42 mm in the control group (Mann Whitney U test; p<0.003), results solely demonstrated a significant group difference regarding the length of a slide from centric occlusion to maximum intercuspation. The results suggest that the slightly pronounced slide could be of clinical importance in the development of increased wear facets in patients with current sleep bruxism activity. Following further evaluation including polysomnographic recordings, the BBMD combined with this new analyzing technique seems to be a clinically feasible instrument that allows the practitioner to quantify abrasion over a short period.

  16. Multiobjective design of aquifer monitoring networks for optimal spatial prediction and geostatistical parameter estimation

    Science.gov (United States)

    Alzraiee, Ayman H.; Bau, Domenico A.; Garcia, Luis A.

    2013-06-01

    Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a Monte-Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that

  17. Self-care and clinical parameters in patients with type 2 diabetes mellitus

    Directory of Open Access Journals (Sweden)

    José Thiago de Sousa

    2015-09-01

    Full Text Available Objective: to verify characteristics related to self-care and clinical parameters in patients with type 2 diabetes mellitus. Methods: descriptive and exploratory, cross-sectional study, conducted with 173 patients assisted in 12 Family Health Units in the urban area of a city in the Northeast region of Brazil. Results: most participants (61.3% were female, aged less than 60 years old. There were significant differences in the lower glycemic control (p = 0.014, capillary glycemia (p = 0.018 and alcohol consumption (p = 0.015 for men as well as higher central obesity indexes for women (p = 0.000. It was observed high frequency of overweight, abdominal obesity, high blood pressure, elevated blood glucose levels and insufficient levels of physical activity. Conclusion: there is the need for nursing actions aimed at improving self-care and control of the clinical parameters in these patients.

  18. Clinical, psychological and demographic parameters of body pain in multiple sclerosis

    Directory of Open Access Journals (Sweden)

    Ghasem Salehpoor

    2017-02-01

    Full Text Available Background: Body pain in multiple sclerosis (MS is a common phenomenon that can create or exacerbate by different parameters of clinical, psychological and demographic. The aim of this study was to investigate the relationship between parameters of clinical (fatigue, clinical course, body mass index and duration, psychological (depression, anxiety and stress and demographic (age, gender, marital status and education characters with multiple sclerosis patient’s body pain. Methods: This cross-sectional study has been performed in the Multiple Sclerosis Society of Guilan Province and Imam Reza Specialized and Sub-specialized Clinic, Rasht City, Iran during June to February 2010. In this study 162 patients with MS were selected by consecutive sampling. We used the clinical and demographic variables inventory, body pain subscale of the health survey questionnaire, depression, anxiety and stress scale and fatigue severity scale along with identical analog-spring balance. The data were analyzed by Pearson correlation coefficient and point bi-serial, one-way analysis of variance, Gabriel test and stepwise multiple regression. Results: The findings showed that patients who scored 3 or higher in relapses experienced significantly more body pain than patients who scored 1-2 times of relapses (P= 0.031. In the meantime, significant differences were not found between the two groups of patients with a score of 3 or higher in relapses and non-relapse and between non-relapse patients and with a score 1-2 times of relapses in terms of body pain. Also, significant differences were not found in different groups of hospitalization in terms of body pain. However, anxiety and fatigue together could explain significantly 25% of the shared variance of body pain (F= 26.29, P≤ 0.0009. Conclusion: This study showed the effect of psychological and clinical factors on body pain exacerbation in MS patients. Therefore, it is necessary for clinicians to consider

  19. Comparing 2 Whiplash Grading Systems to Predict Clinical Outcomes.

    Science.gov (United States)

    Croft, Arthur C; Bagherian, Alireza; Mickelsen, Patrick K; Wagner, Stephen

    2016-06-01

    Two whiplash severity grading systems have been developed: Quebec Task Force on Whiplash-Associated Disorders (QTF-WAD) and the Croft grading system. The majority of clinical studies to date have used the modified grading system published by the QTF-WAD in 1995 and have demonstrated some ability to predict outcome. But most studies include only injuries of lower severity (grades 1 and 2), preventing a broader interpretation. The purpose of this study was assess the ability of these grading systems to predict clinical outcome within the context of a broader injury spectrum. This study evaluated both grading systems for their ability to predict the bivalent outcome, recovery, within a sample of 118 whiplash patients who were part of a previous case-control designed study. Of these, 36% (controls) had recovered, and 64% (cases) had not recovered. The discrete bivariate distribution between recovery status and whiplash grade was analyzed using the 2-tailed cross-tabulation statistics. Applying the criteria of the original 1993 Croft grading system, the subset comprised 1 grade 1 injury, 32 grade 2 injuries, 53 grade 3 injuries, and 32 grade 4 injuries. Applying the criteria of the modified (QTF-WAD) grading system, there were 1 grade 1 injury, 89 grade 2 injuries, and 28 grade 3 injuries. Both whiplash grading systems correlated negatively with recovery; that is, higher severity grades predicted a lower probability of recovery, and statistically significant correlations were observed in both, but the Croft grading system substantially outperformed the QTF-WAD system on this measure. The Croft grading system for whiplash injury severity showed a better predictive measure for recovery status from whiplash injuries as compared with the QTF-WAD grading system.

  20. Nonlinear Prediction As A Tool For Determining Parameters For Phase Space Reconstruction In Meteorology

    Science.gov (United States)

    Miksovsky, J.; Raidl, A.

    Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.

  1. Efficacy and predictive value of clinical stage in non-surgical patients with esophageal cancer

    International Nuclear Information System (INIS)

    Liu Xiao; Wang Guiqi; He Shun

    2014-01-01

    Objective: To investigate the efficacy and predictive value of clinical stage in non-surgical patients with esophageal cancer (EC). Methods: A retrospective study was conducted in 358 EC patients who underwent radical surgery in our hospital from April 2003 to October 2010 and who had preoperative work-up including endoscopic esophageal ultrasound (EUS), esophagoscopy, thoracic CT scans,and contrast esophagography and had detailed information on postoperative pathological stages. The predictive value of preoperative clinical T/N stage based on EUS + CT for postoperative pathological stage was analyzed. The disease free survival (DFS) and overall survival (OS) were analyzed according to the UICC TNM classification (2002/ 2009) and the clinical stage based on imaging findings. Results: The median follow-up was 47 months.A total of 305 (85.2%) of all patients were analyzed by clinical stage based on EUS + CT.Among them, the predictive value of clinical T stage for pathological T stage was 0-88.6%, highest (88.6%) for T1 stage and lowest for T4 stage. The predictive value of clinical N stage (N 0 /N1) was 62.5-100%. The significant differences in OS and DFS rates based on both 2002 and 2009 UICC TNM classifications were noted (P=0.000 and 0.000). There were significant differences in OS between stage groups, except the comparison between two stage Ⅳ patients and other groups, according to 2002 UICC TNM classification. There were usually insignificant differences in OS between stage groups, according to 2009 UICC TNM classification. For the 305 patients staged clinically based on EUS and CT according to 2002 UICC TNM classification, significant differences in OS and DFS rates were noted (P=0.000 and 0.000). Conclusions: Imaging modalities show good predictive value for N stage (N0/N1),even though they cannot accurately provide the number of metastatic lymph nodes. The clinical stage based on EUS + CT can effectively predict the prognosis of non-surgical EC patients

  2. Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

    Science.gov (United States)

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

    2015-02-01

    Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have

  3. Identification of clinical and paraclinical findings predictive for headache occurrence during spontaneous subarachnoid hemorrhage.

    Science.gov (United States)

    Ljubisavljevic, Srdjan; Milosevic, Vuk; Stojanov, Aleksandar; Ljubisavljevic, Marina; Dunjic, Olivera; Zivkovic, Miroslava

    2017-07-01

    Headache is recognized as the main but unwarranted symptom of subarachnoid hemorrhage (SAH). There are no enough findings identified as predictive for headache occurrence in SAH. We evaluated the clinical and paraclinical factors predictive for headache occurrence in SAH. We retrospectively analyzed medical records of 431 consecutive non traumatic SAH patients (264 females and 167 males), ages from 19 to 91 years, presenting with headache (70.3%) and without headache (29.7%) during period of 11years. Among all tested parameters, as negative predictors for headache occurrence were recognized: patients' ages (OR 0.97 [95%CI: 0.96-0.99], p=0.025), persistence of coagulation abnormality (OR 0.23 [95%CI: 0.08-0.67], p=0.006), atrial fibrilation (OR 0.23 [95%CI: 0.09-0.59], p=0.002), chronic renal failure (OR 0.26 [95%CI: 0.09-0.76], p=0.014) and more diseases (OR 0.11 [95%CI: 0.04-0.32], p<0.0001), as higher clinical score (OR 0.94 [95%CI: 0.90-0.99], p=0.018) including positive neurological findings (OR 0.34 [95%CI: 0.21-0.55], p<0.001) and loss of consciousness (OR 0.22 [95%CI: 0.12-0.39], p<0.001) at the SAH onset, while the complaint of neck stiffness was identified as its positive predictor (OR 1.93 [95%CI: 1.19-3.10], p=0.007). Although diagnosis based solely on clinical presentation is not reliable and speculative, our findings could provide physicians with evidence to consider SAH not only in conditions of its headache occurrence but also in those with headache absence. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Predictive probability methods for interim monitoring in clinical trials with longitudinal outcomes.

    Science.gov (United States)

    Zhou, Ming; Tang, Qi; Lang, Lixin; Xing, Jun; Tatsuoka, Kay

    2018-04-17

    In clinical research and development, interim monitoring is critical for better decision-making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on-going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on-going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed-form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed-form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials. Copyright © 2018 John Wiley & Sons, Ltd.

  5. QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.

    Science.gov (United States)

    Golmohammadi, H; Dashtbozorgi, Z

    2016-12-01

    In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors. The variable selection method of ERM was employed to select an optimum subset of descriptors. The five descriptors selected using ERM were used as inputs of SVM to predict the polarity parameter of organic compounds in methanol. The coefficient of determination, r 2 , between experimental and predicted polarity parameters for the prediction set by ERM and SVM were 0.952 and 0.982, respectively. Acceptable results specified that the ERM approach is a very effective method for variable selection and the predictive aptitude of the SVM model is superior to those obtained by ERM. The obtained results demonstrate that SVM can be used as a substitute influential modeling tool for QSPR studies.

  6. A Bayesian approach for parameter estimation and prediction using a computationally intensive model

    International Nuclear Information System (INIS)

    Higdon, Dave; McDonnell, Jordan D; Schunck, Nicolas; Sarich, Jason; Wild, Stefan M

    2015-01-01

    Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model η(θ), where θ denotes the uncertain, best input setting. Hence the statistical model is of the form y=η(θ)+ϵ, where ϵ accounts for measurement, and possibly other, error sources. When nonlinearity is present in η(⋅), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model η(⋅). This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. We also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory. (paper)

  7. Prediction of detonation and JWL eos parameters of energetic materials using EXPLO5 computer code

    CSIR Research Space (South Africa)

    Peter, Xolani

    2016-09-01

    Full Text Available Ballistic Organization Cape Town, South Africa 27-29 September 2016 1 PREDICTION OF DETONATION AND JWL EOS PARAMETERS OF ENERGETIC MATERIALS USING EXPLO5 COMPUTER CODE X. Peter*, Z. Jiba, M. Olivier, I.M. Snyman, F.J. Mostert and T.J. Sono.... Nowadays many numerical methods and programs are being used for carrying out thermodynamic calculations of the detonation parameters of condensed explosives, for example a BKW Fortran (Mader, 1967), Ruby (Cowperthwaite and Zwisler, 1974) TIGER...

  8. Autoreactive T Cells in Human Smokers Is Predictive of Clinical Outcome

    Directory of Open Access Journals (Sweden)

    Chuang eXu

    2012-08-01

    Full Text Available Cross-sectional studies have suggested a role for activation of adaptive immunity in smokers with emphysema, but the clinical application of these findings has not been explored. Here we examined the utility of detecting autoreactive T cells as a screening tool for emphysema in an at-risk population of smokers. We followed 156 former and current (ever-smokers for two years to assess whether peripheral blood CD4 T cell cytokine responses to lung elastin fragments (EFs could discriminate between those with and without emphysema, and to evaluate the relevance of autoreactive T cells to predict changes during follow-up in lung physiological parameters. Volunteers underwent baseline complete phenotypic assessment with pulmonary function tests, quantitative chest CT, yearly six minutes walk distance (6MWD testing, and annual measurement of CD4 T cell cytokine responses to EFs. The areas under the receiver operating characteristic curve to predict emphysema for interferon gamma (IFN-γ, and interleukin 6 (IL-6 responses to EFs were 0.81 (95% CI of 0.74 to 0.88 and 0.79 (95% CI of 0.72 to 0.86 respectively. We developed a dual cytokine enzyme-linked immunocell spot assay, the γ-6 Spot, using CD4 T cell IFN-γ and IL-6 responses and found that it discriminated emphysema with 90% sensitivity. After adjusting for potential confounders, the presence of autoreactive T cells was predictive of a decrease in 6MWD over two years (decline in 6MWD, -19 meters (m per fold change in IFN-γ; P=0.026, and -26 m per fold change in IL-6; P=0.003. These findings collectively suggest that the EF specific autoreactive CD4 T cell assay, γ-6 Spot, could provide a non-invasive diagnostic tool with potential application to large-scale screening to discriminate emphysema in ever-smokers, and predict early relevant physiological outcomes in those at risk.

  9. To Find a Better Dosimetric Parameter in the Predicting of Radiation-Induced Lung Toxicity Individually: Ventilation, Perfusion or CT based.

    Science.gov (United States)

    Xiao, Lin-Lin; Yang, Guoren; Chen, Jinhu; Wang, Xiaohui; Wu, Qingwei; Huo, Zongwei; Yu, Qingxi; Yu, Jinming; Yuan, Shuanghu

    2017-03-15

    This study aimed to find a better dosimetric parameter in predicting of radiation-induced lung toxicity (RILT) in patients with non-small cell lung cancer (NSCLC) individually: ventilation(V), perfusion (Q) or computerized tomography (CT) based. V/Q single-photon emission computerized tomography (SPECT) was performed within 1 week prior to radiotherapy (RT). All V/Q imaging data was integrated into RT planning system, generating functional parameters based on V/Q SPECT. Fifty-seven NSCLC patients were enrolled in this prospective study. Fifteen (26.3%) patients underwent grade ≥2 RILT, the remaining forty-two (73.7%) patients didn't. Q-MLD, Q-V20, V-MLD, V-V20 of functional parameters correlated more significantly with the occurrence of RILT compared to V20, MLD of anatomical parameters (r = 0.630; r = 0.644; r = 0.617; r = 0.651 vs. r = 0.424; r = 0.520 p < 0.05, respectively). In patients with chronic obstructive pulmonary diseases (COPD), V functional parameters reflected significant advantage in predicting RILT; while in patients without COPD, Q functional parameters reflected significant advantage. Analogous results were existed in fractimal analysis of global pulmonary function test (PFT). In patients with central-type NSCLC, V parameters were better than Q parameters; while in patients with peripheral-type NSCLC, the results were inverse. Therefore, this study demonstrated that choosing a suitable dosimetric parameter individually can help us predict RILT accurately.

  10. [Usefulness of clinical prediction rules for ruling out deep vein thrombosis in a hospital emergency department].

    Science.gov (United States)

    Rosa-Jiménez, Francisco; Rosa-Jiménez, Ascensión; Lozano-Rodríguez, Aquiles; Santoro-Martínez, María Del Carmen; Duro-López, María Del Carmen; Carreras-Álvarez de Cienfuegos, Amelia

    2015-01-01

    To compare the efficacy of the most familiar clinical prediction rules in combination with D-dimer testing to rule out a diagnosis of deep vein thrombosis (DVT) in a hospital emergency department. Retrospective cross-sectional analysis of the case records of all patients attending a hospital emergency department with suspected lower-limb DVT between 1998 and 2002. Ten clinical prediction scores were calculated and D-dimer levels were available for all patients. The gold standard was ultrasound diagnosis of DVT by an independent radiologist who was blinded to clinical records. For each prediction rule, we analyzed the effectiveness of the prediction strategy defined by "low clinical probability and negative D-dimer level" against the ultrasound diagnosis. A total of 861 case records were reviewed and 577 cases were selected; the mean (SD) age was 66.7 (14.2) years. DVT was diagnosed in 145 patients (25.1%). Only the Wells clinical prediction rule and 4 other models had a false negative rate under 2%. The Wells criteria and the score published by Johanning and colleagues identified higher percentages of cases (15.6% and 11.6%, respectively). This study shows that several clinical prediction rules can be safely used in the emergency department, although none of them have proven more effective than the Wells criteria.

  11. Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist.

    Science.gov (United States)

    Dolan, M; Doyle, M

    2000-10-01

    Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. To review the current status of violence risk prediction research. Literature search (Medline). Key words: violence, risk prediction, mental disorder. Systematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings. Violence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

  12. Prediction of higher cost of antiretroviral therapy (ART) according to clinical complexity. A validated clinical index.

    Science.gov (United States)

    Velasco, Cesar; Pérez, Inaki; Podzamczer, Daniel; Llibre, Josep Maria; Domingo, Pere; González-García, Juan; Puig, Inma; Ayala, Pilar; Martín, Mayte; Trilla, Antoni; Lázaro, Pablo; Gatell, Josep Maria

    2016-03-01

    The financing of antiretroviral therapy (ART) is generally determined by the cost incurred in the previous year, the number of patients on treatment, and the evidence-based recommendations, but not the clinical characteristics of the population. To establish a score relating the cost of ART and patient clinical complexity in order to understand the costing differences between hospitals in the region that could be explained by the clinical complexity of their population. Retrospective analysis of patients receiving ART in a tertiary hospital between 2009 and 2011. Factors potentially associated with a higher cost of ART were assessed by bivariate and multivariate analysis. Two predictive models of "high-cost" were developed. The normalized estimated (adjusted for the complexity scores) costs were calculated and compared with the normalized real costs. In the Hospital Index, 631 (16.8%) of the 3758 patients receiving ART were responsible for a "high-cost" subgroup, defined as the highest 25% of spending on ART. Baseline variables that were significant predictors of high cost in the Clinic-B model in the multivariate analysis were: route of transmission of HIV, AIDS criteria, Spanish nationality, year of initiation of ART, CD4+ lymphocyte count nadir, and number of hospital admissions. The Clinic-B score ranged from 0 to 13, and the mean value (5.97) was lower than the overall mean value of the four hospitals (6.16). The clinical complexity of the HIV patient influences the cost of ART. The Clinic-B and Clinic-BF scores predicted patients with high cost of ART and could be used to compare and allocate costs corrected for the patient clinical complexity. Copyright © 2015 Elsevier España, S.L.U. y Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

  13. Predictive equations underestimate resting energy expenditure in female adolescents with phenylketonuria

    Science.gov (United States)

    Quirk, Meghan E.; Schmotzer, Brian J.; Schmotzer, Brian J.; Singh, Rani H.

    2010-01-01

    Resting energy expenditure (REE) is often used to estimate total energy needs. The Schofield equation based on weight and height has been reported to underestimate REE in female children with phenylketonuria (PKU). The objective of this observational, cross-sectional study was to evaluate the agreement of measured REE with predicted REE for female adolescents with PKU. A total of 36 females (aged 11.5-18.7 years) with PKU attending Emory University’s Metabolic Camp (June 2002 – June 2008) underwent indirect calorimetry. Measured REE was compared to six predictive equations using paired Student’s t-tests, regression-based analysis, and assessment of clinical accuracy. The differences between measured and predicted REE were modeled against clinical parameters to determine to if a relationship existed. All six selected equations significantly under predicted measured REE (P< 0.005). The Schofield equation based on weight had the greatest level of agreement, with the lowest mean prediction bias (144 kcal) and highest concordance correlation coefficient (0.626). However, the Schofield equation based on weight lacked clinical accuracy, predicting measured REE within ±10% in only 14 of 36 participants. Clinical parameters were not associated with bias for any of the equations. Predictive equations underestimated measured REE in this group of female adolescents with PKU. Currently, there is no accurate and precise alternative for indirect calorimetry in this population. PMID:20497783

  14. Interleukin-1β level in peri-implant crevicular fluid and its correlation with the clinical and radiographic parameters

    Directory of Open Access Journals (Sweden)

    Aniruddha M Kajale

    2014-01-01

    Full Text Available Background and Objectives : Assessing only the clinical and radiographic parameters for evaluation of dental implants may not be enough as they often reflect extensive inflammatory changes in the periodontal tissues. As peri-implant crevicular fluid (PICF can give us a more prompt and objective measure of the disease activity, the purpose of this case series is to assess the peri-implant health status of single tooth dental implants not only clinically and radiographically but also biochemically. Materials and Methods: Thirteen patients were subjected to dental implants at single edentulous sites using a conventional surgical approach. At baseline, 6 months, and 12 months after implant placement, the clinical and radiographic parameters were recorded. Additionally, IL-1β in PICF was estimated using the ELISA kit at 6 th and 12 th month. Results: The clinical and radiographic parameters differed significantly around the implants at different time intervals with IL-1β levels showing highly significant differences between 6 months (31.79 ± 12.26 pg/μl and 12 months (113.09 ± 51.11 pg/μl. However, Spearman′s correlation coefficient showed no correlation with the clinical and radiographic parameters. Interpretation and Conclusion: Assessment of the various parameters confirmed that all the implants had a healthy peri-implant status. Although the levels of IL-1β in PICF were elevated at the 12 th month, they were well within the healthy range as observed by previous studies. This indicates that IL-1β, a biochemical marker, can be used as an adjunct to clinical and radiographic parameters in the assessment of EARLY inflammatory changes around implants.

  15. Use of post-thaw semen quality parameters to predict fertility of water buffalo (Bubalus bubalis) bull during peak breeding season.

    Science.gov (United States)

    Ahmed, H; Andrabi, S M H; Anwar, M; Jahan, S

    2017-05-01

    This study was designed to predict the fertility of water buffalo bull using post-thaw semen quality parameters during peak breeding season. Thirty ejaculates were collected from five bulls with artificial vagina and cryopreserved. At post-thaw, semen was analysed for motility parameters, velocity distribution, kinematics, DNA integrity/fragmentation, viability, mitochondrial transmembrane potential, morphology, plasma membrane and acrosome integrity. Data of 514 inseminations were collected for estimation of in vivo fertility. Pearson's correlation coefficients showed that progressive motility (PM), rapid velocity, average path velocity, straight line velocity, straightness, supravital plasma membrane integrity, viable spermatozoon with intact acrosome or with high mitochondrial activity were correlated with in vivo fertility (r = .81, p fertility was PM. However, combinations of semen quality parameters to predict fertility were better as compared to single parameter. In conclusion, fertility of buffalo bull can be predicted through some of the post-thaw in vitro semen quality tests during peak breeding season. © 2016 Blackwell Verlag GmbH.

  16. CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy.

    Science.gov (United States)

    Ma, Leyuan; Boucher, Jeffrey I; Paulsen, Janet; Matuszewski, Sebastian; Eide, Christopher A; Ou, Jianhong; Eickelberg, Garrett; Press, Richard D; Zhu, Lihua Julie; Druker, Brian J; Branford, Susan; Wolfe, Scot A; Jensen, Jeffrey D; Schiffer, Celia A; Green, Michael R; Bolon, Daniel N

    2017-10-31

    Developing tools to accurately predict the clinical prevalence of drug-resistant mutations is a key step toward generating more effective therapeutics. Here we describe a high-throughput CRISPR-Cas9-based saturated mutagenesis approach to generate comprehensive libraries of point mutations at a defined genomic location and systematically study their effect on cell growth. As proof of concept, we mutagenized a selected region within the leukemic oncogene BCR-ABL1 Using bulk competitions with a deep-sequencing readout, we analyzed hundreds of mutations under multiple drug conditions and found that the effects of mutations on growth in the presence or absence of drug were critical for predicting clinically relevant resistant mutations, many of which were cancer adaptive in the absence of drug pressure. Using this approach, we identified all clinically isolated BCR-ABL1 mutations and achieved a prediction score that correlated highly with their clinical prevalence. The strategy described here can be broadly applied to a variety of oncogenes to predict patient mutations and evaluate resistance susceptibility in the development of new therapeutics. Published under the PNAS license.

  17. Vomiting and migraine-related clinical parameters in pediatric migraine.

    Science.gov (United States)

    Eidlitz-Markus, Tal; Haimi-Cohen, Yishai; Zeharia, Avraham

    2017-06-01

    To investigate the characteristics of vomiting in pediatric migraineurs and the relationship of vomiting with other migraine-related parameters. The cohort included children and adolescents with migraine attending a headache clinic of a tertiary pediatric medical center from 2010 to 2016. Patients were identified by a retrospective database search. Data were collected from medical files. The presence of vomiting was associated with background and headache-related parameters. The study group included 453 patients, 210 boys (46.4%) and 243 girls (53.6%), of mean age 11.3 ± 3.7 years. Vomiting was reported by 161 patients (35.5%). On comparison of patients with and without vomiting, vomiting was found to be significantly associated with male gender (54% vs 42.1%, P migraine onset (8.0 ± 3. years vs 9.6 ± 3.7 years, P migraine (67% vs 58.7%, P migraine (24.1% vs 10.1%, P migraine in both parents (9.3% vs 3.1%, P = .007), and migraine in either parent (57.5% vs 45.5%, P = .02). The higher rate of vomiting in the younger patients and the patients with awakening pain may be explained by a common underlying pathogenetic mechanism of vomiting and migraine involving autonomic nerve dysfunction/immaturity. The association of vomiting with parental migraine points to a genetic component of vomiting and migraine. It should be noted that some of the findings may simply reflect referral patterns in the tertiary clinic. © 2017 American Headache Society.

  18. Clinical predictors of outcome in vitiligo

    Directory of Open Access Journals (Sweden)

    Dave Shriya

    2002-11-01

    Full Text Available The significant inter-patient variability in progression, and response to therapy makes it a great challenge for the physician to predict the outcome of vitiligo at the very outset. Subjective factors like stress, pregnancy, sunburn and illness have been identified as aggravating factors for vitiligo. However, a few studies have evaluated the statistical significance of objective clinical parameters in predicting the outcome of vitiligo. Our retrospective analysis of 199 consecutive patients with vitiligo who presented to our OPD was aimed at evaluation of these objective clinical parameters utilizing a standard proforma. Patients already on treatment, and those with duration of disease less than 6 months were excluded from the study. Progression was defined as an increase in size or number of lesions in the 3 months prior to presentation. In all 76. 9% patients had progression of vitiligo. The clinical parameters significantly associated with progression were a positive family history (p=0. 027, mucosal involvement (p=0. 032, Koebner′s phenomenon (p=0. 036 and nonsegmental vitiligo (p=0. 033. Thrichrome sign, leucotrichia, longer duration and higher age at onset did not correlate significantly with progression. The one significant observation that we found to have the poor prognostic implication in vitiligo is the presence of mucosal vitiligo. The clinical prediction of disease progression at the outset enables the physician to set realistic treatment goals and optimize the therapeutic regimen for the individual patient.

  19. Stepped approach for prediction of syndrome Z in patients attending sleep clinic: a north Indian hospital-based study.

    Science.gov (United States)

    Agrawal, Swastik; Sharma, Surendra Kumar; Sreenivas, Vishnubhatla; Lakshmy, Ramakrishnan; Mishra, Hemant K

    2012-09-01

    Syndrome Z is the occurrence of metabolic syndrome (MS) with obstructive sleep apnea. Knowledge of its risk factors is useful to screen patients requiring further evaluation for syndrome Z. Consecutive patients referred from sleep clinic undergoing polysomnography in the Sleep Laboratory of AIIMS Hospital, New Delhi were screened between June 2008 and May 2010, and 227 patients were recruited. Anthropometry, body composition analysis, blood pressure, fasting blood sugar, and lipid profile were measured. MS was defined using the National Cholesterol Education Program (adult treatment panel III) criteria, with Asian cutoff values for abdominal obesity. Prevalence of MS and syndrome Z was 74% and 65%, respectively. Age, percent body fat, excessive daytime sleepiness (EDS), and ΔSaO(2) (defined as difference between baseline and minimum SaO(2) during polysomnography) were independently associated with syndrome Z. Using a cutoff of 15% for level of desaturation, the stepped predictive score using these risk factors had sensitivity, specificity, positive predictive value, and negative predictive value of 75%, 73%, 84%, and 61%, respectively for the diagnosis of syndrome Z. It correctly characterized presence of syndrome Z 75% of the time and obviated need for detailed evaluation in 42% of the screened subjects. A large proportion of patients presenting to sleep clinics have MS and syndrome Z. Age, percent body fat, EDS, and ΔSaO(2) are independent risk factors for syndrome Z. A stepped predictive score using these parameters is cost-effective and useful in diagnosing syndrome Z in resource-limited settings.

  20. Predicted Interval Plots (PIPS): A Graphical Tool for Data Monitoring of Clinical Trials.

    Science.gov (United States)

    Li, Lingling; Evans, Scott R; Uno, Hajime; Wei, L J

    2009-11-01

    Group sequential designs are often used in clinical trials to evaluate efficacy and/or futility. Many methods have been developed for different types of endpoints and scenarios. However, few of these methods convey information regarding effect sizes (e.g., treatment differences) and none uses prediction to convey information regarding potential effect size estimates and associated precision, with trial continuation. To address these limitations, Evans et al. (2007) proposed to use prediction and predicted intervals as a flexible and practical tool for quantitative monitoring of clinical trials. In this article, we reaffirm the importance and usefulness of this innovative approach and introduce a graphical summary, predicted interval plots (PIPS), to display the information obtained in the prediction process in a straightforward yet comprehensive manner. We outline the construction of PIPS and apply this method in two examples. The results and the interpretations of the PIPS are discussed.

  1. Medical and Periodontal Clinical Parameters in Patients at Different Levels of Chronic Renal Failure

    Directory of Open Access Journals (Sweden)

    Caroline Perozini

    2017-01-01

    Full Text Available Aim. To assess the clinical periodontal and medical parameters in patients with chronic renal failure (CRF at different levels of renal disease. Background. CRF is a progressive and irreversible loss of renal function associated with a decline in the glomerular filtration rate. Periodontal disease is a destructive inflammatory disease affecting periodontal tissues that shows high prevalence in patients with CRF. Materials and Methods. 102 CRF patients were included and divided into an early stage group (EG, predialysis group (PDG, and hemodialysis group (HDG. The medical parameters were taken from the patients’ records. Results. Periodontal clinical condition differed among the CRF groups. Clinical attachment loss was greater in the HDG and PDG group compared to the EG (p=0.0364; the same was observed in the Plaque Index (p=0.0296; the others periodontal parameters did not show any differences. Ferritin levels were significantly higher in the HDG when compared to the EG and PGD (p<0.0001, and fibrinogen was higher in PDG compared with the others (p<0.0001; the triglycerides also showed higher values in the HDG compared with the other groups (p<0.0001. Conclusion. The patients with renal involvement should have a multidisciplinary approach to an improvement in their oral and systemic health.

  2. Can we predict uranium bioavailability based on soil parameters? Part 1: effect of soil parameters on soil solution uranium concentration.

    Science.gov (United States)

    Vandenhove, H; Van Hees, M; Wouters, K; Wannijn, J

    2007-01-01

    Present study aims to quantify the influence of soil parameters on soil solution uranium concentration for (238)U spiked soils. Eighteen soils collected under pasture were selected such that they covered a wide range for those parameters hypothesised as being potentially important in determining U sorption. Maximum soil solution uranium concentrations were observed at alkaline pH, high inorganic carbon content and low cation exchange capacity, organic matter content, clay content, amorphous Fe and phosphate levels. Except for the significant correlation between the solid-liquid distribution coefficients (K(d), L kg(-1)) and the organic matter content (R(2)=0.70) and amorphous Fe content (R(2)=0.63), there was no single soil parameter significantly explaining the soil solution uranium concentration (which varied 100-fold). Above pH=6, log(K(d)) was linearly related with pH [log(K(d))=-1.18 pH+10.8, R(2)=0.65]. Multiple linear regression analysis did result in improved predictions of the soil solution uranium concentration but the model was complex.

  3. Histologic parameters predictive of disease outcome in women with advanced stage ovarian carcinoma treated with neoadjuvant chemotherapy.

    Science.gov (United States)

    Samrao, Damanzoopinder; Wang, Dan; Ough, Faith; Lin, Yvonne G; Liu, Song; Menesses, Teodulo; Yessaian, Annie; Turner, Nicole; Pejovic, Tanja; Mhawech-Fauceglia, Paulette

    2012-12-01

    The use of neoadjuvant chemotherapy followed by tumor reduction surgery, also called interval debulking surgery (IDS), is considered an alternative therapeutic regimen for selected patients with advanced stage epithelial ovarian cancer (EOC). Although minimal residual disease has been proven to be a prognostic factor in traditional cytoreduction for advanced stage EOC, predictive factors after IDS still remain unexplored. The aim of this study was to determine the prognostic value of post-neoadjuvant histologic changes with clinical outcome. Three pathologists evaluated 67 cases for the following parameters: fibrosis, necrosis, residual tumor, and inflammation. The Cohen's kappa statistic was used to measure agreement among pathologists. Univariate and multivariate Cox proportional hazards models were used to determine the association between histologic parameters and recurrence-free survival (RFS) and overall survival (OS). There was substantial to almost perfect agreement among the three pathologists in all four histologic parameters (k ranged from 0.65 to 0.97). Fibrosis was associated with longer RFS (P = 0.0257) with a median of 20 months for tumors with fibrosis (3+) versus 12 months for tumors with fibrosis (1+, 2+) and longer OS (P = 0.0249) with a median of 51 months for tumors with fibrosis (3+) versus 32 months for tumors with fibrosis (1+, 2+). Our results revealed that patients with tumors exhibiting fibrosis (1+, 2+), as well as necrosis (0, 1+), had significant shorter RFS and OS (P = 0.059 and P = 0.0234, respectively). We suggest that the assessment of fibrosis and necrosis should be implemented in pathologic evaluation and prospectively validated in future studies.

  4. Quantitative predictions from competition theory with incomplete information on model parameters tested against experiments across diverse taxa

    OpenAIRE

    Fort, Hugo

    2017-01-01

    We derive an analytical approximation for making quantitative predictions for ecological communities as a function of the mean intensity of the inter-specific competition and the species richness. This method, with only a fraction of the model parameters (carrying capacities and competition coefficients), is able to predict accurately empirical measurements covering a wide variety of taxa (algae, plants, protozoa).

  5. Comparison of clinical and paraclinical parameters as tools for early diagnosis of classical swine fever

    DEFF Research Database (Denmark)

    Lohse, Louise; Uttenthal, Åse; Nielsen, Jens

    Comparison of clinical and paraclinical parameters as tools for early diagnosis of classical swine fever. Louise Lohse, Åse Uttenthal, Jens Nielsen. National Veterinary Institute, Division of Virology, Lindholm, Technical University of Denmark. Introduction: In order to limit the far-reaching socio......-economic as well as the animal welfare consequences of an outbreak of classical swine fever (CSF), early diagnosis is essential. However, host-virus interactions strongly influence the course of CSF disease, and the clinical feature is not clear, thus complicating the diagnostic perspective. At the National...... Veterinary Institute, in Denmark, we are conducting a series of animal experiments under standardized conditions in order to investigate new parameters of clinical as well as paraclinical nature that holds the potential as diagnostic tools to improve early detection of CSF. In three recent studies, weaned...

  6. Modeling and Predicting AD Progression by Regression Analysis of Sequential Clinical Data

    KAUST Repository

    Xie, Qing

    2016-02-23

    Alzheimer\\'s Disease (AD) is currently attracting much attention in elders\\' care. As the increasing availability of massive clinical diagnosis data, especially the medical images of brain scan, it is highly significant to precisely identify and predict the potential AD\\'s progression based on the knowledge in the diagnosis data. In this paper, we follow a novel sequential learning framework to model the disease progression for AD patients\\' care. Different from the conventional approaches using only initial or static diagnosis data to model the disease progression for different durations, we design a score-involved approach and make use of the sequential diagnosis information in different disease stages to jointly simulate the disease progression. The actual clinical scores are utilized in progress to make the prediction more pertinent and reliable. We examined our approach by extensive experiments on the clinical data provided by the Alzheimer\\'s Disease Neuroimaging Initiative (ADNI). The results indicate that the proposed approach is more effective to simulate and predict the disease progression compared with the existing methods.

  7. Modeling and Predicting AD Progression by Regression Analysis of Sequential Clinical Data

    KAUST Repository

    Xie, Qing; Wang, Su; Zhu, Jia; Zhang, Xiangliang

    2016-01-01

    Alzheimer's Disease (AD) is currently attracting much attention in elders' care. As the increasing availability of massive clinical diagnosis data, especially the medical images of brain scan, it is highly significant to precisely identify and predict the potential AD's progression based on the knowledge in the diagnosis data. In this paper, we follow a novel sequential learning framework to model the disease progression for AD patients' care. Different from the conventional approaches using only initial or static diagnosis data to model the disease progression for different durations, we design a score-involved approach and make use of the sequential diagnosis information in different disease stages to jointly simulate the disease progression. The actual clinical scores are utilized in progress to make the prediction more pertinent and reliable. We examined our approach by extensive experiments on the clinical data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results indicate that the proposed approach is more effective to simulate and predict the disease progression compared with the existing methods.

  8. Clinical judgement in the era of big data and predictive analytics.

    Science.gov (United States)

    Chin-Yee, Benjamin; Upshur, Ross

    2017-12-13

    Clinical judgement is a central and longstanding issue in the philosophy of medicine which has generated significant interest over the past few decades. In this article, we explore different approaches to clinical judgement articulated in the literature, focusing in particular on data-driven, mathematical approaches which we contrast with narrative, virtue-based approaches to clinical reasoning. We discuss the tension between these different clinical epistemologies and further explore the implications of big data and machine learning for a philosophy of clinical judgement. We argue for a pluralistic, integrative approach, and demonstrate how narrative, virtue-based clinical reasoning will remain indispensable in an era of big data and predictive analytics. © 2017 John Wiley & Sons, Ltd.

  9. The estimation of soil parameters using observations on crop biophysical variables and the crop model STICS improve the predictions of agro environmental variables.

    Science.gov (United States)

    Varella, H.-V.

    2009-04-01

    Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters, especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless, observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In this work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters is made, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, a Bayesian data assimilation method is chosen (because of available prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prior information and the result with the posterior information provided by the Bayesian data assimilation method. The result of the

  10. Pretreatment data is highly predictive of liver chemistry signals in clinical trials

    Directory of Open Access Journals (Sweden)

    Cai Z

    2012-11-01

    Full Text Available Zhaohui Cai,1,* Anders Bresell,2,* Mark H Steinberg,1 Debra G Silberg,1 Stephen T Furlong11AstraZeneca Pharmaceuticals, Wilmington, DE, USA; 2AstraZeneca Pharmaceuticals, Södertälje, Sweden*These authors contributed equally to this workPurpose: The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline information.Patients and methods: Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results.Results: Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests.Conclusion: It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.Keywords: bilirubin, Hy’s Law, ALT, GGT, baseline, prediction

  11. Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study)

    Science.gov (United States)

    Mehrdad Mirsanjari, Mir; Mohammadyari, Fatemeh

    2018-03-01

    Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.

  12. Predicting clinical decline in progressive agrammatic aphasia and apraxia of speech.

    Science.gov (United States)

    Whitwell, Jennifer L; Weigand, Stephen D; Duffy, Joseph R; Clark, Heather M; Strand, Edythe A; Machulda, Mary M; Spychalla, Anthony J; Senjem, Matthew L; Jack, Clifford R; Josephs, Keith A

    2017-11-28

    To determine whether baseline clinical and MRI features predict rate of clinical decline in patients with progressive apraxia of speech (AOS). Thirty-four patients with progressive AOS, with AOS either in isolation or in the presence of agrammatic aphasia, were followed up longitudinally for up to 4 visits, with clinical testing and MRI at each visit. Linear mixed-effects regression models including all visits (n = 94) were used to assess baseline clinical and MRI variables that predict rate of worsening of aphasia, motor speech, parkinsonism, and behavior. Clinical predictors included baseline severity and AOS type. MRI predictors included baseline frontal, premotor, motor, and striatal gray matter volumes. More severe parkinsonism at baseline was associated with faster rate of decline in parkinsonism. Patients with predominant sound distortions (AOS type 1) showed faster rates of decline in aphasia and motor speech, while patients with segmented speech (AOS type 2) showed faster rates of decline in parkinsonism. On MRI, we observed trends for fastest rates of decline in aphasia in patients with relatively small left, but preserved right, Broca area and precentral cortex. Bilateral reductions in lateral premotor cortex were associated with faster rates of decline of behavior. No associations were observed between volumes and decline in motor speech or parkinsonism. Rate of decline of each of the 4 clinical features assessed was associated with different baseline clinical and regional MRI predictors. Our findings could help improve prognostic estimates for these patients. © 2017 American Academy of Neurology.

  13. A clinical prediction rule for detecting major depressive disorder in primary care : the PREDICT-NL study

    NARCIS (Netherlands)

    Zuithoff, Nicolaas P A; Vergouwe, Yvonne; King, Michael; Nazareth, Irwin; Hak, Eelko; Moons, Karel G M; Geerlings, Mirjam I

    BACKGROUND: Major depressive disorder often remains unrecognized in primary care. OBJECTIVE: Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. METHODS: A total of 1046 subjects, aged 18-65 years, were included from

  14. Determination of Several Clinical Parameters of the Blood for the HealthyEvaluation of the Radiation Worker

    International Nuclear Information System (INIS)

    Yazid, M; Triyono; Aris-Bastianudin

    2000-01-01

    Determination of the several clinical parameters of the blood for healthyevaluation of the radiation worker has been done. This research was done forarrangement of the medical general check up of the radiation worker toobserve pathological indicator of several body organs. The blood sample wastaken from vena mediana cubiti and analyzed by reagent using standardprocedure from Boehringer Mainheim. That procedure is specific for eachclinical parameters. That clinical parameters concentration was measured byClinicon Photometer 4010. The clinical data of the radiation worker wascompared to the non radiation worker. The measurement results of 501patients, shown that total protein concentrations for all worker are > 8.00g%, the cholesterol concentration of 25 patient are > 260 mg%. The glucoseconcentration for fasting condition of 7 patients are > 200 mg/dl, the ureumconcentration of all patients are 7mg% and the creatinine of 112 patients are > 1.4 mg%. From those results canbe concluded that the most pathological indicator can be identified fromliver, heart and kidney function respectively. From the clinical aspects canbe seen that there is no significant difference between the health ofradiation worker and non radiation worker. (author)

  15. [Clinical parameters for molecular testing of Maturity Onset Diabetes of the Young (MODY)].

    Science.gov (United States)

    Datz, N; Nestoris, C; von Schütz, W; Danne, T; Driesel, A J; Maringa, M; Kordonouri, O

    2011-05-01

    Monogenic forms of diabetes are often diagnosed by chance, due to the variety of clinical presentation and limited experience of the diabetologists with this kind of diabetes. Aim of this study was to evaluate clinical parameters for an efficient screening. Clinical parameters were: negative diabetes-specific antibodies at onset of diabetes, positive family history of diabetes, and low to moderate insulin requirements after one year of diabetes treatment. Molecular testing was performed through sequencing of the programming regions of HNF-4alpha (MODY 1), glucokinase (MODY 2) and HNF-1alpha/TCF1 (MODY 3) and in one patient the HNF-1beta/TCF2 region (MODY 5). 39 of 292 patients treated with insulin were negative for GADA and IA2A, and 8 (20.5%) patients fulfilled both other criteria. Positive molecular results were found in five (63%) patients (two with MODY 2, two with MODY 3, one with MODY 5). At diabetes onset, the mean age of the 5 patients with MODY was 10.6 ± 5.3 yrs (range 2.6-15 yrs), HbA(1c) was 8.4 ± 3.1 % (6.5-13.9%), mean diabetes duration until diagnosis of MODY was 3.3 ± 3.6 yrs (0.8-9.6 yrs) with insulin requirements of 0.44 ± 0.17 U/kg/d (0.2-0.6 U/kg/d). Patients with MODY 3 were changed from insulin to repaglinide, those with MODY 2 were recommended discontinuing insulin treatment. In patients with negative diabetes-specific antibodies at onset of diabetes, with a positive family history, and low to moderate insulin needs a genetic screening for MODY is indicated. Watchful consideration of these clinical parameters may lead to an early genetic testing, and to an adequate treatment. © Georg Thieme Verlag KG Stuttgart · New York.

  16. Geoelectrical parameter-based multivariate regression borehole yield model for predicting aquifer yield in managing groundwater resource sustainability

    Directory of Open Access Journals (Sweden)

    Kehinde Anthony Mogaji

    2016-07-01

    Full Text Available This study developed a GIS-based multivariate regression (MVR yield rate prediction model of groundwater resource sustainability in the hard-rock geology terrain of southwestern Nigeria. This model can economically manage the aquifer yield rate potential predictions that are often overlooked in groundwater resources development. The proposed model relates the borehole yield rate inventory of the area to geoelectrically derived parameters. Three sets of borehole yield rate conditioning geoelectrically derived parameters—aquifer unit resistivity (ρ, aquifer unit thickness (D and coefficient of anisotropy (λ—were determined from the acquired and interpreted geophysical data. The extracted borehole yield rate values and the geoelectrically derived parameter values were regressed to develop the MVR relationship model by applying linear regression and GIS techniques. The sensitivity analysis results of the MVR model evaluated at P ⩽ 0.05 for the predictors ρ, D and λ provided values of 2.68 × 10−05, 2 × 10−02 and 2.09 × 10−06, respectively. The accuracy and predictive power tests conducted on the MVR model using the Theil inequality coefficient measurement approach, coupled with the sensitivity analysis results, confirmed the model yield rate estimation and prediction capability. The MVR borehole yield prediction model estimates were processed in a GIS environment to model an aquifer yield potential prediction map of the area. The information on the prediction map can serve as a scientific basis for predicting aquifer yield potential rates relevant in groundwater resources sustainability management. The developed MVR borehole yield rate prediction mode provides a good alternative to other methods used for this purpose.

  17. Clinical and microbiological parameters in patients with self-ligating and conventional brackets during early phase of orthodontic treatment.

    Science.gov (United States)

    Pejda, Slavica; Varga, Marina Lapter; Milosevic, Sandra Anic; Mestrovic, Senka; Slaj, Martina; Repic, Dario; Bosnjak, Andrija

    2013-01-01

    To determine the effect of different bracket designs (conventional brackets and self-ligating brackets) on periodontal clinical parameters and periodontal pathogens in subgingival plaque. The following inclusion criteria were used: requirement of orthodontic treatment plan starting with alignment and leveling, good general health, healthy periodontium, no antibiotic therapy in the previous 6 months before the beginning of the study, and no smoking. The study sample totaled 38 patients (13 male, 25 female; mean age, 14.6 ± 2.0 years). Patients were divided into two groups with random distribution of brackets. Recording of clinical parameters was done before the placement of the orthodontic appliance (T0) and at 6 weeks (T1), 12 weeks (T2), and 18 weeks (T3) after full bonding of orthodontic appliances. Periodontal pathogens of subgingival microflora were detected at T3 using a commercially available polymerase chain reaction test (micro-Dent test) that contains probes for Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Prevotella intermedia, Tannerella forsythia, and Treponema denticola. There was a statistically significant higher prevalence of A actinomycetemcomitans in patients with conventional brackets than in patients with self-ligating brackets, but there was no statistically significant difference for other putative periodontal pathogens. The two different types of brackets did not show statistically significant differences in periodontal clinical parameters. Bracket design does not seem to have a strong influence on periodontal clinical parameters and periodontal pathogens in subgingival plaque. The correlation between some periodontal pathogens and clinical periodontal parameters was weak.

  18. Prediction ofWater Quality Parameters (NO3, CL in Karaj Riverby Usinga Combinationof Wavelet Neural Network, ANN and MLRModels

    Directory of Open Access Journals (Sweden)

    T. Rajaee

    2016-10-01

    Full Text Available IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN, Wavelet Neural Network combination (WANN and multi linear regression (MLR models, to predict next month the Nitrate (NO3 and Chloride (CL ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated. Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL has been put related to the past amounts of the same time series (NO3 / CL and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data as training set, next 10% of data (12 months and 10% of the end of time series (terminal 12 months were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q and mentioned qualityparameters(NO3 / CL were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E and the Root Mean Square

  19. Clinical presentation at first heart failure hospitalization does not predict recurrent heart failure admission.

    Science.gov (United States)

    Kosztin, Annamaria; Costa, Jason; Moss, Arthur J; Biton, Yitschak; Nagy, Vivien Klaudia; Solomon, Scott D; Geller, Laszlo; McNitt, Scott; Polonsky, Bronislava; Merkely, Bela; Kutyifa, Valentina

    2017-11-01

    There are limited data on whether clinical presentation at first heart failure (HF) hospitalization predicts recurrent HF events. We aimed to assess predictors of recurrent HF hospitalizations in mild HF patients with an implantable cardioverter defibrillator or cardiac resynchronization therapy with defibrillator. Data on HF hospitalizations were prospectively collected for patients enrolled in MADIT-CRT. Predictors of recurrent HF hospitalization (HF2) after the first HF hospitalization were assessed using Cox proportional hazards regression models including baseline covariates and clinical presentation or management at first HF hospitalization. There were 193 patients with first HF hospitalization, and 156 patients with recurrent HF events. Recurrent HF rate after the first HF hospitalization was 43% at 1 year, 52% at 2 years, and 55% at 2.5 years. Clinical signs and symptoms, medical treatment, or clinical management of HF at first HF admission was not predictive for HF2. Baseline covariates predicting recurrent HF hospitalization included prior HF hospitalization (HR = 1.59, 95% CI: 1.15-2.20, P = 0.005), digitalis therapy (HR = 1.58, 95% CI: 1.13-2.20, P = 0.008), and left ventricular end-diastolic volume >240 mL (HR = 1.62, 95% CI: 1.17-2.25, P = 0.004). Recurrent HF events are frequent following the first HF hospitalization in patients with implanted implantable cardioverter defibrillator or cardiac resynchronization therapy with defibrillator. Neither clinical presentation nor clinical management during first HF admission was predictive of recurrent HF. Prior HF hospitalization, digitalis therapy, and left ventricular end-diastolic volume at enrolment predicted recurrent HF hospitalization, and these covariates could be used as surrogate markers for identifying a high-risk cohort. © 2017 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiology.

  20. Clinical prediction in early pregnancy of infants small for gestational age by customised birthweight centiles: findings from a healthy nulliparous cohort.

    Directory of Open Access Journals (Sweden)

    Lesley M E McCowan

    Full Text Available Small for gestational age (SGA infants comprise up to 50% of all stillbirths and a minority are detected before birth. We aimed to develop and validate early pregnancy predictive models for SGA infants.5628 participants from SCOPE, a prospective study of nulliparous pregnant women, were interviewed at 15 ± 1 weeks' gestation. Fetal anthropometry, uterine and umbilical Doppler studies were performed at 20 ± 1 weeks'. The cohort was divided into training (n = 3735 and validation datasets (n = 1871. All-SGA (birthweight 12 months to conceive, university student, cigarette smoking, proteinuria, daily vigorous exercise and diastolic blood pressure ≥ 80. Recreational walking ≥ 4 times weekly, rhesus negative blood group and increasing random glucose were protective. AUC for clinical risk factors was 0.63. Fetal abdominal or head circumference z scores <10(th centile and increasing uterine artery Doppler resistance at 20 ± 1 weeks' were associated with increased risk. Addition of these parameters increased the AUC to 0.69. Clinical predictors of Normotensive and Hypertensive-SGA were sub-groups of All-SGA predictors and were quite different. The combined clinical and ultrasound AUC for Normotensive and Hypertensive-SGA were 0.69 and 0.82 respectively.Predictors for SGA of relevance to clinical practice were identified. The identity and predictive potential differed in normotensive women and those who developed hypertension.

  1. Predictive assays of tumor radiocurability: towards a custom-made radiotherapy

    International Nuclear Information System (INIS)

    Cosset, J.M; Girinsky, T.; Guichard, M.; Eschwege, F.; Malaise, E.P.; Peters, L.J.; Mornex, F.

    1990-01-01

    Up to now, radiation oncologists had at their disposal only a number of well-known histological and clinical factors in order to define the optimal dose which should be delivered to a given tumor. Recently, radiobiological studies have suggested additional parameters which may play a major role in tumor radiocurability. These parameters are: the number of clonogenic cells, intrinsic radiosensitivity, hypoxia and proliferation kinetics. Predictive tests are being developed and evaluated for each of these parameters. The more advanced studies deal with intrinsic radiosensitivity; preliminary data show impressive variations in radiosensitivity within groups of clinically homogeneous tumors. Should these tests prove to be reliably predictive of radiocurability, it will be possible in the near future to propose to any given patient a custom-made radiotherapy adapted to the precise features of his or her tumor [fr

  2. Prediction of pharmacokinetic and toxicological parameters of a 4-phenylcoumarin isolated from geopropolis: In silico and in vitro approaches.

    Science.gov (United States)

    da Cunha, Marcos Guilherme; Franco, Gilson César Nobre; Franchin, Marcelo; Beutler, John A; de Alencar, Severino Matias; Ikegaki, Masaharu; Rosalen, Pedro Luiz

    2016-11-30

    In silico and in vitro methodologies have been used as important tools in the drug discovery process, including from natural sources. The aim of this study was to predict pharmacokinetic and toxicity (ADME/Tox) properties of a coumarin isolated from geopropolis using in silico and in vitro approaches. Cinnamoyloxy-mammeisin (CNM) isolated from Brazilian M. scutellaris geopropolis was evaluated for its pharmacokinetic parameters by in silico models (ACD/Percepta™ and MetaDrug™ software). Genotoxicity was assessed by in vitro DNA damage signaling PCR array. CNM did not pass all parameters of Lipinski's rule of five, with a predicted low oral bioavailability and high plasma protein binding, but with good predicted blood brain barrier penetration. CNM was predicted to show low affinity to cytochrome P450 family members. Furthermore, the predicted Ames test indicated potential mutagenicity of CNM. Also, the probability of toxicity for organs and tissues was classified as moderate and high for liver and kidney, and moderate and low for skin and eye irritation, respectively. The PCR array analysis showed that CNM significantly upregulated about 7% of all DNA damage-related genes. By exploring the biological function of these genes, it was found that the predicted CNM genotoxicity is likely to be mediated by apoptosis. The predicted ADME/Tox profile suggests that external use of CNM may be preferable to systemic exposure, while its genotoxicity was characterized by the upregulation of apoptosis-related genes after treatment. The combined use of in silico and in vitro approaches to evaluate these parameters generated useful hypotheses to guide further preclinical studies. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  3. Prediction of Radionuclide transfer based on soil parameters: application to vulnerability studies

    International Nuclear Information System (INIS)

    Roig, M.; Vidal, M.; Rauret, G.

    1998-01-01

    The multi factorial character of the radiocaesium and radiostrontium soil-to-plan transfer, which depends on the radionuclide level in the soil solution amplified by a plant factor, prevents from establishing univariate relationships between transfer factors and soil and/or plant parameters. The plant factor is inversely proportional to the level of competitive species in the soil solution (Ca and Mg, for radiostrontium, and K and NH 4 for radiocaesium). Radionuclide level in soil solution depends on the radionuclide available fraction and its distribution coefficient. For radiostrontium, this may be obtained from the Cationic Exchange Capacity (CEC), whereas for radiocaesium the Specific Interception Potential should be calculate, both corrected by the concentrations of the competitive species and selectivity coefficients. Therefore, the transfer factor eventually depends on soil solution composition, the available fraction and the number of sorption sites, as well as on the plant factor. For a given plant, a relative sequence of transfer can be set up based solely on soil parameters, since the plant factor is cancelled. This prediction model has been compared with transfer data from experiments with Mediterranean, mineral soils, contaminated with a thermo generated aerosol, and with podzolic and organic soils, contaminated by the Chernobyl fallout. These studies revealed that it was possible to predict a relative scale of transfer for any type of soil, also allowing a scale of soil vulnerability to radiostrontium and radiocaesium contamination to be set up. (Author)

  4. A Well-Designed Parameter Estimation Method for Lifetime Prediction of Deteriorating Systems with Both Smooth Degradation and Abrupt Damage

    Directory of Open Access Journals (Sweden)

    Chuanqiang Yu

    2015-01-01

    Full Text Available Deteriorating systems, which are subject to both continuous smooth degradation and additional abrupt damages due to a shock process, can be often encountered in engineering. Modeling the degradation evolution and predicting the lifetime of this kind of systems are both interesting and challenging in practice. In this paper, we model the degradation trajectory of the deteriorating system by a random coefficient regression (RCR model with positive jumps, where the RCR part is used to model the continuous smooth degradation of the system and the jump part is used to characterize the abrupt damages due to random shocks. Based on a specified threshold level, the probability density function (PDF and cumulative distribution function (CDF of the lifetime can be derived analytically. The unknown parameters associated with the derived lifetime distributions can be estimated via a well-designed parameter estimation procedure on the basis of the available degradation recordings of the deteriorating systems. An illustrative example is finally provided to demonstrate the implementation and superiority of the newly proposed lifetime prediction method. The experimental results reveal that our proposed lifetime prediction method with the dedicated parameter estimation strategy can get more accurate lifetime predictions than the rival model in literature.

  5. On-Line Flutter Prediction Tool for Wind Tunnel Flutter Testing using Parameter Varying Estimation Methodology, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — ZONA Technology, Inc. (ZONA) proposes to develop an on-line flutter prediction tool for wind tunnel model using the parameter varying estimation (PVE) technique to...

  6. Role of Transition Zone Index in the Prediction of Clinical Benign Prostatic Hyperplasia

    Directory of Open Access Journals (Sweden)

    Muhammet Güzelsoy

    2016-12-01

    Full Text Available Objective The objective of this study was to determine the role of the transition zone (TZ index (TZI in the prediction of clinical benign prostatic hyperplasia (BPH in patients who underwent transurethral prostatectomy (TUR-P and to analyze the correlation between the amount of resected tissue and TZ volume (TZV. Materials and Methods Twenty-six male clinical BPH patients with obstructive complaints and 17 male benign prostate enlargement (BPE patients without any complaints were included in the study. Both the groups were over the age of 50. Clinical BPH patients underwent complete TUR-P. Statistical analysis was done with SPSS. Sensitivity, specificity, positive and negative predictive values of TZI-as a method of assessing clinical BPH-were measured. Results There was a statistically significant difference in prostate volume, uroflowmetry patterns, prostate-specific antigen (PSA, International prostate symptom score (IPSS, TZV and TZI between the two groups. There was a correlation between TZV and the amount of resected tissue (r=0.97; p0.40 has a high level of sensitivity and specificity in the prediction of clinical BPH among patients who undergo TUR-P due to obstructive symptoms and reported as BPH. There is a strong correlation between the amount of resected tissue and TZV. TZI is a valuable tool in diagnosis, and TZV gives valuable information about the patient to the surgeon.

  7. Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sang Youn; Moon, Sung Kyoung; Hwang, Sung Il; Sung, Chang Kyu; Cho, Jeong Yeon; Kim, Seung Hyup; Lee, Hak Jong [Seoul National University College of Medicine, Seoul (Korea, Republic of); Jung, Dae Chul [National Cancer Center, Ilsan (Korea, Republic of); Lee, Ji Won [Kangwon National University College of Medicine, Chuncheon (Korea, Republic of)

    2011-10-15

    The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models. Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05). The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer. Te performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

  8. De novo sequencing of circulating miRNAs identifies novel markers predicting clinical outcome of locally advanced breast cancer

    Directory of Open Access Journals (Sweden)

    Wu Xiwei

    2012-03-01

    Full Text Available Abstract Background MicroRNAs (miRNAs have been recently detected in the circulation of cancer patients, where they are associated with clinical parameters. Discovery profiling of circulating small RNAs has not been reported in breast cancer (BC, and was carried out in this study to identify blood-based small RNA markers of BC clinical outcome. Methods The pre-treatment sera of 42 stage II-III locally advanced and inflammatory BC patients who received neoadjuvant chemotherapy (NCT followed by surgical tumor resection were analyzed for marker identification by deep sequencing all circulating small RNAs. An independent validation cohort of 26 stage II-III BC patients was used to assess the power of identified miRNA markers. Results More than 800 miRNA species were detected in the circulation, and observed patterns showed association with histopathological profiles of BC. Groups of circulating miRNAs differentially associated with ER/PR/HER2 status and inflammatory BC were identified. The relative levels of selected miRNAs measured by PCR showed consistency with their abundance determined by deep sequencing. Two circulating miRNAs, miR-375 and miR-122, exhibited strong correlations with clinical outcomes, including NCT response and relapse with metastatic disease. In the validation cohort, higher levels of circulating miR-122 specifically predicted metastatic recurrence in stage II-III BC patients. Conclusions Our study indicates that certain miRNAs can serve as potential blood-based biomarkers for NCT response, and that miR-122 prevalence in the circulation predicts BC metastasis in early-stage patients. These results may allow optimized chemotherapy treatments and preventive anti-metastasis interventions in future clinical applications.

  9. External validation of a biomarker and clinical prediction model for hospital mortality in acute respiratory distress syndrome.

    Science.gov (United States)

    Zhao, Zhiguo; Wickersham, Nancy; Kangelaris, Kirsten N; May, Addison K; Bernard, Gordon R; Matthay, Michael A; Calfee, Carolyn S; Koyama, Tatsuki; Ware, Lorraine B

    2017-08-01

    Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort. The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied. The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70-0.79) in FACTT, compared to 0.72 (95% CI 0.67-0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70-0.76) when FACTT and VALID were combined. We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion.

  10. SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Z; Folkert, M; Iyengar, P; Zhang, Y; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining

  11. The Predictive Value of Ultrasound Learning Curves Across Simulated and Clinical Settings

    DEFF Research Database (Denmark)

    Madsen, Mette E; Nørgaard, Lone N; Tabor, Ann

    2017-01-01

    OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation-based tra......OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation......-based training program in transvaginal sonography. The training was conducted on a VR simulator as well as on a physical mannequin. A subgroup of 6 participants underwent subsequent clinical training. During each of the 3 steps, the participants' performance was assessed using instruments with established...... settings. RESULTS: A good correlation was found between time needed to achieve predefined performance levels on the VR simulator and the physical mannequin (Pearson correlation coefficient .78; P VR simulator correlated well to the clinical performance scores (Pearson...

  12. Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles

    International Nuclear Information System (INIS)

    Pei, Lei; Zhu, Chunbo; Wang, Tiansi; Lu, Rengui; Chan, C.C.

    2014-01-01

    The goal of this study is to realize real-time predictions of the peak power/state of power (SOP) for lithium-ion batteries in electric vehicles (EVs). To allow the proposed method to be applicable to different temperature and aging conditions, a training-free battery parameter/state estimator is presented based on an equivalent circuit model using a dual extended Kalman filter (DEKF). In this estimator, the model parameters are no longer taken as functions of factors such as SOC (state of charge), temperature, and aging; instead, all parameters will be directly estimated under the present conditions, and the impact of the temperature and aging on the battery model will be included in the parameter identification results. Then, the peak power/SOP will be calculated using the estimated results under the given limits. As an improvement to the calculation method, a combined limit of current and voltage is proposed to obtain results that are more reasonable. Additionally, novel verification experiments are designed to provide the true values of the cells' peak power under various operating conditions. The proposed methods are implemented in experiments with LiFePO 4 /graphite cells. The validating results demonstrate that the proposed methods have good accuracy and high adaptability. - Highlights: • A real-time peak power/SOP prediction method for lithium-ion batteries is proposed. • A training-free method based on DEKF is presented for parameter identification. • The proposed method can be applied to different temperature and aging conditions. • The calculation of peak power under the current and voltage limits is improved. • Validation experiments are designed to verify the accuracy of prediction results

  13. Volumetric parameters on FDG PET can predict early intrahepatic recurrence-free survival in patients with hepatocellular carcinoma after curative surgical resection

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jeong Won [Catholic Kwandong University College of Medicine, Department of Nuclear Medicine, Incheon (Korea, Republic of); Hwang, Sang Hyun; Kim, Hyun Jeong; Kim, Dongwoo; Cho, Arthur; Yun, Mijin [Yonsei University College of Medicine, Department of Nuclear Medicine, Seoul (Korea, Republic of)

    2017-11-15

    This study assessed the prognostic values of volumetric parameters on {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in predicting early intrahepatic recurrence-free survival (RFS) after curative resection in patients with hepatocellular carcinoma (HCC). A retrospective analysis was performed on 242 patients with HCC who underwent staging FDG PET and subsequent curative surgical resection. The tumor-to-non-tumorous liver uptake ratio, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the HCC lesions on PET were measured. The prognostic values of clinical factors and PET parameters for predicting overall RFS, overall survival (OS), extrahepatic RFS, and early and late intrahepatic RFS were assessed. The median follow-up period was 54.7 months, during which 110 patients (45.5%) experienced HCC recurrence and 62 (25.6%) died. Patients with extrahepatic and early intrahepatic recurrence showed worse OS than did those with no recurrence or late intrahepatic recurrence (p < 0.001). Serum bilirubin level, MTV, and TLG were independent prognostic factors for overall RFS and OS (p < 0.05). Only MTV and TLG were prognostic for extrahepatic RFS (p < 0.05). Serum alpha-fetoprotein and bilirubin levels, MTV, and TLG were prognostic for early intrahepatic RFS (p < 0.05) and hepatitis C virus (HCV) positivity and serum albumin level were independently prognostic for late intrahepatic RFS (p < 0.05). Intrahepatic recurrence showed different prognoses according to the time interval of recurrence in which early recurrence had as poor survival as extrahepatic recurrence. MTV and TLG on initial staging PET were significant independent factors for predicting early intrahepatic and extrahepatic RFS in patients with HCC after curative resection. Only HCV positivity and serum albumin level were significant for late intrahepatic RFS, which is mainly attributable to the de novo formation of new primary HCC. (orig.)

  14. Four hundred or more participants needed for stable contingency table estimates of clinical prediction rule performance

    DEFF Research Database (Denmark)

    Kent, Peter; Boyle, Eleanor; Keating, Jennifer L

    2017-01-01

    OBJECTIVE: To quantify variability in the results of statistical analyses based on contingency tables and discuss the implications for the choice of sample size for studies that derive clinical prediction rules. STUDY DESIGN AND SETTING: An analysis of three pre-existing sets of large cohort data......, odds ratios and risk/prevalence ratios, for each sample size was calculated. RESULTS: There were very wide, and statistically significant, differences in estimates derived from contingency tables from the same dataset when calculated in sample sizes below 400 people, and typically this variability...... stabilized in samples of 400 to 600 people. Although estimates of prevalence also varied significantly in samples below 600 people, that relationship only explains a small component of the variability in these statistical parameters. CONCLUSION: To reduce sample-specific variability, contingency tables...

  15. A Multi-Center Prospective Derivation and Validation of a Clinical Prediction Tool for Severe Clostridium difficile Infection.

    LENUS (Irish Health Repository)

    Na, Xi

    2015-04-23

    Prediction of severe clinical outcomes in Clostridium difficile infection (CDI) is important to inform management decisions for optimum patient care. Currently, treatment recommendations for CDI vary based on disease severity but validated methods to predict severe disease are lacking. The aim of the study was to derive and validate a clinical prediction tool for severe outcomes in CDI.

  16. Mechanomyographic Parameter Extraction Methods: An Appraisal for Clinical Applications

    Directory of Open Access Journals (Sweden)

    Morufu Olusola Ibitoye

    2014-12-01

    Full Text Available The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity.

  17. Prediction of Driving Safety in Individuals with Homonymous Hemianopia and Quadrantanopia from Clinical Neuroimaging

    Directory of Open Access Journals (Sweden)

    Michael S. Vaphiades

    2014-01-01

    Full Text Available Background. This study aimed to determine whether it is possible to predict driving safety of individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuroimages that are routinely available in clinical practice. Methods. Two experienced neuroophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which indicated the site and extent of the lesion and they made predictions regarding whether participants would be safe/unsafe to drive. Driving safety was independently defined at the time of the study using state-recorded motor vehicle crashes (all crashes and at-fault for the previous 5 years and ratings of driving safety determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. Results. The ability to predict driving safety was highly variable regardless of the driving safety measure, ranging from 31% to 63% (kappa levels ranged from −0.29 to 0.04. The level of agreement between the neuroophthalmologists was only fair (kappa = 0.28. Conclusions. Clinical evaluation of summary reports of currently available neuroimages by neuroophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.

  18. Prediction of driving safety in individuals with homonymous hemianopia and quadrantanopia from clinical neuroimaging.

    Science.gov (United States)

    Vaphiades, Michael S; Kline, Lanning B; McGwin, Gerald; Owsley, Cynthia; Shah, Ritu; Wood, Joanne M

    2014-01-01

    Background. This study aimed to determine whether it is possible to predict driving safety of individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuroimages that are routinely available in clinical practice. Methods. Two experienced neuroophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which indicated the site and extent of the lesion and they made predictions regarding whether participants would be safe/unsafe to drive. Driving safety was independently defined at the time of the study using state-recorded motor vehicle crashes (all crashes and at-fault) for the previous 5 years and ratings of driving safety determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. Results. The ability to predict driving safety was highly variable regardless of the driving safety measure, ranging from 31% to 63% (kappa levels ranged from -0.29 to 0.04). The level of agreement between the neuroophthalmologists was only fair (kappa = 0.28). Conclusions. Clinical evaluation of summary reports of currently available neuroimages by neuroophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.

  19. Islet oxygen consumption rate (OCR) dose predicts insulin independence for first clinical islet allotransplants

    Science.gov (United States)

    Kitzmann, JP; O’Gorman, D; Kin, T; Gruessner, AC; Senior, P; Imes, S; Gruessner, RW; Shapiro, AMJ; Papas, KK

    2014-01-01

    Human islet allotransplant (ITx) for the treatment of type 1 diabetes is in phase III clinical registration trials in the US and standard of care in several other countries. Current islet product release criteria include viability based on cell membrane integrity stains, glucose stimulated insulin release (GSIR), and islet equivalent (IE) dose based on counts. However, only a fraction of patients transplanted with islets that meet or exceed these release criteria become insulin independent following one transplant. Measurements of islet oxygen consumption rate (OCR) have been reported as highly predictive of transplant outcome in many models. In this paper we report on the assessment of clinical islet allograft preparations using islet oxygen consumption rate (OCR) dose (or viable IE dose) and current product release assays in a series of 13 first transplant recipients. The predictive capability of each assay was examined and successful graft function was defined as 100% insulin independence within 45 days post-transplant. Results showed that OCR dose was most predictive of CTO. IE dose was also highly predictive, while GSIR and membrane integrity stains were not. In conclusion, OCR dose can predict CTO with high specificity and sensitivity and is a useful tool for evaluating islet preparations prior to clinical ITx. PMID:25131089

  20. Calculation of apparent age by linear combination of facial skin parameters: a predictive tool to evaluate the efficacy of cosmetic treatments and to assess the predisposition to accelerated aging.

    Science.gov (United States)

    Dicanio, Denise; Sparacio, Rose; Declercq, Lieve; Corstjens, Hugo; Muizzuddin, Neelam; Hidalgo, Julie; Giacomoni, Paolo U; Jorgensen, Lise; Maes, Daniel

    2009-12-01

    The estimated apparent age (EAA) was estimated by a panel of trained experts, for the individuals in a cohort. Twelve independent clinical, biophysical and biochemical parameters measured on facial skin, have been identified by multiple regression analysis, which influence the EAA of a person of chronological age (CA) (under eye lines, clinically assessed crow's feet, age spots, clinically evaluated firmness, forehead lines, pores, lip lines, instrumentally evaluated firmness, instrumentally evaluated crow feet, skin texture, in vivo fluorescence related to proliferation and glycation). An algorithm has been devised to obtain the calculated age score (CAS) in a cohort of 452 female volunteers, as CAS(n) = ∑RCiPi(n) (i = 1-13, n = 1-452 and P13 = 1) where the coefficients Ci are obtained by minimizing the difference EAA - CAS, and Pi(n) are the experimental values of the i-th parameter for the n-th volunteer. The determination of CAS before and after a specific cosmetic or pharmacological anti-aging treatment can be used to objectively assess the efficacy of the treatment. The comparison of EAA(n) and of CAS(n) with CA(n) allows one to predict the susceptibility of an individual's face to undergo aging. It has been observed that the biophysical and biochemical parameters play a relevant role in the assessment of the predisposition of skin to undergo accelerated aging.

  1. Islet Oxygen Consumption Rate (OCR) Dose Predicts Insulin Independence in Clinical Islet Autotransplantation.

    Science.gov (United States)

    Papas, Klearchos K; Bellin, Melena D; Sutherland, David E R; Suszynski, Thomas M; Kitzmann, Jennifer P; Avgoustiniatos, Efstathios S; Gruessner, Angelika C; Mueller, Kathryn R; Beilman, Gregory J; Balamurugan, Appakalai N; Loganathan, Gopalakrishnan; Colton, Clark K; Koulmanda, Maria; Weir, Gordon C; Wilhelm, Josh J; Qian, Dajun; Niland, Joyce C; Hering, Bernhard J

    2015-01-01

    Reliable in vitro islet quality assessment assays that can be performed routinely, prospectively, and are able to predict clinical transplant outcomes are needed. In this paper we present data on the utility of an assay based on cellular oxygen consumption rate (OCR) in predicting clinical islet autotransplant (IAT) insulin independence (II). IAT is an attractive model for evaluating characterization assays regarding their utility in predicting II due to an absence of confounding factors such as immune rejection and immunosuppressant toxicity. Membrane integrity staining (FDA/PI), OCR normalized to DNA (OCR/DNA), islet equivalent (IE) and OCR (viable IE) normalized to recipient body weight (IE dose and OCR dose), and OCR/DNA normalized to islet size index (ISI) were used to characterize autoislet preparations (n = 35). Correlation between pre-IAT islet product characteristics and II was determined using receiver operating characteristic analysis. Preparations that resulted in II had significantly higher OCR dose and IE dose (p<0.001). These islet characterization methods were highly correlated with II at 6-12 months post-IAT (area-under-the-curve (AUC) = 0.94 for IE dose and 0.96 for OCR dose). FDA/PI (AUC = 0.49) and OCR/DNA (AUC = 0.58) did not correlate with II. OCR/DNA/ISI may have some utility in predicting outcome (AUC = 0.72). Commonly used assays to determine whether a clinical islet preparation is of high quality prior to transplantation are greatly lacking in sensitivity and specificity. While IE dose is highly predictive, it does not take into account islet cell quality. OCR dose, which takes into consideration both islet cell quality and quantity, may enable a more accurate and prospective evaluation of clinical islet preparations.

  2. Islet Oxygen Consumption Rate (OCR Dose Predicts Insulin Independence in Clinical Islet Autotransplantation.

    Directory of Open Access Journals (Sweden)

    Klearchos K Papas

    Full Text Available Reliable in vitro islet quality assessment assays that can be performed routinely, prospectively, and are able to predict clinical transplant outcomes are needed. In this paper we present data on the utility of an assay based on cellular oxygen consumption rate (OCR in predicting clinical islet autotransplant (IAT insulin independence (II. IAT is an attractive model for evaluating characterization assays regarding their utility in predicting II due to an absence of confounding factors such as immune rejection and immunosuppressant toxicity.Membrane integrity staining (FDA/PI, OCR normalized to DNA (OCR/DNA, islet equivalent (IE and OCR (viable IE normalized to recipient body weight (IE dose and OCR dose, and OCR/DNA normalized to islet size index (ISI were used to characterize autoislet preparations (n = 35. Correlation between pre-IAT islet product characteristics and II was determined using receiver operating characteristic analysis.Preparations that resulted in II had significantly higher OCR dose and IE dose (p<0.001. These islet characterization methods were highly correlated with II at 6-12 months post-IAT (area-under-the-curve (AUC = 0.94 for IE dose and 0.96 for OCR dose. FDA/PI (AUC = 0.49 and OCR/DNA (AUC = 0.58 did not correlate with II. OCR/DNA/ISI may have some utility in predicting outcome (AUC = 0.72.Commonly used assays to determine whether a clinical islet preparation is of high quality prior to transplantation are greatly lacking in sensitivity and specificity. While IE dose is highly predictive, it does not take into account islet cell quality. OCR dose, which takes into consideration both islet cell quality and quantity, may enable a more accurate and prospective evaluation of clinical islet preparations.

  3. Magnetic resonance imaging of injuries to the ankle joint: can it predict clinical outcome?

    Science.gov (United States)

    Zanetti, M; De Simoni, C; Wetz, H H; Zollinger, H; Hodler, J

    1997-02-01

    To predict clinical outcome after ankle sprains on the basis of magnetic resonance (MR) findings. Twenty-nine consecutive patients (mean age 32.9 years, range 13-60 years) were examined clinically and with MR imaging both after trauma and following standardized conservative therapy. Various MR abnormalities were related to a clinical outcome score. There was a tendency for a better clinical outcome in partial, rather than complete, tears of the anterior talofibular ligament and when there was no fluid within the peroneal tendon sheath at the initial MR examination (P = 0.092 for either abnormality). A number of other MR features did not significantly influence clinical outcome, including the presence of a calcaneofibular ligament lesion and a bone bruise of the talar dome. Clinical outcome after ankle sprain cannot consistently be predicted by MR imaging, although MR imaging may be more accurate when the anterior talofibular ligament is only partially torn and there are no signs of injury to the peroneal tendon sheath.

  4. Value of MDCT and Clinical and Laboratory Data for Predicting the Need for Surgical Intervention in Suspected Small-Bowel Obstruction.

    Science.gov (United States)

    Scrima, Andrew; Lubner, Meghan G; King, Scott; Pankratz, Joshua; Kennedy, Gregory; Pickhardt, Perry J

    2017-04-01

    The purpose of this article is to assess the value of a large panel of clinical and MDCT variables in patients with suspected small-bowel obstruction (SBO) for predicting urgent surgical intervention (data were abstracted from electronic medical record review. Univariate and multivariate analyses were performed. Among all 179 patients with suspected SBO, 56 (31.3%) underwent surgical intervention within 72 hours, 10 (5.6%) had ischemia at surgery, and nine (5.0%) required small-bowel resection. On univariate analysis, multiple CT findings were highly significant (p < 0.01) for predicting the main surgical outcomes, including degree of obstruction, 5-point radiology likelihood scores, and the presence of a transition point, closed loop, and mesenteric congestion. None of the objective clinical or laboratory variables (including serum lactate level) reached this level of significance. At multivariate analysis, forward stepwise logistic regression with 0.05 significance level cutoff included both degree of obstruction (p < 0.001) and closed loop (p < 0.01), with the presence of a transition point showing a trend toward significance (p = 0.081). A number of findings at abdominal MDCT are associated with the need for surgery and other important surgical outcomes in patients with suspected SBO. Overall radiologist impression of need for surgical intervention was a better predictor than any clinical or laboratory parameter.

  5. CT assessment of the correlation between clinical examination and bone involvement in oral malignant tumors

    International Nuclear Information System (INIS)

    Albuquerque, Marco Antonio Portela; Oliveira, Ilka Regina Souza; Cavalcanti, Marcelo Gusmao Paraiso; Kuruoshi, Marcia Etsuko

    2009-01-01

    Oral cancers have a tendency to invade the surrounding bone structures, and this has a direct influence on the treatment management and on outcomes. The objective of this study was to correlate the clinical parameters (location, clinical presentation and TNM staging) of oral malignant tumors that can be associated with a potential of bone invasion and determine the accuracy of clinical examination to predict bone involvement, using computed tomography (CT). Twenty five patients, with oral malignant tumors were submitted to clinical and CT examinations. CT was considered the standard parameter to evaluate the presence of bone involvement. Clinical assessment of location, presentation form and TNM staging of the tumors were then compared to the CT findings in predicting bone involvement. Bone involvement was observed in 68% of the cases. It was predicted that tumors located in the retromolar trigone and hard palate, with a clinical aspect of infiltrative ulcer or nodule and classified in stage IV had a high potential to cause bone involvement. The clinical examination assessment of these tumors showed to be a valuable tool to predict bone invasion, with high sensitivity (82%) and specificity (87.5%), based on the results found in the CT images. No statistical significance was found between the CT and clinical examinations regarding bone involvement. The identification of some clinical parameters such as location, clinical presentation, and TNM stage, associated with a detailed clinical examination, was considered a valuable tool for the assessment of bone destruction by oral malignant tumors. (author)

  6. Genetic Algorithms for Estimating Effective Parameters in a Lumped Reactor Model for Reactivity Predictions

    International Nuclear Information System (INIS)

    Marseguerra, Marzio; Zio, Enrico

    2001-01-01

    The control system of a reactor should be able to predict, in real time, the amount of reactivity to be inserted (e.g., by control rod movements and boron injection and dilution) to respond to a given electrical load demand or to undesired, accidental transients. The real-time constraint renders impractical the use of a large, detailed dynamic reactor code. One has, then, to resort to simplified analytical models with lumped effective parameters suitably estimated from the reactor data.The simple and well-known Chernick model for describing the reactor power evolution in the presence of xenon is considered and the feasibility of using genetic algorithms for estimating the effective nuclear parameters involved and the initial nonmeasurable xenon and iodine conditions is investigated. This approach has the advantage of counterbalancing the inherent model simplicity with the periodic reestimation of the effective parameter values pertaining to each reactor on the basis of its recent history. By so doing, other effects, such as burnup, are automatically taken into account

  7. Predictive value of some hematological parameters for non-invasive and invasive mole pregnancies.

    Science.gov (United States)

    Abide Yayla, Cigdem; Özkaya, Enis; Yenidede, Ilter; Eser, Ahmet; Ergen, Evrim Bostancı; Tayyar, Ahter Tanay; Şentürk, Mehmet Baki; Karateke, Ates

    2018-02-01

    The aim of this study was to discriminate mole pregnancies and invasive forms among cases with first trimester vaginal bleeding by the utilization of some complete blood count parameters conjunct to sonographic findings and beta human chorionic gonadotropin concentration. Consecutive 257 cases with histopathologically confirmed mole pregnancies and 199 women without mole pregnancy presented with first trimester vaginal bleeding who admitted to Zeynep Kamil Women and Children's Health Training Hospital between January 2012 and January 2016 were included in this cross-sectional study. The serum beta HCG level at presentation, and beta hCG levels at 1st, 2nd and 3rd weeks of postevacuation with some parameters of complete blood count were utilized to discriminate cases with molar pregnancy and cases with invasive mole among first trimester pregnants presented with vaginal bleeding and abnormal sonographic findings. Levels of beta hCG at baseline (AUC = 0.700, p < 0.05) and 1st (AUC = 0.704, p < 0.05), 2nd (AUC = 0.870, p < 0.001) and 3rd (AUC = 0.916, p < 0.001) weeks of postevacuation period were significant predictors for the cases with persistent disease. While area under curve for mean platelet volume is 0.715, it means that mean platelet volume has 21.5% additional diagnostic value for predicting persistency in molar patients. For 8.55 cut-off point for mean platelet volume, sensitivity is 84.6% and specificity is 51.6%. Area under curve for platelet/lymphocyte ratio is 0.683 means that platelet/lymphocyte ratio has additional 18.3% diagnostic value. For 102.25 cut-off point sensitivity is 86.6% and specificity is 46.2. Simple, widely available complete blood count parameters may be used as an adjunct to other risk factors to diagnose molar pregnancies and predict postevacuation trophoblastic disease.

  8. Neither Single nor a Combination of Routine Laboratory Parameters can Discriminate between Gram-positive and Gram-negative Bacteremia

    Science.gov (United States)

    Ratzinger, Franz; Dedeyan, Michel; Rammerstorfer, Matthias; Perkmann, Thomas; Burgmann, Heinz; Makristathis, Athanasios; Dorffner, Georg; Loetsch, Felix; Blacky, Alexander; Ramharter, Michael

    2015-01-01

    Adequate early empiric antibiotic therapy is pivotal for the outcome of patients with bloodstream infections. In clinical practice the use of surrogate laboratory parameters is frequently proposed to predict underlying bacterial pathogens; however there is no clear evidence for this assumption. In this study, we investigated the discriminatory capacity of predictive models consisting of routinely available laboratory parameters to predict the presence of Gram-positive or Gram-negative bacteremia. Major machine learning algorithms were screened for their capacity to maximize the area under the receiver operating characteristic curve (ROC-AUC) for discriminating between Gram-positive and Gram-negative cases. Data from 23,765 patients with clinically suspected bacteremia were screened and 1,180 bacteremic patients were included in the study. A relative predominance of Gram-negative bacteremia (54.0%), which was more pronounced in females (59.1%), was observed. The final model achieved 0.675 ROC-AUC resulting in 44.57% sensitivity and 79.75% specificity. Various parameters presented a significant difference between both genders. In gender-specific models, the discriminatory potency was slightly improved. The results of this study do not support the use of surrogate laboratory parameters for predicting classes of causative pathogens. In this patient cohort, gender-specific differences in various laboratory parameters were observed, indicating differences in the host response between genders. PMID:26522966

  9. Diagnosis trajectories of prior multi-morbidity predict sepsis mortality

    DEFF Research Database (Denmark)

    Beck, Mette Kristina; Jensen, Anders Boeck; Nielsen, Annelaura Bach

    2016-01-01

    Sepsis affects millions of people every year, many of whom will die. In contrast to current survival prediction models for sepsis patients that primarily are based on data from within-admission clinical measurements (e.g. vital parameters and blood values), we aim for using the full disease histo...... of disease history to scoring based on within-admission clinical measurements emphasizing the value of long term data in novel patient scores that combine the two types of data.......Sepsis affects millions of people every year, many of whom will die. In contrast to current survival prediction models for sepsis patients that primarily are based on data from within-admission clinical measurements (e.g. vital parameters and blood values), we aim for using the full disease history...... recurrent trajectories of time-ordered co-morbidities had significantly increased sepsis mortality compared to those who did not follow a trajectory. We identified trajectories which significantly altered sepsis mortality, and found three major starting points in a combined temporal sepsis network: Alcohol...

  10. Microsatellite Instability Predicts Clinical Outcome in Radiation-Treated Endometrioid Endometrial Cancer

    International Nuclear Information System (INIS)

    Bilbao, Cristina; Lara, Pedro Carlos; Ramirez, Raquel; Henriquez-Hernandez, Luis Alberto; Rodriguez, German; Falcon, Orlando; Leon, Laureano; Perucho, Manuel

    2010-01-01

    Purpose: To elucidate whether microsatellite instability (MSI) predicts clinical outcome in radiation-treated endometrioid endometrial cancer (EEC). Methods and Materials: A consecutive series of 93 patients with EEC treated with extrafascial hysterectomy and postoperative radiotherapy was studied. The median clinical follow-up of patients was 138 months, with a maximum of 232 months. Five quasimonomorphic mononucleotide markers (BAT-25, BAT-26, NR21, NR24, and NR27) were used for MSI classification. Results: Twenty-five patients (22%) were classified as MSI. Both in the whole series and in early stages (I and II), univariate analysis showed a significant association between MSI and poorer 10-year local disease-free survival, disease-free survival, and cancer-specific survival. In multivariate analysis, MSI was excluded from the final regression model in the whole series, but in early stages MSI provided additional significant predictive information independent of traditional prognostic and predictive factors (age, stage, grade, and vascular invasion) for disease-free survival (hazard ratio [HR] 3.25, 95% confidence interval [CI] 1.01-10.49; p = 0.048) and cancer-specific survival (HR 4.20, 95% CI 1.23-14.35; p = 0.022) and was marginally significant for local disease-free survival (HR 3.54, 95% CI 0.93-13.46; p = 0.064). Conclusions: These results suggest that MSI may predict radiotherapy response in early-stage EEC.

  11. The Role of Lipidogram Parameters during Pregnancy in the Prediction of Preeclampsia Development

    Directory of Open Access Journals (Sweden)

    I.B. Ventskovskaya

    2016-08-01

    Full Text Available Aim of investigation. To study the correlation between lipid metabolism during pregnancy and the risk of preeclampsia (PE. Material and methods. We have studied parameters of lipid metabolism in the blood serum of 267 pregnant women with the help of diagnostic kits. Blood sampling was carried out in I and II gestation trimesters. Total cholesterol (TC and triglycerides (TG were determined by colorimetric, enzymatic methods; high density lipoproteins (HDL — with homogeneous method, low density lipoproteins (LDL — with the direct method. Very low density lipoproteins (VLDL concentration was calculated from the Friedwald equation: VLDL = TG / 2.2. Depending on the clinical picture of PE, 43 pregnant women were divided into groups with mild and moderate-to-severe courses of the disease. Results. Among women with PE, we observed significant changes of the lipid profile parameters in the II trimester. These women had elevated TG levels in the blood serum: I group — 1.73 ± 0.14 mM/l, II group — 1.86 ± 0.18 mM/l as compared to the group III (controls — 1.32 ± 0.29 mM/l; decreased HDL indices: I group — 0.79 ± 0.19 mM/l; group II — 0.64 ± 0.04 mM/l in comparison with the control group — 1.17 ± 0.12 mM/l and increased VLDL — 0.78 ± 0.09 mM/l for group I, 0.90 ± 0.06 mM/l in women of group II unlike group III — 0.60 ± 0.16 mM/l. TC and LDL levels among patients with PE did not differ from pregnant women in the control group. Conclusions. It was demonstrated the existence of an imbalance in the synthesis of lipoproteins in women, whose pregnancies were complicated by development of PE that manifested by hypertriglyceridemia with predominance of atherogenic fractions. It was found that the investigation of the parameters of lipid profile in the II trimester of pregnancy allows us to predict the risk of PE and its long-term cardiovascular and metabolic consequences.

  12. [Establishment of A Clinical Prediction Model of Prolonged Air Leak 
after Anatomic Lung Resection].

    Science.gov (United States)

    Wu, Xianning; Xu, Shibin; Ke, Li; Fan, Jun; Wang, Jun; Xie, Mingran; Jiang, Xianliang; Xu, Meiqing

    2017-12-20

    Prolonged air leak (PAL) after anatomic lung resection is a common and challenging complication in thoracic surgery. No available clinical prediction model of PAL has been established in China. The aim of this study was to construct a model to identify patients at increased risk of PAL by using preoperative factors exclusively. We retrospectively reviewed clinical data and PAL occurrence of patients after anatomic lung resection, in department of thoracic surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, from January 2016 to October 2016. 359 patients were in group A, clinical data including age, body mass index (BMI), gender, smoking history, surgical methods, pulmonary function index, pleural adhesion, pathologic diagnosis, side and site of resected lung were analyzed. By using univariate and multivariate analysis, we found the independent predictors of PAL after anatomic lung resection and subsequently established a clinical prediction model. Then, another 112 patients (group B), who underwent anatomic lung resection in different time by different team, were chosen to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curve was constructed using the prediction model. Multivariate Logistic regression analysis was used to identify six clinical characteristics [BMI, gender, smoking history, forced expiratory volume in one second to forced vital capacity ratio (FEV1%), pleural adhesion, site of resection] as independent predictors of PAL after anatomic lung resection. The area under the ROC curve for our model was 0.886 (95%CI: 0.835-0.937). The best predictive P value was 0.299 with sensitivity of 78.5% and specificity of 93.2%. Our prediction model could accurately identify occurrence risk of PAL in patients after anatomic lung resection, which might allow for more effective use of intraoperative prophylactic strategies.
.

  13. ECG dispersion mapping predicts clinical deterioration, measured by increase in the Simple Clinical Score.

    LENUS (Irish Health Repository)

    Kellett, J

    2012-01-01

    Objective: ECG dispersion mapping (ECG-DM) is a novel technique that reports abnormal ECG microalternations. We report the ability of ECG-DM to predict clinical deterioration of acutely ill medical patients, as measured by an increase in the Simple Clinical Score (SCS) the day after admission to hospital. Methods: 453 acutely ill medical patients (mean age 69.7 +\\/- 14.0 years) had the SCS recorded and ECGDM performed immediately after admission to hospital. Results: 46 patients had an SCS increase 20.8 +\\/- 7.6 hours after admission. Abnormal micro-alternations during left ventricular re-polarization had the highest association with SCS increase (p=0.0005). Logistic regression showed that only nursing home residence and abnormal micro-alternations during re-polarization of the left ventricle were independent predictors of SCS increase with an odds ratio of 2.84 and 3.01, respectively. Conclusion: ECG-DM changes during left ventricular re-polarization are independent predictors of clinical deterioration the day after hospital admission.

  14. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

    Science.gov (United States)

    Park, Seong Ho; Han, Kyunghwa

    2018-03-01

    The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires careful confirmation of its clinical utility. Herein, the authors explain key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used, mainly from the standpoints of clinical epidemiology and biostatistics. First, statistical methods for assessing the discrimination and calibration performances of a diagnostic or predictive model are summarized. Next, the effects of disease manifestation spectrum and disease prevalence on the performance results are explained, followed by a discussion of the difference between evaluating the performance with use of internal and external datasets, the importance of using an adequate external dataset obtained from a well-defined clinical cohort to avoid overestimating the clinical performance as a result of overfitting in high-dimensional or overparameterized classification model and spectrum bias, and the essentials for achieving a more robust clinical evaluation. Finally, the authors review the role of clinical trials and observational outcome studies for ultimate clinical verification of diagnostic or predictive artificial intelligence tools through patient outcomes, beyond performance metrics, and how to design such studies. © RSNA, 2018.

  15. Prediction of Clinically Relevant Safety Signals of Nephrotoxicity through Plasma Metabolite Profiling

    Directory of Open Access Journals (Sweden)

    W. B. Mattes

    2013-01-01

    Full Text Available Addressing safety concerns such as drug-induced kidney injury (DIKI early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC. The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound’s well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity, not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.

  16. Evaluation of Clinical Gait Analysis parameters in patients affected by Multiple Sclerosis: Analysis of kinematics.

    Science.gov (United States)

    Severini, Giacomo; Manca, Mario; Ferraresi, Giovanni; Caniatti, Luisa Maria; Cosma, Michela; Baldasso, Francesco; Straudi, Sofia; Morelli, Monica; Basaglia, Nino

    2017-06-01

    Clinical Gait Analysis is commonly used to evaluate specific gait characteristics of patients affected by Multiple Sclerosis. The aim of this report is to present a retrospective cross-sectional analysis of the changes in Clinical Gait Analysis parameters in patients affected by Multiple Sclerosis. In this study a sample of 51 patients with different levels of disability (Expanded Disability Status Scale 2-6.5) was analyzed. We extracted a set of 52 parameters from the Clinical Gait Analysis of each patient and used statistical analysis and linear regression to assess differences among several groups of subjects stratified according to the Expanded Disability Status Scale and 6-Minutes Walking Test. The impact of assistive devices (e.g. canes and crutches) on the kinematics was also assessed in a subsample of patients. Subjects showed decreased range of motion at hip, knee and ankle that translated in increased pelvic tilt and hiking. Comparison between the two stratifications showed that gait speed during 6-Minutes Walking Test is better at discriminating patients' kinematics with respect to Expanded Disability Status Scale. Assistive devices were shown not to significantly impact gait kinematics and the Clinical Gait Analysis parameters analyzed. We were able to characterize disability-related trends in gait kinematics. The results presented in this report provide a small atlas of the changes in gait characteristics associated with different disability levels in the Multiple Sclerosis population. This information could be used to effectively track the progression of MS and the effect of different therapies. Copyright © 2017. Published by Elsevier Ltd.

  17. A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2017-11-01

    Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

  18. Economic Model Predictive Control of Bihormonal Artificial Pancreas System Based on Switching Control and Dynamic R-parameter.

    Science.gov (United States)

    Tang, Fengna; Wang, Youqing

    2017-11-01

    Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

  19. Clinical Relevance of Prognostic and Predictive Molecular Markers in Gliomas.

    Science.gov (United States)

    Siegal, Tali

    2016-01-01

    Sorting and grading of glial tumors by the WHO classification provide clinicians with guidance as to the predicted course of the disease and choice of treatment. Nonetheless, histologically identical tumors may have very different outcome and response to treatment. Molecular markers that carry both diagnostic and prognostic information add useful tools to traditional classification by redefining tumor subtypes within each WHO category. Therefore, molecular markers have become an integral part of tumor assessment in modern neuro-oncology and biomarker status now guides clinical decisions in some subtypes of gliomas. The routine assessment of IDH status improves histological diagnostic accuracy by differentiating diffuse glioma from reactive gliosis. It carries a favorable prognostic implication for all glial tumors and it is predictive for chemotherapeutic response in anaplastic oligodendrogliomas with codeletion of 1p/19q chromosomes. Glial tumors that contain chromosomal codeletion of 1p/19q are defined as tumors of oligodendroglial lineage and have favorable prognosis. MGMT promoter methylation is a favorable prognostic marker in astrocytic high-grade gliomas and it is predictive for chemotherapeutic response in anaplastic gliomas with wild-type IDH1/2 and in glioblastoma of the elderly. The clinical implication of other molecular markers of gliomas like mutations of EGFR and ATRX genes and BRAF fusion or point mutation is highlighted. The potential of molecular biomarker-based classification to guide future therapeutic approach is discussed and accentuated.

  20. Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

    Science.gov (United States)

    Kaklamanos, James; Baise, Laurie G.; Boore, David M.

    2011-01-01

    The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

  1. Clinical and functional criteria for predicting asthma in infants

    OpenAIRE

    Yu. L. Mizemitskiy; V. A. Pavlenko; I. M. Melnikova

    2015-01-01

    Objective: to determine clinical and functional criteria for predicting asthma in children who have sustained acute obstructive bronchitis in infancy. Subjects and methods. A total of 125 infants aged 2 to 36 months who had experienced 1 -2 episodes of acute obstructive bronchitis and treated at hospital were examined when bronchial obstruction syndrome was being relieved. In addition to physical examination, functional studies (computerized bronchophonography and heart rate variability asses...

  2. Physiotherapy students' perceptions and experiences of clinical prediction rules.

    Science.gov (United States)

    Knox, Grahame M; Snodgrass, Suzanne J; Stanton, Tasha R; Kelly, David H; Vicenzino, Bill; Wand, Benedict M; Rivett, Darren A

    2017-09-01

    Clinical reasoning can be difficult to teach to pre-professional physiotherapy students due to their lack of clinical experience. It may be that tools such as clinical prediction rules (CPRs) could aid the process, but there has been little investigation into their use in physiotherapy clinical education. This study aimed to determine the perceptions and experiences of physiotherapy students regarding CPRs, and whether they are learning about CPRs on clinical placement. Cross-sectional survey using a paper-based questionnaire. Final year pre-professional physiotherapy students (n=371, response rate 77%) from five universities across five states of Australia. Sixty percent of respondents had not heard of CPRs, and a further 19% had not clinically used CPRs. Only 21% reported using CPRs, and of these nearly three-quarters were rarely, if ever, learning about CPRs in the clinical setting. However most of those who used CPRs (78%) believed CPRs assisted in the development of clinical reasoning skills and none (0%) was opposed to the teaching of CPRs to students. The CPRs most commonly recognised and used by students were those for determining the need for an X-ray following injuries to the ankle and foot (67%), and for identifying deep venous thrombosis (63%). The large majority of students in this sample knew little, if anything, about CPRs and few had learned about, experienced or practiced them on clinical placement. However, students who were aware of CPRs found them helpful for their clinical reasoning and were in favour of learning more about them. Copyright © 2016 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

  3. [Predictive methods versus clinical titration for the initiation of lithium therapy. A systematic review].

    Science.gov (United States)

    Geeraerts, I; Sienaert, P

    2013-01-01

    When lithium is administered, the clinician needs to know when the lithium in the patient’s blood has reached a therapeutic level. At the initiation of treatment the level is usually achieved gradually through the application of the titration method. In order to increase the efficacy of this procedure several methods for dosing lithium and for predicting lithium levels have been developed. To conduct a systematic review of the publications relating to the various methods for dosing lithium or predicting lithium levels at the initiation of therapy. We searched Medline systematically for articles published in English, French or Dutch between 1966 and April 2012 which described or studied a method for dosing lithium or for predicting the lithium level reached following a specific dosage. We screened the reference lists of relevant articles in order to locate additional papers. We found 38 lithium prediction methods, in addition to the clinical titration method. These methods can be divided into two categories: the ‘a priori’ methods and the ‘test-dose’ methods, the latter requiring the administration of a test dose of lithium. The lithium prediction methods generally achieve a therapeutic blood level faster than the clinical titration method, but none of the methods achieves convincing results. On the basis of our review, we propose that the titration method should be used as the standard method in clinical practice.

  4. High EDSS can predict risk for upper urinary tract damage in patients with multiple sclerosis.

    Science.gov (United States)

    Ineichen, Benjamin V; Schneider, Marc P; Hlavica, Martin; Hagenbuch, Niels; Linnebank, Michael; Kessler, Thomas M

    2018-04-01

    Neurogenic lower urinary tract dysfunction (NLUTD) is very common in patients with multiple sclerosis (MS), and it might jeopardize renal function and thereby increase mortality. Although there are well-known urodynamic risk factors for upper urinary tract damage, no clinical prediction parameters are available. We aimed to assess clinical parameters potentially predicting urodynamic risk factors for upper urinary tract damage. A consecutive series of 141 patients with MS referred from neurologists for primary neuro-urological work-up including urodynamics were prospectively evaluated. Clinical parameters taken into account were age, sex, duration, and clinical course of MS and Expanded Disability Status Scale (EDSS). Multivariate modeling revealed EDSS as a clinical parameter significantly associated with urodynamic risk factors for upper urinary tract damage (odds ratio = 1.34, 95% confidence interval (CI) = 1.06-1.71, p = 0.02). Using receiver operator characteristic (ROC) curves, an EDSS of 5.0 as cutoff showed a sensitivity of 86%-87% and a specificity of 52% for at least one urodynamic risk factor for upper urinary tract damage. High EDSS is significantly associated with urodynamic risk factors for upper urinary tract damage and allows a risk-dependent stratification in daily neurological clinical practice to identify MS patients requiring further neuro-urological assessment and treatment.

  5. Prediction of renal mass aggressiveness using clinical and radiographic features: a global, multicentre prospective study

    NARCIS (Netherlands)

    Golan, Shay; Eggener, Scott; Subotic, Svetozar; Barret, Eric; Cormio, Luigi; Naito, Seiji; Tefekli, Ahmet; Pilar Laguna Pes, M.

    2016-01-01

    To examine the ability of preoperative clinical characteristics to predict histological features of renal masses (RMs). Data from consecutive patients with clinical stage I RMs treated surgically between 2010 and 2011 in the Clinical Research Office of Endourology Society (CROES) Renal Mass Registry

  6. Clinical and atopic parameters and airway inflammatory markers in childhood asthma: a factor analysis

    Science.gov (United States)

    Leung, T; Wong, G; Ko, F; Lam, C; Fok, T

    2005-01-01

    Background: Recent studies have repeatedly shown weak correlations among lung function parameters, atopy, exhaled nitric oxide level (FeNO), and airway inflammatory markers, suggesting that they are non-overlapping characteristics of asthma in adults. A study was undertaken to determine, using factor analysis, whether the above features represent separate dimensions of childhood asthma. Methods: Clinically stable asthmatic patients aged 7–18 years underwent spirometric testing, methacholine bronchial challenge, blood sampling for atopy markers and chemokine levels (macrophage derived chemokine (MDC), thymus and activation regulated chemokine (TARC), and eotaxin), FeNO, and chemokines (MDC and eotaxin) and leukotriene B4 measurements in exhaled breath condensate (EBC). Results: The mean (SD) forced expiratory volume in 1 second (FEV1) and FeNO of 92 patients were 92.1 (15.9)% predicted and 87.3 (65.7) ppb, respectively. 59% of patients received inhaled corticosteroids. Factor analysis selected four different factors, explaining 55.5% of total variance. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.587. Plasma total and specific IgE levels, peripheral blood eosinophil percentage, and FeNO loaded on factor 1; plasma TARC and MDC concentrations on factor 2; MDC, eotaxin and leukotriene B4 concentrations in EBC on factor 3; and plasma eotaxin concentration together with clinical indices including body mass index and disease severity score loaded on factor 4. Post hoc factor analyses revealed similar results when outliers were excluded. Conclusions: The results suggest that atopy related indices and airway inflammation are separate dimensions in the assessment of childhood asthma, and inflammatory markers in peripheral blood and EBC are non-overlapping factors of asthma. PMID:16055623

  7. An IL28B genotype-based clinical prediction model for treatment of chronic hepatitis C.

    Directory of Open Access Journals (Sweden)

    Thomas R O'Brien

    Full Text Available Genetic variation in IL28B and other factors are associated with sustained virological response (SVR after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC. Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables.HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC.Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p10% (43.3% of subjects had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study.A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC.

  8. A predictive thermal dynamic model for parameter generation in the laser assisted direct write process

    International Nuclear Information System (INIS)

    Shang Shuo; Fearon, Eamonn; Wellburn, Dan; Sato, Taku; Edwardson, Stuart; Dearden, G; Watkins, K G

    2011-01-01

    The laser assisted direct write (LADW) method can be used to generate electrical circuitry on a substrate by depositing metallic ink and curing the ink thermally by a laser. Laser curing has emerged over recent years as a novel yet efficient alternative to oven curing. This method can be used in situ, over complicated 3D contours of large parts (e.g. aircraft wings) and selectively cure over heat sensitive substrates, with little or no thermal damage. In previous studies, empirical methods have been used to generate processing windows for this technique, relating to the several interdependent processing parameters on which the curing quality and efficiency strongly depend. Incorrect parameters can result in a track that is cured in some areas and uncured in others, or in damaged substrates. This paper addresses the strong need for a quantitative model which can systematically output the processing conditions for a given combination of ink, substrate and laser source; transforming the LADW technique from a purely empirical approach, to a simple, repeatable, mathematically sound, efficient and predictable process. The method comprises a novel and generic finite element model (FEM) that for the first time predicts the evolution of the thermal profile of the ink track during laser curing and thus generates a parametric map which indicates the most suitable combination of parameters for process optimization. Experimental data are compared with simulation results to verify the accuracy of the model.

  9. The predictive validity of the BioMedical Admissions Test for pre-clinical examination performance.

    Science.gov (United States)

    Emery, Joanne L; Bell, John F

    2009-06-01

    Some medical courses in the UK have many more applicants than places and almost all applicants have the highest possible previous and predicted examination grades. The BioMedical Admissions Test (BMAT) was designed to assist in the student selection process specifically for a number of 'traditional' medical courses with clear pre-clinical and clinical phases and a strong focus on science teaching in the early years. It is intended to supplement the information provided by examination results, interviews and personal statements. This paper reports on the predictive validity of the BMAT and its predecessor, the Medical and Veterinary Admissions Test. Results from the earliest 4 years of the test (2000-2003) were matched to the pre-clinical examination results of those accepted onto the medical course at the University of Cambridge. Correlation and logistic regression analyses were performed for each cohort. Section 2 of the test ('Scientific Knowledge') correlated more strongly with examination marks than did Section 1 ('Aptitude and Skills'). It also had a stronger relationship with the probability of achieving the highest examination class. The BMAT and its predecessor demonstrate predictive validity for the pre-clinical years of the medical course at the University of Cambridge. The test identifies important differences in skills and knowledge between candidates, not shown by their previous attainment, which predict their examination performance. It is thus a valid source of additional admissions information for medical courses with a strong scientific emphasis when previous attainment is very high.

  10. Analysis of a large number of clinical studies for breast cancer radiotherapy: estimation of radiobiological parameters for treatment planning

    International Nuclear Information System (INIS)

    Guerrero, M; Li, X Allen

    2003-01-01

    Numerous studies of early-stage breast cancer treated with breast conserving surgery (BCS) and radiotherapy (RT) have been published in recent years. Both external beam radiotherapy (EBRT) and/or brachytherapy (BT) with different fractionation schemes are currently used. The present RT practice is largely based on empirical experience and it lacks a reliable modelling tool to compare different RT modalities or to design new treatment strategies. The purpose of this work is to derive a plausible set of radiobiological parameters that can be used for RT treatment planning. The derivation is based on existing clinical data and is consistent with the analysis of a large number of published clinical studies on early-stage breast cancer. A large number of published clinical studies on the treatment of early breast cancer with BCS plus RT (including whole breast EBRT with or without a boost to the tumour bed, whole breast EBRT alone, brachytherapy alone) and RT alone are compiled and analysed. The linear quadratic (LQ) model is used in the analysis. Three of these clinical studies are selected to derive a plausible set of LQ parameters. The potential doubling time is set a priori in the derivation according to in vitro measurements from the literature. The impact of considering lower or higher T pot is investigated. The effects of inhomogeneous dose distributions are considered using clinically representative dose volume histograms. The derived LQ parameters are used to compare a large number of clinical studies using different regimes (e.g., RT modality and/or different fractionation schemes with different prescribed dose) in order to validate their applicability. The values of the equivalent uniform dose (EUD) and biologically effective dose (BED) are used as a common metric to compare the biological effectiveness of each treatment regime. We have obtained a plausible set of radiobiological parameters for breast cancer. This set of parameters is consistent with in vitro

  11. Readmission prediction via deep contextual embedding of clinical concepts.

    Science.gov (United States)

    Xiao, Cao; Ma, Tengfei; Dieng, Adji B; Blei, David M; Wang, Fei

    2018-01-01

    Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients. The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions. This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.

  12. Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.

    Directory of Open Access Journals (Sweden)

    Scott B Hu

    Full Text Available Clinical deterioration (ICU transfer and cardiac arrest occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR along with a machine learning algorithm called a neural network to develop a prediction model that would increase the predictive accuracy and decrease false alarm rates.Retrospective cohort study.The hematologic malignancy unit in an academic medical center in the United States.Adult patients admitted to the hematologic malignancy unit from 2009 to 2010.None.Vital signs and laboratory values were obtained from the electronic medical record system and then used as predictors (features. A neural network was used to build a model to predict clinical deterioration events (ICU transfer and cardiac arrest. The performance of the neural network model was compared to the VitalPac Early Warning Score (ViEWS. Five hundred sixty five consecutive total admissions were available with 43 admissions resulting in clinical deterioration. Using simulation, the neural network outperformed the ViEWS model with a positive predictive value of 82% compared to 24%, respectively.We developed and tested a neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit. Our neural network model outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised. This system can be readily implemented in a real-time fashion in existing EMR systems.

  13. Myocardial gene expression of microRNA-133a and myosin heavy and light chains, in conjunction with clinical parameters, predict regression of left ventricular hypertrophy after valve replacement in patients with aortic stenosis.

    Science.gov (United States)

    Villar, Ana V; Merino, David; Wenner, Mareike; Llano, Miguel; Cobo, Manuel; Montalvo, Cecilia; García, Raquel; Martín-Durán, Rafael; Hurlé, Juan M; Hurlé, María A; Nistal, J Francisco

    2011-07-01

    Left ventricular (LV) reverse remodelling after valve replacement in aortic stenosis (AS) has been classically linked to the hydraulic performance of the replacement device, but myocardial status at the time of surgery has received little attention. To establish predictors of LV mass (LVM) regression 1 year after valve replacement in a surgical cohort of patients with AS based on preoperative clinical and echocardiographic parameters and the myocardial gene expression profile at surgery. Transcript levels of remodelling-related proteins and regulators were determined in LV intraoperative biopsies from 46 patients with AS by RT-PCR. Using multiple linear regression analysis, an equation was developed (adjusted R²=0.73; pregression analysis identified microRNA-133a as a significant positive predictor of LVM normalisation, whereas β-myosin heavy chain and BMI constituted negative predictors. Hypertrophy regression 1 year after pressure overload release is related to the preoperative myocardial expression of remodelling-related genes, in conjunction with the patient's clinical background. In this scenario, miR-133 emerges as a key element of the reverse remodelling process. Postoperative improvement of valve haemodynamics does not predict the degree of hypertrophy regression or LVM normalisation. These results led us to reconsider the current reverse remodelling paradigm and (1) to include criteria of hypertrophy reversibility in the decision algorithm used to decide timing for the operation; and (2) to modify other prevailing factors (overweight, diabetes, etc) known to maintain LV hypertrophy.

  14. Predicting functional decline and survival in amyotrophic lateral sclerosis.

    Science.gov (United States)

    Ong, Mei-Lyn; Tan, Pei Fang; Holbrook, Joanna D

    2017-01-01

    Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database. In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors. A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class. Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.

  15. Dynamics of Clinical and Biochemical Parameters in Patients with Liver Cirrhosis Under the Influence of Complex Therapy with Ursodeoxycholic Acid

    Directory of Open Access Journals (Sweden)

    M.I. Shved

    2013-11-01

    Full Text Available It was studied dynamics of clinical and biochemical parameters in patients with liver cirrhosis under the influence of complex treatment using ursosan. It is found that the inclusion of ursosan in complex treatment improves clinical and laboratory parameters, significantly reduces the manifestations of general inflammatory liver syndrome, which prevents the progression of the disease.

  16. [Validation of a clinical prediction rule to distinguish bacterial from aseptic meningitis].

    Science.gov (United States)

    Agüero, Gonzalo; Davenport, María C; Del Valle, María de la P; Gallegos, Paulina; Kannemann, Ana L; Bokser, Vivian; Ferrero, Fernando

    2010-02-01

    Despite most meningitis are not bacterial, antibiotics are usually administered on admission because bacterial meningitis is difficult to be rule-out. Distinguishing bacterial from aseptic meningitis on admission could avoid inappropriate antibiotic use and hospitalization. We aimed to validate a clinical prediction rule to distinguish bacterial from aseptic meningitis in children, on arriving to the emergency room. This prospective study included patients aged or = 1000 cells/mm(3), CSF protein > or = 80 mg/dl, peripheral blood absolute neutrophil count > or = 10.000/mm(3), seizure = 1 point each. Sensitivity (S), specificity (E), positive and negative predictive values (PPV and NPV), positive and negative likelihood ratios (PLR and NLR) of the BMS to predict bacterial meningitis were calculated. Seventy patients with meningitis were included (14 bacterial meningitis). When BMS was calculated, 25 patients showed a BMS= 0 points, 11 BMS= 1 point, and 34 BMS > or = 2 points. A BMS = 0 showed S: 100%, E: 44%, VPP: 31%, VPN: 100%, RVP: 1,81 RVN: 0. A BMS > or = 2 predicted bacterial meningitis with S: 100%, E: 64%, VPP: 41%, VPN: 100%, PLR: 2.8, NLR:0. Using BMS was simple, and allowed identifying children with very low risk of bacterial meningitis. It could be a useful tool to assist clinical decision making.

  17. Gender and age related predictive value of walk test in heart failure: do anthropometrics matter in clinical practice?

    Science.gov (United States)

    Frankenstein, L; Remppis, A; Graham, J; Schellberg, D; Sigg, C; Nelles, M; Katus, H A; Zugck, C

    2008-07-21

    The six-minute walk test (6 WT) is a valid and reliable predictor of morbidity and mortality in chronic heart failure (CHF) patients, frequently used as an endpoint or target in clinical trials. As opposed to spiroergometry, improvement of its prognostic accuracy by correction for height, weight, age and gender has not yet been attempted comprehensively despite known influences of these parameters. We recorded the 6 WT of 1035 CHF patients, attending clinic from 1995 to 2005. The 1-year prognostic value of 6 WT was calculated, alone and after correction for height, weight, BMI and/or age. Analysis was performed on the entire cohort, on males and females separately and stratified according to BMI (30 kg/m(2)). 6 WT weakly correlated with age (r=-0.32; p<0.0001), height (r=0.2; p<0.0001), weight (r=0.11; p<0.001), not with BMI (r=0.01; p=ns). The 6 WT was a strong predictor of 1-year mortality in both genders, both as a single and age corrected parameter. Parameters derived from correction of 6 WT for height, weight or BMI did not improve the prognostic value in univariate analysis for either gender. Comparison of the receiver operated characteristics showed no significant gain in prognostic accuracy from any derived variable, either for males or females. The six-minute walk test is a valid tool for risk prediction in both male and female CHF patients. In both genders, correcting 6 WT distance for height, weight or BMI alone, or adjusting for age, does not increase the prognostic power of this tool.

  18. Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural network

    International Nuclear Information System (INIS)

    Mirvakili, S.M.; Faghihi, F.; Khalafi, H.

    2012-01-01

    Highlights: ► Thermal–hydraulics parameters of a VVER-1000 core based on neural network (ANN), are carried out. ► Required data for ANN training are found based on modified COBRA-EN code and then linked each other using MATLAB software. ► Based on ANN method, average and maximum temperature of fuel and clad as well as MDNBR of each FA are predicted. -- Abstract: The main goal of the present article is to design a computational tool to predict physical parameters of the VVER-1000 nuclear reactor core based on artificial neural network (ANN), taking into account a detailed physical model of the fuel rods and coolant channels in a fuel assembly. Predictions of thermal characteristics of fuel, clad and coolant are performed using cascade feed forward ANN based on linear fission power distribution and power peaking factors of FAs and hot channels factors (which are found based on our previous neutronic calculations). A software package has been developed to prepare the required data for ANN training which applies a modified COBRA-EN code for sub-channel analysis and links the codes using the MATLAB software. Based on the current estimation system, five main core TH parameters are predicted, which include the average and maximum temperatures of fuel and clad as well as the minimum departure from nucleate boiling ratio (MDNBR) for each FA. To get the best conditions for the considered ANNs training, a comprehensive sensitivity study has been performed to examine the effects of variation of hidden neurons, hidden layers, transfer functions, and the learning algorithms on the training and simulation results. Performance evaluation results show that the developed ANN can be trained to estimate the core TH parameters of a typical VVER-1000 reactor quickly without loss of accuracy.

  19. Highly accurate prediction of food challenge outcome using routinely available clinical data.

    Science.gov (United States)

    DunnGalvin, Audrey; Daly, Deirdre; Cullinane, Claire; Stenke, Emily; Keeton, Diane; Erlewyn-Lajeunesse, Mich; Roberts, Graham C; Lucas, Jane; Hourihane, Jonathan O'B

    2011-03-01

    Serum specific IgE or skin prick tests are less useful at levels below accepted decision points. We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge. The proto-algorithm was generated by using a limited data set from 1 service (phase 1). We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center (phase 2). Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center (phase 3). Allergen-specific models were developed for peanut, egg, and milk. Phase 1 (N = 429) identified 5 clinical factors associated with diagnosis of food allergy by food challenge. In phase 2 (N = 289), we examined the predictive ability of 6 clinical factors: skin prick test, serum specific IgE, total IgE minus serum specific IgE, symptoms, sex, and age. In phase 3 (N = 70), 97% of cases were accurately predicted as positive and 94% as negative. Our model showed an advantage in clinical prediction compared with serum specific IgE only, skin prick test only, and serum specific IgE and skin prick test (92% accuracy vs 57%, and 81%, respectively). Our findings have implications for the improved delivery of food allergy-related health care, enhanced food allergy-related quality of life, and economized use of health service resources by decreasing the number of food challenges performed. Copyright © 2011 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

  20. Comparison of predictability for human pharmacokinetics parameters among monkeys, rats, and chimeric mice with humanised liver.

    Science.gov (United States)

    Miyamoto, Maki; Iwasaki, Shinji; Chisaki, Ikumi; Nakagawa, Sayaka; Amano, Nobuyuki; Hirabayashi, Hideki

    2017-12-01

    1. The aim of the present study was to evaluate the usefulness of chimeric mice with humanised liver (PXB mice) for the prediction of clearance (CL t ) and volume of distribution at steady state (Vd ss ), in comparison with monkeys, which have been reported as a reliable model for human pharmacokinetics (PK) prediction, and with rats, as a conventional PK model. 2. CL t and Vd ss values in PXB mice, monkeys and rats were determined following intravenous administration of 30 compounds known to be mainly eliminated in humans via the hepatic metabolism by various drug-metabolising enzymes. Using single-species allometric scaling, human CL t and Vd ss values were predicted from the three animal models. 3. Predicted CL t values from PXB mice exhibited the highest predictability: 25 for PXB mice, 21 for monkeys and 14 for rats were predicted within a three-fold range of actual values among 30 compounds. For predicted human Vd ss values, the number of compounds falling within a three-fold range was 23 for PXB mice, 24 for monkeys, and 16 for rats among 29 compounds. PXB mice indicated a higher predictability for CL t and Vd ss values than the other animal models. 4. These results demonstrate the utility of PXB mice in predicting human PK parameters.

  1. Prediction of Small for Gestational Age Infants in Healthy Nulliparous Women Using Clinical and Ultrasound Risk Factors Combined with Early Pregnancy Biomarkers.

    Directory of Open Access Journals (Sweden)

    Lesley M E McCowan

    Full Text Available Most small for gestational age pregnancies are unrecognised before birth, resulting in substantial avoidable perinatal mortality and morbidity. Our objective was to develop multivariable prediction models for small for gestational age combining clinical risk factors and biomarkers at 15±1 weeks' with ultrasound parameters at 20±1 weeks' gestation.Data from 5606 participants in the Screening for Pregnancy Endpoints (SCOPE cohort study were divided into Training (n = 3735 and Validation datasets (n = 1871. The primary outcomes were All-SGA (small for gestational age with birthweight <10th customised centile, Normotensive-SGA (small for gestational age with a normotensive mother and Hypertensive-SGA (small for gestational age with an hypertensive mother. The comparison group comprised women without the respective small for gestational age phenotype. Multivariable analysis was performed using stepwise logistic regression beginning with clinical variables, and subsequent additions of biomarker and then ultrasound (biometry and Doppler variables. Model performance was assessed in Training and Validation datasets by calculating area under the curve.633 (11.2% infants were All-SGA, 465(8.2% Normotensive-SGA and 168 (3% Hypertensive-SGA. Area under the curve (95% Confidence Intervals for All-SGA using 15±1 weeks' clinical variables, 15±1 weeks' clinical+ biomarker variables and clinical + biomarkers + biometry /Doppler at 20±1 weeks' were: 0.63 (0.59-0.67, 0.64 (0.60-0.68 and 0.69 (0.66-0.73 respectively in the Validation dataset; Normotensive-SGA results were similar: 0.61 (0.57-0.66, 0.61 (0.56-0.66 and 0.68 (0.64-0.73 with small increases in performance in the Training datasets. Area under the curve (95% Confidence Intervals for Hypertensive-SGA were: 0.76 (0.70-0.82, 0.80 (0.75-0.86 and 0.84 (0.78-0.89 with minimal change in the Training datasets.Models for prediction of small for gestational age, which combine biomarkers, clinical and

  2. Critical thresholds of liver function parameters for ketosis prediction in dairy cows using receiver operating characteristic (ROC) analysis.

    Science.gov (United States)

    Sun, Yuhang; Wang, Bo; Shu, Shi; Zhang, Hongyou; Xu, Chuang; Wu, Ling; Xia, Cheng

    2015-01-01

    Fatty liver syndrome and ketosis are important metabolic disorders in high-producing cows during early lactation with fatty liver usually preceding ketosis. To date, parameters for early prediction of the risk of ketosis have not been investigated in China. To determine the predictive value of some parameters on the risk of ketosis in China. In a descriptive study, 48 control and 32 ketotic Holstein Friesian cows were randomly selected from one farm with a serum β-hydroxybutyrate (BHBA) concentration of 1.20 mmol/L as cutoff point. The risk prediction thresholds for ketosis were determined by receiver operating characteristic (ROC) analysis. In line with a high BHBA concentration, blood glucose concentration was significantly lower in ketotic cows compared to control animals (2.77 ± 0.24 versus 3.34 ± 0.03 mmol/L; P = 0.02). Thresholds were more than 0.76 mmol/L for nonesterified fatty acids (NEFA, with 65% sensitivity and 92% specificity), more than 104 U/L for aspartate aminotransferase (AST, 74% and 85%, respectively), less than 140 U/L for cholinesterase (CHE, 75% and 59%, respectively), and more than 3.3 µmol/L for total bilirubin (TBIL, 58% and 83%, respectively). There were significant correlations between BHBA and glucose (R = -4.74), or CHE (R = -0.262), BHBA and NEFA (R = 0.520), or AST (R = 0.525), or TBIL (R = 0.278), or direct bilirubin (DBIL, R = 0.348). AST, CHE, TBIL and NEFA may be useful parameters for risk prediction of ketosis. This study might be of value in addressing novel directions for future research on the connection between ketosis and liver dysfunction.

  3. Prediction of ecotoxicity of hydrocarbon-contaminated soils using physicochemical parameters

    Energy Technology Data Exchange (ETDEWEB)

    Wong, D.C.L.; Chai, E.Y.; Chu, K.K.; Dorn, P.B.

    1999-11-01

    The physicochemical properties of eight hydrocarbon-contaminated soils were used to predict toxicity to earthworms (Eisenia fetida) and plants. The toxicity of these preremediated soils was assessed using earthworm avoidance, survival, and reproduction and seed germination and root growth in four plant species. No-observed-effect and 25% inhibitory concentrations were determined from the earthworm and plant assays. Physical property measurements and metals analyses of the soils were conducted. Hydrocarbon contamination was characterized by total petroleum hydrocarbons, oil and grease, and GC boiling-point distribution. Univariate and multivariate statistical methods were used to examine relationships between physical and chemical properties and biological endpoints. Soil groupings based on physicochemical properties and toxicity from cluster and principal component analyses were generally similar. Correlation analysis identified a number of significant relationships between soil parameters and toxicity that were used in univariate model development. Total petroleum hydrocarbons by gas chromatography and polars were identified as predictors of earthworm avoidance and survival and seed germination, explaining 65 to 75% of the variation in the data. Asphaltenes also explained 83% of the variation in seed germination. Gravimetric total petroleum hydrocarbons explained 40% of the variation in earthworm reproduction, whereas 43% of the variation in plant root growth was explained by asphaltenes. Multivariate one-component partial least squares models, which identified predictors similar to those identified by the univariate models, were also developed for worm avoidance and survival and seed germination and had predictive powers of 42 and 29%, respectively.

  4. The Prognostic Influence of BRAF Mutation and other Molecular, Clinical and Laboratory Parameters in Stage IV Colorectal Cancer.

    Science.gov (United States)

    Karadima, Maria L; Saetta, Angelica A; Chatziandreou, Ilenia; Lazaris, Andreas C; Patsouris, Efstratios; Tsavaris, Nikolaos

    2016-10-01

    Our aim was to evaluate the predictive and prognostic influence of BRAF mutation and other molecular, clinical and laboratory parameters in stage IV colorectal cancer (CRC). 60 patients were included in this retrospective analysis, and 17 variables were examined for their relation with treatment response and survival. KRAS mutation was identified in 40.3 % of cases, BRAF and PIK3CA in 8.8 % and 10.5 % respectively. 29.8 % of patients responded to treatment. Median survival time was 14.3 months. Weight loss, fever, abdominal metastases, blood transfusion, hypoalbuminaimia, BRAF and PIK3CA mutations, CRP and DNA Index were associated with survival. In multivariate analysis, male patients had 3.8 times higher probability of response, increased DNA Index was inversely correlated with response and one unit raise of DNA Index augmented 6 times the probability of death. Our findings potentiate the prognostic role of BRAF, PIK3CA mutations and ploidy in advanced CRC.

  5. Effect of tamsulosin versus prazosin on clinical and urodynamic parameters in women with voiding difficulty: a randomized clinical trial

    Directory of Open Access Journals (Sweden)

    Sakineh Hajebrahimi

    2011-01-01

    Full Text Available Sakineh Hajebrahimi1, Yadollah Ahmadi Asrbadr1, Arash Azaripour1, Homayoun Sadeghi-Bazargani2,31Urology Department, Imam Reza University Hospital, Tabriz, Iran; 2Neuroscience Research Center and RDCC, Tabriz University of Medical Sciences, Tabriz, Iran; 3Karolinska Institute, Stockholm, SwedenObjective: To compare the effects of tamsulosin and prazosin on clinical and urodynamic parameters in women with voiding dysfunction.Methods: Forty women aged 20–65 years with a clinical diagnosis of voiding dysfunction were blindly randomized to two equal groups for treatment with tamsulosin 0.4 mg or 1–2 mg of prazosin daily. Symptom assessment with the American Urological Association Symptom Score (AUASS and urodynamic evaluation was performed initially and after three months of treatment. Patient satisfaction was evaluated and severe adverse drug effects recorded. Statistical analysis was carried out using the Student’s t-test and Mann–Whitney U test.Results: Although AUASS improved in both groups, the rate of improvement was larger in the tamsulosin group. Urodynamic parameters improved but did not normalize in both groups. Adverse side effects from medication in the prazosin group were more common than in the tamsulosin group. Most of the patients in the tamsulosin group (80% were satisfied with their treatment compared with those in the prazosin group (45%.Conclusion: Tamsulosin and prazosin are both effective in palliating symptoms of women with voiding dysfunction and improving their urodynamic parameters. Tamsulosin may be the preferred drug to prescribe because of its more amenable side effect profile and greater patient satisfaction.Keywords: tamsulosin, prazosin, voiding dysfunction 

  6. Effect of steroids on inflammatory markers and clinical parameters in congenital open heart surgery: a randomised controlled trial.

    Science.gov (United States)

    Amanullah, Muhammad M; Hamid, Mohammad; Hanif, Hashim M; Muzaffar, Marium; Siddiqui, Maria T; Adhi, Fatima; Ahmad, Khabir; Khan, Shahjahan; Hasan, Zahra

    2016-03-01

    Cardiopulmonary bypass is associated with systemic inflammatory response. Steroids suppress this response, although the therapeutic evidence remains controversial. We hypothesised that intravenous steroids in children undergoing open-heart surgery would decrease inflammation leading to better early post-operative outcomes. We conducted a randomised controlled trial to evaluate the trends in the levels of immunomodulators and their effects on clinical parameters. To assess the effects of intravenous steroids on early post-operative inflammatory markers and clinical parameters in children undergoing open-heart surgery. A randomised controlled trial involving 152 patients, from one month up to 18 years of age, who underwent open-heart surgery for congenital heart disease from April 2010-2012 was carried out. Patients were randomised and administered either three scheduled intravenous pulse doses of dexamethasone (1 mg/kg) or placebo. Blood samples were drawn at four time intervals and serum levels of inflammatory cytokines - Interleukin-6, 8, 10, 18, and tumour necrosis factor-alpha - were measured. Clinical parameters were also assessed. Blood cytokine levels were compared between the dexamethasone (n=65) and placebo (n=64) groups. Interleukin-6 levels were lower at 6 and 24 hours post-operatively (p<0.001), and Interleukin-10 levels were higher 6 hours post-operatively (p<0.001) in the steroid group. Interleukin-8, 18, and tumour necrosis factor-alpha levels did not differ between the groups at any time intervals. The clinical parameters were similar in both the groups. Dexamethasone caused quantitative suppression of Interleukin-6 and increased Interleukin-10 activation, contributing to reduced immunopathology, but it did not translate into clinical benefit in the short term.

  7. SU-F-R-04: Radiomics for Survival Prediction in Glioblastoma (GBM)

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, H; Molitoris, J; Bhooshan, N; Choi, W; Lu, W; Mehta, M; D’Souza, W [University of Maryland School of Medicine, Baltimore, MD (United States); Tan, S [Huazhong University of Science & Technology, Wuhan (China); Giacomelli, I; Scartoni, D [University of Florence, Florence (Italy); Gzell, C [Northern Sydney Cancer Centre, Sydney (Australia)

    2016-06-15

    Purpose: To develop a quantitative radiomics approach for survival prediction of glioblastoma (GBM) patients treated with chemoradiotherapy (CRT). Methods: 28 GBM patients who received CRT at our institution were retrospectively studied. 255 radiomic features were extracted from 3 gadolinium-enhanced T1 weighted MRIs for 2 regions of interest (ROIs) (the surgical cavity and its surrounding enhancement rim). The 3 MRIs were at pre-treatment, 1-month and 3-month post-CRT. The imaging features comprehensively quantified the intensity, spatial variation (texture), geometric property and their spatial-temporal changes for the 2 ROIs. 3 demographics features (age, race, gender) and 12 clinical parameters (KPS, extent of resection, whether concurrent temozolomide was adjusted/stopped and radiotherapy related information) were also included. 4 Machine learning models (logistic regression (LR), support vector machine (SVM), decision tree (DT), neural network (NN)) were applied to predict overall survival (OS) and progression-free survival (PFS). The number of cases and percentage of cases predicted correctly were collected and AUC (area under the receiver operating characteristic (ROC) curve) were determined after leave-one-out cross-validation. Results: From univariate analysis, 27 features (1 demographic, 1 clinical and 25 imaging) were statistically significant (p<0.05) for both OS and PFS. Two sets of features (each contained 24 features) were algorithmically selected from all features to predict OS and PFS. High prediction accuracy of OS was achieved by using NN (96%, 27 of 28 cases were correctly predicted, AUC = 0.99), LR (93%, 26 of 28 cases were correctly predicted, AUC = 0.95) and SVM (93%, 26 of 28 cases were correctly predicted, AUC = 0.90). When predicting PFS, NN obtained the highest prediction accuracy (89%, 25 of 28 cases were correctly predicted, AUC = 0.92). Conclusion: Radiomics approach combined with patients’ demographics and clinical parameters can

  8. Parameter prediction for microwave garnets

    International Nuclear Information System (INIS)

    Ramer, R.

    1996-01-01

    Full text: Linearity of the microwave parameters (resonance linewidth ΔH and effective linewidth ΔH eff ) is demonstrated and their use in the Computer-aided design (CAD)/Computer-aided manufacturing (CAM) of new microwave garnets is proposed. Such an approach would combine a numerical database of microwave data and several computational programs. The model is an applied formulation of the analysis of a wide range of microwave garnets

  9. Artificial neural networks to predict presence of significant pathology in patients presenting to routine colorectal clinics.

    Science.gov (United States)

    Maslekar, S; Gardiner, A B; Monson, J R T; Duthie, G S

    2010-12-01

    Artificial neural networks (ANNs) are computer programs used to identify complex relations within data. Routine predictions of presence of colorectal pathology based on population statistics have little meaning for individual patient. This results in large number of unnecessary lower gastrointestinal endoscopies (LGEs - colonoscopies and flexible sigmoidoscopies). We aimed to develop a neural network algorithm that can accurately predict presence of significant pathology in patients attending routine outpatient clinics for gastrointestinal symptoms. Ethics approval was obtained and the study was monitored according to International Committee on Harmonisation - Good Clinical Practice (ICH-GCP) standards. Three-hundred patients undergoing LGE prospectively completed a specifically developed questionnaire, which included 40 variables based on clinical symptoms, signs, past- and family history. Complete data sets of 100 patients were used to train the ANN; the remaining data was used for internal validation. The primary output used was positive finding on LGE, including polyps, cancer, diverticular disease or colitis. For external validation, the ANN was applied to data from 50 patients in primary care and also compared with the predictions of four clinicians. Clear correlation between actual data value and ANN predictions were found (r = 0.931; P = 0.0001). The predictive accuracy of ANN was 95% in training group and 90% (95% CI 84-96) in the internal validation set and this was significantly higher than the clinical accuracy (75%). ANN also showed high accuracy in the external validation group (89%). Artificial neural networks offer the possibility of personal prediction of outcome for individual patients presenting in clinics with colorectal symptoms, making it possible to make more appropriate requests for lower gastrointestinal endoscopy. © 2010 The Authors. Colorectal Disease © 2010 The Association of Coloproctology of Great Britain and Ireland.

  10. Preoperative radiochemotherapy in locally advanced or recurrent rectal cancer: regional radiofrequency hyperthermia correlates with clinical parameters

    International Nuclear Information System (INIS)

    Rau, B.; Wust, P.; Tilly, W.; Gellermann, J.; Harder, C.; Riess, H.; Budach, V.; Felix, R.; Schlag, P.M.

    2000-01-01

    Purpose: Preoperative radiochemotherapy (RCT) is a widely used means of treatment for patients suffering from primary, locally advanced, or recurrent rectal cancer. We evaluated the efficacy of treatment due to additional application of regional hyperthermia (HRCT) to this conventional therapy regime in a Phase II study, employing the annular phased-array system BSD-2000 (SIGMA-60 applicator). The clinical results of the trial were encouraging. We investigated the relationship between a variety of thermal and clinical parameters in order to assess the adequacy of thermometry, the effectiveness of hyperthermia therapy, and its potential contribution to clinical endpoints. Methods and Materials: A preoperative combination of radiotherapy (1.8 Gy for 5 days a week, total dose 45 Gy applied over 5 weeks) and chemotherapy (low-dose 5-fluorouracil [5-FU] plus leucovorin in the first and fourth week) was administered to 37 patients with primary rectal cancer (PRC) and 18 patients with recurrent rectal cancer (RRC). Regional hyperthermia (RHT) was applied once a week prior to the daily irradiation fraction of 1.8 Gy. Temperatures were registered along rectal catheters using Bowman thermistors. Measurement points related to the tumor were specified after estimating the section of the catheter in near contact with the tumor. Three patients with local recurrence after abdominoperineal resection, had their catheters positioned transgluteally under CT guidance, where the section of the catheter related to the tumor was estimated from the CT scans. Index temperatures (especially T max , T 90 ) averaged over time, cumulative minutes (cum min) (here for T 90 > reference temperature 40.5 deg. C), and equivalent minutes (equ min) (with respect to 43 deg. C) were derived from repetitive temperature-position scans (5- to 10-min intervals) utilizing software specially developed for this purpose on a PC platform. Using the statistical software package SPSS a careful analysis was

  11. A Design of Experiment approach to predict product and process parameters for a spray dried influenza vaccine.

    Science.gov (United States)

    Kanojia, Gaurav; Willems, Geert-Jan; Frijlink, Henderik W; Kersten, Gideon F A; Soema, Peter C; Amorij, Jean-Pierre

    2016-09-25

    Spray dried vaccine formulations might be an alternative to traditional lyophilized vaccines. Compared to lyophilization, spray drying is a fast and cheap process extensively used for drying biologicals. The current study provides an approach that utilizes Design of Experiments for spray drying process to stabilize whole inactivated influenza virus (WIV) vaccine. The approach included systematically screening and optimizing the spray drying process variables, determining the desired process parameters and predicting product quality parameters. The process parameters inlet air temperature, nozzle gas flow rate and feed flow rate and their effect on WIV vaccine powder characteristics such as particle size, residual moisture content (RMC) and powder yield were investigated. Vaccine powders with a broad range of physical characteristics (RMC 1.2-4.9%, particle size 2.4-8.5μm and powder yield 42-82%) were obtained. WIV showed no significant loss in antigenicity as revealed by hemagglutination test. Furthermore, descriptive models generated by DoE software could be used to determine and select (set) spray drying process parameter. This was used to generate a dried WIV powder with predefined (predicted) characteristics. Moreover, the spray dried vaccine powders retained their antigenic stability even after storage for 3 months at 60°C. The approach used here enabled the generation of a thermostable, antigenic WIV vaccine powder with desired physical characteristics that could be potentially used for pulmonary administration. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  12. Dst Prediction Based on Solar Wind Parameters

    Directory of Open Access Journals (Sweden)

    Yoon-Kyung Park

    2009-12-01

    Full Text Available We reevaluate the Burton equation (Burton et al. 1975 of predicting Dst index using high quality hourly solar wind data supplied by the ACE satellite for the period from 1998 to 2006. Sixty magnetic storms with monotonously decreasing main phase are selected. In order to determine the injection term (Q and the decay time (tau of the equation, we examine the relationships between Dst* and VB_s, Delta Dst* and VB_s, and Delta Dst* and Dst* during the magnetic storms. For this analysis, we take into account one hour of the propagation time from the ACE satellite to the magnetopause, and a half hour of the response time of the magnetosphere/ring current to the solar wind forcing. The injection term is found to be Q({nT}/h=-3.56VB_s for VB_s>0.5mV/m and Q({nT}/h=0 for VB_s leq0.5mV/m. The tau (hour is estimated as 0.060 Dst* + 16.65 for Dst*>-175nT and 6.15 hours for Dst* leq -175nT. Based on these empirical relationships, we predict the 60 magnetic storms and find that the correlation coefficient between the observed and predicted Dst* is 0.88. To evaluate the performance of our prediction scheme, the 60 magnetic storms are predicted again using the models by Burton et al. (1975 and O'Brien & McPherron (2000a. The correlation coefficients thus obtained are 0.85, the same value for both of the two models. In this respect, our model is slightly improved over the other two models as far as the correlation coefficients is concerned. Particularly our model does a better job than the other two models in predicting intense magnetic storms (Dst* lesssim -200nT.

  13. Comprehensive reference ranges for hematology and clinical chemistry laboratory parameters derived from normal Nigerian adults.

    Science.gov (United States)

    Miri-Dashe, Timzing; Osawe, Sophia; Tokdung, Monday; Daniel, Monday Tokdung Nenbammun; Daniel, Nenbammun; Choji, Rahila Pam; Mamman, Ille; Deme, Kurt; Damulak, Dapus; Abimiku, Alash'le

    2014-01-01

    Interpretation of laboratory test results with appropriate diagnostic accuracy requires reference or cutoff values. This study is a comprehensive determination of reference values for hematology and clinical chemistry in apparently healthy voluntary non-remunerated blood donors and pregnant women. Consented clients were clinically screened and counseled before testing for HIV, Hepatitis B, Hepatitis C and Syphilis. Standard national blood donors' questionnaire was administered to consented blood donors. Blood from qualified volunteers was used for measurement of complete hematology and chemistry parameters. Blood samples were analyzed from a total of 383 participants, 124 (32.4%) males, 125 (32.6%) non-pregnant females and 134 pregnant females (35.2%) with a mean age of 31 years. Our results showed that the red blood cells count (RBC), Hemoglobin (HB) and Hematocrit (HCT) had significant gender difference (p = 0.000) but not for total white blood count (p>0.05) which was only significantly higher in pregnant verses non-pregnant women (p = 0.000). Hemoglobin and Hematocrit values were lower in pregnancy (P = 0.000). Platelets were significantly higher in females than men (p = 0.001) but lower in pregnant women (p =  .001) with marked difference in gestational period. For clinical chemistry parameters, there was no significant difference for sodium, potassium and chloride (p>0.05) but gender difference exists for Bicarbonate (HCO3), Urea nitrogen, Creatinine as well as the lipids (pchemistry parameters between pregnant and non-pregnant women in this study (p0.05). Hematological and Clinical Chemistry reference ranges established in this study showed significant gender differences. Pregnant women also differed from non-pregnant females and during pregnancy. This is the first of such comprehensive study to establish reference values among adult Nigerians and difference observed underscore the need to establish reference values for different populations.

  14. Comparison of Model Predictions of Image Quality with Results of Clinical Trials in Chest and Lumbar Spine Screen-film Imaging

    International Nuclear Information System (INIS)

    Sandborg, M.; McVey, G.; Dance, D.R.; Carlsson, G.A.

    2000-01-01

    The ability to predict image quality from known physical and technical parameters is a prerequisite for making successful dose optimisation. In this study, imaging systems have been simulated using a Monte Carlo model of the imaging systems. The model includes a voxelised human anatomy and quantifies image quality in terms of contrast and signal-to-noise ratio for 5-6 anatomical details included in the anatomy. The imaging systems used in clinical trials were simulated and the ranking of the systems by the model and radiologists compared. The model and the results of the trial for chest PA both show that using a high maximum optical density was significantly better than using a low one. The model predicts that a good system is characterised by a large dynamic range and a high contrast of the blood vessels in the retrocardiac area. The ranking by the radiologists and the model agreed for the lumbar spine AP. (author)

  15. Predictive Performance of Echocardiographic Parameters for Cardiovascular Events Among Elderly Treated Hypertensive Patients.

    Science.gov (United States)

    Chowdhury, Enayet K; Jennings, Garry L R; Dewar, Elizabeth; Wing, Lindon M H; Reid, Christopher M

    2016-07-01

    Hypertension leads to cardiac structural and functional changes, commonly assessed by echocardiography. In this study, we assessed the predictive performance of different echocardiographic parameters including left ventricular hypertrophy (LVH) on future cardiovascular outcomes in elderly hypertensive patients without heart failure. Data from LVH substudy of the Second Australian National Blood Pressure trial were used. Echocardiograms were performed at entry into the study. Cardiovascular outcomes were identified over short term (median 4.2 years) and long term (median 10.9 years). LVH was defined using threshold values of LV mass (LVM) indexed to either body surface area (BSA) or height(2.7): >115/95g/m(2) (LVH-BSA(115/95)) or ≥49/45g/m(2.7) (LVH-ht(49/45)) in males/females, respectively, and ≥125g/m(2) (LVH-BSA(125)) or ≥51g/m(2.7) (LVH-ht(51)) for both sexes. In the 666 participants aged ≥65 years in this analysis, LVH prevalence at baseline was 33%-70% depending on definition; and after adjusting for potential risk factors, only LVH-BSA(115/95) predicted both short- and long-term cardiovascular outcomes. Participants having LVH-BSA(115/95) (69%) at baseline had twice the risk of having any first cardiovascular event over the short term (hazard ratio, 95% confidence interval: 2.00, 1.12-3.57, P = 0.02) and any fatal cardiovascular events (2.11, 1.21-3.68, P = 0.01) over the longer term. Among other echocardiographic parameters, LVM and LVM indexed to either BSA or height(2.7) predicted cardiovascular events over both short and longer term. In elderly treated hypertensive patients without heart failure, determining LVH by echocardiography is highly dependent on the methodology adopted. LVH-BSA(115/95) is a reliable predictor of future cardiovascular outcomes in the elderly. © American Journal of Hypertension, Ltd 2016. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  16. Prediction of surface roughness in turning of Ti-6Al-4V using cutting parameters, forces and tool vibration

    Science.gov (United States)

    Sahu, Neelesh Kumar; Andhare, Atul B.; Andhale, Sandip; Raju Abraham, Roja

    2018-04-01

    Present work deals with prediction of surface roughness using cutting parameters along with in-process measured cutting force and tool vibration (acceleration) during turning of Ti-6Al-4V with cubic boron nitride (CBN) inserts. Full factorial design is used for design of experiments using cutting speed, feed rate and depth of cut as design variables. Prediction model for surface roughness is developed using response surface methodology with cutting speed, feed rate, depth of cut, resultant cutting force and acceleration as control variables. Analysis of variance (ANOVA) is performed to find out significant terms in the model. Insignificant terms are removed after performing statistical test using backward elimination approach. Effect of each control variables on surface roughness is also studied. Correlation coefficient (R2 pred) of 99.4% shows that model correctly explains the experiment results and it behaves well even when adjustment is made in factors or new factors are added or eliminated. Validation of model is done with five fresh experiments and measured forces and acceleration values. Average absolute error between RSM model and experimental measured surface roughness is found to be 10.2%. Additionally, an artificial neural network model is also developed for prediction of surface roughness. The prediction results of modified regression model are compared with ANN. It is found that RSM model and ANN (average absolute error 7.5%) are predicting roughness with more than 90% accuracy. From the results obtained it is found that including cutting force and vibration for prediction of surface roughness gives better prediction than considering only cutting parameters. Also, ANN gives better prediction over RSM models.

  17. Efficiency of Calatonia on clinical parameters in the immediate post-surgery period: a clinical study

    Directory of Open Access Journals (Sweden)

    Elaine Ferreira Lasaponari

    2013-09-01

    Full Text Available OBJECTIVE: to assess the efficiency of the Calatonia technique about clinical parameters and pain in the immediate post-surgical phase. METHOD: a randomised study was carried out with 116 patients subjected to a cholecystectomy, by laparoscopy, divided into an experimental group (58 patients and a placebo group (58 patients. The experimental group received the Calatonia technique, while the placebo was only subjected to non-intentional touches. RESULTS: The placebo group and the experimental group were considered homogeneous in terms of the variables: sex, age, physical status classification, duration of surgical procedures and also the time spent recovering in the Post-Anaesthetic Recovery Room. The only variable to show a statistically significant difference was the axillary temperature of the body. In relation to pain, the experimental group showed significant results, and hence it is possible to deduce that the relaxation caused by the Calatonia technique brought some relief of the general situation of pain. CONCLUSION: The application of Calatonia can take up the function of a resource complementary to assistance in the period immediately after surgery. Brazilian Register of Clinical Trials, UTN U1111-1129-9629.

  18. Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

    Science.gov (United States)

    Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

    2013-04-01

    Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

  19. Have the Findings from Clinical Risk Prediction and Trials Any Key Messages for Safety Pharmacology?

    Directory of Open Access Journals (Sweden)

    Jem D. Lane

    2017-11-01

    Full Text Available Anti-arrhythmic drugs are a mainstay in the management of symptoms related to arrhythmias, and are adjuncts in prevention and treatment of life-threatening ventricular arrhythmias. However, they also have the potential for pro-arrhythmia and thus the prediction of arrhythmia predisposition and drug response are critical issues. Clinical trials are the latter stages in the safety testing and efficacy process prior to market release, and as such serve as a critical safeguard. In this review, we look at some of the lessons to be learned from approaches to arrhythmia prediction in patients, clinical trials of drugs used in the treatment of arrhythmias, and the implications for the design of pre-clinical safety pharmacology testing.

  20. The potential role of biomarkers in predicting gestational diabetes

    Directory of Open Access Journals (Sweden)

    Huguette S Brink

    2016-08-01

    Full Text Available Gestational diabetes (GD is a frequent complication during pregnancy and is associated with maternal and neonatal complications. It is suggested that a disturbing environment for the foetus, such as impaired glucose metabolism during intrauterine life, may result in enduring epigenetic changes leading to increased disease risk in adult life. Hence, early prediction of GD is vital. Current risk prediction models are based on maternal and clinical parameters, lacking a strong predictive value. Adipokines are mainly produced by adipocytes and suggested to be a link between obesity and its cardiovascular complications. Various adipokines, including adiponectin, leptin and TNFα, have shown to be dysregulated in GD. This review aims to outline biomarkers potentially associated with the pathophysiology of GD and discuss the role of integrating predictive biomarkers in current clinical risk prediction models, in order to enhance the identification of those at risk.

  1. Effect of the probiotic Lactobacillus murinus LbP2 on clinical parameters of dogs with distemper-associated diarrhea.

    Science.gov (United States)

    Delucchi, Luis; Fraga, Martín; Zunino, Pablo

    2017-04-01

    The objective of this study was to assess the effect of the probiotic Lactobacillus murinus native strain (LbP2) on general clinical parameters of dogs with distemper-associated diarrhea. Two groups of dogs over 60 d of age with distemper and diarrhea were used in the study, which was done at the Animal Hospital of the Veterinary Faculty of the University of Uruguay, Montevideo, Uruguay. The dogs were treated orally each day for 5 d with the probiotic or with a placebo (vehicle without bacteria). Clinical parameters were assessed and scored according to a system specially designed for this study. Blood parameters were also measured. Administration of the probiotic significantly improved the clinical score of the patients, whereas administration of the placebo did not. Stool output, fecal consistency, mental status, and appetite all improved in the probiotic-treated dogs. These results support previous findings of beneficial effects with the probiotic L. murinus LbP2 in dogs. Thus, combined with other therapeutic measures, probiotic treatment appears to be promising for the management of canine distemper-associated diarrhea.

  2. Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

    International Nuclear Information System (INIS)

    De Ruyck, Kim; Sabbe, Nick; Oberije, Cary; Vandecasteele, Katrien; Thas, Olivier; De Ruysscher, Dirk; Lambin, Phillipe; Van Meerbeeck, Jan; De Neve, Wilfried; Thierens, Hubert

    2011-01-01

    Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade ≥2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.

  3. CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

    Science.gov (United States)

    Kong, Hualei; Tong, Pan; Zhao, Xiaodong; Sun, Jielin; Li, Hua

    2018-03-01

    In the past decade, molecular classification of cancer has gained high popularity owing to its high predictive power on clinical outcomes as compared with traditional methods commonly used in clinical practice. In particular, using gene expression profiles, recent studies have successfully identified a number of gene sets for the delineation of cancer subtypes that are associated with distinct prognosis. However, identification of such gene sets remains a laborious task due to the lack of tools with flexibility, integration and ease of use. To reduce the burden, we have developed an R package, CAsubtype, to efficiently identify gene sets predictive of cancer subtypes and clinical outcomes. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes. In conclusion, CAsubtype is a flexible and well-integrated tool in the R environment to identify gene sets for cancer subtype identification and clinical outcome prediction. Its simple R commands and comprehensive data sets enable efficient examination of the clinical value of any given gene set, thus facilitating hypothesis generating and testing in biological and

  4. Risk determination after an acute myocardial infarction: review of 3 clinical risk prediction tools.

    Science.gov (United States)

    Scruth, Elizabeth Ann; Page, Karen; Cheng, Eugene; Campbell, Michelle; Worrall-Carter, Linda

    2012-01-01

    The objective of the study was to provide comprehensive information for the clinical nurse specialist (CNS) on commonly used clinical prediction (risk assessment) tools used to estimate risk of a secondary cardiac or noncardiac event and mortality in patients undergoing primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI). The evolution and widespread adoption of primary PCI represent major advances in the treatment of acute myocardial infarction, specifically STEMI. The American College of Cardiology and the American Heart Association have recommended early risk stratification for patients presenting with acute coronary syndromes using several clinical risk scores to identify patients' mortality and secondary event risk after PCI. Clinical nurse specialists are integral to any performance improvement strategy. Their knowledge and understandings of clinical prediction tools will be essential in carrying out important assessment, identifying and managing risk in patients who have sustained a STEMI, and enhancing discharge education including counseling on medications and lifestyle changes. Over the past 2 decades, risk scores have been developed from clinical trials to facilitate risk assessment. There are several risk scores that can be used to determine in-hospital and short-term survival. This article critiques the most common tools: the Thrombolytic in Myocardial Infarction risk score, the Global Registry of Acute Coronary Events risk score, and the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications risk score. The importance of incorporating risk screening assessment tools (that are important for clinical prediction models) to guide therapeutic management of patients cannot be underestimated. The ability to forecast secondary risk after a STEMI will assist in determining which patients would require the most aggressive level of treatment and monitoring postintervention including

  5. A clinical tool for predicting survival in ALS.

    Science.gov (United States)

    Knibb, Jonathan A; Keren, Noa; Kulka, Anna; Leigh, P Nigel; Martin, Sarah; Shaw, Christopher E; Tsuda, Miho; Al-Chalabi, Ammar

    2016-12-01

    Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic factors. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  6. Serum parameters predict the severity of ultrasonographicifndingsinnon-alcoholic fatty liver disease

    Institute of Scientific and Technical Information of China (English)

    Mohsen Razavizade; Raika Jamali; Abbas Arj; Hamidreza Talari

    2012-01-01

    BACKGROUND: Controversy exists about the correlation between liver ultrasonography and serum parameters for evaluating the severity of liver involvement in non-alcoholic fatty liver disease (NAFLD). This study was designed to determine the association between liver ultrasonography staging in NAFLD and serum parameters correlated with disease severity in previous studies; and set optimal cut-off points for those serum parameters correlated with NAFLD staging at ultrasonography, in order to differentiate ultrasonographic groups (USGs). METHODS: This cross-sectional study evaluated outpatients with evidence of NAFLD in ultrasonography referred to a general hospital. Those with positive viral markers, abnormal serum ceruloplasmin or gamma-globulin concentrations were excluded. A radiologist performed the ultrasonography staging and stratiifed the patients into mild, moderate, and severe groups. Fasting serum alanine aminotransferase (ALT), aspartate aminotransferase, alkaline phosphatase, triglyceride (TG), high and low density lipoprotein (HDL, LDL), and cholesterol were checked. RESULTS:Two hundred and forty-ifve patients with a mean age (±standard deviation) of 41.63(±11.46) years were included. There were no signiifcant differences when mean laboratory concentrations were compared between moderate and severe USGs. Therefore, these groups were combined to create revised USGs ("mild"versus"moderate or severe"). There were associations between the revised USGs, and ALT, TG, HDL levels, and diabetes mellitus [odds ratios=2.81 (95%conifdence interval (CI):1.37-5.76), 2.48 (95%CI:1.29-4.78), 0.36 (95%CI:0.18-0.74), and 5.65 (95%CI:2.86-11.16) respectively;all P values CONCLUSIONS: Serum ALT, TG, and HDL concentrations seem to be associated with the staging by liver ultrasonography in NAFLD. They might be used to predict the staging of liver ultrasonography in these patients.

  7. Metallic ureteral stents in malignant ureteral obstruction: clinical factors predicting stent failure.

    Science.gov (United States)

    Chow, Po-Ming; Hsu, Jui-Shan; Huang, Chao-Yuan; Wang, Shuo-Meng; Lee, Yuan-Ju; Huang, Kuo-How; Yu, Hong-Jheng; Pu, Yeong-Shiau; Liang, Po-Chin

    2014-06-01

    To provide clinical outcomes of the Resonance metallic ureteral stent in patients with malignant ureteral obstruction, as well as clinical factors predicting stent failure. Cancer patients who have received Resonance stents from July 2009 to March 2012 for ureteral obstruction were included for chart review. Stent failure was detected by clinical symptoms, image studies, and renal function tests. Survival analysis for stent duration was used to estimate patency rate and factors predicting stent failure. A total of 117 stents were inserted successfully into 94 ureteral units in 79 patients. There were no major complications. These stents underwent survival analysis and proportional hazard regression. The median duration for the stents was 5.77 months. In multivariate analysis, age (P=0.043), preoperative serum creatinine level (P=0.0174), and cancer type (P=0.0494) were significant factors associated with stent failure. Cancer treatment before and after stent insertion had no effect on stent duration. Resonance stents are effective and safe in relieving malignant ureteral obstructions. Old age and high serum creatinine level are predictors for stent failure. Stents in patients with lower gastrointestinal cancers have longer functional duration.

  8. Association of the Pro12Ala Polymorphism with the Metabolic Parameters in Women with Polycystic Ovary Syndrome

    Directory of Open Access Journals (Sweden)

    Moushira Zaki

    2017-06-01

    CONCLUSION: The PPARG Pro12Ala polymorphism might contribute to the risk of PCOS and abnormal metabolic parameters and could be considered as a biomarker for early diagnosis and clinic prediction of metabolic complications.

  9. Predicting dynamic knee joint load with clinical measures in people with medial knee osteoarthritis.

    Science.gov (United States)

    Hunt, Michael A; Bennell, Kim L

    2011-08-01

    Knee joint loading, as measured by the knee adduction moment (KAM), has been implicated in the pathogenesis of knee osteoarthritis (OA). Given that the KAM can only currently be accurately measured in the laboratory setting with sophisticated and expensive equipment, its utility in the clinical setting is limited. This study aimed to determine the ability of a combination of four clinical measures to predict KAM values. Three-dimensional motion analysis was used to calculate the peak KAM at a self-selected walking speed in 47 consecutive individuals with medial compartment knee OA and varus malalignment. Clinical predictors included: body mass; tibial angle measured using an inclinometer; walking speed; and visually observed trunk lean toward the affected limb during the stance phase of walking. Multiple linear regression was performed to predict KAM magnitudes using the four clinical measures. A regression model including body mass (41% explained variance), tibial angle (17% explained variance), and walking speed (9% explained variance) explained a total of 67% of variance in the peak KAM. Our study demonstrates that a set of measures easily obtained in the clinical setting (body mass, tibial alignment, and walking speed) can help predict the KAM in people with medial knee OA. Identifying those patients who are more likely to experience high medial knee loads could assist clinicians in deciding whether load-modifying interventions may be appropriate for patients, whilst repeated assessment of joint load could provide a mechanism to monitor disease progression or success of treatment. Copyright © 2010 Elsevier B.V. All rights reserved.

  10. Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

    Directory of Open Access Journals (Sweden)

    Adel Taieb

    2017-01-01

    Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.

  11. External validation of a clinical prediction rule to predict full recovery and ongoing moderate/severe disability following acute whiplash injury.

    Science.gov (United States)

    Ritchie, Carrie; Hendrikz, Joan; Jull, Gwendolen; Elliott, James; Sterling, Michele

    2015-04-01

    Retrospective secondary analysis of data. To investigate the external validity of the whiplash clinical prediction rule (CPR). We recently derived a whiplash CPR to consolidate previously established prognostic factors for poor recovery from a whiplash injury and predicted 2 recovery pathways. Prognostic factors for full recovery were being less than 35 years of age and having an initial Neck Disability Index (NDI) score of 32% or less. Prognostic factors for ongoing moderate/severe pain and disability were being 35 years of age or older, having an initial NDI score of 40% or more, and the presence of hyperarousal symptoms. Validation is required to confirm the reproducibility and accuracy of this CPR. Clinician feedback on the usefulness of the CPR is also important to gauge acceptability. A secondary analysis of data from 101 individuals with acute whiplash-associated disorder who had previously participated in either a randomized controlled clinical trial or prospective cohort study was performed using accuracy statistics. Full recovery was defined as NDI score at 6 months of 10% or less, and ongoing moderate/severe pain and disability were defined as an NDI score at 6 months of 30% or greater. In addition, a small sample of physical therapists completed an anonymous survey on the clinical acceptability and usability of the tool. Results The positive predictive value of ongoing moderate/severe pain and disability was 90.9% in the validation cohort, and the positive predictive value of full recovery was 80.0%. Surveyed physical therapists reported that the whiplash CPR was simple, understandable, would be easy to use, and was an acceptable prognostic tool. External validation of the whiplash CPR confirmed the reproducibility and accuracy of this dual-pathway tool for individuals with acute whiplash-associated disorder. Further research is needed to assess prospective validation, the impact of inclusion on practice, and to examine the efficacy of linking treatment

  12. Predicting occupational asthma and rhinitis in bakery workers referred for clinical evaluation

    NARCIS (Netherlands)

    Jonaid, Badri Sadat; Rooyackers, Jos; Stigter, Erik; Portengen, Lützen; Krop, Esmeralda; Heederik, Dick

    2017-01-01

    BACKGROUND: Occupational allergic diseases are a major problem in some workplaces like in the baking industry. Diagnostic rules have been used in surveillance but not yet in the occupational respiratory clinic. OBJECTIVE: To develop diagnostic models predicting baker's asthma and rhinitis among

  13. A clinical prediction model to assess the risk of operative delivery

    NARCIS (Netherlands)

    Schuit, E.; Kwee, A.; Westerhuis, M. E. M. H.; van Dessel, H. J. H. M.; Graziosi, G. C. M.; van Lith, J. M. M.; Nijhuis, J. G.; Oei, S. G.; Oosterbaan, H. P.; Schuitemaker, N. W. E.; Wouters, M. G. A. J.; Visser, G. H. A.; Mol, B. W. J.; Moons, K. G. M.; Groenwold, R. H. H.

    2012-01-01

    Please cite this paper as: Schuit E, Kwee A, Westerhuis M, Van Dessel H, Graziosi G, Van Lith J, Nijhuis J, Oei S, Oosterbaan H, Schuitemaker N, Wouters M, Visser G, Mol B, Moons K, Groenwold R. A clinical prediction model to assess the risk of operative delivery. BJOG 2012;119:915923. Objective To

  14. Prediction and optimization of friction welding parameters for super duplex stainless steel (UNS S32760) joints

    International Nuclear Information System (INIS)

    Udayakumar, T.; Raja, K.; Afsal Husain, T.M.; Sathiya, P.

    2014-01-01

    Highlights: • Corrosion resistance and impact strength – predicted by response surface methodology. • Burn off length has highest significance on corrosion resistance. • Friction force is a strong determinant in changing impact strength. • Pareto front points generated by genetic algorithm aid to fix input control variable. • Pareto front will be a trade-off between corrosion resistance and impact strength. - Abstract: Friction welding finds widespread industrial use as a mass production process for joining materials. Friction welding process allows welding of several materials that are extremely difficult to fusion weld. Friction welding process parameters play a significant role in making good quality joints. To produce a good quality joint it is important to set up proper welding process parameters. This can be done by employing optimization techniques. This paper presents a multi objective optimization method for optimizing the process parameters during friction welding process. The proposed method combines the response surface methodology (RSM) with an intelligent optimization algorithm, i.e. genetic algorithm (GA). Corrosion resistance and impact strength of friction welded super duplex stainless steel (SDSS) (UNS S32760) joints were investigated considering three process parameters: friction force (F), upset force (U) and burn off length (B). Mathematical models were developed and the responses were adequately predicted. Direct and interaction effects of process parameters on responses were studied by plotting graphs. Burn off length has high significance on corrosion current followed by upset force and friction force. In the case of impact strength, friction force has high significance followed by upset force and burn off length. Multi objective optimization for maximizing the impact strength and minimizing the corrosion current (maximizing corrosion resistance) was carried out using GA with the RSM model. The optimization procedure resulted in

  15. Comparison of different contouring definitions of the rectum as organ at risk (OAR) and dose-volume parameters predicting rectal inflammation in radiotherapy of prostate cancer: which definition to use?

    Science.gov (United States)

    Nitsche, Mirko; Brannath, Werner; Brückner, Matthias; Wagner, Dirk; Kaltenborn, Alexander; Temme, Nils; Hermann, Robert M

    2017-02-01

    The objective of this retrospective planning study was to find a contouring definition for the rectum as an organ at risk (OAR) in curative three-dimensional external beam radiotherapy (EBRT) for prostate cancer (PCa) with a predictive correlation between the dose-volume histogram (DVH) and rectal toxicity. In a pre-study, the planning CT scans of 23 patients with PCa receiving definitive EBRT were analyzed. The rectum was contoured according to 13 different definitions, and the dose distribution was correlated with the respective rectal volumes by generating DVH curves. Three definitions were identified to represent the most distinct differences in the shapes of the DVH curves: one anatomical definition recommended by the Radiation Therapy Oncology Group (RTOG) and two functional definitions based on the target volume. In the main study, the correlation between different relative DVH parameters derived from these three contouring definitions and the occurrence of rectal toxicity during and after EBRT was studied in two consecutive collectives. The first cohort consisted of 97 patients receiving primary curative EBRT and the second cohort consisted of 66 patients treated for biochemical recurrence after prostatectomy. Rectal toxicity was investigated by clinical investigation and scored according to the Common Terminology Criteria for Adverse Events. Candidate parameters were the volume of the rectum, mean dose, maximal dose, volume receiving at least 60 Gy (V 60 ), area under the DVH curve up to 25 Gy and area under the DVH curve up to 75 Gy in dependence of each chosen rectum definition. Multivariable logistic regression considered other clinical factors such as pelvine lymphatics vs local target volume, diabetes, prior rectal surgery, anticoagulation or haemorrhoids too. In Cohort 1 (primary EBRT), the mean rectal volumes for definitions "RTOG", planning target volume "(PTV)-based" and "PTV-linked" were 100 cm 3 [standard deviation (SD) 43 cm 3 ], 60

  16. Can machine-learning improve cardiovascular risk prediction using routine clinical data?

    Science.gov (United States)

    Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

    2017-01-01

    Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

  17. A proposal of parameter determination method in the residual strength degradation model for the prediction of fatigue life (I)

    International Nuclear Information System (INIS)

    Kim, Sang Tae; Jang, Seong Soo

    2001-01-01

    The static and fatigue tests have been carried out to verify the validity of a generalized residual strength degradation model. And a new method of parameter determination in the model is verified experimentally to account for the effect of tension-compression fatigue loading of spheroidal graphite cast iron. It is shown that the correlation between the experimental results and the theoretical prediction on the statistical distribution of fatigue life by using the proposed method is very reasonable. Furthermore, it is found that the correlation between the theoretical prediction and the experimental results of fatigue life in case of tension-tension fatigue data in composite material appears to be reasonable. Therefore, the proposed method is more adjustable in the determination of the parameter than maximum likelihood method and minimization technique

  18. Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk.

    Science.gov (United States)

    Walsh, Colin G; Sharman, Kavya; Hripcsak, George

    2017-12-01

    Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration

  19. Preoperative Prediction of Ki-67 Labeling Index By Three-dimensional CT Image Parameters for Differential Diagnosis Of Ground-Glass Opacity (GGO.

    Directory of Open Access Journals (Sweden)

    Mingzheng Peng

    Full Text Available The aim of this study was to predict Ki-67 labeling index (LI preoperatively by three-dimensional (3D CT image parameters for pathologic assessment of GGO nodules. Diameter, total volume (TV, the maximum CT number (MAX, average CT number (AVG and standard deviation of CT number within the whole GGO nodule (STD were measured by 3D CT workstation. By detection of immunohistochemistry and Image Software Pro Plus 6.0, different Ki-67 LI were measured and statistically analyzed among preinvasive adenocarcinoma (PIA, minimally invasive adenocarcinoma (MIA and invasive adenocarcinoma (IAC. Receiver operating characteristic (ROC curve, Spearman correlation analysis and multiple linear regression analysis with cross-validation were performed to further research a quantitative correlation between Ki-67 labeling index and radiological parameters. Diameter, TV, MAX, AVG and STD increased along with PIA, MIA and IAC significantly and consecutively. In the multiple linear regression model by a stepwise way, we obtained an equation: prediction of Ki-67 LI=0.022*STD+0.001* TV+2.137 (R=0.595, R's square=0.354, p<0.001, which can predict Ki-67 LI as a proliferative marker preoperatively. Diameter, TV, MAX, AVG and STD could discriminate pathologic categories of GGO nodules significantly. Ki-67 LI of early lung adenocarcinoma presenting GGO can be predicted by radiologic parameters based on 3D CT for differential diagnosis.

  20. Implant-supported mandibular removable partial dentures: Functional, clinical and radiographical parameters in relation to implant position.

    Science.gov (United States)

    Jensen, Charlotte; Speksnijder, Caroline M; Raghoebar, Gerry M; Kerdijk, Wouter; Meijer, Henny J A; Cune, Marco S

    2017-06-01

    Patients with a Kennedy class I situation often encounter problems with their removable partial denture (RPD). To assess the functional benefits of implant support to RPDs, the clinical performance of the implants and teeth and to determine the most favorable implant position: the premolar (PM) or molar (M) region. Thirty subjects received 2 PM and 2 M implants. A new RPD was made. Implant support was provided 3 months later. In a cross-over model, randomly, 2 implants (PM or M) supported the RPD during 3 months. Masticatory performance was assessed using the mixing ability index (MAI). Clinical and radiographic parameters were assessed. Non-parametric statistical analysis for related samples and post hoc comparisons were performed. Masticatory performance differed significantly between the stages of treatment (P < .001). MAI-scores improved with implant support although the implant position had no significant effect. No complications to the implants or RPD were observed and clinical and radiographical parameters for both implants and teeth were favorable. Higher scores for bleeding on probing were seen for molar implants. Implant support to a Kennedy class I RPD significantly improves masticatory function, regardless of implant position. No major clinical problems were observed. © 2017 Wiley Periodicals, Inc.

  1. Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning Health System.

    Science.gov (United States)

    Keim-Malpass, Jessica; Kitzmiller, Rebecca R; Skeeles-Worley, Angela; Lindberg, Curt; Clark, Matthew T; Tai, Robert; Calland, James Forrest; Sullivan, Kevin; Randall Moorman, J; Anderson, Ruth A

    2018-06-01

    In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS. Copyright © 2018 Elsevier Inc. All rights reserved.

  2. Application of a simple parameter estimation method to predict effluent transport in the Savannah River

    International Nuclear Information System (INIS)

    Hensel, S.J.; Hayes, D.W.

    1993-01-01

    A simple parameter estimation method has been developed to determine the dispersion and velocity parameters associated with stream/river transport. The unsteady one dimensional Burgers' equation was chosen as the model equation, and the method has been applied to recent Savannah River dye tracer studies. The computed Savannah River transport coefficients compare favorably with documented values, and the time/concentration curves calculated from these coefficients compare well with the actual tracer data. The coefficients were used as a predictive capability and applied to Savannah River tritium concentration data obtained during the December 1991 accidental tritium discharge from the Savannah River Site. The peak tritium concentration at the intersection of Highway 301 and the Savannah River was underpredicted by only 5% using the coefficients computed from the dye data

  3. Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

    Science.gov (United States)

    Wang, Xun-Heng; Jiao, Yun; Li, Lihua

    2017-10-24

    Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is twofold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity. To achieve this goal, a cohort of 74 boys with ADHD and a cohort of 69 age-matched normal controls were recruited from the ADHD-200 Consortium. Both structural and resting-state fMRI images were obtained for each participant. Temporal patterns between and within intrinsic connectivity networks (ICNs) were applied as raw features in the predictive models. Specifically, sample entropy was taken asan intra-ICN feature, and phase synchronization (PS) was used asan inter-ICN feature. The predictive models were based on the least absolute shrinkage and selectionator operator (LASSO) algorithm. The performance of the predictive model for inattention is r=0.79 (p<10 -8 ), and the performance of the predictive model for impulsivity is r=0.48 (p<10 -8 ). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  4. A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records.

    Science.gov (United States)

    Huang, Zhengxing; Dong, Wei; Duan, Huilong; Liu, Jiquan

    2018-05-01

    Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention and treatment. Existing ACS risk scoring models are based mainly on a small set of hand-picked risk factors and often dichotomize predictive variables to simplify the score calculation. This study develops a regularized stacked denoising autoencoder (SDAE) model to stratify clinical risks of ACS patients from a large volume of electronic health records (EHR). To capture characteristics of patients at similar risk levels, and preserve the discriminating information across different risk levels, two constraints are added on SDAE to make the reconstructed feature representations contain more risk information of patients, which contribute to a better clinical risk prediction result. We validate our approach on a real clinical dataset consisting of 3464 ACS patient samples. The performance of our approach for predicting ACS risk remains robust and reaches 0.868 and 0.73 in terms of both AUC and accuracy, respectively. The obtained results show that the proposed approach achieves a competitive performance compared to state-of-the-art models in dealing with the clinical risk prediction problem. In addition, our approach can extract informative risk factors of ACS via a reconstructive learning strategy. Some of these extracted risk factors are not only consistent with existing medical domain knowledge, but also contain suggestive hypotheses that could be validated by further investigations in the medical domain.

  5. Prediction efficiency of the hydrographical parameters as related to distribution patterns of the Pleuromamma species in the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Saraswathy, M.

    . Multiple regression model of P. indica abundance on the parameters: temperature, salinity, dissolved oxygen and phosphate-phosphorus could explain more than 85% of the variation in the predicted abundance, while those of 8 species obtained from...

  6. Blood coagulation parameters and platelet indices: changes in normal and preeclamptic pregnancies and predictive values for preeclampsia.

    Directory of Open Access Journals (Sweden)

    Lei Han

    Full Text Available Preeclampsia (PE is an obstetric disorder with high morbidity and mortality rates but without clear pathogeny. The dysfunction of the blood coagulation-fibrinolysis system is a salient characteristic of PE that varies in severity, and necessitates different treatments. Therefore, it is necessary to find suitable predictors for the onset and severity of PE.We aimed to evaluate blood coagulation parameters and platelet indices as potential predictors for the onset and severity of PE.Blood samples from 3 groups of subjects, normal pregnant women (n = 79, mild preeclampsia (mPE (n = 53 and severe preeclampsia (sPE (n = 42, were collected during early and late pregnancy. The levels of coagulative parameters and platelet indices were measured and compared among the groups. The receiver-operating characteristic (ROC curves of these indices were generated, and the area under the curve (AUC was calculated. The predictive values of the selected potential parameters were examined in binary regression analysis.During late pregnancy in the normal pregnancy group, the activated partial thromboplastin time (APTT, prothrombin time (PT, thrombin time (TT and platelet count decreased, while the fibrinogen level and mean platelet volume (MPV increased compared to early pregnancy (p<0.05. However, the PE patients presented with increased APTT, TT, MPV and D-dimer (DD during the third trimester. In the analysis of subjects with and without PE, TT showed the largest AUC (0.743 and high predictive value. In PE patients with different severities, MPV showed the largest AUC (0.671 and ideal predictive efficiency.Normal pregnancy causes a maternal physiological hypercoagulable state in late pregnancy. PE may trigger complex disorders in the endogenous coagulative pathways and consume platelets and FIB, subsequently activating thrombopoiesis and fibrinolysis. Thrombin time and MPV may serve as early monitoring markers for the onset and severity of PE

  7. OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.

    Science.gov (United States)

    Chen, Jonathan H; Podchiyska, Tanya; Altman, Russ B

    2016-03-01

    To answer a "grand challenge" in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com's product recommender. EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender's ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% (P < 10(-10)) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% (P < 10(-16)). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from "correct" ones. Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. "interesting" suggestions). Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government

  8. Robust parameter extraction for decision support using multimodal intensive care data

    Science.gov (United States)

    Clifford, G.D.; Long, W.J.; Moody, G.B.; Szolovits, P.

    2008-01-01

    Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU. PMID:18936019

  9. Predicting the risk of suicide by analyzing the text of clinical notes.

    Science.gov (United States)

    Poulin, Chris; Shiner, Brian; Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

    2014-01-01

    We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.

  10. Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

    Science.gov (United States)

    Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

    2014-01-01

    We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients. PMID:24489669

  11. Rapid determination of thermodynamic parameters from one-dimensional programmed-temperature gas chromatography for use in retention time prediction in comprehensive multidimensional chromatography.

    Science.gov (United States)

    McGinitie, Teague M; Ebrahimi-Najafabadi, Heshmatollah; Harynuk, James J

    2014-01-17

    A new method for estimating the thermodynamic parameters of ΔH(T0), ΔS(T0), and ΔCP for use in thermodynamic modeling of GC×GC separations has been developed. The method is an alternative to the traditional isothermal separations required to fit a three-parameter thermodynamic model to retention data. Herein, a non-linear optimization technique is used to estimate the parameters from a series of temperature-programmed separations using the Nelder-Mead simplex algorithm. With this method, the time required to obtain estimates of thermodynamic parameters a series of analytes is significantly reduced. This new method allows for precise predictions of retention time with the average error being only 0.2s for 1D separations. Predictions for GC×GC separations were also in agreement with experimental measurements; having an average relative error of 0.37% for (1)tr and 2.1% for (2)tr. Copyright © 2013 Elsevier B.V. All rights reserved.

  12. Different minimally important clinical difference (MCID) scores lead to different clinical prediction rules for the Oswestry disability index for the same sample of patients.

    Science.gov (United States)

    Schwind, Julie; Learman, Kenneth; O'Halloran, Bryan; Showalter, Christopher; Cook, Chad

    2013-05-01

    Minimal clinically important difference (MCID) scores for outcome measures are frequently used evidence-based guides to gage meaningful changes. There are numerous outcome instruments used for analyzing pain, disability, and dysfunction of the low back; perhaps the most common of these is the Oswestry disability index (ODI). A single agreed-upon MCID score for the ODI has yet to be established. What is also unknown is whether selected baseline variables will be universal predictors regardless of the MCID used for a particular outcome measure. To explore the relationship between predictive models and the MCID cutpoint on the ODI. Data were collected from 16 outpatient physical therapy clinics in 10 states. Secondary database analysis using backward stepwise deletion logistic regression of data from a randomized controlled trial (RCT) to create prognostic clinical prediction rules (CPR). One hundred and forty-nine patients with low back pain (LBP) were enrolled in the RCT. All were treated with manual therapy, with a majority also receiving spine-strengthening exercises. The resultant predictive models were dependent upon the MCID used and baseline sample characteristics. All CPR were statistically significant (P < 001). All six MCID cutpoints used resulted in completely different significant predictor variables with no predictor significant across all models. The primary limitations include sub-optimal sample size and study design. There is extreme variability among predictive models created using different MCIDs on the ODI within the same patient population. Our findings highlight the instability of predictive modeling, as these models are significantly affected by population baseline characteristics along with the MCID used. Clinicians must be aware of the fragility of CPR prior to applying each in clinical practice.

  13. Predicting clinically unrecognized coronary artery disease: use of two- dimensional echocardiography

    Directory of Open Access Journals (Sweden)

    Nagueh Sherif F

    2009-03-01

    Full Text Available Abstract Background 2-D Echo is often performed in patients without history of coronary artery disease (CAD. We sought to determine echo features predictive of CAD. Methods 2-D Echo of 328 patients without known CAD performed within one year prior to stress myocardial SPECT and angiography were reviewed. Echo features examined were left ventricular and atrial enlargement, LV hypertrophy, wall motion abnormality (WMA, LV ejection fraction (EF 15% LV perfusion defect or multivessel distribution. Severe coronary artery stenosis (CAS was defined as left main, 3 VD or 2VD involving proximal LAD. Results The mean age was 62 ± 13 years, 59% men, 29% diabetic (DM and 148 (45% had > 2 risk factors. Pharmacologic stress was performed in 109 patients (33%. MPA was present in 200 pts (60% of which, 137 were high risk. CAS was present in 166 pts (51%, 75 were severe. Of 87 patients with WMA, 83% had MPA and 78% had CAS. Multivariate analysis identified age >65, male, inability to exercise, DM, WMA, MAC and AS as independent predictors of MPA and CAS. Independent predictors of high risk MPA and severe CAS were age, DM, inability to exercise and WMA. 2-D echo findings offered incremental value over clinical information in predicting CAD by angiography. (Chi square: 360 vs. 320 p = 0.02. Conclusion 2-D Echo was valuable in predicting presence of physiological and anatomical CAD in addition to clinical information.

  14. Data Science Solution to Event Prediction in Outsourced Clinical Trial Models.

    Science.gov (United States)

    Dalevi, Daniel; Lovick, Susan; Mann, Helen; Metcalfe, Paul D; Spencer, Stuart; Hollis, Sally; Ruau, David

    2015-01-01

    Late phase clinical trials are regularly outsourced to a Contract Research Organisation (CRO) while the risk and accountability remain within the sponsor company. Many statistical tasks are delivered by the CRO and later revalidated by the sponsor. Here, we report a technological approach to standardised event prediction. We have built a dynamic web application around an R-package with the aim of delivering reliable event predictions, simplifying communication and increasing trust between the CRO and the in-house statisticians via transparency. Short learning curve, interactivity, reproducibility and data diagnostics are key here. The current implementation is motivated by time-to-event prediction in oncology. We demonstrate a clear benefit of standardisation for both parties. The tool can be used for exploration, communication, sensitivity analysis and generating standard reports. At this point we wish to present this tool and share some of the insights we have gained during the development.

  15. Prediction of dosage-based parameters from the puff dispersion of airborne materials in urban environments using the CFD-RANS methodology

    Science.gov (United States)

    Efthimiou, G. C.; Andronopoulos, S.; Bartzis, J. G.

    2018-02-01

    One of the key issues of recent research on the dispersion inside complex urban environments is the ability to predict dosage-based parameters from the puff release of an airborne material from a point source in the atmospheric boundary layer inside the built-up area. The present work addresses the question of whether the computational fluid dynamics (CFD)-Reynolds-averaged Navier-Stokes (RANS) methodology can be used to predict ensemble-average dosage-based parameters that are related with the puff dispersion. RANS simulations with the ADREA-HF code were, therefore, performed, where a single puff was released in each case. The present method is validated against the data sets from two wind-tunnel experiments. In each experiment, more than 200 puffs were released from which ensemble-averaged dosage-based parameters were calculated and compared to the model's predictions. The performance of the model was evaluated using scatter plots and three validation metrics: fractional bias, normalized mean square error, and factor of two. The model presented a better performance for the temporal parameters (i.e., ensemble-average times of puff arrival, peak, leaving, duration, ascent, and descent) than for the ensemble-average dosage and peak concentration. The majority of the obtained values of validation metrics were inside established acceptance limits. Based on the obtained model performance indices, the CFD-RANS methodology as implemented in the code ADREA-HF is able to predict the ensemble-average temporal quantities related to transient emissions of airborne material in urban areas within the range of the model performance acceptance criteria established in the literature. The CFD-RANS methodology as implemented in the code ADREA-HF is also able to predict the ensemble-average dosage, but the dosage results should be treated with some caution; as in one case, the observed ensemble-average dosage was under-estimated slightly more than the acceptance criteria. Ensemble

  16. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma

    DEFF Research Database (Denmark)

    Lauss, Martin; Donia, Marco; Harbst, Katja

    2017-01-01

    Adoptive T-cell therapy (ACT) is a highly intensive immunotherapy regime that has yielded remarkable response rates and many durable responses in clinical trials in melanoma; however, 50-60% of the patients have no clinical benefit. Here, we searched for predictive biomarkers to ACT in melanoma. ...

  17. Logic-based models in systems biology: a predictive and parameter-free network analysis method.

    Science.gov (United States)

    Wynn, Michelle L; Consul, Nikita; Merajver, Sofia D; Schnell, Santiago

    2012-11-01

    Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

  18. Evaluating the predictive accuracy and the clinical benefit of a nomogram aimed to predict survival in node-positive prostate cancer patients: External validation on a multi-institutional database.

    Science.gov (United States)

    Bianchi, Lorenzo; Schiavina, Riccardo; Borghesi, Marco; Bianchi, Federico Mineo; Briganti, Alberto; Carini, Marco; Terrone, Carlo; Mottrie, Alex; Gacci, Mauro; Gontero, Paolo; Imbimbo, Ciro; Marchioro, Giansilvio; Milanese, Giulio; Mirone, Vincenzo; Montorsi, Francesco; Morgia, Giuseppe; Novara, Giacomo; Porreca, Angelo; Volpe, Alessandro; Brunocilla, Eugenio

    2018-04-06

    To assess the predictive accuracy and the clinical value of a recent nomogram predicting cancer-specific mortality-free survival after surgery in pN1 prostate cancer patients through an external validation. We evaluated 518 prostate cancer patients treated with radical prostatectomy and pelvic lymph node dissection with evidence of nodal metastases at final pathology, at 10 tertiary centers. External validation was carried out using regression coefficients of the previously published nomogram. The performance characteristics of the model were assessed by quantifying predictive accuracy, according to the area under the curve in the receiver operating characteristic curve and model calibration. Furthermore, we systematically analyzed the specificity, sensitivity, positive predictive value and negative predictive value for each nomogram-derived probability cut-off. Finally, we implemented decision curve analysis, in order to quantify the nomogram's clinical value in routine practice. External validation showed inferior predictive accuracy as referred to in the internal validation (65.8% vs 83.3%, respectively). The discrimination (area under the curve) of the multivariable model was 66.7% (95% CI 60.1-73.0%) by testing with receiver operating characteristic curve analysis. The calibration plot showed an overestimation throughout the range of predicted cancer-specific mortality-free survival rates probabilities. However, in decision curve analysis, the nomogram's use showed a net benefit when compared with the scenarios of treating all patients or none. In an external setting, the nomogram showed inferior predictive accuracy and suboptimal calibration characteristics as compared to that reported in the original population. However, decision curve analysis showed a clinical net benefit, suggesting a clinical implication to correctly manage pN1 prostate cancer patients after surgery. © 2018 The Japanese Urological Association.

  19. Prevalence of herpesviruses in gingivitis and chronic periodontitis: relationship to clinical parameters and effect of treatment

    Directory of Open Access Journals (Sweden)

    Rucha Shah

    2016-01-01

    Full Text Available Background: Assess the prevalence of herpesviruses in healthy subjects, gingivitis, and chronic periodontitis patients, to assess the relationship between the prevalence of herpesviruses and periodontal clinical parameters, and to evaluate the effect of phase-I therapy on the level of viral detection. Materials and Methods: Hundred patients consisting of 20 healthy subjects, 40 gingivitis, and 40 chronic periodontitis were included in the study. Clinical parameters recorded included plaque index, gingival index, sulcus bleeding index, probing depth, and clinical attachment level. The gingivitis and chronic periodontitis patients received phase-I periodontal therapy including oral hygiene instructions, full mouth scaling for gingivitis patients and scaling and root planing for chronic periodontitis patients. Gingival crevicular fluid (GCF was collected, and the presence of herpes simplex virus-1 (HSV-1, HSV-2, cytomegalovirus, and Epstein–Barr virus (EBV was analyzed using polymerase chain reaction (PCR. Recording of periodontal parameters as well as GCF collection was performed at baseline and 6 weeks postphase-I therapy. Results: At baseline, the levels of HSV-1 and EBV detection were lower in healthy controls as compared to gingivitis (P < 0.05 and chronic periodontitis cases (P < 0.001. Phase-I therapy led to reduction in the amount of HSV-1 and EBV in gingivitis patients (P < 0.05 and for HSV-1, human cytomegalovirus and EBV in chronic periodontitis patients (P < 0.05 in comparison to baseline. The prevalence of EBV in chronic periodontitis patients was positively associated with increased gingival index, probing depth and loss of clinical attachment (P < 0.05. Conclusions: Higher prevalence of HSV-1 and EBV viruses in GCF of gingivitis and chronic periodontitis suggests a strong association between these viruses and periodontal diseases and periodontal therapy can lead to a reduction in herpesviruses at infected sites.

  20. Prediction of room acoustical parameters (A)

    DEFF Research Database (Denmark)

    Gade, Anders Christian

    1991-01-01

    -averaged acoustical data. The results are presented in the form of linear, multiple regression formulas that may be used to predict the values of the newer measures of level, clarity, spaciousness, and musicians' conditions on the orchestra platform in halls with given RT and geometry....

  1. Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite

    OpenAIRE

    Schlaudraff, Kai-Uwe; Kiessling, Maren C; Császár, Nikolaus BM; Schmitz, Christoph

    2014-01-01

    Kai-Uwe Schlaudraff,1 Maren C Kiessling,2 Nikolaus BM Császár,2 Christoph Schmitz21Concept Clinic, Geneva, Switzerland; 2Department of Anatomy II, Ludwig-Maximilians-University of Munich, Munich, GermanyBackground: Extracorporeal shock wave therapy has been successfully introduced for the treatment of cellulite in recent years. However, it is still unknown whether the individual clinical outcome of cellulite treatment with extracorporeal shock wave therapy can be predict...

  2. Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

    Science.gov (United States)

    Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

    2017-09-01

    Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  3. Impacts of Earth rotation parameters on GNSS ultra-rapid orbit prediction: Derivation and real-time correction

    Science.gov (United States)

    Wang, Qianxin; Hu, Chao; Xu, Tianhe; Chang, Guobin; Hernández Moraleda, Alberto

    2017-12-01

    Analysis centers (ACs) for global navigation satellite systems (GNSSs) cannot accurately obtain real-time Earth rotation parameters (ERPs). Thus, the prediction of ultra-rapid orbits in the international terrestrial reference system (ITRS) has to utilize the predicted ERPs issued by the International Earth Rotation and Reference Systems Service (IERS) or the International GNSS Service (IGS). In this study, the accuracy of ERPs predicted by IERS and IGS is analyzed. The error of the ERPs predicted for one day can reach 0.15 mas and 0.053 ms in polar motion and UT1-UTC direction, respectively. Then, the impact of ERP errors on ultra-rapid orbit prediction by GNSS is studied. The methods for orbit integration and frame transformation in orbit prediction with introduced ERP errors dominate the accuracy of the predicted orbit. Experimental results show that the transformation from the geocentric celestial references system (GCRS) to ITRS exerts the strongest effect on the accuracy of the predicted ultra-rapid orbit. To obtain the most accurate predicted ultra-rapid orbit, a corresponding real-time orbit correction method is developed. First, orbits without ERP-related errors are predicted on the basis of ITRS observed part of ultra-rapid orbit for use as reference. Then, the corresponding predicted orbit is transformed from GCRS to ITRS to adjust for the predicted ERPs. Finally, the corrected ERPs with error slopes are re-introduced to correct the predicted orbit in ITRS. To validate the proposed method, three experimental schemes are designed: function extrapolation, simulation experiments, and experiments with predicted ultra-rapid orbits and international GNSS Monitoring and Assessment System (iGMAS) products. Experimental results show that using the proposed correction method with IERS products considerably improved the accuracy of ultra-rapid orbit prediction (except the geosynchronous BeiDou orbits). The accuracy of orbit prediction is enhanced by at least 50

  4. Dose-Volume Histogram Parameters and Clinical Factors Associated With Pleural Effusion After Chemoradiotherapy in Esophageal Cancer Patients

    International Nuclear Information System (INIS)

    Shirai, Katsuyuki; Tamaki, Yoshio; Kitamoto, Yoshizumi; Murata, Kazutoshi; Satoh, Yumi; Higuchi, Keiko; Nonaka, Tetsuo; Ishikawa, Hitoshi; Katoh, Hiroyuki; Takahashi, Takeo; Nakano, Takashi

    2011-01-01

    Purpose: To investigate the dose-volume histogram parameters and clinical factors as predictors of pleural effusion in esophageal cancer patients treated with concurrent chemoradiotherapy (CRT). Methods and Materials: Forty-three esophageal cancer patients treated with definitive CRT from January 2001 to March 2007 were reviewed retrospectively on the basis of the following criteria: pathologically confirmed esophageal cancer, available computed tomography scan for treatment planning, 6-month follow-up after CRT, and radiation dose ≥50 Gy. Exclusion criteria were lung metastasis, malignant pleural effusion, and surgery. Mean heart dose, mean total lung dose, and percentages of heart or total lung volume receiving ≥10-60 Gy (Heart-V 10 to V 60 and Lung-V 10 to V 60 , respectively) were analyzed in relation to pleural effusion. Results: The median follow-up time was 26.9 months (range, 6.7-70.2) after CRT. Of the 43 patients, 15 (35%) developed pleural effusion. By univariate analysis, mean heart dose, Heart-V 10 to V 60 , and Lung-V 50 to V 60 were significantly associated with pleural effusion. Poor performance status, primary tumor of the distal esophagus, and age ≥65 years were significantly related with pleural effusion. Multivariate analysis identified Heart-V 50 as the strongest predictive factor for pleural effusion (p = 0.01). Patients with Heart-V 50 50 50 ≥40% had 6%, 44%, and 64% of pleural effusion, respectively (p 50 is a useful parameter for assessing the risk of pleural effusion and should be reduced to avoid pleural effusion.

  5. Dynamic interactions between hydrogeological and exposure parameters in daily dose prediction under uncertainty and temporal variability

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Vikas, E-mail: vikas.kumar@urv.cat [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Barros, Felipe P.J. de [Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles 90089, CA (United States); Schuhmacher, Marta [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier [Hydrogeology Group, Department of Geotechnical Engineering and Geosciences, University Politècnica de Catalunya-BarcelonaTech, Barcelona 08034 (Spain)

    2013-12-15

    Highlights: • Dynamic parametric interaction in daily dose prediction under uncertainty. • Importance of temporal dynamics associated with the dose. • Different dose experienced by different population cohorts as a function of time. • Relevance of uncertainty reduction in the input parameters shows temporal dynamism. -- Abstract: We study the time dependent interaction between hydrogeological and exposure parameters in daily dose predictions due to exposure of humans to groundwater contamination. Dose predictions are treated stochastically to account for an incomplete hydrogeological and geochemical field characterization, and an incomplete knowledge of the physiological response. We used a nested Monte Carlo framework to account for uncertainty and variability arising from both hydrogeological and exposure variables. Our interest is in the temporal dynamics of the total dose and their effects on parametric uncertainty reduction. We illustrate the approach to a HCH (lindane) pollution problem at the Ebro River, Spain. The temporal distribution of lindane in the river water can have a strong impact in the evaluation of risk. The total dose displays a non-linear effect on different population cohorts, indicating the need to account for population variability. We then expand the concept of Comparative Information Yield Curves developed earlier (see de Barros et al. [29]) to evaluate parametric uncertainty reduction under temporally variable exposure dose. Results show that the importance of parametric uncertainty reduction varies according to the temporal dynamics of the lindane plume. The approach could be used for any chemical to aid decision makers to better allocate resources towards reducing uncertainty.

  6. SU-G-BRC-01: A Data-Driven Pre-Optimization Method for Prediction of Achievability of Clinical Objectives in IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Ranganathan, V; Kumar, P [Philips India Limited, Bangalore, Karnataka (India); Bzdusek, K [Philips, Fitchburg, WI (United States); Das, J Maria [Sanjay Gandhi PG Inst Med Scienes, Lucknow (India)

    2016-06-15

    Purpose: We propose a novel data-driven method to predict the achievability of clinical objectives upfront before invoking the IMRT optimization. Methods: A new metric called “Geometric Complexity (GC)” is used to estimate the achievability of clinical objectives. Here, GC is the measure of the number of “unmodulated” beamlets or rays that intersect the Region-of-interest (ROI) and the target volume. We first compute the geometric complexity ratio (GCratio) between the GC of a ROI (say, parotid) in a reference plan and the GC of the same ROI in a given plan. The GCratio of a ROI indicates the relative geometric complexity of the ROI as compared to the same ROI in the reference plan. Hence GCratio can be used to predict if a defined clinical objective associated with the ROI can be met by the optimizer for a given case. Basically a higher GCratio indicates a lesser likelihood for the optimizer to achieve the clinical objective defined for a given ROI. Similarly, a lower GCratio indicates a higher likelihood for the optimizer to achieve the clinical objective defined for the given ROI. We have evaluated the proposed method on four Head and Neck cases using Pinnacle3 (version 9.10.0) Treatment Planning System (TPS). Results: Out of the total of 28 clinical objectives from four head and neck cases included in the study, 25 were in agreement with the prediction, which implies an agreement of about 85% between predicted and obtained results. The Pearson correlation test shows a positive correlation between predicted and obtained results (Correlation = 0.82, r2 = 0.64, p < 0.005). Conclusion: The study demonstrates the feasibility of the proposed method in head and neck cases for predicting the achievability of clinical objectives with reasonable accuracy.

  7. An Adaptive Medium Access Parameter Prediction Scheme for IEEE 802.11 Real-Time Applications

    Directory of Open Access Journals (Sweden)

    Estefanía Coronado

    2017-01-01

    Full Text Available Multimedia communications have experienced an unprecedented growth due mainly to the increase in the content quality and the emergence of smart devices. The demand for these contents is tending towards wireless technologies. However, these transmissions are quite sensitive to network delays. Therefore, ensuring an optimum QoS level becomes of great importance. The IEEE 802.11e amendment was released to address the lack of QoS capabilities in the original IEEE 802.11 standard. Accordingly, the Enhanced Distributed Channel Access (EDCA function was introduced, allowing it to differentiate traffic streams through a group of Medium Access Control (MAC parameters. Although EDCA recommends a default configuration for these parameters, it has been proved that it is not optimum in many scenarios. In this work a dynamic prediction scheme for these parameters is presented. This approach ensures an appropriate traffic differentiation while maintaining compatibility with the stations without QoS support. As the APs are the only devices that use this algorithm, no changes are required to current network cards. The results show improvements in both voice and video transmissions, as well as in the QoS level of the network that the proposal achieves with regard to EDCA.

  8. The Effect of Process and Model Parameters in Temperature Prediction for Hot Stamping of Boron Steel

    Directory of Open Access Journals (Sweden)

    Chaoyang Sun

    2013-01-01

    Full Text Available Finite element models of the hot stamping and cold die quenching process for boron steel sheet were developed using either rigid or elastic tools. The effect of tool elasticity and process parameters on workpiece temperature was investigated. Heat transfer coefficient between blank and tools was modelled as a function of gap and contact pressure. Temperature distribution and thermal history in the blank were predicted, and thickness distribution of the blank was obtained. Tests were carried out and the test results are used for the validation of numerical predictions. The effect of holding load and the size of cooling ducts on temperature distribution during the forming and the cool die quenching process was also studied by using two models. The results show that higher accuracy predictions of blank thickness and temperature distribution during deformation were obtained using the elastic tool model. However, temperature results obtained using the rigid tool model were close to those using the elastic tool model for a range of holding load.

  9. Long-term urine biobanking: storage stability of clinical chemical parameters under moderate freezing conditions without use of preservatives.

    Science.gov (United States)

    Remer, Thomas; Montenegro-Bethancourt, Gabriela; Shi, Lijie

    2014-12-01

    To examine the long-term stability and validity of analyte concentrations of 21 clinical biochemistry parameters in 24-h urine samples stored for 12 or 15 yr at -22°C and preservative free. Healthy children's 24-h urine samples in which the respective analytes had been measured shortly after sample collection (baseline) were reanalyzed. Second measurement was performed after 12 yr (organic acids) and 15 yr (creatinine, urea, osmolality, iodine, nitrogen, anions, cations, acid-base parameters) with the same analytical methodology. Paired comparisons and correlations between the baseline and repeated measurements were done. Recovery rates were calculated. More than half of the analytes (creatinine, urea, iodine, nitrogen, sodium, potassium, magnesium, calcium, ammonium, bicarbonate, citric & uric acid) showed measurement values after >10 yr of storage not significantly different from baseline. 15 of the 21 parameters were highly correlated (r=0.99) between baseline and second measurement. Poorest correlation was r=0.77 for oxalate. Recovery ranged from 73% (oxalate) to 105% (phosphate). Our results suggest high long-term stability and measurement validity for numerous clinical chemistry parameters stored at -22°C without addition of any urine preservative. Prospective storage of urine aliquots at -22°C for periods even exceeding 10 yr, appears to be an acceptable and valid tool in epidemiological settings for later quantification of several urine analytes. Copyright © 2014 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  10. Search for morphological parameters influential for prediction of the mechanical characteristics of an austeno-ferritic duplex stainless steel

    International Nuclear Information System (INIS)

    Messiaen, L.

    1997-01-01

    Duplex stainless steels are commonly used (among others in nuclear industry) for their good properties. However these steels may 'age' in service condition at high temperatures. As their mechanical properties (Charpy impact toughness, resistance to ductile tearing) are often very scattered and tend to decrease after ageing, it has become essential to predict them with high precision. For this, we propose to explain a part of the scattering of the mechanical properties with measurable parameters in relation with the particularly complicated two-phase morphology. The two-phase and bi-percolated morphology of the ferrite and austenite phases is first characterised from the observation of 2D images and from the reconstitution of a 3D image. At the same time we precise the genesis of the formation's mechanisms of the structure (germination and growth of the austenitic phase in the solidified ferri tic one) in relation with the literature. The morphological characteristics so observed corresponding with classical notions of mathematical morphology, - size, covariance, connexity -, we use morphological operators to measure morphological variables by image analysis. We establish then a link between toughness and a parameter measuring fineness of the morphology. The lack of data for very aged steels prevent us from proposing a model of toughness which could take this parameter into consideration at these ageing states, for which it is properly the more crucial to obtain specially precise predictions. A mathematical mo del of the 3D structure of the steel is finally proposed. We choose an homogeneous Markov chain of 3D spatial processes, whose evolution in time mimes the solidification. The morphology of the microstructure is so summarised with 8 parameters. This model is liable to be coupled with a model of toughness, for which it would so enlarge the possibilities of prediction. It could also be used to simulate subsequently the damage and the rupture of two

  11. Location of brain lesions predicts conversion of clinically isolated syndromes to multiple sclerosis

    DEFF Research Database (Denmark)

    Giorgio, Antonio; Battaglini, Marco; Rocca, Maria Assunta

    2013-01-01

    OBJECTIVES: To assess in a large population of patients with clinically isolated syndrome (CIS) the relevance of brain lesion location and frequency in predicting 1-year conversion to multiple sclerosis (MS). METHODS: In this multicenter, retrospective study, clinical and MRI data at onset......: In CIS patients with hemispheric, multifocal, and brainstem/cerebellar onset, lesion probability map clusters were seen in clinically eloquent brain regions. Significant lesion clusters were not found in CIS patients with optic nerve and spinal cord onset. At 1 year, clinically definite MS developed...... in the converting group in projection, association, and commissural WM tracts, with larger clusters being in the corpus callosum, corona radiata, and cingulum. CONCLUSIONS: Higher frequency of lesion occurrence in clinically eloquent WM tracts can characterize CIS subjects with different types of onset...

  12. Relationship of periodontal clinical parameters with bacterial composition in human dental plaque.

    Science.gov (United States)

    Fujinaka, Hidetake; Takeshita, Toru; Sato, Hirayuki; Yamamoto, Tetsuji; Nakamura, Junji; Hase, Tadashi; Yamashita, Yoshihisa

    2013-06-01

    More than 600 bacterial species have been identified in the oral cavity, but only a limited number of species show a strong association with periodontitis. The purpose of the present study was to provide a comprehensive outline of the microbiota in dental plaque related to periodontal status. Dental plaque from 90 subjects was sampled, and the subjects were clustered based on bacterial composition using the terminal restriction fragment length polymorphism of 16S rRNA genes. Here, we evaluated (1) periodontal clinical parameters between clusters; (2) the correlation of subgingival bacterial composition with supragingival bacterial composition; and (3) the association between bacterial interspecies in dental plaque using a graphical Gaussian model. Cluster 1 (C1) having high prevalence of pathogenic bacteria in subgingival plaque showed increasing values of the parameters. The values of the parameters in Cluster 2a (C2a) having high prevalence of non-pathogenic bacteria were markedly lower than those in C1. A cluster having low prevalence of non-pathogenic bacteria in supragingival plaque showed increasing values of the parameters. The bacterial patterns between subgingival plaque and supragingival plaque were significantly correlated. Chief pathogens, such as Porphyromonas gingivalis, formed a network with other pathogenic species in C1, whereas a network of non-pathogenic species, such as Rothia sp. and Lautropia sp., tended to compete with a network of pathogenic species in C2a. Periodontal status relates to non-pathogenic species as well as to pathogenic species, suggesting that the bacterial interspecies connection affects dental plaque virulence.

  13. Prediction of meteorological parameters - 3: Rainfall and droughts

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-11-01

    We describe two new methods by which rainfall and hence meteorological droughts at any location on the earth may be predicted. The first method is based upon well supported observations that rainfall distribution at a given location during any local sunspot-related temperature/heat cycle is approximately similar to the distribution during another cycle associated with approximately similar sunspot cycle provided that the two temperature/heat cycles involved are immediately preceded by approximately similar sunspot cycles. The second method is based upon the fact that rainfall belts or patterns seem to be closely related to certain spatial and time-dependent temperature/heat patterns in the earth-atmosphere system. Reasonable predictions of these temperature/heat patterns may be made, and hence the associated rainfall patterns or belts may correspondingly be predicted. Specific examples are given to illustrate the two prediction methods. (author). 12 refs, 11 figs, 1 tab

  14. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  15. Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

    Directory of Open Access Journals (Sweden)

    Bart V J Cuppen

    Full Text Available In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA respond insufficiently to TNF-α inhibitors (TNFis. The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124. The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC, sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI. The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28, erythrocyte sedimentation rate (ESR or C-reactive protein (CRP. Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6, sn1-LPC(15:0, ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01. The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23 and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05. Our study established an accurate

  16. Prediction of clinical response based on pharmacokinetic/pharmacodynamic models of 5-hydroxytryptamine reuptake inhibitors in mice

    DEFF Research Database (Denmark)

    Kreilgaard, Mads; Smith, D. G.; Brennum, L. T.

    2008-01-01

    Bridging the gap between preclinical research and clinical trials is vital for drug development. Predicting clinically relevant steady-state drug concentrations (Css) in serum from preclinical animal models may facilitate this transition. Here we used a pharmacokinetic/pharmacodynamic (PK...

  17. Dose-volumetric parameters for predicting hypothyroidism after radiotherapy for head and neck cancer

    International Nuclear Information System (INIS)

    Kim, Mi Young; Yu, Tosol; Wu, Hong-Gyun

    2014-01-01

    To investigate predictors affecting the development of hypothyroidism after radiotherapy for head and neck cancer, focusing on radiation dose-volumetric parameters, and to determine the appropriate radiation dose-volumetric threshold of radiation-induced hypothyroidism. A total of 114 patients with head and neck cancer whose radiotherapy fields included the thyroid gland were analysed. The purpose of the radiotherapy was either definitive (n=81) or post-operative (n=33). Thyroid function was monitored before starting radiotherapy and after completion of radiotherapy at 1 month, 6 months, 1 year and 2 years. A diagnosis of hypothyroidism was based on a thyroid stimulating hormone value greater than the maximum value of laboratory range, regardless of symptoms. In all patients, dose volumetric parameters were analysed. Median follow-up duration was 25 months (range; 6-38). Forty-six percent of the patients were diagnosed as hypothyroidism after a median time of 8 months (range; 1-24). There were no significant differences in the distribution of age, gender, surgery, radiotherapy technique and chemotherapy between the euthyroid group and the hypothyroid group. In univariate analysis, the mean dose and V35-V50 results were significantly associated with hypothyroidism. The V45 is the only variable that independently contributes to the prediction of hypothyroidism in multivariate analysis and V45 of 50% was a threshold value. If V45 was <50%, the cumulative incidence of hypothyroidism at 1 year was 22.8%, whereas the incidence was 56.1% if V45 was ≥50%. (P=0.034). The V45 may predict risk of developing hypothyroidism after radiotherapy for head and neck cancer, and a V45 of 50% can be a useful dose-volumetric threshold of radiation-induced hypothyroidism. (author)

  18. Generation and mid-IR measurement of a gas-phase to predict security parameters of aviation jet fuel.

    Science.gov (United States)

    Gómez-Carracedo, M P; Andrade, J M; Calviño, M A; Prada, D; Fernández, E; Muniategui, S

    2003-07-27

    The worldwide use of kerosene as aviation jet fuel makes its safety considerations of most importance not only for aircraft security but for the workers' health (chronic and/or acute exposure). As most kerosene risks come from its vapours, this work focuses on predicting seven characteristics (flash point, freezing point, % of aromatics and four distillation points) which assess its potential hazards. Two experimental devices were implemented in order to, first, generate a kerosene vapour phase and, then, to measure its mid-IR spectrum. All the working conditions required to generate the gas phase were optimised either in a univariate or a multivariate (SIMPLEX) approach. Next, multivariate prediction models were deployed using partial least squares regression and it was found that both the average prediction errors and precision parameters were satisfactory, almost always well below the reference figures.

  19. Evaluation of clinical pathology parameters in fecal PCR-positive or PCR-negative goats for Johne's disease.

    Science.gov (United States)

    Bonelli, Francesca; Fratini, F; Turchi, B; Cantile, C; Ebani, V V; Colombani, G; Galiero, A; Sgorbini, M

    2017-10-01

    Johne's disease (JD) is an economically important infectious disease of ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP). This study evaluated the differences in various hematological and biochemical parameters between healthy goats and goats with JD. Forty goats were chosen randomly from a herd endemic for JD. A complete physical examination was performed. Blood and fresh fecal samples were collected from each goat. A complete blood cell (CBC) count and a protein electrophoresis were performed. Polymerase chain reaction (PCR) on fecal samples was performed in order to divide goats into two groups: group A "positive PCR on feces"; and group B "control (negative)." A Student's t test was performed for each parameter to verify differences between groups A vs B. Twenty goats were included in each group. Clinical signs likely related to JD were found in the history of 4/40 (10%) goats, while 36/40 (90%) goats were reported to be asymptomatic. CBC and electrophoresis values were within reference intervals in both groups. No differences were found for CBC parameters between the two groups. Values for alpha 1, beta, gamma globulins, and total protein (TP) were statistically higher in group A vs those in group B, while those for albumin and albumin/globulin (A/G) ratio were lower. An increase in TP, hypoalbuminemia, and hypergammaglobulinemia has been reported in group A, while no abnormalities were found concerning CBC. JD-positive goats seem to show earlier clinical pathological alternations than clinical signs. Protein electrophoresis may help the diagnosis of JD in asymptomatic goat herds, acting as an economical screening method.

  20. A core competency-based objective structured clinical examination (OSCE) can predict future resident performance.

    Science.gov (United States)

    Wallenstein, Joshua; Heron, Sheryl; Santen, Sally; Shayne, Philip; Ander, Douglas

    2010-10-01

    This study evaluated the ability of an objective structured clinical examination (OSCE) administered in the first month of residency to predict future resident performance in the Accreditation Council for Graduate Medical Education (ACGME) core competencies. Eighteen Postgraduate Year 1 (PGY-1) residents completed a five-station OSCE in the first month of postgraduate training. Performance was graded in each of the ACGME core competencies. At the end of 18 months of training, faculty evaluations of resident performance in the emergency department (ED) were used to calculate a cumulative clinical evaluation score for each core competency. The correlations between OSCE scores and clinical evaluation scores at 18 months were assessed on an overall level and in each core competency. There was a statistically significant correlation between overall OSCE scores and overall clinical evaluation scores (R = 0.48, p competencies of patient care (R = 0.49, p competencies. An early-residency OSCE has the ability to predict future postgraduate performance on a global level and in specific core competencies. Used appropriately, such information can be a valuable tool for program directors in monitoring residents' progress and providing more tailored guidance. © 2010 by the Society for Academic Emergency Medicine.

  1. Model parameter-related optimal perturbations and their contributions to El Niño prediction errors

    Science.gov (United States)

    Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua

    2018-04-01

    Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.

  2. Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

    Science.gov (United States)

    Kirouac, Daniel C; Schaefer, Gabriele; Chan, Jocelyn; Merchant, Mark; Orr, Christine; Huang, Shih-Min A; Moffat, John; Liu, Lichuan; Gadkar, Kapil; Ramanujan, Saroja

    2017-01-01

    Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

  3. Personality and Defense Styles: Clinical Specificities and Predictive Factors of Alcohol Use Disorder in Women.

    Science.gov (United States)

    Ribadier, Aurélien; Dorard, Géraldine; Varescon, Isabelle

    2016-01-01

    This study investigated personality traits and defense styles in order to determine clinical specificities and predictive factors of alcohol use disorders (AUDs) in women. A female sample, composed of AUD outpatients (n = 48) and a control group (n = 50), completed a sociodemographic self-report and questionnaires assessing personality traits (BFI), defense mechanisms and defense styles (DSQ-40). Comparative and correlational analyses, as well as univariate and multivariate logistic regressions, were performed. AUD women presented with higher neuroticism and lower extraversion and conscientiousness. They used less mature and more neurotic and immature defense styles than the control group. Concerning personality traits, high neuroticism and lower conscientiousness were predictive of AUD, as well as low mature, high neurotic, and immature defense styles. Including personality traits and defense styles in a logistic model, high neuroticism was the only AUD predictive factor. AUD women presented clinical specificities and predictive factors in personality traits and defense styles that must be taken into account in AUD studies. Implications for specific treatment for women are discussed.

  4. A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors

    NARCIS (Netherlands)

    Schoups, G.; Vrugt, J.A.

    2010-01-01

    Estimation of parameter and predictive uncertainty of hydrologic models has traditionally relied on several simplifying assumptions. Residual errors are often assumed to be independent and to be adequately described by a Gaussian probability distribution with a mean of zero and a constant variance.

  5. Predictive value of specific radiographic findings of disability in patients with rheumatoid arthritis

    International Nuclear Information System (INIS)

    Kaye, J.J.; Nance, E.P. Jr.; Callahan, L.F.; Pincus, T.

    1986-01-01

    This study was carried out to determine whether and to what extend radiographic erosion, joint space narrowing, and malalignment are predictive of clinical disability in patients with rheumatoid arthristis (RA). Radiographs of the hands and wrists of 224 patients with RA were scored for these radiographic parameters. To determine which of these findings best explained variation in clinical measures of disability, a series of regression analyses was performed. Malalignment scores were the best predictor of joint deformity and limitation of motion. Erosion scores were most predictive of variation in functional tests. The author concludes that specific radiographic findings of malalignment and erosion are significantly predictive of disability in patients with RA

  6. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    field monitoring. Vibration prediction diminishes the importance of trial-and-error procedures such as drill-off tests, which are valid only for short sections. It also solves an existing lapse in Mechanical Specific Energy (MSE) real-time drilling control programs applying the theory of Teale, which states that a drilling system is perfectly efficient when it spends the exact energy to overcome the in situ rock strength. Using the proprietary software tool this paper will examine the resonant vibration modes that may be initiated while drilling with different BHA's and drill string designs, showing that the combination of a proper BHA design along with the correct selection of input parameters results in an overall improvement to drilling efficiency. Also, being the BHA predictively analyzed, it will be reduced the potential for vibration or stress fatigue in the drill string components, leading to a safer operation. In the recent years there has been an increased focus on vibration detection, analysis, and mitigation techniques, where new technologies, like the Drilling Dynamics Data Recorders (DDDR), may provide the capability to capture high frequency dynamics data at multiple points along the drilling system. These tools allow the achievement of drilling performance improvements not possible before, opening a whole new array of opportunities for optimization and for verification of predictions calculated by the drill string dynamics modeling software tool. The results of this study will identify how the dynamics from the drilling system, interacting with formation, directly relate to inefficiencies and to the possible solutions to mitigate drilling vibrations in order to improve drilling performance. Software vibration prediction and downhole measurements can be used for non-drilling operations like drilling out casing or reaming, where extremely high vibration levels - devastating to the cutting structure of the bit before it has even touched bottom - have

  7. Development, external validation and clinical usefulness of a practical prediction model for radiation-induced dysphagia in lung cancer patients

    International Nuclear Information System (INIS)

    Dehing-Oberije, Cary; De Ruysscher, Dirk; Petit, Steven; Van Meerbeeck, Jan; Vandecasteele, Katrien; De Neve, Wilfried; Dingemans, Anne Marie C.; El Naqa, Issam; Deasy, Joseph; Bradley, Jeff; Huang, Ellen; Lambin, Philippe

    2010-01-01

    Introduction: Acute dysphagia is a distressing dose-limiting toxicity occurring frequently during concurrent chemo-radiation or high-dose radiotherapy for lung cancer. It can lead to treatment interruptions and thus jeopardize survival. Although a number of predictive factors have been identified, it is still not clear how these could offer assistance for treatment decision making in daily clinical practice. Therefore, we have developed and validated a nomogram to predict this side-effect. In addition, clinical usefulness was assessed by comparing model predictions to physicians' predictions. Materials and methods: Clinical data from 469 inoperable lung cancer patients, treated with curative intent, were collected prospectively. A prediction model for acute radiation-induced dysphagia was developed. Model performance was evaluated by the c-statistic and assessed using bootstrapping as well as two external datasets. In addition, a prospective study was conducted comparing model to physicians' predictions in 138 patients. Results: The final multivariate model consisted of age, gender, WHO performance status, mean esophageal dose (MED), maximum esophageal dose (MAXED) and overall treatment time (OTT). The c-statistic, assessed by bootstrapping, was 0.77. External validation yielded an AUC of 0.94 on the Ghent data and 0.77 on the Washington University St. Louis data for dysphagia ≥ grade 3. Comparing model predictions to the physicians' predictions resulted in an AUC of 0.75 versus 0.53, respectively. Conclusions: The proposed model performed well was successfully validated and demonstrated the ability to predict acute severe dysphagia remarkably better than the physicians. Therefore, this model could be used in clinical practice to identify patients at high or low risk.

  8. Sputum biomarkers and the prediction of clinical outcomes in patients with cystic fibrosis.

    Directory of Open Access Journals (Sweden)

    Theodore G Liou

    Full Text Available Lung function, acute pulmonary exacerbations (APE, and weight are the best clinical predictors of survival in cystic fibrosis (CF; however, underlying mechanisms are incompletely understood. Biomarkers of current disease state predictive of future outcomes might identify mechanisms and provide treatment targets, trial endpoints and objective clinical monitoring tools. Such CF-specific biomarkers have previously been elusive. Using observational and validation cohorts comprising 97 non-transplanted consecutively-recruited adult CF patients at the Intermountain Adult CF Center, University of Utah, we identified biomarkers informative of current disease and predictive of future clinical outcomes. Patients represented the majority of sputum producers. They were recruited March 2004-April 2007 and followed through May 2011. Sputum biomarker concentrations were measured and clinical outcomes meticulously recorded for a median 5.9 (interquartile range 5.0 to 6.6 years to study associations between biomarkers and future APE and time-to-lung transplantation or death. After multivariate modeling, only high mobility group box-1 protein (HMGB-1, mean=5.84 [log ng/ml], standard deviation [SD] =1.75 predicted time-to-first APE (hazard ratio [HR] per log-unit HMGB-1=1.56, p-value=0.005, number of future APE within 5 years (0.338 APE per log-unit HMGB-1, p<0.001 by quasi-Poisson regression and time-to-lung transplantation or death (HR=1.59, p=0.02. At APE onset, sputum granulocyte macrophage colony stimulating factor (GM-CSF, mean 4.8 [log pg/ml], SD=1.26 was significantly associated with APE-associated declines in lung function (-10.8 FEV(1% points per log-unit GM-CSF, p<0.001 by linear regression. Evaluation of validation cohorts produced similar results that passed tests of mutual consistency. In CF sputum, high HMGB-1 predicts incidence and recurrence of APE and survival, plausibly because it mediates long-term airway inflammation. High APE-associated GM

  9. Hepatic steatosis after islet transplantation: Can ultrasound predict the clinical outcome? A longitudinal study in 108 patients.

    Science.gov (United States)

    Venturini, Massimo; Maffi, Paola; Querques, Giulia; Agostini, Giulia; Piemonti, Lorenzo; Sironi, Sandro; De Cobelli, Francesco; Fiorina, Paolo; Secchi, Antonio; Del Maschio, Alessandro

    2015-08-01

    Percutaneous intra-portal islet transplantation (PIPIT) is a less invasive, safer, and repeatable therapeutic option for brittle type 1 diabetes, compared to surgical pancreas transplantation. Hepatic steatosis is a consequence of the islet engraftment but it is curiously present in a limited number of patients and its meaning is controversial. The aims of this study were to assess hepatic steatosis at ultrasound (US) after PIPIT investigating its relationship with graft function and its role in predicting the clinical outcome. From 1996 to 2012, 108 patients underwent PIPIT: 83 type-1 diabetic patients underwent allo-transplantation, 25 auto-transplantation. US was performed at baseline, 6, 12, and 24 months, recording steatosis prevalence, first detection, duration, and distribution. Contemporaneously, steatotic and non-steatotic patients were compared for the following parameters: infused islet mass, insulin independence rate, β-score, C-peptide, glycated hemoglobin, exogenous insulin requirement, and fasting plasma glucose. Steatosis at US was detected in 21/108 patients, 20/83 allo-transplanted and 1/25 auto-transplanted, mostly at 6 and 12 months. Infused islet mass was significantly higher in steatotic than non-steatotic patients (IE/kg: S=10.822; NS=6138; p=0.001). Metabolically, steatotic patients had worse basal conditions, but better islet function when steatosis was first detected, after which progressive islet exhaustion, along with steatosis disappearance, was observed. Conversely, in non-steatotic patients these parameters remained stable in time. Number of re-transplantations was significantly higher in steatotic than in non-steatotic patients (1.8 vs 1.1; p=0.001). Steatosis at US seems to be related to the islet mass and local overworking activity. It precedes metabolic alterations and can predict graft dysfunction addressing to therapeutic decisions before islet exhaustion. If steatosis does not appear, no conclusion can be drawn. Copyright

  10. Lungscape: resected non-small-cell lung cancer outcome by clinical and pathological parameters.

    Science.gov (United States)

    Peters, Solange; Weder, Walter; Dafni, Urania; Kerr, Keith M; Bubendorf, Lukas; Meldgaard, Peter; O'Byrne, Kenneth J; Wrona, Anna; Vansteenkiste, Johan; Felip, Enriqueta; Marchetti, Antonio; Savic, Spasenija; Lu, Shun; Smit, Egbert; Dingemans, Anne-Marie; Blackhall, Fiona H; Baas, Paul; Camps, Carlos; Rosell, Rafael; Stahel, Rolf A

    2014-11-01

    The Lungscape project was designed to address the impact of clinical, pathological, and molecular characteristics on outcome in resected non-small- cell lung cancer (NSCLC). A decentralized biobank with fully annotated tissue samples was established. Selection criteria for participating centers included sufficient number of cases, tissue microarray building capability, and documented ethical approval. Patient selection was based on availability of comprehensive clinical data, radical resection between 2003 and 2009 with adequate follow-up, and adequate quantity and quality of formalin-fixed tissue. Fifteen centers contributed 2449 cases. The 5-year overall survival (OS) was 69.6% and 63.6% for stages IA and IB, 51.6% and 47.7% for stages IIA and IIB, and 29.0% and 13.0% for stages IIIA and IIIB, respectively (p < 0.001). Median and 5-year relapse-free survival (RFS) were 52.8 months and 47.3%, respectively. Distant relapse was recorded for 44.4%, local for 26.0%, and both for 16.9% of patients. Based on multivariate analysis for the OS, RFS, and time to relapse, the factors significantly associated with all of them are performance status and pathological stage. The aim of this report is to present the results from Lungscape, the first large series reporting on NSCLC surgical outcome measured not only by OS but also by RFS and time to relapse and including multivariate analysis by significant clinical and pathological prognostic parameters. As tissue from all patients is preserved locally and is available for detailed molecular investigations, Lungscape provides an excellent basis to evaluate the influence of molecular parameters on the disease outcome after radical resection, besides providing an overview of the molecular landscape of stage I to III NSCLC.

  11. Prediction of clinical infection in women with preterm labour with intact membranes: a score based on ultrasonographic, clinical and biological markers.

    Science.gov (United States)

    Kayem, Gilles; Maillard, Françoise; Schmitz, Thomas; Jarreau, Pierre H; Cabrol, Dominique; Breart, Gérard; Goffinet, François

    2009-07-01

    To predict maternal and neonatal clinical infection at admission in women hospitalized for preterm labour (PTL) with intact membranes. Prospective study of 371 women hospitalized for preterm labour with intact membranes. The primary outcome was clinical infection, defined by clinical chorioamnionitis at delivery or early-onset neonatal infection. Clinical infection was identified in 21 cases (5.7%) and was associated with earlier gestational age at admission for PTL, elevated maternal C-reactive protein (CRP) and white blood cell count (WBC), shorter cervical length, and a cervical funnelling on ultrasound. We used ROC curves to determine the cut-off values that minimized the number of false positives and false negatives. The cut-off points chosen were 30 weeks for gestational age at admission, 25 mm for cervical length, 8 mg/l for CRP and 12,000 c/mm(3) for WBC. Each of these variables was assigned a weight on the basis of the adjusted odds ratios in a clinical infection risk score (CIRS). We set a threshold corresponding to a specificity close to 90%, and calculated the positive and negative predictive values and likelihood ratios of each marker and of the CIRS. The CIRS had a sensitivity of 61.9%, while the sensitivity of the other markers ranged from 19.0% to 42.9%. Internal cross-validation was used to estimate the performance of the CIRS in new subjects. The diagnostic values found remained close to the initial values. A clinical infection risk score built from data known at admission for preterm labour helps to identify women and newborns at high risk of clinical infection.

  12. SITE-94. Discrete-feature modelling of the Aespoe Site: 3. Predictions of hydrogeological parameters for performance assessment

    International Nuclear Information System (INIS)

    Geier, J.E.

    1996-12-01

    A 3-dimensional, discrete-feature hydrological model is developed. The model integrates structural and hydrologic data for the Aespoe site, on scales ranging from semi regional fracture zones to individual fractures in the vicinity of the nuclear waste canisters. Predicted parameters for the near field include fracture spacing, fracture aperture, and Darcy velocity at each of forty canister deposition holes. Parameters for the far field include discharge location, Darcy velocity, effective longitudinal dispersion coefficient and head gradient, flow porosity, and flow wetted surface, for each canister source that discharges to the biosphere. Results are presented in the form of statistical summaries for a total of 42 calculation cases, which treat a set of 25 model variants in various combinations. The variants for the SITE-94 Reference Case model address conceptual and parametric uncertainty related to the site-scale hydrogeologic model and its properties, the fracture network within the repository, effective semi regional boundary conditions for the model, and the disturbed-rock zone around the repository tunnels and shafts. Two calculation cases simulate hydrologic conditions that are predicted to occur during future glacial episodes. 30 refs

  13. Prediction of the intensity and diversity of day-to-day activities among people with schizophrenia using parameters obtained during acute hospitalization.

    Science.gov (United States)

    Lipskaya-Velikovsky, Lena; Jarus, Tal; Kotler, Moshe

    2017-06-01

    Participation in day-to-day activities of people with schizophrenia is restricted, causing concern to them, their families, service providers and the communities at large. Participation is a significant component of health and recovery; however, factors predicting participation are still not well established. This study examines whether the parameters obtained during acute hospitalization can predict the intensity and diversity of participation in day-to-day activities six months after discharge. In-patients with chronic schizophrenia (N = 104) were enrolled into the study and assessed for cognitive functioning, functional capacity in instrumental activities of daily living (IADL), and symptoms. Six months after discharge, the intensity and diversity of participation in day-to-day activities were evaluated (N = 70). Multiple correlations were found between parameters obtained during hospitalization and participation diversity, but not participation intensity. The model that is better suited to the prediction of participation diversity contains cognitive ability of construction, negative symptoms and number of previous hospitalizations. The total explained variance is 37.8% (F 3,66  =   14.99, p process for the prediction of participation diversity in day-to-day activities six months after discharge. Participation diversity is best predicted through a set of factors reflecting personal and environmental indicators. Implications for rehabilitation Results of in-patient evaluations can predict the diversity of participation in day-to-day activities six months after discharge. Higher prediction of participation diversity is obtained using a holistic evaluation model that includes assessments for cognitive abilities, negative symptoms severity and number of hospitalizations.

  14. Clinical Prediction Models for Cardiovascular Disease: The Tufts PACE CPM Database

    Science.gov (United States)

    Wessler, Benjamin S.; Lana Lai, YH; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S.; Kent, David M.

    2015-01-01

    Background Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease (CVD) there are numerous CPMs available though the extent of this literature is not well described. Methods and Results We conducted a systematic review for articles containing CPMs for CVD published between January 1990 through May 2012. CVD includes coronary heart disease (CHD), heart failure (HF), arrhythmias, stroke, venous thromboembolism (VTE) and peripheral vascular disease (PVD). We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. 717 (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions including 215 CPMs for patients with CAD, 168 CPMs for population samples, and 79 models for patients with HF. There are 77 distinct index/ outcome (I/O) pairings. Of the de novo models in this database 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. Conclusions There is an abundance of CPMs available for a wide assortment of CVD conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. PMID:26152680

  15. Clinical use of interface pressure to predict pressure ulcer development: A systematic review

    NARCIS (Netherlands)

    Reenalda, Jasper; Jannink, M.J.A.; Nederhand, Marcus Johannes; IJzerman, Maarten Joost

    2009-01-01

    Pressure ulcers are a large problem in subjects who use a wheelchair for their mobility. These ulcers originate beneath the bony prominences of the pelvis and progress outward as a consequence of prolonged pressure. Interface pressure is used clinically to predict and prevent pressure ulcers.

  16. Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

    Science.gov (United States)

    Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

  17. System Predicts Critical Runway Performance Parameters

    Science.gov (United States)

    Millen, Ernest W.; Person, Lee H., Jr.

    1990-01-01

    Runway-navigation-monitor (RNM) and critical-distances-process electronic equipment designed to provide pilot with timely and reliable predictive navigation information relating to takeoff, landing and runway-turnoff operations. Enables pilot to make critical decisions about runway maneuvers with high confidence during emergencies. Utilizes ground-referenced position data only to drive purely navigational monitor system independent of statuses of systems in aircraft.

  18. Logic-based models in systems biology: a predictive and parameter-free network analysis method†

    Science.gov (United States)

    Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

    2012-01-01

    Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

  19. Pre-stimulation parameters predicting live birth after IVF in the long GnRH agonist protocol

    DEFF Research Database (Denmark)

    Pettersson, Göran; Andersen, Anders Nyboe; Broberg, Per

    2010-01-01

    This retrospective study aimed to identify novel pre-stimulation parameters associated with live birth in IVF and to develop a model for prediction of the chances of live birth at an early phase of the treatment cycle. Data were collected from a randomized trial in couples with unexplained...... infertility, tubal factor, mild male factor or other reason for infertility. All women (n=731) had undergone an IVF cycle (no intracytoplasmic sperm injection) after stimulation with human menopausal gonadotrophin or follicle-stimulating hormone following the long gonadotrophin-releasing hormone agonist...

  20. Pre-stimulation parameters predicting live birth after IVF in the long GnRH agonist protocol

    DEFF Research Database (Denmark)

    Pettersson, Göran; Andersen, Anders Nyboe; Broberg, Per

    2010-01-01

    infertility, tubal factor, mild male factor or other reason for infertility. All women (n=731) had undergone an IVF cycle (no intracytoplasmic sperm injection) after stimulation with human menopausal gonadotrophin or follicle-stimulating hormone following the long gonadotrophin-releasing hormone agonist......This retrospective study aimed to identify novel pre-stimulation parameters associated with live birth in IVF and to develop a model for prediction of the chances of live birth at an early phase of the treatment cycle. Data were collected from a randomized trial in couples with unexplained...

  1. Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy

    International Nuclear Information System (INIS)

    Pickles, Martin D.; Manton, David J.; Lowry, Martin; Turnbull, Lindsay W.

    2009-01-01

    The purpose of this study was to investigate whether dynamic contrast enhanced MRI (DCE-MRI) data, both pharmacokinetic and empirical, can predict, prior to neoadjuvant chemotherapy, which patients are likely to have a shorter disease free survival (DFS) and overall survival (OS) interval following surgery. Traditional prognostic parameters were also included in the survival analysis. Consequently, a comparison of the prognostic value could be made between all the parameters studied. MR examinations were conducted on a 1.5 T system in 68 patients prior to the initiation of neoadjuvant chemotherapy. DCE-MRI consisted of a fast spoiled gradient echo sequence acquired over 35 phases with a mean temporal resolution of 11.3 s. Both pharmacokinetic and empirical parameters were derived from the DCE-MRI data. Kaplan-Meier survival plots were generated for each parameter and group comparisons were made utilising logrank tests. The results from the 54 patients entered into the univariate survival analysis demonstrated that traditional prognostic parameters (tumour grade, hormonal status and size), empirical parameters (maximum enhancement index, enhancement index at 30 s, area under the curve and initial slope) and adjuvant therapies demonstrated significant differences in survival intervals. Further multivariate Cox regression survival analysis revealed that empirical enhancement parameters contributed the greatest prediction of both DFS and OS in the resulting models. In conclusion, this study has demonstrated that in patients who exhibit high levels of perfusion and vessel permeability pre-treatment, evidenced by elevated empirical DCE-MRI parameters, a significantly lower disease free survival and overall survival can be expected.

  2. Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Pickles, Martin D. [Centre for Magnetic Resonance Investigations, Division of Cancer, Postgraduate Medical School, University of Hull, Hull Royal Infirmary, Anlaby Road, Hull, HU3 2JZ (United Kingdom)], E-mail: m.pickles@hull.ac.uk; Manton, David J. [Centre for Magnetic Resonance Investigations, Division of Cancer, Postgraduate Medical School, University of Hull, Hull Royal Infirmary, Anlaby Road, Hull, HU3 2JZ (United Kingdom)], E-mail: d.j.manton@hull.ac.uk; Lowry, Martin [Centre for Magnetic Resonance Investigations, Division of Cancer, Postgraduate Medical School, University of Hull, Hull Royal Infirmary, Anlaby Road, Hull, HU3 2JZ (United Kingdom)], E-mail: m.lowry@hull.ac.uk; Turnbull, Lindsay W. [Centre for Magnetic Resonance Investigations, Division of Cancer, Postgraduate Medical School, University of Hull, Hull Royal Infirmary, Anlaby Road, Hull, HU3 2JZ (United Kingdom)], E-mail: l.w.turnbull@hull.ac.uk

    2009-09-15

    The purpose of this study was to investigate whether dynamic contrast enhanced MRI (DCE-MRI) data, both pharmacokinetic and empirical, can predict, prior to neoadjuvant chemotherapy, which patients are likely to have a shorter disease free survival (DFS) and overall survival (OS) interval following surgery. Traditional prognostic parameters were also included in the survival analysis. Consequently, a comparison of the prognostic value could be made between all the parameters studied. MR examinations were conducted on a 1.5 T system in 68 patients prior to the initiation of neoadjuvant chemotherapy. DCE-MRI consisted of a fast spoiled gradient echo sequence acquired over 35 phases with a mean temporal resolution of 11.3 s. Both pharmacokinetic and empirical parameters were derived from the DCE-MRI data. Kaplan-Meier survival plots were generated for each parameter and group comparisons were made utilising logrank tests. The results from the 54 patients entered into the univariate survival analysis demonstrated that traditional prognostic parameters (tumour grade, hormonal status and size), empirical parameters (maximum enhancement index, enhancement index at 30 s, area under the curve and initial slope) and adjuvant therapies demonstrated significant differences in survival intervals. Further multivariate Cox regression survival analysis revealed that empirical enhancement parameters contributed the greatest prediction of both DFS and OS in the resulting models. In conclusion, this study has demonstrated that in patients who exhibit high levels of perfusion and vessel permeability pre-treatment, evidenced by elevated empirical DCE-MRI parameters, a significantly lower disease free survival and overall survival can be expected.

  3. PERSPECTIVES OF FACTORIAL ANALYSIS IN STUDYING ASSOCIATIONS BETWEEN IMMUNE SYSTEM PARAMETERS AND CLINICAL CHARACTERISTICS IN GASTRIC CANCER

    Directory of Open Access Journals (Sweden)

    I. G. Solovyeva

    2010-01-01

    Full Text Available When studying functional features of immune system, a lot of quantitative and functional parameters are determined. A multifactorial analysis allows of detecting interdependent immunological parameters and defining them as significant factors. In present study, four factors are revealed, which are associated with certain clinical characteristics of gastric cancer (tumor invasion depth, lymph node status and distant metastases, tumor stage, histological type. The data obtained are of interest, with regard of systemic approach to functional studies of immune functions.

  4. A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic

    International Nuclear Information System (INIS)

    Chow, Edward; Fung, KinWah; Panzarella, Tony; Bezjak, Andrea; Danjoux, Cyril; Tannock, Ian

    2002-01-01

    Purpose: To develop a predictive model for survival from the time of presentation in an outpatient palliative radiotherapy clinic. Methods and Materials: Sixteen factors were analyzed prospectively in 395 patients seen in a dedicated palliative radiotherapy clinic in a large tertiary cancer center using Cox's proportional hazards regression model. Results: Six prognostic factors had a statistically significant impact on survival, as follows: primary cancer site, site of metastases, Karnofsky performance score (KPS), and fatigue, appetite, and shortness of breath scores from the modified Edmonton Symptom Assessment Scale. Risk group stratification was performed (1) by assigning weights to the prognostic factors based on their levels of significance, and (2) by the number of risk factors present. The weighting method provided a Survival Prediction Score (SPS), ranging from 0 to 32. The survival probability at 3, 6, and 12 months was 83%, 70%, and 51%, respectively, for patients with SPS ≤13 (n=133); 67%, 41%, and 20% for patients with SPS 14-19 (n=129); and 36%, 18%, and 4% for patients with SPS ≥20 (n=133) (p<0.0001). Corresponding survival probabilities based on number of risk factors were as follows: 85%, 72%, and 52% (≤3 risk factors) (n=98); 68%, 47%, and 24% (4 risk factors) (n=117); and 46%, 24%, and 11% (≥5 factors) (n=180) (p<0.0001). Conclusion: Clinical prognostic factors can be used to predict prognosis among patients attending a palliative radiotherapy clinic. If validated in an independent series of patients, the model can be used to guide clinical decisions, plan supportive services, and allocate resource use

  5. Current V3 genotyping algorithms are inadequate for predicting X4 co-receptor usage in clinical isolates.

    Science.gov (United States)

    Low, Andrew J; Dong, Winnie; Chan, Dennison; Sing, Tobias; Swanstrom, Ronald; Jensen, Mark; Pillai, Satish; Good, Benjamin; Harrigan, P Richard

    2007-09-12

    Integrating CCR5 antagonists into clinical practice would benefit from accurate assays of co-receptor usage (CCR5 versus CXCR4) with fast turnaround and low cost. Published HIV V3-loop based predictors of co-receptor usage were compared with actual phenotypic tropism results in a large cohort of antiretroviral naive individuals to determine accuracy on clinical samples and identify areas for improvement. Aligned HIV envelope V3 loop sequences (n = 977), derived by bulk sequencing were analyzed by six methods: the 11/25 rule; a neural network (NN), two support vector machines, and two subtype-B position specific scoring matrices (PSSM). Co-receptor phenotype results (Trofile Co-receptor Phenotype Assay; Monogram Biosciences) were stratified by CXCR4 relative light unit (RLU) readout and CD4 cell count. Co-receptor phenotype was available for 920 clinical samples with V3 genotypes having fewer than seven amino acid mixtures (n = 769 R5; n = 151 X4-capable). Sensitivity and specificity for predicting X4 capacity were evaluated for the 11/25 rule (30% sensitivity/93% specificity), NN (44%/88%), PSSM(sinsi) (34%/96%), PSSM(x4r5) (24%/97%), SVMgenomiac (22%/90%) and SVMgeno2pheno (50%/89%). Quantitative increases in sensitivity could be obtained by optimizing the cut-off for methods with continuous output (PSSM methods), and/or integrating clinical data (CD4%). Sensitivity was directly proportional to strength of X4 signal in the phenotype assay (P < 0.05). Current default implementations of co-receptor prediction algorithms are inadequate for predicting HIV X4 co-receptor usage in clinical samples, particularly those X4 phenotypes with low CXCR4 RLU signals. Significant improvements can be made to genotypic predictors, including training on clinical samples, using additional data to improve predictions and optimizing cutoffs and increasing genotype sensitivity.

  6. Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review.

    Science.gov (United States)

    Yoo, Byong Chul; Yeo, Seung-Gu

    2017-03-01

    Approximately 20% of all patients with locally advanced rectal cancer experience pathologically complete responses following neoadjuvant chemoradiotherapy (CRT) and standard surgery. The utility of radical surgery for patients exhibiting good CRT responses has been challenged. Organ-sparing strategies for selected patients exhibiting complete clinical responses include local excision or no immediate surgery. The subjects of this tailored management are patients whose presenting disease corresponds to current indications of neoadjuvant CRT, and their post-CRT tumor response is assessed by clinical and radiological examinations. However, a model predictive of the CRT response, applied before any treatment commenced, would be valuable to facilitate such a personalized approach. This would increase organ preservation, particularly in patients for whom upfront CRT is not generally prescribed. Molecular biomarkers hold the greatest promise for development of a pretreatment predictive model of CRT response. A combination of clinicopathological, radiological, and molecular markers will be necessary to render the model robust. Molecular research will also contribute to the development of drugs that can overcome the radioresistance of rectal tumors. Current treatments for rectal cancer are based on the expected prognosis given the presenting disease extent. In the future, treatment schemes may be modified by including the predicted CRT response evaluated at presentation.

  7. A probabilistic model to predict clinical phenotypic traits from genome sequencing.

    Science.gov (United States)

    Chen, Yun-Ching; Douville, Christopher; Wang, Cheng; Niknafs, Noushin; Yeo, Grace; Beleva-Guthrie, Violeta; Carter, Hannah; Stenson, Peter D; Cooper, David N; Li, Biao; Mooney, Sean; Karchin, Rachel

    2014-09-01

    Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.

  8. Regional Longitudinal Deformation Improves Prediction of Ventricular Tachyarrhythmias in Patients With Heart Failure With Reduced Ejection Fraction

    DEFF Research Database (Denmark)

    Biering-Sørensen, Tor; Knappe, Dorit; Pouleur, Anne-Catherine

    2017-01-01

    BACKGROUND: Left ventricular dysfunction is a known predictor of ventricular arrhythmias. We hypothesized that measures of regional longitudinal deformation by speckle-tracking echocardiography predict ventricular tachyarrhythmias and provide incremental prognostic information over clinical...... in the model, only a decreasing myocardial function in the inferior myocardial wall predicted VT/VF (hazard ratio, 1.05 [1.00-1.11]; P=0.039). Only strain obtained from the inferior myocardial wall provided incremental prognostic information for VT/VF over clinical and echocardiographic parameters (C statistic...... 0.71 versus 0.69; P=0.005). CONCLUSIONS: Assessment of regional longitudinal myocardial deformation in the inferior region provided incremental prognostic information over clinical and echocardiographic risk factors in predicting ventricular tachyarrhythmias. CLINICAL TRIAL REGISTRATION: URL: http...

  9. A comparative study: classification vs. user-based collaborative filtering for clinical prediction.

    Science.gov (United States)

    Hao, Fang; Blair, Rachael Hageman

    2016-12-08

    Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.). User-based collaborative filtering is a popular recommender system, which leverages an individuals' prior satisfaction with items, as well as the satisfaction of individuals that are "similar". Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the "Big Data" era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records). In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity), Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT), chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR) or Missing Completely At Random (MCAR) under various degrees of missingness and levels of class imbalance in the response variable. Our results demonstrate that user-based collaborative filtering is consistently inferior to logistic regression and random forests with different

  10. A comparative study: classification vs. user-based collaborative filtering for clinical prediction

    Directory of Open Access Journals (Sweden)

    Fang Hao

    2016-12-01

    Full Text Available Abstract Background Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.. User-based collaborative filtering is a popular recommender system, which leverages an individuals’ prior satisfaction with items, as well as the satisfaction of individuals that are “similar”. Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Methods Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the “Big Data” era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records. In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity, Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT, chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR or Missing Completely At Random (MCAR under various degrees of missingness and levels of class imbalance in the response variable. Results Our results demonstrate that user-based collaborative filtering is consistently inferior

  11. Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

    Directory of Open Access Journals (Sweden)

    G Khalili-Zadeh-Mahani

    2016-07-01

    Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

  12. Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation.

    Science.gov (United States)

    Johnson, Tracy L; Brewer, Daniel; Estacio, Raymond; Vlasimsky, Tara; Durfee, Michael J; Thompson, Kathy R; Everhart, Rachel M; Rinehart, Deborath J; Batal, Holly

    2015-01-01

    The Center for Medicare and Medicaid Innovation (CMMI) awarded Denver Health's (DH) integrated, safety net health care system $19.8 million to implement a "population health" approach into the delivery of primary care. This major practice transformation builds on the Patient Centered Medical Home (PCMH) and Wagner's Chronic Care Model (CCM) to achieve the "Triple Aim": improved health for populations, care to individuals, and lower per capita costs. This paper presents a case study of how DH integrated published predictive models and front-line clinical judgment to implement a clinically actionable, risk stratification of patients. This population segmentation approach was used to deploy enhanced care team staff resources and to tailor care-management services to patient need, especially for patients at high risk of avoidable hospitalization. Developing, implementing, and gaining clinical acceptance of the Health Information Technology (HIT) solution for patient risk stratification was a major grant objective. In addition to describing the Information Technology (IT) solution itself, we focus on the leadership and organizational processes that facilitated its multidisciplinary development and ongoing iterative refinement, including the following: team composition, target population definition, algorithm rule development, performance assessment, and clinical-workflow optimization. We provide examples of how dynamic business intelligence tools facilitated clinical accessibility for program design decisions by enabling real-time data views from a population perspective down to patient-specific variables. We conclude that population segmentation approaches that integrate clinical perspectives with predictive modeling results can better identify high opportunity patients amenable to medical home-based, enhanced care team interventions.

  13. Impact of Humidity on In Vitro Human Skin Permeation Experiments for Predicting In Vivo Permeability.

    Science.gov (United States)

    Ishida, Masahiro; Takeuchi, Hiroyuki; Endo, Hiromi; Yamaguchi, Jun-Ichi

    2015-12-01

    In vitro skin permeation studies have been commonly conducted to predict in vivo permeability for the development of transdermal therapeutic systems (TTSs). We clarified the impact of humidity on in vitro human skin permeation of two TTSs having different breathability and then elucidated the predictability of in vivo permeability based on in vitro experimental data. Nicotinell(®) TTS(®) 20 and Frandol(®) tape 40mg were used as model TTSs in this study. The in vitro human skin permeation experiments were conducted under humidity levels similar to those used in clinical trials (approximately 50%) as well as under higher humidity levels (approximately 95%). The skin permeability values of drugs at 95% humidity were higher than those at 50% humidity. The time profiles of the human plasma concentrations after TTS application fitted well with the clinical data when predicted based on the in vitro permeation parameters at 50% humidity. On the other hand, those profiles predicted based on the parameters at 95% humidity were overestimated. The impact of humidity was higher for the more breathable TTS; Frandol(®) tape 40mg. These results show that in vitro human skin permeation experiments should be investigated under realistic clinical humidity levels especially for breathable TTSs. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

  14. Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

    Science.gov (United States)

    Pironti, Alejandro; Pfeifer, Nico; Kaiser, Rolf; Walter, Hauke; Lengauer, Thomas

    2014-01-01

    Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set

  15. Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

    Science.gov (United States)

    Johnson, Martin; Kozielska, Magdalena; Pilla Reddy, Venkatesh; Vermeulen, An; Barton, Hugh A; Grimwood, Sarah; de Greef, Rik; Groothuis, Geny M M; Danhof, Meindert; Proost, Johannes H

    2016-04-01

    To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

  16. A review of clinical and histological parameters associated with contralateral neck metastases in oral squamous cell carcinoma

    Science.gov (United States)

    Fan, Song; Tang, Qiong-lan; Lin, Ying-jin; Chen, Wei-liang; Li, Jin-song; Huang, Zhi-quan; Yang, Zhao-hui; Wang, You-yuan; Zhang, Da-ming; Wang, Hui-jing; Dias-Ribeiro, Eduardo; Cai, Qiang; Wang, Lei

    2011-01-01

    Oral squamous cell carcinoma (OSCC) has a high incidence of cervical micrometastases and sometimes metastasizes contralaterally because of the rich lymphatic intercommunications relative to submucosal plexus of oral cavity that freely communicate across the midline, and it can facilitate the spread of neoplastic cells to any area of the neck consequently. Clinical and histopathologic factors continue to provide predictive information to contralateral neck metastases (CLNM) in OSCC, which determine prophylactic and adjuvant treatments for an individual patient. This review describes the predictive value of clinical-histopathologic factors, which relate to primary tumor and cervical lymph nodes, and surgical dissection and adjuvant treatments. In addition, the indications for elective contralateral neck dissection and adjuvant radiotherapy (aRT) and strategies for follow-up are offered, which is strongly focused by clinicians to prevent later CLNM and poor prognosis subsequently. PMID:22010576

  17. Evaluation of clinical and cytogenetic parameters in rheumatoid arthritis patients for effective diagnosis.

    Science.gov (United States)

    Chandirasekar, R; Kumar, B Lakshman; Jayakumar, R; Uthayakumar, V; Jacob, Raichel; Sasikala, K

    2015-01-15

    Rheumatoid arthritis is the commonest inflammatory joint disease, affecting nearly 1% of the adult population worldwide. Early and accurate diagnosis and prognosis of rheumatoid arthritis (RA) have become increasingly important. In the present study, we aimed to elucidate the relationships between hematological, biochemical, immunological and cytogenetic parameters in rheumatoid arthritis patients and healthy normal controls. The study group comprised of 126 RA patients and equal number of healthy normal control subjects. The blood was collected and analyzed for biochemical, immunological, enzymatic and cytogenetic parameters. Results of the present study indicated that 20% of RA patient's hematological, 31% of biochemical and 70% immunological parameters had a significant difference from the controls and reference range. The RF and anti-CCP antibody levels were also positive in 70% of RA patients. A significant increase in minor chromosomal abnormalities was also observed in patients as compared to controls. The knowledge about autoimmune diseases is very low among the South Indian population. The present study has thus helped in understanding the RA disease in a better way based on a pattern of various clinical markers of the disease condition which might help in planning therapeutic intervention strategies and create awareness about the disease management among RA patients of the population studied. Copyright © 2014. Published by Elsevier B.V.

  18. A Clinical Prediction Algorithm to Stratify Pediatric Musculoskeletal Infection by Severity

    Science.gov (United States)

    Benvenuti, Michael A; An, Thomas J; Mignemi, Megan E; Martus, Jeffrey E; Mencio, Gregory A; Lovejoy, Stephen A; Thomsen, Isaac P; Schoenecker, Jonathan G; Williams, Derek J

    2016-01-01

    Objective There are currently no algorithms for early stratification of pediatric musculoskeletal infection (MSKI) severity that are applicable to all types of tissue involvement. In this study, the authors sought to develop a clinical prediction algorithm that accurately stratifies infection severity based on clinical and laboratory data at presentation to the emergency department. Methods An IRB-approved retrospective review was conducted to identify patients aged 0–18 who presented to the pediatric emergency department at a tertiary care children’s hospital with concern for acute MSKI over a five-year period (2008–2013). Qualifying records were reviewed to obtain clinical and laboratory data and to classify in-hospital outcomes using a three-tiered severity stratification system. Ordinal regression was used to estimate risk for each outcome. Candidate predictors included age, temperature, respiratory rate, heart rate, C-reactive protein, and peripheral white blood cell count. We fit fully specified (all predictors) and reduced models (retaining predictors with a p-value ≤ 0.2). Discriminatory power of the models was assessed using the concordance (c)-index. Results Of the 273 identified children, 191 (70%) met inclusion criteria. Median age was 5.8 years. Outcomes included 47 (25%) children with inflammation only, 41 (21%) with local infection, and 103 (54%) with disseminated infection. Both the full and reduced models accurately demonstrated excellent performance (full model c-index 0.83, 95% CI [0.79–0.88]; reduced model 0.83, 95% CI [0.78–0.87]). Model fit was also similar, indicating preference for the reduced model. Variables in this model included C-reactive protein, pulse, temperature, and an interaction term for pulse and temperature. The odds of a more severe outcome increased by 30% for every 10-unit increase in C-reactive protein. Conclusions Clinical and laboratory data obtained in the emergency department may be used to accurately

  19. Clinical utility of polymorphisms in one-carbon metabolism for breast cancer risk prediction

    Directory of Open Access Journals (Sweden)

    Shaik Mohammad Naushad

    2011-01-01

    Full Text Available This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1 G80A, thymidylate synthase (TYMS 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR C677T and catecholamine-O-methyl transferase (COMT genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII C1561T and cytosolic serine hydroxymethyl transferase (cSHMT C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC curves were plotted and the area under ROC curve (C was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93 while the predictability was lost in subjects with one protective allele (C=0.60. However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles. The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.

  20. Genetic parameters for the prediction of abdominal fat traits using blood biochemical indicators in broilers.

    Science.gov (United States)

    Zhang, H L; Xu, Z Q; Yang, L L; Wang, Y X; Li, Y M; Dong, J Q; Zhang, X Y; Jiang, X Y; Jiang, X F; Li, H; Zhang, D X; Zhang, H

    2018-02-01

    1. Excessive deposition of body fat, especially abdominal fat, is detrimental in chickens and the prevention of excessive fat accumulation is an important problem. The aim of this study was to identify blood biochemical indicators that could be used as criteria to select lean Yellow-feathered chicken lines. 2. Levels of blood biochemical indicators in the fed and fasted states and the abdominal fat traits were measured in 332 Guangxi Yellow chickens. In the fed state, the genetic correlations (r g ) of triglycerides and very low density lipoprotein levels were positive for the abdominal fat traits (0.47 ≤ r g  ≤ 0.67), whereas total cholesterol, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) showed higher negative correlations with abdominal fat traits (-0.59 ≤ r g  ≤ -0.33). Heritabilities of these blood biochemical parameters were high, varying from 0.26 to 0.60. 3. In the fasted state, HDL-C:LDL-C level was positively correlated with abdominal fat traits (0.35 ≤ r g  ≤ 0.38), but triglycerides, total cholesterol, HDL-C, LDL-C, total protein, albumin, aspartate transaminase, uric acid and creatinine levels were negatively correlated with abdominal fat traits (-0.79 ≤ r g  ≤ -0.35). The heritabilities of these 10 blood biochemical parameters were high (0.22 ≤ h 2  ≤ 0.59). 4. In the fed state, optimal multiple regression models were constructed to predict abdominal fat traits by using triglycerides and LDL-C. In the fasted state, triglycerides, total cholesterol, HDL-C, LDL-C, total protein, albumin and uric acid could be used to predict abdominal fat content. 5. It was concluded that these models in both nutritional states could be used to predict abdominal fat content in Guangxi Yellow broiler chickens.

  1. Predictive value of clinical and laboratory variables for vesicoureteral reflux in children.

    Science.gov (United States)

    Soylu, Alper; Kasap, Belde; Demir, Korcan; Türkmen, Mehmet; Kavukçu, Salih

    2007-06-01

    We aimed to determine the predictability of clinical and laboratory variables for vesicoureteral reflux (VUR) in children with urinary tract infection (UTI). Data of children with febrile UTI who underwent voiding cystoureterography between 2002 and 2005 were evaluated retrospectively for clinical (age, gender, fever > or = 38.5 degrees C, recurrent UTI), laboratory [leukocytosis, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), pyuria, serum creatinine (S(Cr))] and imaging (renal ultrasonography) variables. Children with VUR (group 1) vs. no VUR (group 2) and children with high-grade (III-V) VUR (group 3) vs. no or low-grade (I-II) VUR (group 4) were compared. Among 88 patients (24 male), 38 had VUR and 21 high-grade VUR. Fever > or = 38.5 degrees C was associated with VUR [odds ratio (OR): 7.5]. CRP level of 50 mg/l was the best cut-off level for predicting high-grade VUR (OR 15.5; discriminative ability 0.89 +/- 0.05). Performing voiding cystourethrography based on this CRP level would result in failure to notice 9% of patients with high-grade VUR, whereas 69% of children with no/low-grade VUR would be spared from this invasive test. In conclusion, fever > or = 38 degrees C and CRP > 50 mg/l seem to be potentially useful clinical predictors of VUR and high-grade VUR, respectively, in pediatric patients with UTI. Further validation of these findings could limit unnecessary voiding cystourethrography.

  2. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    Science.gov (United States)

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. [Hepatic transit times and liver elasticity compared with meld in predicting a 1 year adverse clinical outcome of a clinically diagnosed cirrhosis].

    Science.gov (United States)

    Koller, Tomáš; Piešťanská, Zuzana; Hlavatý, Tibor; Holomáň, Jozef; Glasa, Jozef; Payer, Juraj

    Hepatic transit times measured by the contrast enhanced ultrasonography and liver elasticity were found to predict a clinically significant portal hypertension. However, these modalities we not yet sufficiently evaluated in predicting adverse clinical outcome in patients with clinically diagnosed cirrhosis (D´Amico stages > 1), having a clinically significant portal hypertension. The aim of our study was to assess the predictive power of the liver transit times and the liver elasticity on an adverse clinical outcome of clinically diagnosed cirrhosis compared with the MELD score. The study group included 48 consecutive outpatients with cirrhosis in the 2., 3. and 4. DAmico stages. Patients with stage 4 could have jaundice, patients with other complications of portal hypertension were excluded. Transit times were measured from the time of intravenous administration of contrast agent (Sonovue) to a signal appearance in a hepatic vein (hepatic vein arrival time, HVAT) or time difference between the contrast signal in the hepatic artery and hepatic vein (hepatic transit time, HTT) in seconds. Elasticity was measured using the transient elastography (Fibroscan). The transit times and elasticity were measured at baseline and patients were followed for up for 1 year. Adverse outcome of cirrhosis was defined as the appearance of clinically apparent ascites and/or hospitalization for liver disease and/or death within 1 year. The mean age was 61 years, with female/male ratio 23/25. At baseline, the median Child-Pugh score was 5 (IQR 5.0-6.0), MELD 9.5 (IQR 7.6 to 12.1), median HVAT was 22 s (IQR 19-25) and HTT 6 (IQR 5-9). HTT and HVAT negatively correlated with Child-Pugh (-0.351 and -0.441, p = 0.002) and MELD (-0.479 and -0.388, p = 0.006) scores. The adverse outcome at 1-year was observed in 11 cases (22.9 %), including 6 deaths and 5 hospitalizations. Median HVAT in those with/without the adverse outcome was 20 seconds (IQR 19.3-23.5) compared with 22 s (IQR 19-26, p

  4. Prediction of Marginal Mass Required for Successful Islet Transplantation

    Science.gov (United States)

    Papas, Klearchos K.; Colton, Clark K.; Qipo, Andi; Wu, Haiyan; Nelson, Rebecca A.; Hering, Bernhard J.; Weir, Gordon C.; Koulmanda, Maria

    2013-01-01

    Islet quality assessment methods for predicting diabetes reversal (DR) following transplantation are needed. We investigated two islet parameters, oxygen consumption rate (OCR) and OCR per DNA content, to predict transplantation outcome and explored the impact of islet quality on marginal islet mass for DR. Outcomes in immunosuppressed diabetic mice were evaluated by transplanting mixtures of healthy and purposely damaged rat islets for systematic variation of OCR/DNA over a wide range. The probability of DR increased with increasing transplanted OCR and OCR/DNA. On coordinates of OCR versus OCR/DNA, data fell into regions in which DR occurred in all, some, or none of the animals with a sharp threshold of around 150-nmol/min mg DNA. A model incorporating both parameters predicted transplantation outcome with sensitivity and specificity of 93% and 94%, respectively. Marginal mass was not constant, depended on OCR/DNA, and increased from 2,800 to over 100,000 islet equivalents/kg body weight as OCR/DNA decreased. We conclude that measurements of OCR and OCR/DNA are useful for predicting transplantation outcome in this model system, and OCR/DNA can be used to estimate the marginal mass required for reversing diabetes. Because human clinical islet preparations in a previous study had OCR/DNA values in the range of 100–150-nmol/min mg DNA, our findings suggest that substantial improvement in transplantation outcome may accompany increasedOCR/DNAin clinical islet preparations. PMID:20233002

  5. Comprehensive reference ranges for hematology and clinical chemistry laboratory parameters derived from normal Nigerian adults.

    Directory of Open Access Journals (Sweden)

    Timzing Miri-Dashe

    Full Text Available Interpretation of laboratory test results with appropriate diagnostic accuracy requires reference or cutoff values. This study is a comprehensive determination of reference values for hematology and clinical chemistry in apparently healthy voluntary non-remunerated blood donors and pregnant women.Consented clients were clinically screened and counseled before testing for HIV, Hepatitis B, Hepatitis C and Syphilis. Standard national blood donors' questionnaire was administered to consented blood donors. Blood from qualified volunteers was used for measurement of complete hematology and chemistry parameters. Blood samples were analyzed from a total of 383 participants, 124 (32.4% males, 125 (32.6% non-pregnant females and 134 pregnant females (35.2% with a mean age of 31 years. Our results showed that the red blood cells count (RBC, Hemoglobin (HB and Hematocrit (HCT had significant gender difference (p = 0.000 but not for total white blood count (p>0.05 which was only significantly higher in pregnant verses non-pregnant women (p = 0.000. Hemoglobin and Hematocrit values were lower in pregnancy (P = 0.000. Platelets were significantly higher in females than men (p = 0.001 but lower in pregnant women (p =  .001 with marked difference in gestational period. For clinical chemistry parameters, there was no significant difference for sodium, potassium and chloride (p>0.05 but gender difference exists for Bicarbonate (HCO3, Urea nitrogen, Creatinine as well as the lipids (p0.05.Hematological and Clinical Chemistry reference ranges established in this study showed significant gender differences. Pregnant women also differed from non-pregnant females and during pregnancy. This is the first of such comprehensive study to establish reference values among adult Nigerians and difference observed underscore the need to establish reference values for different populations.

  6. Correlated regions of cerebral blood flow with clinical parameters in Parkinson's disease. Comparison using 'Anatomy' and 'Talairach Daemon' software

    International Nuclear Information System (INIS)

    Yoon, Hyun Jin; Cheon, Sang Myung; Jeong, Young Jin; Kang, Do Young

    2012-01-01

    We assign the anatomical names of functional activation regions in the brain, based on the probabilistic cyto-architectonic atlas by Anatomy 1.7 from an analysis of correlations between regional cerebral blood flow (rCBF) and clinical parameters of the non-demented Parkinson's disease (PD) patients by statistical parametric mapping (SPM) 8. We evaluated Anatomy 1.7 of SPM toolbox compared to 'Talairach Daemon' (TD) Client 2.4.2 software. One hundred and thirty-six patients (mean age 60.0±9.09 years; 73 women and 63 men) with non-demented PD were selected. Tc-99m-HMPAO brain single-photon emission computed tomography (SPECT) scans were performed on the patients using a two-head gamma-camera. We analyzed the brain image of PD patients by SPM8 and found the anatomical names of correlated regions of rCBF perfusion with the clinical parameters using TD Client 2.4.2 and Anatomy 1.7. The SPM8 provided a correlation coefficient between clinical parameters and cerebral hypoperfusion by a simple regression method. To the clinical parameters were added age, duration of disease, education period, Hoehn and Yahr (H and Y) stage and Korean mini-mental state examination (K-MMSE) score. Age was correlated with cerebral perfusion in the Brodmann area (BA) 6 and BA 3b assigned by Anatomy 1.7 and BA 6 and pyramis in gray matter by TD Client 2.4.2 with p<0.001 uncorrected. Also, assigned significant correlated regions were found in the left and right lobules VI (Hem) with duration of disease, in left and right lobules VIIa crus I (Hem) with education, in left insula (Ig2), left and right lobules VI (Hem) with H and Y, and in BA 4a and 6 with K-MMSE score with p<0.05 uncorrected by Anatomy 1.7, respectively. Most areas of correlation were overlapped by two different anatomical labeling methods, but some correlation areas were found with different names. Age was the most significantly correlated clinical parameter with rCBF. TD Client found the exact anatomical name by the peak

  7. Outcome prediction in mild traumatic brain injury: age and clinical variables are stronger predictors than CT abnormalities.

    NARCIS (Netherlands)

    Jacobs, B.; Beems, T.; Stulemeijer, M.; Vugt, A.B. van; Vliet, A.M. van der; Borm, G.F.; Vos, P.E.

    2010-01-01

    Mild traumatic brain injury (mTBI) is a common heterogeneous neurological disorder with a wide range of possible clinical outcomes. Accurate prediction of outcome is desirable for optimal treatment. This study aimed both to identify the demographic, clinical, and computed tomographic (CT)

  8. Clinical findings just after return to play predict hamstring re-injury, but baseline MRI findings do not

    NARCIS (Netherlands)

    R.J. de Vos (Robert-Jan); G. Reurink (Gustaaf); G.J. Goudswaard (Gert Jan); M.H. Moen (Maaike); A. Weir (Adam); J.L. Tol (Johannes)

    2014-01-01

    markdownabstract__Abstract__ Background Acute hamstring re-injuries are common and hard to predict. The aim of this study was to investigate the association between clinical and imaging findings and the occurrence of hamstring re-injuries. Methods We obtained baseline data (clinical and MRI

  9. Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room.

    Directory of Open Access Journals (Sweden)

    Anna M Kauppi

    Full Text Available A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69-0.99 and a specificity 0.84 (95% CI 0.58-0.94 with an AUC of 0.93 (95% CI 0.89-1.01. Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85-1.00 and specificity of 0.95 (95% CI 0.74-0.99, and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.

  10. Association of lean body mass with nutritional parameters and mortality in hemodialysis patients: A long-term follow-up clinical study.

    Science.gov (United States)

    Zhou, Dong Chi; Yang, Xiu Hong; Zhan, Xiao Li; Gu, Yan Hong; Guo, Li Li; Jin, Hui Min

    2018-06-01

    This study aimed to evaluate the correlation between lean body mass (LBM) and nutritional status in hemodialysis (HD) patients to better predict their long-term prognosis. Anthropometric body measurements and biochemical parameters were recorded from 222 patients on maintenance hemodialysis (MHD) at the Shanghai Pudong Hospital Hemodialysis Center. LBM was calculated using the serum creatinine index (LBM-SCR), mid-arm muscle circumference (LBM-MAMC), and dominant-arm hand-grip strength (LBM-HGS). Patient mortality and hospitalization were observed after 24 months. LBMs measured from LBM-SCR and LBM-MAMC were associated with sex, body mass index (BMI), serum albumin, and serum creatinine (SCR) ( p LBM evaluation, low LBM was shown to be associated with a higher mortality in patients undergoing HD ( p LBM-SCR and LBM-HGS are strongly associated with hospitalization and mortality in HD patients, indicating LBM is an important factor in prediction of outcomes in those patients. LBM is associated with nutritional parameters in HD patients, and LBM-SCR, HGS, and MAMC are simple approaches for accurately predicting the patient's risk of hospitalization and/or death.

  11. The importance of histopathological and clinical variables in predicting the evolution of colon cancer.

    Science.gov (United States)

    Diculescu, Mircea; Iacob, Răzvan; Iacob, Speranţa; Croitoru, Adina; Becheanu, Gabriel; Popeneciu, Valentin

    2002-09-01

    It has been a consensus that prognostic factors should always be taken into account before planning treatment in colorectal cancer. A 5 year prospective study was conducted, in order to assess the importance of several histopathological and clinical prognostic variables in the prediction of evolution in colon cancer. Some of the factors included in the analysis are still subject to dispute by different authors. 46 of 53 screened patients qualified to enter the study and underwent a potentially curative resection of the tumor, followed, when necessary, by adjuvant chemotherapy. Univariate and multivariate analyses were carried out in order to identify independent prognostic indicators. The endpoint of the study was considered the recurrence of the tumor or the detection of metastases. 65.2% of the patients had a good evolution during the follow up period. Multivariate survival analysis performed by Cox proportional hazard model identified 3 independent prognostic factors: Dukes stage (p = 0.00002), the grade of differentiation (p = 0.0009) and the weight loss index, representing the weight loss of the patient divided by the number of months when it was actually lost (p = 0.02). Age under 40 years, sex, microscopic aspect of the tumor, tumor location, anemia degree were not identified by our analysis as having prognostic importance. Histopathological factors continue to be the most valuable source of information regarding the possible evolution of patients with colorectal cancer. Individual clinical symptoms or biological parameters such as erytrocyte sedimentation rate or hemoglobin level are of little or no prognostic value. More research is required relating to the impact of a performance status index (which could include also weight loss index) as another reliable prognostic variable.

  12. Predicting future conflict between team-members with parameter-free models of social networks

    Science.gov (United States)

    Rovira-Asenjo, Núria; Gumí, Tània; Sales-Pardo, Marta; Guimerà, Roger

    2013-06-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  13. Evaluation of selected predictive models and parameters for the environmental transport and dosimetry of radionuclides

    International Nuclear Information System (INIS)

    Miller, C.W.; Dunning, D.E. Jr.; Etnier, E.L.; Hoffman, F.O.; Little, C.A.; Meyer, H.R.; Shaeffer, D.L.; Till, J.E.

    1979-07-01

    Evaluations of selected predictive models and parameters used in the assessment of the environmental transport and dosimetry of radionuclides are summarized. Mator sections of this report include a validation of the Gaussian plume disperson model, comparison of the output of a model for the transport of 131 I from vegetation to milk with field data, validation of a model for the fraction of aerosols intercepted by vegetation, an evaluation of dose conversion factors for 232 Th, an evaluation of considering the effect of age dependency on population dose estimates, and a summary of validation results for hydrologic transport models

  14. Critical reappraisal of embryo quality as a predictive parameter for pregnancy outcome: a pilot study.

    Science.gov (United States)

    Campo, R; Binda, M M; Van Kerkhoven, G; Frederickx, V; Serneels, A; Roziers, P; Lopes, A S; Gordts, S; Puttemans, P; Gordts, S

    2010-01-01

    Pilot study to analyse the efficacy and embryo morphology using a new human embryo culture medium (GM501) versus the conventional used medium (ISM1). Over a four-month period, all patients at the Leuven Institute of Fertility and Embryology (LIFE) were -randomly allocated to have their embryos cultured in either the standard sequential culture medium ISM1 (control) or in a new universal medium (GM501) (study group). Primary outcome parameters were clinical pregnancy and live birth rate. The secondary outcome parameter was the correlation of embryo fragmentation rate with pregnancy outcome. We did not observe any differences between the ISM1 control group and GM501 study group with regard to fertilization, pregnancy, implantation rates, ongoing pregnancy, and babies born. The number of embryos with a minimal fragmentation rate (less than 30%) was significantly higher in the GM501 study group. Although a significant higher embryo fragmentation rate was seen in In vitro culture of embryos in GM501, pregnancy outcome results were comparable to those of embryos cultured in ISM1. According to our results the value of embryo morphological criteria as a parameter for pregnancy outcome should be examined and discussed again.

  15. Improved noninvasive prediction of liver fibrosis by liver stiffness measurement in patients with nonalcoholic fatty liver disease accounting for controlled attenuation parameter values.

    Science.gov (United States)

    Petta, Salvatore; Wong, Vincent Wai-Sun; Cammà, Calogero; Hiriart, Jean-Baptiste; Wong, Grace Lai-Hung; Marra, Fabio; Vergniol, Julien; Chan, Anthony Wing-Hung; Di Marco, Vito; Merrouche, Wassil; Chan, Henry Lik-Yuen; Barbara, Marco; Le-Bail, Brigitte; Arena, Umberto; Craxì, Antonio; de Ledinghen, Victor

    2017-04-01

    Liver stiffness measurement (LSM) frequently overestimates the severity of liver fibrosis in nonalcoholic fatty liver disease (NAFLD). Controlled attenuation parameter (CAP) is a new parameter provided by the same machine used for LSM and associated with both steatosis and body mass index, the two factors mostly affecting LSM performance in NAFLD. We aimed to determine whether prediction of liver fibrosis by LSM in NAFLD patients is affected by CAP values. Patients (n = 324) were assessed by clinical and histological (Kleiner score) features. LSM and CAP were performed using the M probe. CAP values were grouped by tertiles (lower 132-298, middle 299-338, higher 339-400 dB/m). Among patients with F0-F2 fibrosis, mean LSM values, expressed in kilopascals, increased according to CAP tertiles (6.8 versus 8.6 versus 9.4, P = 0.001), and along this line the area under the curve of LSM for the diagnosis of F3-F4 fibrosis was progressively reduced from lower to middle and further to higher CAP tertiles (0.915, 0.848-0.982; 0.830, 0.753-0.908; 0.806, 0.723-0.890). As a consequence, in subjects with F0-F2 fibrosis, the rates of false-positive LSM results for F3-F4 fibrosis increased according to CAP tertiles (7.2% in lower versus 16.6% in middle versus 18.1% in higher). Consistent with this, a decisional flowchart for predicting fibrosis was suggested by combining both LSM and CAP values. In patients with NAFLD, CAP values should always be taken into account in order to avoid overestimations of liver fibrosis assessed by transient elastography. (Hepatology 2017;65:1145-1155). © 2016 by the American Association for the Study of Liver Diseases.

  16. Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning.

    Science.gov (United States)

    Fusar-Poli, P; Schultze-Lutter, F

    2016-02-01

    Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes' theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  17. Predictive Models for Different Roughness Parameters During Machining Process of Peek Composites Using Response Surface Methodology

    Directory of Open Access Journals (Sweden)

    Mata-Cabrera Francisco

    2013-10-01

    Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

  18. 3P: Personalized Pregnancy Prediction in IVF Treatment Process

    Science.gov (United States)

    Uyar, Asli; Ciray, H. Nadir; Bener, Ayse; Bahceci, Mustafa

    We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.

  19. Sensitivity of corneal biomechanical and optical behavior to material parameters using design of experiments method.

    Science.gov (United States)

    Xu, Mengchen; Lerner, Amy L; Funkenbusch, Paul D; Richhariya, Ashutosh; Yoon, Geunyoung

    2018-02-01

    The optical performance of the human cornea under intraocular pressure (IOP) is the result of complex material properties and their interactions. The measurement of the numerous material parameters that define this material behavior may be key in the refinement of patient-specific models. The goal of this study was to investigate the relative contribution of these parameters to the biomechanical and optical responses of human cornea predicted by a widely accepted anisotropic hyperelastic finite element model, with regional variations in the alignment of fibers. Design of experiments methods were used to quantify the relative importance of material properties including matrix stiffness, fiber stiffness, fiber nonlinearity and fiber dispersion under physiological IOP. Our sensitivity results showed that corneal apical displacement was influenced nearly evenly by matrix stiffness, fiber stiffness and nonlinearity. However, the variations in corneal optical aberrations (refractive power and spherical aberration) were primarily dependent on the value of the matrix stiffness. The optical aberrations predicted by variations in this material parameter were sufficiently large to predict clinically important changes in retinal image quality. Therefore, well-characterized individual variations in matrix stiffness could be critical in cornea modeling in order to reliably predict optical behavior under different IOPs or after corneal surgery.

  20. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

    Science.gov (United States)

    Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

    2018-01-01

    The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.