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Sample records for clinical prediction rule

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

    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.

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

    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).

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

    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.

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

    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.

  5. Physiotherapy students' perceptions and experiences of clinical prediction rules.

    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.

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

    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

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

    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...

  8. Translation of clinical prediction rules for febrile children to primary care practice : an observational cohort study

    van Ierland, Yvette; Elshout, Gijs; Berger, Marjolein Y.; Vergouwe, Yvonne; de Wilde, Marcel; van der Lei, Johan; Mol, Henritte A.; Oostenbrink, Rianne

    Background Clinical prediction rules (CPRs) to identify children with serious infections lack validation in low-prevalence populations, which hampers their implementation in primary care practice. Aim To evaluate the diagnostic value of published CPRs for febrile children in primary care. Design and

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

    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

  10. [Validation of a clinical prediction rule to distinguish bacterial from aseptic meningitis].

    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.

  11. The risk of severe postoperative pain: Modification and validation of a clinical prediction rule

    Janssen, Kristel J. M.; Kalkman, Cor J.; Grobbee, Diederick E.; Bonsel, Gouke J.; Moons, Karel G. M.; Vergouwe, Yvonne

    2008-01-01

    BACKGROUND: Recently, a prediction rule was developed to preoperatively predict the risk of severe pain in the first postoperative hour in surgical inpatients. We aimed to modify the rule to enhance its use in both surgical inpatients and outpatients (ambulatory patients). Subsequently, we

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

    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. Sepsis and meningitis in hospitalized children: performance of clinical signs and their prediction rules in a case-control study.

    Verbakel, Jan Y; MacFaul, Roderick; Aertgeerts, Bert; Buntinx, Frank; Thompson, Matthew

    2014-06-01

    Feverish illness is a common presentation to acute pediatric services. Clinical staff faces the challenge of differentiating the few children with meningitis or sepsis from the majority with self-limiting illness. We aimed to determine the diagnostic value of clinical features and their prediction rules (CPR) for identifying children with sepsis or meningitis among those children admitted to a District General Hospital with acute febrile illness. Acutely ill children admitted to a District General Hospital in England were included in this case-control study between 2000 and 2005. We examined the diagnostic accuracy of individual clinical signs and 6 CPRs, including the National Institute for Clinical Excellence "traffic light" system, to determine clinical utility in identifying children with a diagnosis of sepsis or meningitis. Loss of consciousness, prolonged capillary refill, decreased alertness, respiratory effort, and the physician's illness assessment had high positive likelihood ratios (9-114), although with wide confidence intervals, to rule in sepsis or meningitis. The National Institute for Clinical Excellence traffic light system, the modified Yale Observation Scale, and the Pediatric Advanced Warning Score performed poorly with positive likelihood ratios ranging from 1 to 3. The pediatrician's overall illness assessment was the most useful feature to rule in sepsis or meningitis in these hospitalized children. Clinical prediction rules did not effectively rule in sepsis or meningitis. The modified Yale Observation Scale should be used with caution. Single clinical signs could complement these scores to rule in sepsis or meningitis. Further research is needed to validate these CPRs.

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

    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. Deriving a clinical prediction rule to target sexual healthcare to women attending British General Practices.

    Edelman, N L; Cassell, J A; Mercer, C H; Bremner, S A; Jones, C I; Gersten, A; deVisser, R O

    2018-07-01

    Some women attending General Practices (GPs) are at higher risk of unintended pregnancy (RUIP) and sexually transmitted infections (STI) than others. A clinical prediction rule (CPR) may help target resources using psychosocial questions as an acceptable, effective means of assessment. The aim was to derive a CPR that discriminates women who would benefit from sexual health discussion and intervention. Participants were recruited to a cross-sectional survey from six GPs in a city in South-East England in 2016. On arrival, female patients aged 16-44 years were invited to complete a questionnaire that addressed psychosocial factors, and the following self-reported outcomes: 2+ sexual partners in the last year (2PP) and RUIP. For each sexual risk, psychosocial questions were retained from logistic regression modelling which best discriminated women at risk using the C-statistic. Sensitivity and specificity were established in consultation with GP staff. The final sample comprised N = 1238 women. 2PP was predicted by 11 questions including age, binge-drinking weekly, ever having a partner who insulted you often, current smoking, and not cohabiting (C-statistic = 0.83, sensitivity = 73% and specificity = 77%). RUIP was predicted by 5 questions including sexual debut years, and emergency contraception use in the last 6 months (C-statistic = 0.70, sensitivity = 69% and specificity = 57%). 2PP was better discriminated than RUIP but neither to a clinically-useful degree. The finding that different psychosocial factors predicted each outcome has implications for prevention strategies. Further research should investigate causal links between psychosocial factors and sexual risk. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)

    Wallace, Emma

    2011-10-14

    Abstract Clinical Prediction Rules (CPRs) are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with only a minority progressing to validation and very few undergoing impact analysis. Impact analysis studies remain the most efficient way of assessing whether incorporating CPRs into a decision making process improves patient care. However there is a lack of clear methodology for the design of high quality impact analysis studies. We have developed a sequential four-phased framework based on the literature and the collective experience of our international working group to help researchers identify and overcome the specific challenges in designing and conducting an impact analysis of a CPR. There is a need to shift emphasis from deriving new CPRs to validating and implementing existing CPRs. The proposed framework provides a structured approach to this topical and complex area of research.

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

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

    2009-08-01

    Major depressive disorder often remains unrecognized in primary care. Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. A total of 1046 subjects, aged 18-65 years, were included from seven large general practices in the center of The Netherlands. All subjects were recruited in the general practice waiting room, irrespective of their presenting complaint. Major depressive disorder according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Text Revision edition criteria was assessed with the Composite International Diagnostic Interview. Candidate predictors were gender, age, educational level, being single, number of presented complaints, presence of non-somatic complaints, whether a diagnosis was assigned, consultation rate in past 12 months, presentation of depressive complaints or prescription of antidepressants in past 12 months, number of life events in past 6 months and any history of depression. The first multivariable logistic regression model including only predictors that require no confronting depression-related questions had a reasonable degree of discrimination (area under the receiver operating characteristic curve or concordance-statistic (c-statistic) = 0.71; 95% Confidence Interval (CI): 0.67-0.76). Addition of three simple though more depression-related predictors, number of life events and history of depression, significantly increased the c-statistic to 0.80 (95% CI: 0.76-0.83). After transforming this second model to an easily to use risk score, the lowest risk category (sum score depression, which increased to 49% in the highest category (sum score > or = 30). A clinical prediction rule allows GPs to identify patients-irrespective of their complaints-in whom diagnostic workup for major depressive disorder is indicated.

  18. A simplified approach to the pooled analysis of calibration of clinical prediction rules for systematic reviews of validation studies

    Dimitrov BD

    2015-04-01

    Full Text Available Borislav D Dimitrov,1,2 Nicola Motterlini,2,† Tom Fahey2 1Academic Unit of Primary Care and Population Sciences, University of Southampton, Southampton, United Kingdom; 2HRB Centre for Primary Care Research, Department of General Medicine, Division of Population Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland †Nicola Motterlini passed away on November 11, 2012 Objective: Estimating calibration performance of clinical prediction rules (CPRs in systematic reviews of validation studies is not possible when predicted values are neither published nor accessible or sufficient or no individual participant or patient data are available. Our aims were to describe a simplified approach for outcomes prediction and calibration assessment and evaluate its functionality and validity. Study design and methods: Methodological study of systematic reviews of validation studies of CPRs: a ABCD2 rule for prediction of 7 day stroke; and b CRB-65 rule for prediction of 30 day mortality. Predicted outcomes in a sample validation study were computed by CPR distribution patterns (“derivation model”. As confirmation, a logistic regression model (with derivation study coefficients was applied to CPR-based dummy variables in the validation study. Meta-analysis of validation studies provided pooled estimates of “predicted:observed” risk ratios (RRs, 95% confidence intervals (CIs, and indexes of heterogeneity (I2 on forest plots (fixed and random effects models, with and without adjustment of intercepts. The above approach was also applied to the CRB-65 rule. Results: Our simplified method, applied to ABCD2 rule in three risk strata (low, 0–3; intermediate, 4–5; high, 6–7 points, indicated that predictions are identical to those computed by univariate, CPR-based logistic regression model. Discrimination was good (c-statistics =0.61–0.82, however, calibration in some studies was low. In such cases with miscalibration, the under-prediction

  19. Diagnostic accuracy of the STRATIFY clinical prediction rule for falls: A systematic review and meta-analysis

    Billington, Jennifer

    2012-08-07

    AbstractBackgroundThe STRATIFY score is a clinical prediction rule (CPR) derived to assist clinicians to identify patients at risk of falling. The purpose of this systematic review and meta-analysis is to determine the overall diagnostic accuracy of the STRATIFY rule across a variety of clinical settings.MethodsA literature search was performed to identify all studies that validated the STRATIFY rule. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A STRATIFY score of ≥2 points was used to identify individuals at higher risk of falling. All included studies were combined using a bivariate random effects model to generate pooled sensitivity and specificity of STRATIFY at ≥2 points. Heterogeneity was assessed using the variance of logit transformed sensitivity and specificity.ResultsSeventeen studies were included in our meta-analysis, incorporating 11,378 patients. At a score ≥2 points, the STRATIFY rule is more useful at ruling out falls in those classified as low risk, with a greater pooled sensitivity estimate (0.67, 95% CI 0.52–0.80) than specificity (0.57, 95% CI 0.45 – 0.69). The sensitivity analysis which examined the performance of the rule in different settings and subgroups also showed broadly comparable results, indicating that the STRATIFY rule performs in a similar manner across a variety of different ‘at risk’ patient groups in different clinical settings.ConclusionThis systematic review shows that the diagnostic accuracy of the STRATIFY rule is limited and should not be used in isolation for identifying individuals at high risk of falls in clinical practice.

  20. How well do clinical prediction rules perform in identifying serious infections in acutely ill children across an international network of ambulatory care datasets?

    Verbakel Jan Y

    2013-01-01

    Full Text Available Abstract Background Diagnosing serious infections in children is challenging, because of the low incidence of such infections and their non-specific presentation early in the course of illness. Prediction rules are promoted as a means to improve recognition of serious infections. A recent systematic review identified seven clinical prediction rules, of which only one had been prospectively validated, calling into question their appropriateness for clinical practice. We aimed to examine the diagnostic accuracy of these rules in multiple ambulatory care populations in Europe. Methods Four clinical prediction rules and two national guidelines, based on signs and symptoms, were validated retrospectively in seven individual patient datasets from primary care and emergency departments, comprising 11,023 children from the UK, the Netherlands, and Belgium. The accuracy of each rule was tested, with pre-test and post-test probabilities displayed using dumbbell plots, with serious infection settings stratified as low prevalence (LP; 20% . In LP and IP settings, sensitivity should be >90% for effective ruling out infection. Results In LP settings, a five-stage decision tree and a pneumonia rule had sensitivities of >90% (at a negative likelihood ratio (NLR of Conclusions None of the clinical prediction rules examined in this study provided perfect diagnostic accuracy. In LP or IP settings, prediction rules and evidence-based guidelines had high sensitivity, providing promising rule-out value for serious infections in these datasets, although all had a percentage of residual uncertainty. Additional clinical assessment or testing such as point-of-care laboratory tests may be needed to increase clinical certainty. None of the prediction rules identified seemed to be valuable for HP settings such as emergency departments.

  1. Hospitalization for community-acquired febrile urinary tract infection: validation and impact assessment of a clinical prediction rule.

    Stalenhoef, Janneke E; van der Starre, Willize E; Vollaard, Albert M; Steyerberg, Ewout W; Delfos, Nathalie M; Leyten, Eliane M S; Koster, Ted; Ablij, Hans C; Van't Wout, Jan W; van Dissel, Jaap T; van Nieuwkoop, Cees

    2017-06-06

    There is a lack of severity assessment tools to identify adults presenting with febrile urinary tract infection (FUTI) at risk for complicated outcome and guide admission policy. We aimed to validate the Prediction Rule for Admission policy in Complicated urinary Tract InfeCtion LEiden (PRACTICE), a modified form of the pneumonia severity index, and to subsequentially assess its use in clinical practice. A prospective observational multicenter study for model validation (2004-2009), followed by a multicenter controlled clinical trial with stepped wedge cluster-randomization for impact assessment (2010-2014), with a follow up of 3 months. Paricipants were 1157 consecutive patients with a presumptive diagnosis of acute febrile UTI (787 in validation cohort and 370 in the randomized trial), enrolled at emergency departments of 7 hospitals and 35 primary care centers in the Netherlands. The clinical prediction rule contained 12 predictors of complicated course. In the randomized trial the PRACTICE included guidance on hospitalization for high risk (>100 points) and home discharge for low risk patients (urinary tract infection, futher improvement is necessary to reduce the occurrence of secondary hospital admissions. NTR4480 http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4480 , registered retrospectively 25 mrt 2014 (during enrollment of subjects).

  2. A Multistep Maturity Model for the Implementation of Electronic and Computable Diagnostic Clinical Prediction Rules (eCPRs).

    Corrigan, Derek; McDonnell, Ronan; Zarabzadeh, Atieh; Fahey, Tom

    2015-01-01

    The use of Clinical Prediction Rules (CPRs) has been advocated as one way of implementing actionable evidence-based rules in clinical practice. The current highly manual nature of deriving CPRs makes them difficult to use and maintain. Addressing the known limitations of CPRs requires implementing more flexible and dynamic models of CPR development. We describe the application of Information and Communication Technology (ICT) to provide a platform for the derivation and dissemination of CPRs derived through analysis and continual learning from electronic patient data. We propose a multistep maturity model for constructing electronic and computable CPRs (eCPRs). The model has six levels - from the lowest level of CPR maturity (literaturebased CPRs) to a fully electronic and computable service-oriented model of CPRs that are sensitive to specific demographic patient populations. We describe examples of implementations of the core model components - focusing on CPR representation, interoperability, electronic dissemination, CPR learning, and user interface requirements. The traditional focus on derivation and narrow validation of CPRs has severely limited their wider acceptance. The evolution and maturity model described here outlines a progression toward eCPRs consistent with the vision of a learning health system (LHS) - using central repositories of CPR knowledge, accessible open standards, and generalizable models to avoid repetition of previous work. This is useful for developing more ambitious strategies to address limitations of the traditional CPR development life cycle. The model described here is a starting point for promoting discussion about what a more dynamic CPR development process should look like.

  3. External validation of a clinical prediction rule to predict full recovery and ongoing moderate/severe disability following acute whiplash injury.

    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

  4. Different minimally important clinical difference (MCID) scores lead to different clinical prediction rules for the Oswestry disability index for the same sample of patients.

    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.

  5. Comparison of a Clinical Prediction Rule and a LAM Antigen-Detection Assay for the Rapid Diagnosis of TBM in a High HIV Prevalence Setting

    Patel, Vinod B.; Singh, Ravesh; Connolly, Cathy; Kasprowicz, Victoria; Zumla, Allimudin; Ndungu, Thumbi; Dheda, Keertan

    2010-01-01

    Background/Objective The diagnosis of tuberculous meningitis (TBM) in resource poor TB endemic environments is challenging. The accuracy of current tools for the rapid diagnosis of TBM is suboptimal. We sought to develop a clinical-prediction rule for the diagnosis of TBM in a high HIV prevalence setting, and to compare performance outcomes to conventional diagnostic modalities and a novel lipoarabinomannan (LAM) antigen detection test (Clearview-TB®) using cerebrospinal fluid (CSF). Methods Patients with suspected TBM were classified as definite-TBM (CSF culture or PCR positive), probable-TBM and non-TBM. Results Of the 150 patients, 84% were HIV-infected (median [IQR] CD4 count = 132 [54; 241] cells/µl). There were 39, 55 and 54 patients in the definite, probable and non-TBM groups, respectively. The LAM sensitivity and specificity (95%CI) was 31% (17;48) and 94% (85;99), respectively (cut-point ≥0.18). By contrast, smear-microscopy was 100% specific but detected none of the definite-TBM cases. LAM positivity was associated with HIV co-infection and low CD4 T cell count (CD4200 cells/µl; p = 0.03). The sensitivity and specificity in those with a CD4<100 cells/µl was 50% (27;73) and 95% (74;99), respectively. A clinical-prediction rule ≥6 derived from multivariate analysis had a sensitivity and specificity (95%CI) of 47% (31;64) and 98% (90;100), respectively. When LAM was combined with the clinical-prediction-rule, the sensitivity increased significantly (p<0.001) to 63% (47;68) and specificity remained high at 93% (82;98). Conclusions Despite its modest sensitivity the LAM ELISA is an accurate rapid rule-in test for TBM that has incremental value over smear-microscopy. The rule-in value of LAM can be further increased by combination with a clinical-prediction rule, thus enhancing the rapid diagnosis of TBM in HIV-infected persons with advanced immunosuppression. PMID:21203513

  6. Pooled individual patient data from five countries were used to derive a clinical prediction rule for coronary artery disease in primary care.

    Aerts, Marc; Minalu, Girma; Bösner, Stefan; Buntinx, Frank; Burnand, Bernard; Haasenritter, Jörg; Herzig, Lilli; Knottnerus, J André; Nilsson, Staffan; Renier, Walter; Sox, Carol; Sox, Harold; Donner-Banzhoff, Norbert

    2017-01-01

    To construct a clinical prediction rule for coronary artery disease (CAD) presenting with chest pain in primary care. Meta-Analysis using 3,099 patients from five studies. To identify candidate predictors, we used random forest trees, multiple imputation of missing values, and logistic regression within individual studies. To generate a prediction rule on the pooled data, we applied a regression model that took account of the differing standard data sets collected by the five studies. The most parsimonious rule included six equally weighted predictors: age ≥55 (males) or ≥65 (females) (+1); attending physician suspected a serious diagnosis (+1); history of CAD (+1); pain brought on by exertion (+1); pain feels like "pressure" (+1); pain reproducible by palpation (-1). CAD was considered absent if the prediction score is data sets based on electronic health records from diverse sites create opportunities for improving their internal and external validity. Our patient-level meta-analysis from five primary care sites should improve external validity. Our strategy for addressing site-to-site systematic variation in missing data should improve internal validity. Using principles derived from decision theory, we also discuss the problem of setting the cutoff prediction score for taking action. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. The Cynomolgus Macaque Natural History Model of Pneumonic Tularemia for Predicting Clinical Efficacy Under the Animal Rule

    Guina, Tina; Lanning, Lynda L.; Omland, Kristian S.; Williams, Mark S.; Wolfraim, Larry A.; Heyse, Stephen P.; Houchens, Christopher R.; Sanz, Patrick; Hewitt, Judith A.

    2018-01-01

    Francisella tularensis is a highly infectious Gram-negative bacterium that is the etiologic agent of tularemia in animals and humans and a Tier 1 select agent. The natural incidence of pneumonic tularemia worldwide is very low; therefore, it is not feasible to conduct clinical efficacy testing of tularemia medical countermeasures (MCM) in human populations. Development and licensure of tularemia therapeutics and vaccines need to occur under the Food and Drug Administration's (FDA's) Animal Rule under which efficacy studies are conducted in well-characterized animal models that reflect the pathophysiology of human disease. The Tularemia Animal Model Qualification (AMQ) Working Group is seeking qualification of the cynomolgus macaque (Macaca fascicularis) model of pneumonic tularemia under Drug Development Tools Qualification Programs with the FDA based upon the results of studies described in this manuscript. Analysis of data on survival, average time to death, average time to fever onset, average interval between fever and death, and bacteremia; together with summaries of clinical signs, necropsy findings, and histopathology from the animals exposed to aerosolized F. tularensis Schu S4 in five natural history studies and one antibiotic efficacy study form the basis for the proposed cynomolgus macaque model. Results support the conclusion that signs of pneumonic tularemia in cynomolgus macaques exposed to 300–3,000 colony forming units (cfu) aerosolized F. tularensis Schu S4, under the conditions described herein, and human pneumonic tularemia cases are highly similar. Animal age, weight, and sex of animals challenged with 300–3,000 cfu Schu S4 did not impact fever onset in studies described herein. This study summarizes critical parameters and endpoints of a well-characterized cynomolgus macaque model of pneumonic tularemia and demonstrates this model is appropriate for qualification, and for testing efficacy of tularemia therapeutics under Animal Rule. PMID

  8. Rationale, design, and implementation protocol of an electronic health record integrated clinical prediction rule (iCPR randomized trial in primary care

    Wisnivesky Juan

    2011-09-01

    Full Text Available Abstract Background Clinical prediction rules (CPRs represent well-validated but underutilized evidence-based medicine tools at the point-of-care. To date, an inability to integrate these rules into an electronic health record (EHR has been a major limitation and we are not aware of a study demonstrating the use of CPR's in an ambulatory EHR setting. The integrated clinical prediction rule (iCPR trial integrates two CPR's in an EHR and assesses both the usability and the effect on evidence-based practice in the primary care setting. Methods A multi-disciplinary design team was assembled to develop a prototype iCPR for validated streptococcal pharyngitis and bacterial pneumonia CPRs. The iCPR tool was built as an active Clinical Decision Support (CDS tool that can be triggered by user action during typical workflow. Using the EHR CDS toolkit, the iCPR risk score calculator was linked to tailored ordered sets, documentation, and patient instructions. The team subsequently conducted two levels of 'real world' usability testing with eight providers per group. Usability data were used to refine and create a production tool. Participating primary care providers (n = 149 were randomized and intervention providers were trained in the use of the new iCPR tool. Rates of iCPR tool triggering in the intervention and control (simulated groups are monitored and subsequent use of the various components of the iCPR tool among intervention encounters is also tracked. The primary outcome is the difference in antibiotic prescribing rates (strep and pneumonia iCPR's encounters and chest x-rays (pneumonia iCPR only between intervention and control providers. Discussion Using iterative usability testing and development paired with provider training, the iCPR CDS tool leverages user-centered design principles to overcome pervasive underutilization of EBM and support evidence-based practice at the point-of-care. The ongoing trial will determine if this collaborative

  9. Development and Validation of a Clinical Prediction Rule of the Return-to-Work Status of Injured Employees in Minnesota.

    Hankins, A Bentley; Reid, Christine A

    2015-09-01

    Vocational rehabilitation services can be a valuable resource to injured employees at risk for sustaining permanent disability. The aim of this study was to develop and validate a predictive model of return-to-work (RTW) status at workers' compensation claim closure that may assist rehabilitation counselors tasked with determining how to allocate such services. A cross-sectional, retrospective study was conducted using data obtained from 15,372 workers' compensation claims in Minnesota's administrative claims database. The association between a set of 15 predictor variables representing medical and contextual factors and the RTW status as of claim closure of the accessible population was assessed using backward stepwise logistic regression. The most parsimonious set of variables that reliably predicted the outcome was selected as the optimal RTW model. This model was then internally validated via a split-dataset approach. Risk factors for failure to RTW by claim closure include the following: (1) attorney involvement; (2) higher level of permanent impairment (PI); (3) shorter job tenure; (4) lower pre-injury average weekly wage (AWW); (5) injury affecting the head and neck or the back; and (6) lower level of educational attainment. The optimal RTW model included four main effects (attorney involvement; severity of PI; age; job tenure) and three first-order interaction effects (pre-injury AWW × pre-injury industry; attorney involvement × severity of PI; attorney involvement × job tenure). When applied to the full dataset, the overall classification rate was 74.7%. This study's optimal RTW model offers further support for evaluating disability from a biopsychosocial perspective. Given the model's performance, it may be of value to those assessing rehabilitation potential within Minnesota's, and possibly other, workers' compensation system(s).

  10. The application of a clinical prediction rule for patients with neck pain likely to benefit from cervical traction: A case report.

    Bernstetter, Andrew

    2016-10-01

    Cervical traction is a commonly utilized intervention in the treatment of patients with neck pain. In 2009, a clinical prediction rule (CPR) was developed as a way to assist clinicians in determining the patient population most likely to respond to cervical traction, though this CPR has yet to be validated. The purpose of this case report is to demonstrate the application of that CPR. The patient was a 46-year-old female with a four-week history of right-sided neck and shoulder pain, with numbness and tingling of her thumb and index finger. Treatment consisted of five sessions provided over 3 weeks. The plan of care included home mechanical cervical traction, exercise, and manual therapy. The patient achieved pain-free cervical range of motion. Neck disability index scores decreased from 28% to 6%, and the Patient-Specific Functional Scale average score improved from 5.5 to 10 out of 10. This case report demonstrates the application of a CPR to assist in deciding if cervical traction is an appropriate intervention. Further research is needed to validate the CPR and to establish the optimal mode of delivery for traction.

  11. Amsterdam wrist rules: A clinical decision aid

    Bentohami Abdelali

    2011-10-01

    Full Text Available Abstract Background Acute trauma of the wrist is one of the most frequent reasons for visiting the Emergency Department. These patients are routinely referred for radiological examination. Most X-rays however, do not reveal any fractures. A clinical decision rule determining the need for X-rays in patients with acute wrist trauma may help to percolate and select patients with fractures. Methods/Design This study will be a multi-center observational diagnostic study in which the data will be collected cross-sectionally. The study population will consist of all consecutive adult patients (≥18 years presenting with acute wrist trauma at the Emergency Department in the participating hospitals. This research comprises two components: one study will be conducted to determine which clinical parameters are predictive for the presence of a distal radius fracture in adult patients presenting to the Emergency Department following acute wrist trauma. These clinical parameters are defined by trauma-mechanism, physical examination, and functional testing. This data will be collected in two of the three participating hospitals and will be assessed by using logistic regression modelling to estimate the regression coefficients after which a reduced model will be created by means of a log likelihood ratio test. The accuracy of the model will be estimated by a goodness of fit test and an ROC curve. The final model will be validated internally through bootstrapping and by shrinking it, an adjusted model will be generated. In the second component of this study, the developed prediction model will be validated in a new dataset consisting of a population of patients from the third hospital. If necessary, the model will be calibrated using the data from the validation study. Discussion Wrist trauma is frequently encountered at the Emergency Department. However, to this date, no decision rule regarding this type of trauma has been created. Ideally, radiographs are

  12. Comparison of the effectiveness of three manual physical therapy techniques in a subgroup of patients with low back pain who satisfy a clinical prediction rule: Study protocol of a randomized clinical trial [NCT00257998

    Childs John D

    2006-02-01

    Full Text Available Abstract Background Recently a clinical prediction rule (CPR has been developed and validated that accurately identifies patients with low back pain (LBP that are likely to benefit from a lumbo-pelvic thrust manipulation. The studies that developed and validated the rule used the identical manipulation procedure. However, recent evidence suggests that different manual therapy techniques may result similar outcomes. The purpose of this study is to investigate the effectiveness of three different manual therapy techniques in a subgroup of patient with low back pain that satisfy the CPR. Methods/Design Consecutive patients with LBP referred to physical therapy clinics in one of four geographical locations who satisfy the CPR will be invited to participate in this randomized clinical trial. Subjects who agree to participate will undergo a standard evaluation and complete a number of patient self-report questionnaires including the Oswestry Disability Index (OSW, which will serve as the primary outcome measure. Following the baseline examination patients will be randomly assigned to receive the lumbopelvic manipulation used in the development of the CPR, an alternative lumbar manipulation technique, or non-thrust lumbar mobilization technique for the first 2 visits. Beginning on visit 3, all 3 groups will receive an identical standard exercise program for 3 visits (visits 3,4,5. Outcomes of interest will be captured by a therapist blind to group assignment at 1 week (3rd visit, 4 weeks (6th visit and at a 6-month follow-up. The primary aim of the study will be tested with analysis of variance (ANOVA using the change in OSW score from baseline to 4-weeks (OSWBaseline – OSW4-weeks as the dependent variable. The independent variable will be treatment with three levels (lumbo-pelvic manipulation, alternative lumbar manipulation, lumbar mobilization. Discussion This trial will be the first to investigate the effectiveness of various manual therapy

  13. Feasibility of automatic evaluation of clinical rules in general practice.

    Opondo, D.; Visscher, S.; Eslami, S.; Medlock, S.; Verheij, R.; Korevaar, J.C.; Abu-Hanna, A.

    2017-01-01

    Purpose: To assess the extent to which clinical rules (CRs) can be implemented for automatic evaluationof quality of care in general practice.Methods: We assessed 81 clinical rules (CRs) adapted from a subset of Assessing Care of Vulnerable Elders(ACOVE) clinical rules, against Dutch College of

  14. Development of a clinical prediction rule for identifying women with tension-type headache who are likely to achieve short-term success with joint mobilization and muscle trigger point therapy.

    Fernández-de-las-Peñas, César; Cleland, Joshua A; Palomeque-del-Cerro, Luis; Caminero, Ana Belén; Guillem-Mesado, Amparo; Jiménez-García, Rodrigo

    2011-02-01

    To identify prognostic factors from the history and physical examination in women with tension-type headache (TTH) who are likely to experience self-perceived clinical improvement following a multimodal physical therapy session including joint mobilization and muscle trigger point (TrP) therapies. No definitive therapeutic intervention is available for TTH. It would be useful for clinicians to have a clinical prediction rule for selecting which TTH patients may experience improved outcomes following a multimodal physical therapy program. Women diagnosed with pure TTH by 3 experienced neurologists according to the International Headache Society criteria from different neurology departments were included. They underwent a standardized examination (neck mobility, pressure pain thresholds, total tenderness score, presence of muscle TrPs, Medical Outcomes Study 36-Item Short Form, the Neck Disability Index [NDI], the Beck Depression Inventory, and the Headache Disability Inventory) and then a multimodal physical therapy session including joint mobilization and TrP therapies. The treatment session included a 30-second grade III or IV central posterior-anterior nonthrust mobilization applied from T4 to T1 thoracic vertebrae, at C7-T1 cervico-thoracic junction and C1-C2 vertebrae for an overall intervention time of 5 minutes Different TrP techniques, particularly soft tissue stroke, pressure release, or muscle energy were applied to head and neck-shoulder muscles (temporalis, suboccipital, upper trapezius, splenius capitis, semispinalis capitis, sternocleidomastoid) to inactivate active muscle TrPs. Participants were classified as having achieved a successful outcome 1 week after the session based on their self-perceived recovery. Potential prognostic variables were entered into a stepwise logistic regression model to determine the most accurate set of variables for prediction of success. Data for 76 subjects were included in the analysis, of which 36 experienced a

  15. Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

    Saidi, Rabie

    2017-08-28

    It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

  16. Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

    Saidi, Rabie; Boudellioua, Imene; Martin, Maria J.; Solovyev, Victor

    2017-01-01

    It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

  17. Using data-driven rules to predict mortality in severe community acquired pneumonia.

    Chuang Wu

    Full Text Available Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available.

  18. A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules

    Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

    2012-01-01

    Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present ...

  19. The Clinical Prediction of Dangerousness.

    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

  20. Appropriateness guidelines and predictive rules to select patients for upper endoscopy: a nationwide multicenter study.

    Buri, Luigi; Hassan, Cesare; Bersani, Gianluca; Anti, Marcello; Bianco, Maria Antonietta; Cipolletta, Livio; Di Giulio, Emilio; Di Matteo, Giovanni; Familiari, Luigi; Ficano, Leonardo; Loriga, Pietro; Morini, Sergio; Pietropaolo, Vincenzo; Zambelli, Alessandro; Grossi, Enzo; Intraligi, Marco; Buscema, Massimo

    2010-06-01

    Selecting patients appropriately for upper endoscopy (EGD) is crucial for efficient use of endoscopy. The objective of this study was to compare different clinical strategies and statistical methods to select patients for EGD, namely appropriateness guidelines, age and/or alarm features, and multivariate and artificial neural network (ANN) models. A nationwide, multicenter, prospective study was undertaken in which consecutive patients referred for EGD during a 1-month period were enrolled. Before EGD, the endoscopist assessed referral appropriateness according to the American Society for Gastrointestinal Endoscopy (ASGE) guidelines, also collecting clinical and demographic variables. Outcomes of the study were detection of relevant findings and new diagnosis of malignancy at EGD. The accuracy of the following clinical strategies and predictive rules was compared: (i) ASGE appropriateness guidelines (indicated vs. not indicated), (ii) simplified rule (>or=45 years or alarm features vs. <45 years without alarm features), (iii) logistic regression model, and (iv) ANN models. A total of 8,252 patients were enrolled in 57 centers. Overall, 3,803 (46%) relevant findings and 132 (1.6%) new malignancies were detected. Sensitivity, specificity, and area under the receiver-operating characteristic curve (AUC) of the simplified rule were similar to that of the ASGE guidelines for both relevant findings (82%/26%/0.55 vs. 88%/27%/0.52) and cancer (97%/22%/0.58 vs. 98%/20%/0.58). Both logistic regression and ANN models seemed to be substantially more accurate in predicting new cases of malignancy, with an AUC of 0.82 and 0.87, respectively. A simple predictive rule based on age and alarm features is similarly effective to the more complex ASGE guidelines in selecting patients for EGD. Regression and ANN models may be useful in identifying a relatively small subgroup of patients at higher risk of cancer.

  1. A random walk rule for phase I clinical trials.

    Durham, S D; Flournoy, N; Rosenberger, W F

    1997-06-01

    We describe a family of random walk rules for the sequential allocation of dose levels to patients in a dose-response study, or phase I clinical trial. Patients are sequentially assigned the next higher, same, or next lower dose level according to some probability distribution, which may be determined by ethical considerations as well as the patient's response. It is shown that one can choose these probabilities in order to center dose level assignments unimodally around any target quantile of interest. Estimation of the quantile is discussed; the maximum likelihood estimator and its variance are derived under a two-parameter logistic distribution, and the maximum likelihood estimator is compared with other nonparametric estimators. Random walk rules have clear advantages: they are simple to implement, and finite and asymptotic distribution theory is completely worked out. For a specific random walk rule, we compute finite and asymptotic properties and give examples of its use in planning studies. Having the finite distribution theory available and tractable obviates the need for elaborate simulation studies to analyze the properties of the design. The small sample properties of our rule, as determined by exact theory, compare favorably to those of the continual reassessment method, determined by simulation.

  2. Development of a clinical prediction rule to improve peripheral intravenous cannulae first attempt success in the emergency department and reduce post insertion failure rates: the Vascular Access Decisions in the Emergency Room (VADER) study protocol.

    Carr, Peter J; Rippey, James C R; Cooke, Marie L; Bharat, Chrianna; Murray, Kevin; Higgins, Niall S; Foale, Aileen; Rickard, Claire M

    2016-02-11

    Peripheral intravenous cannula (PIVC) insertion is one of the most common clinical interventions performed in emergency care worldwide. However, factors associated with successful PIVC placement and maintenance are not well understood. This study seeks to determine the predictors of first time PIVC insertion success in emergency department (ED) and identify the rationale for removal of the ED inserted PIVC in patients admitted to the hospital ward. Reducing failed insertion attempts and improving peripheral intravenous cannulation practice could lead to better staff and patient experiences, as well as improving hospital efficiency. We propose an observational cohort study of PIVC insertions in a patient population presenting to ED, with follow-up observation of the PIVC in subsequent admissions to the hospital ward. We will collect specific PIVC observational data such as; clinician factors, patient factors, device information and clinical practice variables. Trained researchers will gather ED PIVC insertion data to identify predictors of insertion success. In those admitted from the ED, we will determine the dwell time of the ED-inserted PIVC. Multivariate regression analyses will be used to identify factors associated with insertions success and PIVC failure and standard statistical validation techniques will be used to create and assess the effectiveness of a clinical predication rule. The findings of our study will provide new evidence to improve insertion success rates in the ED setting and identify strategies to reduce premature device failure for patients admitted to hospital wards. Results will unravel a complexity of factors that contribute to unsuccessful PIVC attempts such as patient and clinician factors along with the products, technologies and infusates used. ACTRN12615000588594; Pre-results. 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/

  3. A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules.

    Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

    Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods.

  4. Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

    Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

    2017-09-01

    This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

  5. Rule base system in developing groundwater pollution expert system: predicting model

    Mongkon Ta-oun; Mohamed Daud; Mohd Zohadie Bardaie; Shamshuddin Jusop

    2000-01-01

    New techniques are now available for use in the protection of the environment. One of these techniques is the use of expert system for prediction groundwater pollution potential. Groundwater Pollution Expert system (GWPES) rules are a collection of principles and procedures used to know the comprehension of groundwater pollution prediction. The rules of groundwater pollution expert system in the form of questions, choice, radio-box, slide rule, button or frame are translated in to IF-THEN rule. The rules including of variables, types, domains and descriptions were used by the function of wxCLIPS (C Language Integrate Production System) expert system shell. (author)

  6. A clinical return-to-work rule for patients with back pain.

    Dionne, Clermont E; Bourbonnais, Renée; Frémont, Pierre; Rossignol, Michel; Stock, Susan R; Larocque, Isabelle

    2005-06-07

    Tools for early identification of workers with back pain who are at high risk of adverse occupational outcome would help concentrate clinical attention on the patients who need it most, while helping reduce unnecessary interventions (and costs) among the others. This study was conducted to develop and validate clinical rules to predict the 2-year work disability status of people consulting for nonspecific back pain in primary care settings. This was a 2-year prospective cohort study conducted in 7 primary care settings in the Quebec City area. The study enrolled 1007 workers (participation, 68.4% of potential participants expected to be eligible) aged 18-64 years who consulted for nonspecific back pain associated with at least 1 day's absence from work. The majority (86%) completed 5 telephone interviews documenting a large array of variables. Clinical information was abstracted from the medical files. The outcome measure was "return to work in good health" at 2 years, a variable that combined patients' occupational status, functional limitations and recurrences of work absence. Predictive models of 2-year outcome were developed with a recursive partitioning approach on a 40% random sample of our study subjects, then validated on the rest. The best predictive model included 7 baseline variables (patient's recovery expectations, radiating pain, previous back surgery, pain intensity, frequent change of position because of back pain, irritability and bad temper, and difficulty sleeping) and was particularly efficient at identifying patients with no adverse occupational outcome (negative predictive value 78%- 94%). A clinical prediction rule accurately identified a large proportion of workers with back pain consulting in a primary care setting who were at a low risk of an adverse occupational outcome.

  7. Design of a Fuzzy Rule Base Expert System to Predict and Classify ...

    The main objective of design of a rule base expert system using fuzzy logic approach is to predict and forecast the risk level of cardiac patients to avoid sudden death. In this proposed system, uncertainty is captured using rule base and classification using fuzzy c-means clustering is discussed to overcome the risk level, ...

  8. Clinical studies of biomarkers in suicide prediction

    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...

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

    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.

  10. Predictability of Technical Trading Rules: Evidence from the Taiwan Stock Market

    Kung, James J.

    2009-01-01

    Using the Taiwan Stock Exchange Weighted Index from the first trading day in 1975 to the last trading day in 2007, we investigate the predictability of two popular technical rules (variable-length moving average and trading range breakout) in the Taiwan stock market and assess its bearing on market efficiency. Our results show that, for the two rules, returns from buy signals are generally higher than those from sell signals. In addition, they exhibit considerable predictive power over 1975-1...

  11. A modified Miner rule to predict crack initiation

    Taheri, S.

    1992-07-01

    We propose a damage cumulation rule and an upper bound of cumulated damage before propagation using: linear damage function, linear cumulation in proportional or non proportional loading in presence of overloading or ratchetting. We take into account effect of prehardening on Cyclic Stress Strain Curve (C.S.S.C), and define a non proportional state cyclically equivalent to an uniaxial one. We show an important difference between a load controlled and a strain controlled experiment for difficult cross-slip materials as 316 stainless steel described by a non stable C.S.S.C. (author). 17 refs., 10 figs

  12. Prediction on carbon dioxide emissions based on fuzzy rules

    Pauzi, Herrini; Abdullah, Lazim

    2014-06-01

    There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

  13. Testing the performance of technical trading rules in the Chinese markets based on superior predictive test

    Wang, Shan; Jiang, Zhi-Qiang; Li, Sai-Ping; Zhou, Wei-Xing

    2015-12-01

    Technical trading rules have a long history of being used by practitioners in financial markets. The profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousand traditional technical trading rules on the Shanghai Securities Composite Index (SSCI) from May 21, 1992 through June 30, 2013 and China Securities Index 300 (CSI 300) from April 8, 2005 through June 30, 2013 to check whether an effective trading strategy could be found by using the performance measurements based on the return and Sharpe ratio. To correct for the influence of the data-snooping effect, we adopt the Superior Predictive Ability test to evaluate if there exists a trading rule that can significantly outperform the benchmark. The result shows that for SSCI, technical trading rules offer significant profitability, while for CSI 300, this ability is lost. We further partition the SSCI into two sub-series and find that the efficiency of technical trading in sub-series, which have exactly the same spanning period as that of CSI 300, is severely weakened. By testing the trading rules on both indexes with a five-year moving window, we find that during the financial bubble from 2005 to 2007, the effectiveness of technical trading rules is greatly improved. This is consistent with the predictive ability of technical trading rules which appears when the market is less efficient.

  14. Predictive performance of universal termination of resuscitation rules in an Asian community: are they accurate enough?

    Chiang, Wen-Chu; Ko, Patrick Chow-In; Chang, Anna Marie; Liu, Sot Shih-Hung; Wang, Hui-Chih; Yang, Chih-Wei; Hsieh, Ming-Ju; Chen, Shey-Ying; Lai, Mei-Shu; Ma, Matthew Huei-Ming

    2015-04-01

    Prehospital termination of resuscitation (TOR) rules have not been widely validated outside of Western countries. This study evaluated the performance of TOR rules in an Asian metropolitan with a mixed-tier emergency medical service (EMS). We analysed the Utstein registry of adult, non-traumatic out-of-hospital cardiac arrests (OHCAs) in Taipei to test the performance of TOR rules for advanced life support (ALS) or basic life support (BLS) providers. ALS and BLS-TOR rules were tested in OHCAs among three subgroups: (1) resuscitated by ALS, (2) by BLS and (3) by mixed ALS and BLS. Outcome definition was in-hospital death. Sensitivity, specificity, positive predictive value (PPV), negative predictive value and decreased transport rate (DTR) among various provider combinations were calculated. Of the 3489 OHCAs included, 240 were resuscitated by ALS, 1727 by BLS and 1522 by ALS and BLS. Overall survival to hospital discharge was 197 patients (5.6%). Specificity and PPV of ALS-TOR and BLS-TOR for identifying death ranged from 70.7% to 81.8% and 95.1% to 98.1%, respectively. Applying the TOR rules would have a DTR of 34.2-63.9%. BLS rules had better predictive accuracy and DTR than ALS rules among all subgroups. Application of the ALS and BLS TOR rules would have decreased OHCA transported to the hospital, and BLS rules are reasonable as the universal criteria in a mixed-tier EMS. However, 1.9-4.9% of those who survived would be misclassified as non-survivors, raising concern of compromising patient safety for the implementation of the rules. 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.

  15. A rule-based backchannel prediction model using pitch and pause information

    Truong, Khiet Phuong; Poppe, Ronald Walter; Heylen, Dirk K.J.

    We manually designed rules for a backchannel (BC) prediction model based on pitch and pause information. In short, the model predicts a BC when there is a pause of a certain length that is preceded by a falling or rising pitch. This model was validated against the Dutch IFADV Corpus in a

  16. Performance of thirteen clinical rules to distinguish bacterial and presumed viral meningitis in Vietnamese children.

    Nguyen Tien Huy

    Full Text Available BACKGROUND AND PURPOSE: Successful outcomes from bacterial meningitis require rapid antibiotic treatment; however, unnecessary treatment of viral meningitis may lead to increased toxicities and expense. Thus, improved diagnostics are required to maximize treatment and minimize side effects and cost. Thirteen clinical decision rules have been reported to identify bacterial from viral meningitis. However, few rules have been tested and compared in a single study, while several rules are yet to be tested by independent researchers or in pediatric populations. Thus, simultaneous test and comparison of these rules are required to enable clinicians to select an optimal diagnostic rule for bacterial meningitis in settings and populations similar to ours. METHODS: A retrospective cross-sectional study was conducted at the Infectious Department of Pediatric Hospital Number 1, Ho Chi Minh City, Vietnam. The performance of the clinical rules was evaluated by area under a receiver operating characteristic curve (ROC-AUC using the method of DeLong and McNemar test for specificity comparison. RESULTS: Our study included 129 patients, of whom 80 had bacterial meningitis and 49 had presumed viral meningitis. Spanos's rule had the highest AUC at 0.938 but was not significantly greater than other rules. No rule provided 100% sensitivity with a specificity higher than 50%. Based on our calculation of theoretical sensitivity and specificity, we suggest that a perfect rule requires at least four independent variables that posses both sensitivity and specificity higher than 85-90%. CONCLUSIONS: No clinical decision rules provided an acceptable specificity (>50% with 100% sensitivity when applying our data set in children. More studies in Vietnam and developing countries are required to develop and/or validate clinical rules and more very good biomarkers are required to develop such a perfect rule.

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

    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.

  18. A prediction rule for shoulder pain related sick leave: a prospective cohort study

    van der Heijden Geert JMG

    2006-12-01

    Full Text Available Abstract Background Shoulder pain is common in primary care, and has an unfavourable outcome in many patients. Information about predictors of shoulder pain related sick leave in workers is scarce and inconsistent. The objective was to develop a clinical prediction rule for calculating the risk of shoulder pain related sick leave for individual workers, during the 6 months following first consultation in general practice. Methods A prospective cohort study with 6 months follow-up was conducted among 350 workers with a new episode of shoulder pain. Potential predictors included the results of a physical examination, sociodemographic variables, disease characteristics (duration of symptoms, sick leave in the 2 months prior to consultation, pain intensity, disability, comorbidity, physical activity, physical work load, psychological factors, and the psychosocial work environment. The main outcome measure was sick leave during 6 months following first consultation in general practice. Results Response rate to the follow-up questionnaire at 6 months was 85%. During the 6 months after first consultation 30% (89/298 of the workers reported sick leave. 16% (47 reported 10 days sick leave or more. Sick leave during this period was predicted in a multivariable model by a longer duration of sick leave prior to consultation, more shoulder pain, a perceived cause of strain or overuse during regular activities, and co-existing psychological complaints. The discriminative ability of the prediction model was satisfactory with an area under the curve of 0.70 (95% CI 0.64–0.76. Conclusion Although 30% of all workers with shoulder pain reported sick leave during follow-up, the duration of sick leave was limited to a few days in most workers. We developed a prediction rule and a score chart that can be used by general practitioners and occupational health care providers to calculate the absolute risk of sick leave in individual workers with shoulder pain, which

  19. A Prediction Rule for Risk Stratification of Incidentally Discovered Gallstones

    Shabanzadeh, Daniel Mønsted; Sørensen, Lars Tue; Jørgensen, Torben

    2016-01-01

    % confidence interval [CI], 1.45-3.69), acute cholecystitis (HR, 9.49; 95% CI, 2.05-43.92), and uncomplicated events (HR, 2.55; 95% CI, 1.38-4.71), including cholecystectomy (HR, 2.69; 95% CI, 1.29-5.60). Multiple stones were associated with all events (HR, 1.68; 95% CI, 1.00-2.81), complicated events (HR, 2...... the highest risk for events (HR, 11.05; 95% CI, 3.76-32.44; unadjusted absolute risk, 0.0235 events/person-years). CONCLUSIONS: Fewer than 20% of subjects with gallstones develop clinical events. Larger, multiple, and older gallstones are associated with events. Further studies are needed to confirm...

  20. Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data

    Osamu Komori

    2013-01-01

    Full Text Available This paper discusses mathematical and statistical aspects in analysis methods applied to microarray gene expressions. We focus on pattern recognition to extract informative features embedded in the data for prediction of phenotypes. It has been pointed out that there are severely difficult problems due to the unbalance in the number of observed genes compared with the number of observed subjects. We make a reanalysis of microarray gene expression published data to detect many other gene sets with almost the same performance. We conclude in the current stage that it is not possible to extract only informative genes with high performance in the all observed genes. We investigate the reason why this difficulty still exists even though there are actively proposed analysis methods and learning algorithms in statistical machine learning approaches. We focus on the mutual coherence or the absolute value of the Pearson correlations between two genes and describe the distributions of the correlation for the selected set of genes and the total set. We show that the problem of finding informative genes in high dimensional data is ill-posed and that the difficulty is closely related with the mutual coherence.

  1. Radiogenomics: predicting clinical normal tissue radiosensitivity

    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...

  2. What predicts performance during clinical psychology training?

    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...

  3. A study of diverse clinical decision support rule authoring environments and requirements for integration

    Zhou Li

    2012-11-01

    Full Text Available Abstract Background Efficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs, Software Engineers (SEs, and Subject Matter Experts (SMEs to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules. Methods The authors evaluated RAEs in active use across Partners Healthcare, including enterprise wide, ambulatory only, and system specific tools, with a focus on rule editors for reminder and medication rules. We conducted meetings with users of these RAEs to discuss their general experience and perceived advantages and limitations of these tools. Results While the overall rule authoring process is similar across the 10 separate RAEs, the system capabilities and architecture vary widely. Most current RAEs limit the ability of the clinical decision support (CDS interventions to be standardized, sharable, interoperable, and extensible. No existing system meets all requirements defined by knowledge management users. Conclusions A successful, scalable, integrated rule authoring environment will need to support a number of key requirements and functions in the areas of knowledge representation, metadata, terminology, authoring collaboration, user interface, integration with electronic health record (EHR systems, testing, and reporting.

  4. Effect of mixing rule boundary conditions on high pressure (liquid + liquid) equilibrium prediction

    Hsieh, Min-Kang; Lin, Shiang-Tai

    2012-01-01

    Highlights: ► Prediction of LLE from the combined use of EOS and liquid model are examined. ► The mixing rule used affects the predicted pressure dependence of LLE. ► MHV1 mixing rule predicts decent LLE at low pressures. ► WS mixing rule predicts more accurate excess volume and LLE at high pressures. ► The hybrid of MHV1 and WS mixing rule gives overall the best predictions. - Abstract: We examine the prediction of high pressure (liquid + liquid) equilibrium (LLE) from the Peng–Robinson equation with three excess Gibbs free energy (G ex )-based mixing rules (MR): the first order modified Huron–Vidal (MHV1), the Wong–Sandler (WS), and a hybrid of these two (referred to as G ex B 2 ). These mixing rules differ by the boundary conditions used for determination of the temperature and composition dependence of parameters a and b in the PR EOS. The condition of matching the excess Gibbs free energy from the EOS at zero pressure to that from the G ex model, used in MHV1 and G ex B 2 MR, leads to a similar miscibility gap from PR EOS and the G ex model used. On the other hand, the condition of matching excess Helmholtz energy from the EOS at infinite pressure to that from the G ex model, used in the WS MR, shows remarkable deviations. The condition of quadratic composition dependence in the second virial coefficient (B 2 ), used in WS and G ex B 2 MR, allows for both positive and negative values in the molar excess volume. Depending on the mixture, either the increase or decrease of the miscibility gap with pressure can be observed when the WS or the G ex B 2 MR is used. The condition of linear combination of molecular sizes of each component used in the MHV1 MR, however, often leads to small, positive molar excess volumes. As a consequence, the predicted LLE from using the MHV1 MR are insensitive to pressure. Therefore, we find that the G ex B 2 mixing rule provides the best predictive power for the LLE over a wide range of temperature and pressure.

  5. Injury and death in clinical trials and compensation: Rule 122 DAB

    Ravindra B Ghooi

    2013-01-01

    Full Text Available Three amendments to the drugs and cosmetics rules were published in quick succession in 2013. These addressed the issues of compensation of injury and death in clinical trials in addition to the role and registration of Ethics Committees. Of the three, the first and the third make an impact on the clinical research activities in India. The second amendment has codified the conduct of clinical trials, putting together rules, which appeared in different sections of Schedule Y. The first amendment deals with the compensation for injuries and deaths taking place during clinical trials while the third deals with registration of Ethics Committees. Despite the long delay in the issue of compensation rules, there appears significant room for improvement. The most problematic are conditions of injury and death in which compensation has to be paid. When compared with other countries, the Indian rules seem unduly harsh on sponsors and are at significant variance with those in UK. The rules are very generous toward subjects and compensation is likely to become an alternative to insurance in terminally ill subjects. The implementation of these rules will make clinical trials in India more expensive and hurt the industry that is already struggling through other handicaps. There is an urgent need to make the the environment more industry friendly to attract more clinical work.

  6. Re-Evaluation of Acid-Base Prediction Rules in Patients with Chronic Respiratory Acidosis

    Tereza Martinu

    2003-01-01

    Full Text Available RATIONALE: The prediction rules for the evaluation of the acid-base status in patients with chronic respiratory acidosis, derived primarily from an experimental canine model, suggest that complete compensation should not occur. This appears to contradict frequent observations of normal or near-normal pH levels in patients with chronic hypercapnia.

  7. A clinical decision rule for the use of plain radiography in children after acute wrist injury: development and external validation of the Amsterdam Pediatric Wrist Rules

    Slaar, Annelie; Maas, Mario; Rijn, Rick R. van [University of Amsterdam, Department of Radiology, Academic Medical Centre, Meibergdreef 9, 1105, AZ, Amsterdam (Netherlands); Walenkamp, Monique M.J.; Bentohami, Abdelali; Goslings, J.C. [University of Amsterdam, Trauma Unit, Department of Surgery, Academic Medical Centre, Amsterdam (Netherlands); Steyerberg, Ewout W. [Erasmus MC - University Medical Centre, Department of Public Health, Rotterdam (Netherlands); Jager, L.C. [University of Amsterdam, Emergency Department, Academic Medical Centre, Amsterdam (Netherlands); Sosef, Nico L. [Spaarne Hospital, Department of Surgery, Hoofddorp (Netherlands); Velde, Romuald van [Tergooi Hospitals, Department of Surgery, Hilversum (Netherlands); Ultee, Jan M. [Sint Lucas Andreas Hospital, Department of Surgery, Amsterdam (Netherlands); Schep, Niels W.L. [University of Amsterdam, Trauma Unit, Department of Surgery, Academic Medical Centre, Amsterdam (Netherlands); Maasstadziekenhuis Rotterdam, Department of Surgery, Rotterdam (Netherlands)

    2016-01-15

    In most hospitals, children with acute wrist trauma are routinely referred for radiography. To develop and validate a clinical decision rule to decide whether radiography in children with wrist trauma is required. We prospectively developed and validated a clinical decision rule in two study populations. All children who presented in the emergency department of four hospitals with pain following wrist trauma were included and evaluated for 18 clinical variables. The outcome was a wrist fracture diagnosed by plain radiography. Included in the study were 787 children. The prediction model consisted of six variables: age, swelling of the distal radius, visible deformation, distal radius tender to palpation, anatomical snuffbox tender to palpation, and painful or abnormal supination. The model showed an area under the receiver operator characteristics curve of 0.79 (95% CI: 0.76-0.83). The sensitivity and specificity were 95.9% and 37.3%, respectively. The use of this model would have resulted in a 22% absolute reduction of radiographic examinations. In a validation study, 7/170 fractures (4.1%, 95% CI: 1.7-8.3%) would have been missed using the decision model. The decision model may be a valuable tool to decide whether radiography in children after wrist trauma is required. (orig.)

  8. A clinical decision rule for the use of plain radiography in children after acute wrist injury: development and external validation of the Amsterdam Pediatric Wrist Rules

    Slaar, Annelie; Maas, Mario; Rijn, Rick R. van; Walenkamp, Monique M.J.; Bentohami, Abdelali; Goslings, J.C.; Steyerberg, Ewout W.; Jager, L.C.; Sosef, Nico L.; Velde, Romuald van; Ultee, Jan M.; Schep, Niels W.L.

    2016-01-01

    In most hospitals, children with acute wrist trauma are routinely referred for radiography. To develop and validate a clinical decision rule to decide whether radiography in children with wrist trauma is required. We prospectively developed and validated a clinical decision rule in two study populations. All children who presented in the emergency department of four hospitals with pain following wrist trauma were included and evaluated for 18 clinical variables. The outcome was a wrist fracture diagnosed by plain radiography. Included in the study were 787 children. The prediction model consisted of six variables: age, swelling of the distal radius, visible deformation, distal radius tender to palpation, anatomical snuffbox tender to palpation, and painful or abnormal supination. The model showed an area under the receiver operator characteristics curve of 0.79 (95% CI: 0.76-0.83). The sensitivity and specificity were 95.9% and 37.3%, respectively. The use of this model would have resulted in a 22% absolute reduction of radiographic examinations. In a validation study, 7/170 fractures (4.1%, 95% CI: 1.7-8.3%) would have been missed using the decision model. The decision model may be a valuable tool to decide whether radiography in children after wrist trauma is required. (orig.)

  9. Clinical value of the Ottawa ankle rules for diagnosis of fractures in acute ankle injuries.

    Xin Wang

    Full Text Available BACKGROUND: The Ottawa ankle rules (OAR are clinical decision guidelines used to identify whether patients with ankle injuries need to undergo radiography. The OAR have been proven that their application reduces unnecessary radiography. They have nearly perfect sensitivity for identifying clinically significant ankle fractures. OBJECTIVES: The purpose of this study was to assess the applicability of the OAR in China, to examine their accuracy for the diagnosis of fractures in patients with acute ankle sprains, and to assess their clinical utility for the detection of occult fractures. METHODS: In this prospective study, patients with acute ankle injuries were enrolled during a 6-month period. The eligible patients were examined by emergency orthopedic specialists using the OAR, and then underwent ankle radiography. The results of examination using the OAR were compared with the radiographic results to assess the accuracy of the OAR for ankle fractures. Patients with OAR results highly suggestive of fracture, but no evidence of a fracture on radiographs, were advised to undergo 3-dimensional computed tomography (3D-CT. RESULTS: 183 patients with ankle injuries were enrolled in the study and 63 of these injuries involved fractures. The pooled sensitivity, specificity, positive predictive value and negative predictive value of the OAR for detection of fractures of the ankle were 96.8%, 45.8%, 48.4% and 96.5%, respectively. Our results suggest that clinical application of the OAR could decrease unnecessary radiographs by 31.1%. Of the 21 patients with positive OAR results and negative radiographic findings who underwent 3D-CT examination, five had occult fractures of the lateral malleolus. CONCLUSIONS: The OAR are applicable in the Chinese population, and have high sensitivity and modest specificity for the diagnosis of fractures associated with acute ankle injury. They may detect some occult fractures of the malleoli that are not visible on

  10. Behavioral rules of bank’s point-of-sale for segments description and scoring prediction

    Mehdi Bizhani

    2011-04-01

    Full Text Available One of the important factors for the success of a bank industry is to monitor their customers' behavior and their point-of-sale (POS. The bank needs to know its merchants' behavior to find interesting ones to attract more transactions which results in the growth of its income and assets. The recency, frequency and monetary (RFM analysis is a famous approach for extracting behavior of customers and is a basis for marketing and customer relationship management (CRM, but it is not aligned enough for banking context. Introducing RF*M* in this article results in a better understanding of groups of merchants. Another artifact of RF*M* is RF*M* scoring which is applied in two ways, preprocessing the POSs and assigning behavioral meaningful labels to the merchants’ segments. The class labels and the RF*M* parameters are entered into a rule-based classification algorithm to achieve descriptive rules of the clusters. These descriptive rules outlined the boundaries of RF*M* parameters for each cluster. Since the rules are generated by a classification algorithm, they can also be applied for predicting the behavioral label and scoring of the upcoming POSs. These rules are called behavioral rules.

  11. Insuring Care: Paperwork, Insurance Rules, and Clinical Labor at a U.S. Transgender Clinic.

    van Eijk, Marieke

    2017-12-01

    What is a clinician to do when people needing medical care do not have access to consistent or sufficient health insurance coverage and cannot pay for care privately? Analyzing ethnographically how clinicians at a university-based transgender clinic in the United States responded to this challenge, I examine the U.S. health insurance system, insurance paperwork, and administrative procedures that shape transgender care delivery. To buffer the impact of the system's failure to provide sufficient health insurance coverage for transgender care, clinicians blended administrative routines with psychological therapy, counseled people's minds and finances, and leveraged the prestige of their clinic in attempts to create space for gender nonconforming embodiments in gender conservative insurance policies. My analysis demonstrates that in a market-based health insurance system with multiple payers and gender binary insurance rules, health care may be unaffordable, or remain financially challenging, even for transgender people with health insurance. Moreover, insurance carriers' "reliance" on clinicians' insurance-related labor is problematic as it exacerbates existing insurance barriers to the accessibility and affordability of transgender care and obscures the workings of a financial payment model that prioritizes economic expediency over gender nonconforming health.

  12. Program grants for black lung clinics--PHS. Final rule.

    1985-02-27

    The Public Health Service hereby revises the regulations governing the grants program for black lung clinics established under section 427(a) of the Federal Mine Safety and Health Act of 1977. The revision is in keeping with the Department of Health and Human Services' desire to remove as many programmatic burdens and restrictions from grantees as possible, while maintaining program integrity.

  13. Implications of Derived Rule Following of Roulette Gambling for Clinical Practice.

    Wilson, Alyssa N; Grant, Tara

    2015-05-01

    Problem gambling is a global concern, and behavior analytic attention has increasingly focused on reasons for why problem gambling occurs and conditions under which it is maintained. However, limited knowledge currently exists on the process to which self-generated rules maintain gambling behaviors. Therefore, the current study assessed six recreational gamblers on a roulette game before and after discrimination training to establish a self-rule to wager on red or black. Following discrimination training, all six participants altered their response allocation among red or black and consistently responded according to the newly derived self-rule. Results maintained during 1-week follow-up sessions across all participants. Implications for clinical application of self-awareness and self-generated rule following are discussed. Implications for practice • Demonstration of how stimuli such as color can alter gambling behavior • Procedures to assist clients with changing self-rules about gambling behavior • Using self-generated rule formulation for more contextually appropriate target behaviors • Highlights how self-generated rules can be altered to change clinical target behaviors.

  14. Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules.

    Lezcano, Leonardo; Sicilia, Miguel-Angel; Rodríguez-Solano, Carlos

    2011-04-01

    Semantic interoperability is essential to facilitate the computerized support for alerts, workflow management and evidence-based healthcare across heterogeneous electronic health record (EHR) systems. Clinical archetypes, which are formal definitions of specific clinical concepts defined as specializations of a generic reference (information) model, provide a mechanism to express data structures in a shared and interoperable way. However, currently available archetype languages do not provide direct support for mapping to formal ontologies and then exploiting reasoning on clinical knowledge, which are key ingredients of full semantic interoperability, as stated in the SemanticHEALTH report [1]. This paper reports on an approach to translate definitions expressed in the openEHR Archetype Definition Language (ADL) to a formal representation expressed using the Ontology Web Language (OWL). The formal representations are then integrated with rules expressed with Semantic Web Rule Language (SWRL) expressions, providing an approach to apply the SWRL rules to concrete instances of clinical data. Sharing the knowledge expressed in the form of rules is consistent with the philosophy of open sharing, encouraged by archetypes. Our approach also allows the reuse of formal knowledge, expressed through ontologies, and extends reuse to propositions of declarative knowledge, such as those encoded in clinical guidelines. This paper describes the ADL-to-OWL translation approach, describes the techniques to map archetypes to formal ontologies, and demonstrates how rules can be applied to the resulting representation. We provide examples taken from a patient safety alerting system to illustrate our approach. Copyright © 2010 Elsevier Inc. All rights reserved.

  15. Predicting occupational asthma and rhinitis in bakery workers referred for clinical evaluation

    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

  16. Predictions for the Dirac C P -violating phase from sum rules

    Delgadillo, Luis A.; Everett, Lisa L.; Ramos, Raymundo; Stuart, Alexander J.

    2018-05-01

    We explore the implications of recent results relating the Dirac C P -violating phase to predicted and measured leptonic mixing angles within a standard set of theoretical scenarios in which charged lepton corrections are responsible for generating a nonzero value of the reactor mixing angle. We employ a full set of leptonic sum rules as required by the unitarity of the lepton mixing matrix, which can be reduced to predictions for the observable mixing angles and the Dirac C P -violating phase in terms of model parameters. These sum rules are investigated within a given set of theoretical scenarios for the neutrino sector diagonalization matrix for several known classes of charged lepton corrections. The results provide explicit maps of the allowed model parameter space within each given scenario and assumed form of charged lepton perturbations.

  17. Sensitivity of a Clinical Decision Rule and Early Computed Tomography in Aneurysmal Subarachnoid Hemorrhage

    Dustin G. Mark

    2015-10-01

    Full Text Available Introduction: Application of a clinical decision rule for subarachnoid hemorrhage, in combination with cranial computed tomography (CT performed within six hours of ictus (early cranial CT, may be able to reasonably exclude a diagnosis of aneurysmal subarachnoid hemorrhage (aSAH. This study’s objective was to examine the sensitivity of both early cranial CT and a previously validated clinical decision rule among emergency department (ED patients with aSAH and a normal mental status. Methods: Patients were evaluated in the 21 EDs of an integrated health delivery system between January 2007 and June 2013. We identified by chart review a retrospective cohort of patients diagnosed with aSAH in the setting of a normal mental status and performance of early cranial CT. Variables comprising the SAH clinical decision rule (age >40, presence of neck pain or stiffness, headache onset with exertion, loss of consciousness at headache onset were abstracted from the chart and assessed for inter-rater reliability. Results: One hundred fifty-five patients with aSAH met study inclusion criteria. The sensitivity of early cranial CT was 95.5% (95% CI [90.9-98.2]. The sensitivity of the SAH clinical decision rule was also 95.5% (95% CI [90.9-98.2]. Since all false negative cases for each diagnostic modality were mutually independent, the combined use of both early cranial CT and the clinical decision rule improved sensitivity to 100% (95% CI [97.6-100.0]. Conclusion: Neither early cranial CT nor the SAH clinical decision rule demonstrated ideal sensitivity for aSAH in this retrospective cohort. However, the combination of both strategies might optimize sensitivity for this life-threatening disease.

  18. A programmable rules engine to provide clinical decision support using HTML forms.

    Heusinkveld, J; Geissbuhler, A; Sheshelidze, D; Miller, R

    1999-01-01

    The authors have developed a simple method for specifying rules to be applied to information on HTML forms. This approach allows clinical experts, who lack the programming expertise needed to write CGI scripts, to construct and maintain domain-specific knowledge and ordering capabilities within WizOrder, the order-entry and decision support system used at Vanderbilt Hospital. The clinical knowledge base maintainers use HTML editors to create forms and spreadsheet programs for rule entry. A test environment has been developed which uses Netscape to display forms; the production environment displays forms using an embedded browser.

  19. The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents

    Ziad Salem

    2014-12-01

    Full Text Available Learning is the act of obtaining new or modifying existing knowledge, behaviours, skills or preferences. The ability to learn is found in humans, other organisms and some machines. Learning is always based on some sort of observations or data such as examples, direct experience or instruction. This paper presents a classification algorithm to learn the density of agents in an arena based on the measurements of six proximity sensors of a combined actuator sensor units (CASUs. Rules are presented that were induced by the learning algorithm that was trained with data-sets based on the CASU’s sensor data streams collected during a number of experiments with “Bristlebots (agents in the arena (environment”. It was found that a set of rules generated by the learning algorithm is able to predict the number of bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.

  20. ABOUT CLINICAL EXPERT SYSTEM BASED ON RULES USING DATA MINING TECHNOLOGY

    V. P. Martsenyuk

    2015-05-01

    Full Text Available In the work the topics of software implementation of rule induction method based on sequential covering algorithm are considered. Such approach allows us to develop clinical decision support system. The project is implemented within Netbeans IDE based on Java-classes.

  1. Promoting Changes in Children's Predictive Rules about Natural Phenomena: The Role of Computer-Based Modelling Strategies. Technical Report.

    Frenette, Micheline

    Trying to change the predictive rule for the sinking and floating phenomena, students have a great difficulty in understanding density and they are insensitive to empirical counter-examples designed to challenge their own rule. The purpose of this study is to examine the process whereby students from sixth and seventh grades relinquish their…

  2. Using prediction markets of market scoring rule to forecast infectious diseases: a case study in Taiwan.

    Tung, Chen-yuan; Chou, Tzu-chuan; Lin, Jih-wen

    2015-08-11

    The Taiwan CDC relied on the historical average number of disease cases or rate (AVG) to depict the trend of epidemic diseases in Taiwan. By comparing the historical average data with prediction markets, we show that the latter have a better prediction capability than the former. Given the volatility of the infectious diseases in Taiwan, historical average is unlikely to be an effective prediction mechanism. We designed and built the Epidemic Prediction Markets (EPM) system based upon the trading mechanism of market scoring rule. By using this system, we aggregated dispersed information from various medical professionals to predict influenza, enterovirus, and dengue fever in Taiwan. EPM was more accurate in 701 out of 1,085 prediction events than the traditional baseline of historical average and the winning ratio of EPM versus AVG was 64.6 % for the target week. For the absolute prediction error of five diseases indicators of three infectious diseases, EPM was more accurate for the target week than AVG except for dengue fever confirmed cases. The winning ratios of EPM versus AVG for the confirmed cases of severe complicated influenza case, the rate of enterovirus infection, and the rate of influenza-like illness in the target week were 69.6 %, 83.9 and 76.0 %, respectively; instead, for the prediction of the confirmed cases of dengue fever and the confirmed cases of severe complicated enterovirus infection, the winning ratios of EPM were all below 50 %. Except confirmed cases of dengue fever, EPM provided accurate, continuous and real-time predictions of four indicators of three infectious diseases for the target week in Taiwan and outperformed the historical average data of infectious diseases.

  3. Prediction of high pressure vapor-liquid equilibria with mixing rule using ASOG group contribution method

    Tochigi, K.; Kojima, K.; Kurihara, K.

    1985-02-01

    To develop a widely applicable method for predicting high-pressure vapor-liquid equilibria by the equation of state, a mixing rule is proposed in which mixture energy parameter ''..cap alpha..'' of theSoave-RedlichKwong, Peng-Robinson, and Martin cubic equations of state is expressed by using the ASOG group contribution method. The group pair parameters are then determined for 14 group pairs constituted by six groups, i.e. CH/sub 4/, CH/sub 3/, CH/sub 2/, N/sub 2/, H/sub 2/, and CO/sub 2/ groups. By using the group pair parameters determined, high-pressure vapor-liquid equilibria are predicted with good accuracy for binary and ternary systems constituted by n-paraffins, nitrogen, hydrogen, and carbon dioxide in the temperature range of 100 - 450K.

  4. Prediction of high-grade vesicoureteral reflux after pediatric urinary tract infection: external validation study of procalcitonin-based decision rule.

    Sandrine Leroy

    Full Text Available Predicting vesico-ureteral reflux (VUR ≥3 at the time of the first urinary tract infection (UTI would make it possible to restrict cystography to high-risk children. We previously derived the following clinical decision rule for that purpose: cystography should be performed in cases with ureteral dilation and a serum procalcitonin level ≥0.17 ng/mL, or without ureteral dilatation when the serum procalcitonin level ≥0.63 ng/mL. The rule yielded a 86% sensitivity with a 46% specificity. We aimed to test its reproducibility.A secondary analysis of prospective series of children with a first UTI. The rule was applied, and predictive ability was calculated.The study included 413 patients (157 boys, VUR ≥3 in 11% from eight centers in five countries. The rule offered a 46% specificity (95% CI, 41-52, not different from the one in the derivation study. However, the sensitivity significantly decreased to 64% (95%CI, 50-76, leading to a difference of 20% (95%CI, 17-36. In all, 16 (34% patients among the 47 with VUR ≥3 were misdiagnosed by the rule. This lack of reproducibility might result primarily from a difference between derivation and validation populations regarding inflammatory parameters (CRP, PCT; the validation set samples may have been collected earlier than for the derivation one.The rule built to predict VUR ≥3 had a stable specificity (ie. 46%, but a decreased sensitivity (ie. 64% because of the time variability of PCT measurement. Some refinement may be warranted.

  5. External Validation and Update of a Prediction Rule for the Duration of Sickness Absence Due to Common Mental Disorders

    Norder, Giny; Roelen, Corne A. M.; van der Klink, Jac J. L.; Bultmann, Ute; Sluiter, J. K.; Nieuwenhuijsen, K.

    Purpose The objective of the present study was to validate an existing prediction rule (including age, education, depressive/anxiety symptoms, and recovery expectations) for predictions of the duration of sickness absence due to common mental disorders (CMDs) and investigate the added value of

  6. Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

    Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

    2017-12-01

    This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.

  7. A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm

    Yudong Zhang

    2013-01-01

    Full Text Available In this paper, we proposed a hybrid system to predict corporate bankruptcy. The whole procedure consists of the following four stages: first, sequential forward selection was used to extract the most important features; second, a rule-based model was chosen to fit the given dataset since it can present physical meaning; third, a genetic ant colony algorithm (GACA was introduced; the fitness scaling strategy and the chaotic operator were incorporated with GACA, forming a new algorithm—fitness-scaling chaotic GACA (FSCGACA, which was used to seek the optimal parameters of the rule-based model; and finally, the stratified K-fold cross-validation technique was used to enhance the generalization of the model. Simulation experiments of 1000 corporations’ data collected from 2006 to 2009 demonstrated that the proposed model was effective. It selected the 5 most important factors as “net income to stock broker’s equality,” “quick ratio,” “retained earnings to total assets,” “stockholders’ equity to total assets,” and “financial expenses to sales.” The total misclassification error of the proposed FSCGACA was only 7.9%, exceeding the results of genetic algorithm (GA, ant colony algorithm (ACA, and GACA. The average computation time of the model is 2.02 s.

  8. Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

    Boudellioua, Imene; Saidi, Rabie; Hoehndorf, Robert; Martin, Maria J.; Solovyev, Victor

    2016-01-01

    The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.

  9. Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

    Boudellioua, Imene

    2016-07-08

    The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.

  10. The Diagnosis of Urinary Tract Infection in Young Children (DUTY) Study Clinical Rule: Economic Evaluation.

    Hollingworth, William; Busby, John; Butler, Christopher C; O'Brien, Kathryn; Sterne, Jonathan A C; Hood, Kerenza; Little, Paul; Lawton, Michael; Birnie, Kate; Thomas-Jones, Emma; Harman, Kim; Hay, Alastair D

    2017-04-01

    To estimate the cost-effectiveness of a two-step clinical rule using symptoms, signs and dipstick testing to guide the diagnosis and antibiotic treatment of urinary tract infection (UTI) in acutely unwell young children presenting to primary care. Decision analytic model synthesising data from a multicentre, prospective cohort study (DUTY) and the wider literature to estimate the short-term and lifetime costs and healthcare outcomes (symptomatic days, recurrent UTI, quality adjusted life years) of eight diagnostic strategies. We compared GP clinical judgement with three strategies based on a 'coefficient score' combining seven symptoms and signs independently associated with UTI and four strategies based on weighted scores according to the presence/absence of five symptoms and signs. We compared dipstick testing versus laboratory culture in children at intermediate risk of UTI. Sampling, culture and antibiotic costs were lowest in high-specificity DUTY strategies (£1.22 and £1.08) compared to clinical judgement (£1.99). These strategies also approximately halved urine sampling (4.8% versus 9.1% in clinical judgement) without reducing sensitivity (58.2% versus 56.4%). Outcomes were very similar across all diagnostic strategies. High-specificity DUTY strategies were more cost-effective than clinical judgement in the short- (iNMB = £0.78 and £0.84) and long-term (iNMB =£2.31 and £2.50). Dipstick tests had poorer cost-effectiveness than laboratory culture in children at intermediate risk of UTI (iNMB = £-1.41). Compared to GPs' clinical judgement, high specificity clinical rules from the DUTY study could substantially reduce urine sampling, achieving lower costs and equivalent patient outcomes. Dipstick testing children for UTI is not cost-effective. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

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

    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.

  12. Clinical abdominal palpation for predicting oligohydramnios in ...

    with normal AFV being reassuring for expectant management, and reduced AFV a trigger for labour induction.[3] According to textbooks, clinical evidence of reduced AFV (oligohydramnios) includes ... [11,12] AFI was used as the reference.

  13. Excusing exclusion: Accounting for rule-breaking and sanctions in a Swedish methadone clinic.

    Petersson, Frida J M

    2013-11-01

    Methadone maintenance treatment has been subjected to much debate and controversy in Sweden during the last decades. Thresholds for getting access are high and control policies strict within the programmes. This article analyses how professionals working in a Swedish methadone clinic discuss and decide on appropriate responses to clients' rule-breaking behaviour. The research data consist of field notes from observations of three interprofessional team meetings where different clients' illicit drug use is discussed. A micro-sociological approach and accounts analysis was applied to the data. During their decision-oriented talk at the meetings, the professionals account for: (1) sanctions, (2) nonsanction, (3) mildness. In accounting for (2) and (3), they also account for clients' rule-breaking behaviour. Analysis shows how these ways of accounting are concerned with locating blame and responsibility for the act in question, as well as with constructing excuses and justifications for the clients and for the professionals themselves. In general, these results demonstrate that maintenance treatment in everyday professional decision-making, far from being a neutral evidence-based practice, involves a substantial amount of professional discretion and moral judgements. Sanctions are chosen according to the way in which a deviance from the rules is explained and, in doing so, a certain behaviour is deemed to be serious, dangerous and unacceptable - or excusable. Copyright © 2013 Elsevier B.V. All rights reserved.

  14. Estimating the risk of gestational diabetes mellitus : a clinical prediction model based on patient characteristics and medical history

    van Leeuwen, M.; Opmeer, B. C.; Zweers, E. J. K.; van Ballegooie, E.; ter Brugge, H. G.; de Valk, H. W.; Visser, G. H. A.; Mol, B. W. J.

    Objective To develop a clinical prediction rule that can help the clinician to identify women at high and low risk for gestational diabetes mellitus (GDM) early in pregnancy in order to improve the efficiency of GDM screening. Design We used data from a prospective cohort study to develop the

  15. Clinical abdominal palpation for predicting oligohydramnios in ...

    Objective. In view of the scarcity of ultrasound in low-resource settings, to evaluate abdominal palpation for prediction of oligohydramnios in suspected prolonged pregnancy, using the ultrasound-obtained amniotic fluid index (AFI) as a gold standard, taking into account maternal and fetal factors that may affect amniotic fluid ...

  16. Ensemble Classifiers for Predicting HIV-1 Resistance from Three Rule-Based Genotypic Resistance Interpretation Systems.

    Raposo, Letícia M; Nobre, Flavio F

    2017-08-30

    Resistance to antiretrovirals (ARVs) is a major problem faced by HIV-infected individuals. Different rule-based algorithms were developed to infer HIV-1 susceptibility to antiretrovirals from genotypic data. However, there is discordance between them, resulting in difficulties for clinical decisions about which treatment to use. Here, we developed ensemble classifiers integrating three interpretation algorithms: Agence Nationale de Recherche sur le SIDA (ANRS), Rega, and the genotypic resistance interpretation system from Stanford HIV Drug Resistance Database (HIVdb). Three approaches were applied to develop a classifier with a single resistance profile: stacked generalization, a simple plurality vote scheme and the selection of the interpretation system with the best performance. The strategies were compared with the Friedman's test and the performance of the classifiers was evaluated using the F-measure, sensitivity and specificity values. We found that the three strategies had similar performances for the selected antiretrovirals. For some cases, the stacking technique with naïve Bayes as the learning algorithm showed a statistically superior F-measure. This study demonstrates that ensemble classifiers can be an alternative tool for clinical decision-making since they provide a single resistance profile from the most commonly used resistance interpretation systems.

  17. Is the Factor-of-2 Rule Broadly Applicable for Evaluating the Prediction Accuracy of Metal-Toxicity Models?

    Meyer, Joseph S; Traudt, Elizabeth M; Ranville, James F

    2018-01-01

    In aquatic toxicology, a toxicity-prediction model is generally deemed acceptable if its predicted median lethal concentrations (LC50 values) or median effect concentrations (EC50 values) are within a factor of 2 of their paired, observed LC50 or EC50 values. However, that rule of thumb is based on results from only two studies: multiple LC50 values for the fathead minnow (Pimephales promelas) exposed to Cu in one type of exposure water, and multiple EC50 values for Daphnia magna exposed to Zn in another type of exposure water. We tested whether the factor-of-2 rule of thumb also is supported in a different dataset in which D. magna were exposed separately to Cd, Cu, Ni, or Zn. Overall, the factor-of-2 rule of thumb appeared to be a good guide to evaluating the acceptability of a toxicity model's underprediction or overprediction of observed LC50 or EC50 values in these acute toxicity tests.

  18. Age-Adjusted D-Dimer in the Prediction of Pulmonary Embolism: Does a Normal Age-Adjusted D-Dimer Rule Out PE?

    Jacob Ortiz

    2017-01-01

    Full Text Available Risk assessment for pulmonary embolism (PE currently relies on physician judgment, clinical decision rules (CDR, and D-dimer testing. There is still controversy regarding the role of D-dimer testing in low or intermediate risk patients. The objective of the study was to define the role of clinical decision rules and D-dimer testing in patients suspected of having a PE. Records of 894 patients referred for computed tomography pulmonary angiography (CTPA at a University medical center were analyzed. The clinical decision rules overall had an ROC of approximately 0.70, while signs of DVT had the highest ROC (0.80. A low probability CDR coupled with a negative age-adjusted D-dimer largely excluded PE. The negative predictive value (NPV of an intermediate CDR was 86–89%, while the addition of a negative D-dimer resulted in NPVs of 94%. Thus, in patients suspected of having a PE, a low or intermediate CDR does not exclude PE; however, in patients with an intermediate CDR, a normal age-adjusted D-dimer increases the NPV.

  19. Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

    Pesesky, Mitchell W; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burnham, Carey-Ann D; Dantas, Gautam

    2016-01-01

    The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and

  20. Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

    Mitchell Pesesky

    2016-11-01

    Full Text Available The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitate initial use of empiric (frequently broad-spectrum antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0% and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance

  1. Prognosis of patients with nonspecific neck pain: development and external validation of a prediction rule for persistence of complaints

    Schellingerhout, J.M.; Heijmans, M.W.; Verhagen, A.P.; Lewis, M.; de Vet, H.C.W.; Koes, B.W.

    2010-01-01

    Study Design.: Reanalysis of data from 3 randomized controlled trials. Objective.: Development and validation of a prediction rule that estimates the probability of complaints persisting for at least 6 months in patients presenting with nonspecific neck pain in primary care. Sumary of Background

  2. Quality prediction modeling for multistage manufacturing based on classification and association rule mining

    Kao Hung-An

    2017-01-01

    Full Text Available For manufacturing enterprises, product quality is a key factor to assess production capability and increase their core competence. To reduce external failure cost, many research and methodology have been introduced in order to improve process yield rate, such as TQC/TQM, Shewhart CycleDeming's 14 Points, etc. Nowadays, impressive progress has been made in process monitoring and industrial data analysis because of the Industry 4.0 trend. Industries start to utilize quality control (QC methodology to lower inspection overhead and internal failure cost. Currently, the focus of QC is mostly in the inspection of single workstation and final product, however, for multistage manufacturing, many factors (like equipment, operators, parameters, etc. can have cumulative and interactive effects to the final quality. When failure occurs, it is difficult to resume the original settings for cause analysis. To address these problems, this research proposes a combination of principal components analysis (PCA with classification and association rule mining algorithms to extract features representing relationship of multiple workstations, predict final product quality, and analyze the root-cause of product defect. The method is demonstrated on a semiconductor data set.

  3. Leveraging Bibliographic RDF Data for Keyword Prediction with Association Rule Mining (ARM

    Nidhi Kushwaha

    2014-11-01

    Full Text Available The Semantic Web (Web 3.0 has been proposed as an efficient way to access the increasingly large amounts of data on the internet. The Linked Open Data Cloud project at present is the major effort to implement the concepts of the Seamtic Web, addressing the problems of inhomogeneity and large data volumes. RKBExplorer is one of many repositories implementing Open Data and contains considerable bibliographic information. This paper discusses bibliographic data, an important part of cloud data. Effective searching of bibiographic datasets can be a challenge as many of the papers residing in these databases do not have sufficient or comprehensive keyword information. In these cases however, a search engine based on RKBExplorer is only able to use information to retrieve papers based on author names and title of papers without keywords. In this paper we attempt to address this problem by using the data mining algorithm Association Rule Mining (ARM to develop keywords based on features retrieved from Resource Description Framework (RDF data within a bibliographic citation. We have demonstrate the applicability of this method for predicting missing keywords for bibliographic entries in several typical databases. −−−−− Paper presented at 1st International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2014 March 27-28, 2014. Organized by VIT University, Chennai, India. Sponsored by BRNS.

  4. Simple Decision-Analytic Functions of the AUC for Ruling Out a Risk Prediction Model and an Added Predictor.

    Baker, Stuart G

    2018-02-01

    When using risk prediction models, an important consideration is weighing performance against the cost (monetary and harms) of ascertaining predictors. The minimum test tradeoff (MTT) for ruling out a model is the minimum number of all-predictor ascertainments per correct prediction to yield a positive overall expected utility. The MTT for ruling out an added predictor is the minimum number of added-predictor ascertainments per correct prediction to yield a positive overall expected utility. An approximation to the MTT for ruling out a model is 1/[P (H(AUC model )], where H(AUC) = AUC - {½ (1-AUC)} ½ , AUC is the area under the receiver operating characteristic (ROC) curve, and P is the probability of the predicted event in the target population. An approximation to the MTT for ruling out an added predictor is 1 /[P {(H(AUC Model:2 ) - H(AUC Model:1 )], where Model 2 includes an added predictor relative to Model 1. The latter approximation requires the Tangent Condition that the true positive rate at the point on the ROC curve with a slope of 1 is larger for Model 2 than Model 1. These approximations are suitable for back-of-the-envelope calculations. For example, in a study predicting the risk of invasive breast cancer, Model 2 adds to the predictors in Model 1 a set of 7 single nucleotide polymorphisms (SNPs). Based on the AUCs and the Tangent Condition, an MTT of 7200 was computed, which indicates that 7200 sets of SNPs are needed for every correct prediction of breast cancer to yield a positive overall expected utility. If ascertaining the SNPs costs $500, this MTT suggests that SNP ascertainment is not likely worthwhile for this risk prediction.

  5. Clinical impression and ascites appearance do not rule out bacterial peritonitis.

    Chinnock, Brian; Hendey, Gregory W; Minnigan, Hal; Butler, Jack; Afarian, Hagop

    2013-05-01

    Previous research has demonstrated that physician clinical suspicion, determined without assessing fluid appearance, is not adequate to rule out spontaneous bacterial peritonitis (SBP) without fluid testing. To determine the sensitivity of physician clinical suspicion, including a bedside assessment of fluid appearance, in the detection of SBP in Emergency Department (ED) patients undergoing paracentesis. We conducted a prospective, observational study of ED patients with ascites undergoing paracentesis at three academic facilities. The enrolling physician recorded the clinical suspicion of SBP ("none," "low," "moderate," or "high"), and ascites appearance ("clear," "hazy," "cloudy," or "bloody"). SBP was defined as an absolute neutrophil count ≥ 250 cells/mm(3), or culture pathogen growth. We defined "clear" ascites fluid as negative for SBP, and "hazy," "cloudy," or "bloody" as positive. A physician clinical suspicion of "none" or "low" was considered negative for SBP, and an assessment of "moderate" or "high" was considered positive. The primary outcome measure was sensitivity of physician clinical impression and ascites appearance for SBP. There were 348 cases enrolled, with SBP diagnosed in 43 (12%). Physician clinical suspicion had a sensitivity of 42% (95% confidence interval [CI] 29-55%) for the detection of SBP. Fluid appearance had a sensitivity of 72% (95% CI 58-83%). Physician clinical impression, which included an assessment of fluid appearance, had poor sensitivity for the detection of SBP and cannot be used to exclude the diagnosis. Routine laboratory fluid analysis is indicated after ED paracentesis, even in patients considered to have a low degree of suspicion for SBP. Copyright © 2013 Elsevier Inc. All rights reserved.

  6. A diagnosis-based clinical decision rule for spinal pain part 2: review of the literature

    Hurwitz Eric L

    2008-08-01

    Full Text Available Abstract Background Spinal pain is a common and often disabling problem. The research on various treatments for spinal pain has, for the most part, suggested that while several interventions have demonstrated mild to moderate short-term benefit, no single treatment has a major impact on either pain or disability. There is great need for more accurate diagnosis in patients with spinal pain. In a previous paper, the theoretical model of a diagnosis-based clinical decision rule was presented. The approach is designed to provide the clinician with a strategy for arriving at a specific working diagnosis from which treatment decisions can be made. It is based on three questions of diagnosis. In the current paper, the literature on the reliability and validity of the assessment procedures that are included in the diagnosis-based clinical decision rule is presented. Methods The databases of Medline, Cinahl, Embase and MANTIS were searched for studies that evaluated the reliability and validity of clinic-based diagnostic procedures for patients with spinal pain that have relevance for questions 2 (which investigates characteristics of the pain source and 3 (which investigates perpetuating factors of the pain experience. In addition, the reference list of identified papers and authors' libraries were searched. Results A total of 1769 articles were retrieved, of which 138 were deemed relevant. Fifty-one studies related to reliability and 76 related to validity. One study evaluated both reliability and validity. Conclusion Regarding some aspects of the DBCDR, there are a number of studies that allow the clinician to have a reasonable degree of confidence in his or her findings. This is particularly true for centralization signs, neurodynamic signs and psychological perpetuating factors. There are other aspects of the DBCDR in which a lesser degree of confidence is warranted, and in which further research is needed.

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

    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...

  8. Failsafe automation of Phase II clinical trial interim monitoring for stopping rules.

    Day, Roger S

    2010-02-01

    In Phase II clinical trials in cancer, preventing the treatment of patients on a study when current data demonstrate that the treatment is insufficiently active or too toxic has obvious benefits, both in protecting patients and in reducing sponsor costs. Considerable efforts have gone into experimental designs for Phase II clinical trials with flexible sample size, usually implemented by early stopping rules. The intended benefits will not ensue, however, if the design is not followed. Despite the best intentions, failures can occur for many reasons. The main goal is to develop an automated system for interim monitoring, as a backup system supplementing the protocol team, to ensure that patients are protected. A secondary goal is to stimulate timely recording of patient assessments. We developed key concepts and performance needs, then designed, implemented, and deployed a software solution embedded in the clinical trials database system. The system has been in place since October 2007. One clinical trial tripped the automated monitor, resulting in e-mails that initiated statistician/investigator review in timely fashion. Several essential contributing activities still require human intervention, institutional policy decisions, and institutional commitment of resources. We believe that implementing the concepts presented here will provide greater assurance that interim monitoring plans are followed and that patients are protected from inadequate response or excessive toxicity. This approach may also facilitate wider acceptance and quicker implementation of new interim monitoring algorithms.

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

    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. Clinical classification in low back pain: best-evidence diagnostic rules based on systematic reviews.

    Petersen, Tom; Laslett, Mark; Juhl, Carsten

    2017-05-12

    Clinical examination findings are used in primary care to give an initial diagnosis to patients with low back pain and related leg symptoms. The purpose of this study was to develop best evidence Clinical Diagnostic Rules (CDR] for the identification of the most common patho-anatomical disorders in the lumbar spine; i.e. intervertebral discs, sacroiliac joints, facet joints, bone, muscles, nerve roots, muscles, peripheral nerve tissue, and central nervous system sensitization. A sensitive electronic search strategy using MEDLINE, EMBASE and CINAHL databases was combined with hand searching and citation tracking to identify eligible studies. Criteria for inclusion were: persons with low back pain with or without related leg symptoms, history or physical examination findings suitable for use in primary care, comparison with acceptable reference standards, and statistical reporting permitting calculation of diagnostic value. Quality assessments were made independently by two reviewers using the Quality Assessment of Diagnostic Accuracy Studies tool. Clinical examination findings that were investigated by at least two studies were included and results that met our predefined threshold of positive likelihood ratio ≥ 2 or negative likelihood ratio ≤ 0.5 were considered for the CDR. Sixty-four studies satisfied our eligible criteria. We were able to construct promising CDRs for symptomatic intervertebral disc, sacroiliac joint, spondylolisthesis, disc herniation with nerve root involvement, and spinal stenosis. Single clinical test appear not to be as useful as clusters of tests that are more closely in line with clinical decision making. This is the first comprehensive systematic review of diagnostic accuracy studies that evaluate clinical examination findings for their ability to identify the most common patho-anatomical disorders in the lumbar spine. In some diagnostic categories we have sufficient evidence to recommend a CDR. In others, we have only

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

    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.

  12. Change-Point Detection Method for Clinical Decision Support System Rule Monitoring.

    Liu, Siqi; Wright, Adam; Hauskrecht, Milos

    2017-06-01

    A clinical decision support system (CDSS) and its components can malfunction due to various reasons. Monitoring the system and detecting its malfunctions can help one to avoid any potential mistakes and associated costs. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. The detection should be performed online, that is whenever a new datum arrives, we want to have a score indicating how likely there is a change in the system. We develop a new method based on Seasonal-Trend decomposition and likelihood ratio statistics to detect the changes. Experiments on real and simulated data show that our method has a lower delay in detection compared with existing change-point detection methods.

  13. Induction and pruning of classification rules for prediction of microseismic hazards in coal mines

    Sikora, M. [Silesian Technical University, Gliwice (Poland)

    2011-06-15

    The paper presents results of application of a rule induction and pruning algorithm for classification of a microseismic hazard state in coal mines. Due to imbalanced distribution of examples describing states 'hazardous' and 'safe', the special algorithm was used for induction and rule pruning. The algorithm selects optimal parameters' values influencing rule induction and pruning based on training and tuning sets. A rule quality measure which decides about a form and classification abilities of rules that are induced is the basic parameter of the algorithm. The specificity and sensitivity of a classifier were used to evaluate its quality. Conducted tests show that the admitted method of rules induction and classifier's quality evaluation enables to get better results of classification of microseismic hazards than by methods currently used in mining practice. Results obtained by the rules-based classifier were also compared with results got by a decision tree induction algorithm and by a neuro-fuzzy system.

  14. Can clinical prediction tools predict the need for computed tomography in blunt abdominal? A systematic review.

    Sharples, Alistair; Brohi, Karim

    2016-08-01

    Blunt abdominal trauma is a common reason for admission to the Emergency Department. Early detection of injuries is an important goal but is often not straightforward as physical examination alone is not a good predictor of serious injury. Computed tomography (CT) has become the primary method for assessing the stable trauma patient. It has high sensitivity and specificity but there remains concern regarding the long term consequences of high doses of radiation. Therefore an accurate and reliable method of assessing which patients are at higher risk of injury and hence require a CT would be clinically useful. We perform a systematic review to investigate the use of clinical prediction tools (CPTs) for the identification of abdominal injuries in patients suffering blunt trauma. A literature search was performed using Medline, Embase, The Cochrane Library and NHS Evidence up to August 2014. English language, prospective and retrospective studies were included if they derived, validated or assessed a CPT, aimed at identifying intra-abdominal injuries or the need for intervention to treat an intra-abdominal after blunt trauma. Methodological quality was assessed using a 14 point scale. Performance was assessed predominantly by sensitivity. Seven relevant studies were identified. All studies were derivative studies and no CPT was validated in a separate study. There were large differences in the study design, composition of the CPTs, the outcomes analysed and the methodological quality of the included studies. Sensitivities ranged from 86 to 100%. The highest performing CPT had a lower limit of the 95% CI of 95.8% and was of high methodological quality (11 of 14). Had this rule been applied to the population then 25.1% of patients would have avoided a CT scan. Seven CPTs were identified of varying designs and methodological quality. All demonstrate relatively high sensitivity with some achieving very high sensitivity whilst still managing to reduce the number of CTs

  15. Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset

    Liu, Zhao; Zhu, Yunhong; Wu, Chenxue

    2016-01-01

    Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified. PMID:27508502

  16. European Marketing Authorizations Granted Based on a Single Pivotal Clinical Trial: The Rule or the Exception?

    Morant, Anne Vinther; Vestergaard, Henrik Tang

    2018-07-01

    A minimum of two positive, adequate, and well-controlled clinical trials has historically been the gold standard for providing substantial evidence to support regulatory approval of a new medicine. Nevertheless, the present analysis of European Marketing Authorizations granted between 2012 and 2016 showed that 45% of new active substances were approved based on a single pivotal clinical trial. For therapeutic areas such as oncology and cardiovascular diseases, approvals based on a single pivotal trial are the rule rather than the exception, whereas new medicines within the nervous system area were generally supported by two or more pivotal trials. While overall similar trends have been observed in the US, the recent US Food and Drug Administration approvals of nervous system medicines based on a single pivotal trial suggest that a case-by-case scientific evaluation of the totality of evidence is increasingly applied to facilitate faster access of new medicines to patients suffering from serious diseases. © 2017 American Society for Clinical Pharmacology and Therapeutics.

  17. Validity and clinical utility of the simplified Wells rule for assessing clinical probability for the exclusion of pulmonary embolism

    Douma, Renée A.; Gibson, Nadine S.; Gerdes, Victor E. A.; Büller, Harry R.; Wells, Philip S.; Perrier, Arnaud; Le Gal, Grégoire

    2009-01-01

    The recently introduced simplified Wells rule for the exclusion of pulmonary embolism (PE) assigns only one point to the seven variables of the original Wells rule. This study was performed to independently validate the simplified Wells rule for the exclusion of PE. We retrospectively calculated the

  18. Applicability of Alignment and Combination Rules to Burst Pressure Prediction of Multiple-flawed Steam Generator Tube

    Lee, Myeong Woo; Kim, Ji Seok; Kim, Yun Jae [Korea University, Seoul (Korea, Republic of); Jeon, Jun Young [Doosan Heavy Industries and Consruction, Seoul (Korea, Republic of); Lee, Dong Min [Korea Plant Service and Engineering, Technical Research and Development Institute, Naju (Korea, Republic of)

    2016-05-15

    Alignment and combination rules are provided by various codes and standards. These rules are used to determine whether multiple flaws should be treated as non-aligned or as coplanar, and independent or combined flaws. Experimental results on steam generator (SG) tube specimens containing multiple axial part-through-wall (PTW) flaws at room temperature (RT) are compared with assessment results based on the alignment and combination rules of the codes and standards. In case of axial collinear flaws, ASME, JSME, and BS7910 treated multiple flaws as independent flaws and API 579, A16, and FKM treated multiple flaws as combined single flaw. Assessment results of combined flaws were conservative. In case of axial non-aligned flaws, almost flaws were aligned and assessment results well correlate with experimental data. In case of axial parallel flaws, both effective flaw lengths of aligned flaws and separated flaws was are same because of each flaw length were same. This study investigates the applicability of alignment and combination rules for multiple flaws on the failure behavior of Alloy 690TT steam generator (SG) tubes that widely used in the nuclear power plan. Experimental data of burst tests on Alloy 690TT tubes with single and multiple flaws that conducted at room temperature (RT) by Kim el al. compared with the alignment rules of these codes and standards. Burst pressure of SG tubes with flaws are predicted using limit load solutions that provide by EPRI Handbook.

  19. A modification of the Schomaker—Stevenson rule for prediction of single bond distances

    Blom, Richard; Haaland, Arne

    1985-04-01

    A modification of the Schomaker—Stevenson rule: ?c = 8.5 pm, n = 1.4, significantly reduces the discrepancy between experimental calculated bond lengths for every polar bonds between main group elements.

  20. Modifications to the HIPAA Privacy, Security, Enforcement, and Breach Notification rules under the Health Information Technology for Economic and Clinical Health Act and the Genetic Information Nondiscrimination Act; other modifications to the HIPAA rules.

    2013-01-25

    The Department of Health and Human Services (HHS or ``the Department'') is issuing this final rule to: Modify the Health Insurance Portability and Accountability Act (HIPAA) Privacy, Security, and Enforcement Rules to implement statutory amendments under the Health Information Technology for Economic and Clinical Health Act (``the HITECH Act'' or ``the Act'') to strengthen the privacy and security protection for individuals' health information; modify the rule for Breach Notification for Unsecured Protected Health Information (Breach Notification Rule) under the HITECH Act to address public comment received on the interim final rule; modify the HIPAA Privacy Rule to strengthen the privacy protections for genetic information by implementing section 105 of Title I of the Genetic Information Nondiscrimination Act of 2008 (GINA); and make certain other modifications to the HIPAA Privacy, Security, Breach Notification, and Enforcement Rules (the HIPAA Rules) to improve their workability and effectiveness and to increase flexibility for and decrease burden on the regulated entities.

  1. Prediction value of the Canadian CT head rule and the New Orleans criteria for positive head CT scan and acute neurosurgical procedures in minor head trauma: a multicenter external validation study.

    Bouida, Wahid; Marghli, Soudani; Souissi, Sami; Ksibi, Hichem; Methammem, Mehdi; Haguiga, Habib; Khedher, Sonia; Boubaker, Hamdi; Beltaief, Kaouthar; Grissa, Mohamed Habib; Trimech, Mohamed Naceur; Kerkeni, Wiem; Chebili, Nawfel; Halila, Imen; Rejeb, Imen; Boukef, Riadh; Rekik, Noureddine; Bouhaja, Bechir; Letaief, Mondher; Nouira, Semir

    2013-05-01

    The New Orleans Criteria and the Canadian CT Head Rule have been developed to decrease the number of normal computed tomography (CT) results in mild head injury. We compare the performance of both decision rules for identifying patients with intracranial traumatic lesions and those who require an urgent neurosurgical intervention after mild head injury. This was an observational cohort study performed between 2008 and 2011 on patients with mild head injury who were aged 10 years or older. We collected prospectively clinical head CT scan findings and outcome. Primary outcome was need for neurosurgical intervention, defined as either death or craniotomy, or the need of intubation within 15 days of the traumatic event. Secondary outcome was the presence of traumatic lesions on head CT scan. New Orleans Criteria and Canadian CT Head Rule decision rules were compared by using sensitivity specifications and positive and negative predictive value. We enrolled 1,582 patients. Neurosurgical intervention was performed in 34 patients (2.1%) and positive CT findings were demonstrated in 218 patients (13.8%). Sensitivity and specificity for need for neurosurgical intervention were 100% (95% confidence interval [CI] 90% to 100%) and 60% (95% CI 44% to 76%) for the Canadian CT Head Rule and 82% (95% CI 69% to 95%) and 26% (95% CI 24% to 28%) for the New Orleans Criteria. Negative predictive values for the above-mentioned clinical decision rules were 100% and 99% and positive values were 5% and 2%, respectively, for the Canadian CT Head Rule and New Orleans Criteria. Sensitivity and specificity for clinical significant head CT findings were 95% (95% CI 92% to 98%) and 65% (95% CI 62% to 68%) for the Canadian CT Head Rule and 86% (95% CI 81% to 91%) and 28% (95% CI 26% to 30%) for the New Orleans Criteria. A similar trend of results was found in the subgroup of patients with a Glasgow Coma Scale score of 15. For patients with mild head injury, the Canadian CT Head Rule had higher

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

    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.

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

    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

  4. FDA publishes conflict of interest rules for clinical trials. Food and Drug Administration.

    James, J S

    1998-03-06

    The Food and Drug Administration (FDA) published new rules defining conflict of interests between drug companies and medical researchers and clinicians. Certain financial arrangements will need to be disclosed, although the FDA estimates that only one to ten percent of pharmaceutical companies will need to submit disclosures for one or more of their investigators. The purpose of the new rule is to prevent bias in safety and efficacy studies of drugs and medical devices. The full rule is published in the Federal Register.

  5. Difference rule-a new thermodynamic principle: prediction of standard thermodynamic data for inorganic solvates.

    Jenkins, H Donald Brooke; Glasser, Leslie

    2004-12-08

    We present a quite general thermodynamic "difference" rule, derived from thermochemical first principles, quantifying the difference between the standard thermodynamic properties, P, of a solid n-solvate (or n-hydrate), n-S, containing n molecules of solvate, S (water or other) and the corresponding solid parent (unsolvated) salt: [P[n-solvate] - P[parent

  6. Utilizing change effort prediction to analyze modifiability of business rule architectures at the NHS

    dr. Martijn Zoet; Koen Smit

    2016-01-01

    From the article: Abstract Business rules (BR’s) play a critical role in an organization’s daily activities. With the increased use of BR (solutions) and ever increasing change frequency of BR’s the interest in modifiability guidelines that address the manageability of BR’s has increased as well.

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

    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.

  8. Prefrontal and parietal activity is modulated by the rule complexity of inductive reasoning and can be predicted by a cognitive model.

    Jia, Xiuqin; Liang, Peipeng; Shi, Lin; Wang, Defeng; Li, Kuncheng

    2015-01-01

    In neuroimaging studies, increased task complexity can lead to increased activation in task-specific regions or to activation of additional regions. How the brain adapts to increased rule complexity during inductive reasoning remains unclear. In the current study, three types of problems were created: simple rule induction (i.e., SI, with rule complexity of 1), complex rule induction (i.e., CI, with rule complexity of 2), and perceptual control. Our findings revealed that increased activations accompany increased rule complexity in the right dorsal lateral prefrontal cortex (DLPFC) and medial posterior parietal cortex (precuneus). A cognitive model predicted both the behavioral and brain imaging results. The current findings suggest that neural activity in frontal and parietal regions is modulated by rule complexity, which may shed light on the neural mechanisms of inductive reasoning. Copyright © 2014. Published by Elsevier Ltd.

  9. Predicting higher selection in elite junior Australian Rules football: The influence of physical performance and anthropometric attributes.

    Robertson, Sam; Woods, Carl; Gastin, Paul

    2015-09-01

    To develop a physiological performance and anthropometric attribute model to predict Australian Football League draft selection. Cross-sectional observational. Data was obtained (n=4902) from three Under-18 Australian football competitions between 2010 and 2013. Players were allocated into one of the three groups, based on their highest level of selection in their final year of junior football (Australian Football League Drafted, n=292; National Championship, n=293; State-level club, n=4317). Physiological performance (vertical jumps, agility, speed and running endurance) and anthropometric (body mass and height) data were obtained. Hedge's effect sizes were calculated to assess the influence of selection-level and competition on these physical attributes, with logistic regression models constructed to discriminate Australian Football League Drafted and National Championship players. Rule induction analysis was undertaken to determine a set of rules for discriminating selection-level. Effect size comparisons revealed a range of small to moderate differences between State-level club players and both other groups for all attributes, with trivial to small differences between Australian Football League Drafted and National Championship players noted. Logistic regression models showed multistage fitness test, height and 20 m sprint time as the most important attributes in predicting Draft success. Rule induction analysis showed that players displaying multistage fitness test scores of >14.01 and/or 20 m sprint times of football players being recruited to the highest level of the sport. Copyright © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  10. How the brain predicts people's behavior in relation to rules and desires. Evidence of a medio-prefrontal dissociation.

    Corradi-Dell'Acqua, Corrado; Turri, Francesco; Kaufmann, Laurence; Clément, Fabrice; Schwartz, Sophie

    2015-09-01

    Forming and updating impressions about others is critical in everyday life and engages portions of the dorsomedial prefrontal cortex (dMPFC), the posterior cingulate cortex (PCC) and the amygdala. Some of these activations are attributed to "mentalizing" functions necessary to represent people's mental states, such as beliefs or desires. Evolutionary psychology and developmental studies, however, suggest that interpersonal inferences can also be obtained through the aid of deontic heuristics, which dictate what must (or must not) be done in given circumstances. We used fMRI and asked 18 participants to predict whether unknown characters would follow their desires or obey external rules. Participants had no means, at the beginning, to make accurate predictions, but slowly learned (throughout the experiment) each character's behavioral profile. We isolated brain regions whose activity changed during the experiment, as a neural signature of impression updating: whereas dMPFC was progressively more involved in predicting characters' behavior in relation to their desires, the medial orbitofrontal cortex and the amygdala were progressively more recruited in predicting rule-based behavior. Our data provide evidence of a neural dissociation between deontic inference and theory-of-mind (ToM), and support a differentiation of orbital and dorsal prefrontal cortex in terms of low- and high-level social cognition. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

    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.

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

    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.

  13. Isokinetic strength testing does not predict hamstring injury in Australian Rules footballers

    Bennell, K.; Wajswelner, H.; Lew, P.; Schall-Riaucour, A.; Leslie, S.; Plant, D.; Cirone, J.

    1998-01-01

    OBJECTIVE: To determine the relation of hamstring and quadriceps muscle strength and imbalance to hamstring injury using a prospective observational cohort study METHOD: A total of 102 senior male Australian Rules footballers aged 22.2 (3.6) years were tested at the start of a football season. Maximum voluntary concentric and eccentric torque of the hamstring and quadriceps muscles of both legs was assessed using a Kin-Com isokinetic dynamometer at angular velocities of 60 and 180 degre...

  14. Confirming a predicted selection rule in inelastic neutron scattering spectroscopy: the quantum translator-rotator H2 entrapped inside C60.

    Xu, Minzhong; Jiménez-Ruiz, Mónica; Johnson, Mark R; Rols, Stéphane; Ye, Shufeng; Carravetta, Marina; Denning, Mark S; Lei, Xuegong; Bačić, Zlatko; Horsewill, Anthony J

    2014-09-19

    We report an inelastic neutron scattering (INS) study of a H2 molecule encapsulated inside the fullerene C60 which confirms the recently predicted selection rule, the first to be established for the INS spectroscopy of aperiodic, discrete molecular compounds. Several transitions from the ground state of para-H2 to certain excited translation-rotation states, forbidden according to the selection rule, are systematically absent from the INS spectra, thus validating the selection rule with a high degree of confidence. Its confirmation sets a precedent, as it runs counter to the widely held view that the INS spectroscopy of molecular compounds is not subject to any selection rules.

  15. Ethics in research: from science fairs to clinical trials, the same golden rules apply

    Wright, C

    2011-07-01

    Full Text Available opinion and not causing anyone physical harm. Similar ?golden rules? apply when you do research and these are called research ethics. When you decide to do a research project that involves humans or animals, whether you are at school, at university... or a professor, there are important steps and rules that you need to follow. Why? Most importantly the steps and rules are there to protect you, the researcher, from making a mistake that may harm a person or an animal. There are important...

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

    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.

  17. Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems.

    DesAutels, Spencer J; Fox, Zachary E; Giuse, Dario A; Williams, Annette M; Kou, Qing-Hua; Weitkamp, Asli; Neal R, Patel; Bettinsoli Giuse, Nunzia

    2016-01-01

    Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support. Driven by an institutional need to prioritize decision support content for migration to new clinical systems, the Center for Knowledge Management and Health Information Technology teams applied their unique expertise to extract content from individual systems, organize it through a single extensible schema, and present it for discovery and reuse through a newly created Clinical Support Knowledge Acquisition and Archival Tool (CS-KAAT). CS-KAAT can build and maintain the underlying knowledge infrastructure needed by clinical systems.

  18. Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems

    DesAutels, Spencer J.; Fox, Zachary E.; Giuse, Dario A.; Williams, Annette M.; Kou, Qing-hua; Weitkamp, Asli; Neal R, Patel; Bettinsoli Giuse, Nunzia

    2016-01-01

    Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support. Driven by an institutional need to prioritize decision support content for migration to new clinical systems, the Center for Knowledge Management and Health Information Technology teams applied their unique expertise to extract content from individual systems, organize it through a single extensible schema, and present it for discovery and reuse through a newly created Clinical Support Knowledge Acquisition and Archival Tool (CS-KAAT). CS-KAAT can build and maintain the underlying knowledge infrastructure needed by clinical systems. PMID:28269846

  19. Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

    Jin, Y.; Rong, X.; Liu, Z.

    2017-12-01

    This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.

  20. Clinical and functional criteria for predicting asthma in infants

    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...

  1. Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

    Benjamin W. Y. Lo

    2013-01-01

    Full Text Available Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH. Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients. Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs. Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.

  2. Clinical and functional criteria for predicting asthma in infants

    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.

  3. A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion

    Hossain, Mohammad Shahadat; Hossain, Emran; Khalid, Md. Saifuddin

    2014-01-01

    conditions of uncertainty. The Belief Rule-Based Inference Methodology Using the Evidential Reasoning (RIMER) approach was adopted to develop this expert system; which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation...... and inference procedures. The knowledge base of this system was constructed by using real patient data and expert opinion. Practical case studies were used to validate the system. The system-generated results are more effective and reliable in terms of accuracy than the results generated by a manual system....

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

    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.

  5. Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation.

    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.

  6. Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert.

    MacRae, Jayden; Love, Tom; Baker, Michael G; Dowell, Anthony; Carnachan, Matthew; Stubbe, Maria; McBain, Lynn

    2015-10-06

    We designed and validated a rule-based expert system to identify influenza like illness (ILI) from routinely recorded general practice clinical narrative to aid a larger retrospective research study into the impact of the 2009 influenza pandemic in New Zealand. Rules were assessed using pattern matching heuristics on routine clinical narrative. The system was trained using data from 623 clinical encounters and validated using a clinical expert as a gold standard against a mutually exclusive set of 901 records. We calculated a 98.2 % specificity and 90.2 % sensitivity across an ILI incidence of 12.4 % measured against clinical expert classification. Peak problem list identification of ILI by clinical coding in any month was 9.2 % of all detected ILI presentations. Our system addressed an unusual problem domain for clinical narrative classification; using notational, unstructured, clinician entered information in a community care setting. It performed well compared with other approaches and domains. It has potential applications in real-time surveillance of disease, and in assisted problem list coding for clinicians. Our system identified ILI presentation with sufficient accuracy for use at a population level in the wider research study. The peak coding of 9.2 % illustrated the need for automated coding of unstructured narrative in our study.

  7. Clinical Relevance of Prognostic and Predictive Molecular Markers in Gliomas.

    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.

  8. Selecting short-statured children needing growth hormone testing: Derivation and validation of a clinical decision rule

    Bréart Gérard

    2008-07-01

    Full Text Available Abstract Background Numerous short-statured children are evaluated for growth hormone (GH deficiency (GHD. In most patients, GH provocative tests are normal and are thus in retrospect unnecessary. Methods A retrospective cohort study was conducted to identify predictors of growth hormone (GH deficiency (GHD in children seen for short stature, and to construct a very sensitive and fairly specific predictive tool to avoid unnecessary GH provocative tests. GHD was defined by the presence of 2 GH concentration peaks Results The initial study included 167 patients, 36 (22% of whom had GHD, including 5 (3% with certain GHD. Independent predictors of GHD were: growth rate Conclusion We have derived and performed an internal validation of a highly sensitive decision rule that could safely help to avoid more than 2/3 of the unnecessary GH tests. External validation of this rule is needed before any application.

  9. Readmission prediction via deep contextual embedding of clinical concepts.

    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.

  10. Addition of host genetic variants in a prediction rule for post meningitis hearing loss in childhood: a model updating study

    Sanders, Marieke S.; de Jonge, Rogier C. J.; Terwee, Caroline B.; Heymans, Martijn W.; Koomen, Irene; Ouburg, Sander; Spanjaard, Lodewijk; Morré, Servaas A.; van Furth, A. Marceline

    2013-01-01

    Sensorineural hearing loss is the most common sequela in survivors of bacterial meningitis (BM). In the past we developed a validated prediction model to identify children at risk for post-meningitis hearing loss. It is known that host genetic variations, besides clinical factors, contribute to

  11. Medicare program; revisions to payment policies under the physician fee schedule, clinical laboratory fee schedule & other revisions to Part B for CY 2014. Final rule with comment period.

    2013-12-10

    This major final rule with comment period addresses changes to the physician fee schedule, clinical laboratory fee schedule, and other Medicare Part B payment policies to ensure that our payment systems are updated to reflect changes in medical practice and the relative value of services. This final rule with comment period also includes a discussion in the Supplementary Information regarding various programs. (See the Table of Contents for a listing of the specific issues addressed in the final rule with comment period.)

  12. A Business Intelligence Model to Predict Bankruptcy using Financial Domain Ontology with Association Rule Mining Algorithm

    Martin, A.; Manjula, M.; Venkatesan, Dr. V. Prasanna

    2011-01-01

    Today in every organization financial analysis provides the basis for understanding and evaluating the results of business operations and delivering how well a business is doing. This means that the organizations can control the operational activities primarily related to corporate finance. One way that doing this is by analysis of bankruptcy prediction. This paper develops an ontological model from financial information of an organization by analyzing the Semantics of the financial statement...

  13. Addition of host genetic variants in a prediction rule for post meningitis hearing loss in childhood: a model updating study.

    Sanders, Marieke S; de Jonge, Rogier C J; Terwee, Caroline B; Heymans, Martijn W; Koomen, Irene; Ouburg, Sander; Spanjaard, Lodewijk; Morré, Servaas A; van Furth, A Marceline

    2013-07-23

    Sensorineural hearing loss is the most common sequela in survivors of bacterial meningitis (BM). In the past we developed a validated prediction model to identify children at risk for post-meningitis hearing loss. It is known that host genetic variations, besides clinical factors, contribute to severity and outcome of BM. In this study it was determined whether host genetic risk factors improve the predictive abilities of an existing model regarding hearing loss after childhood BM. Four hundred and seventy-one Dutch Caucasian childhood BM were genotyped for 11 single nucleotide polymorphisms (SNPs) in seven different genes involved in pathogen recognition. Genetic data were added to the original clinical prediction model and performance of new models was compared to the original model by likelihood ratio tests and the area under the curve (AUC) of the receiver operating characteristic curves. Addition of TLR9-1237 SNPs and the combination of TLR2 + 2477 and TLR4 + 896 SNPs improved the clinical prediction model, but not significantly (increase of AUC's from 0.856 to 0.861 and from 0.856 to 0.875 (p = 0.570 and 0.335, respectively). Other SNPs analysed were not linked to hearing loss. Although addition of genetic risk factors did not significantly improve the clinical prediction model for post-meningitis hearing loss, AUC's of the pre-existing model remain high after addition of genetic factors. Future studies should evaluate whether more combinations of SNPs in larger cohorts has an additional value to the existing prediction model for post meningitis hearing loss.

  14. Current V3 genotyping algorithms are inadequate for predicting X4 co-receptor usage in clinical isolates.

    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.

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

    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.

  16. Do Institutional Logics Predict Interpretation of Contract Rules at the Dental Chair-side?

    Harris, Rebecca; Brown, Stephen; Holt, Robin

    2014-01-01

    understands opportunism as fully rational, individual responses selecting maximally efficient outcomes from a set of possibilities. We take a more emotive and collective view of contracting, exploring the influence of institutional logics in relation to the opportunistic behaviour of dentists. Following...... earlier qualitative work where we identified four institutional logics in English general dental practice, and six dental contract areas where there was scope for opportunism; in 2013 we surveyed 924 dentists to investigate these logics and whether they had predictive purchase over dentists' chair...

  17. Exceptions to the rule: case studies in the prediction of pathogenicity for genetic variants in hereditary cancer genes.

    Rosenthal, E T; Bowles, K R; Pruss, D; van Kan, A; Vail, P J; McElroy, H; Wenstrup, R J

    2015-12-01

    Based on current consensus guidelines and standard practice, many genetic variants detected in clinical testing are classified as disease causing based on their predicted impact on the normal expression or function of the gene in the absence of additional data. However, our laboratory has identified a subset of such variants in hereditary cancer genes for which compelling contradictory evidence emerged after the initial evaluation following the first observation of the variant. Three representative examples of variants in BRCA1, BRCA2 and MSH2 that are predicted to disrupt splicing, prematurely truncate the protein, or remove the start codon were evaluated for pathogenicity by analyzing clinical data with multiple classification algorithms. Available clinical data for all three variants contradicts the expected pathogenic classification. These variants illustrate potential pitfalls associated with standard approaches to variant classification as well as the challenges associated with monitoring data, updating classifications, and reporting potentially contradictory interpretations to the clinicians responsible for translating test outcomes to appropriate clinical action. It is important to address these challenges now as the model for clinical testing moves toward the use of large multi-gene panels and whole exome/genome analysis, which will dramatically increase the number of genetic variants identified. © 2015 The Authors. Clinical Genetics published by John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  18. A Clinical Prediction Model for Postcardiac Surgery Atrial Fibrillation in an Asian Population.

    Zhang, Wei; Liu, Weiling; Chew, Sophia T H; Shen, Liang; Ti, Lian Kah

    2016-08-01

    Postoperative atrial fibrillation (AF) is associated with increased morbidity, mortality, and resource utilization. Current prediction models for postoperative AF are based primarily on Western populations. In this study, we sought to develop a clinical prediction rule for postcardiac surgery AF for a multiethnic Asian population. Two thousand one hundred sixty-eight patients undergoing coronary artery bypass graft or valve surgery with cardiopulmonary bypass were prospectively enrolled in this observational study between August 2008 and July 2012 at Singapore's 2 national heart centers. Postoperative AF was defined as an irregularly irregular electrocardiogram rhythm without identifiable P wave after surgery and before hospital discharge that lasted more than an hour, or affected hemodynamics (ie, systolic blood pressure 120 minutes (OR, 1.92; 95% CI, 1.47-2.52, P Chinese ethnicity (Chinese versus Indian OR, 2.09; 95% CI, 1.28-3.41, P = 0.003) or Malay (Malay versus Indian OR, 2.43; 95% CI, 1.36-4.05, P = 0.002) to be independently associated with postoperative AF. The area under the receiver-operator characteristic curve of the model was 0.704 (95% CI, 0.674-0.734). Internal validation produced an area under the receiver-operator characteristic curve of 0.756 (95% CI, 0.690-0.821). Clinical risk factors for AF after cardiac surgery in an Asian population are similar to that reported from primarily Western populations, but specific ethnicity influences susceptibility.

  19. Allocating health care resources: a questionnaire experiment on the predictive success of rules.

    Ahlert, Marlies; Schwettmann, Lars

    2017-06-26

    The topic of this paper is related to equity in health within a country. In public health care sectors of many countries decisions on priority setting with respect to treatment of different types of diseases or patient groups are implicitly or explicitly made. Priorities are realized by allocation decisions for medical resources where moral judgments play an important role with respect to goals and measures that should be applied. The aim of this study is to explore the moral intuitions held in the German society related to priorities in medical treatment. We use an experimental questionnaire method established in the Empirical Social Choice literature. Participants are asked to make decisions in a sequence of distributive problems where a limited amount of treatment time has to be allocated to hypothetically described patients. The decision problems serve as an intuition pump. Situations are systematically varied with respect to patients' initial health levels, their ability to benefit from treatment time, and the amount of treatment time available. Subjects are also asked to describe their deliberations. We focus on the acceptance of different allocation principles including equity concepts and utilitarian properties. We investigate rule characteristics like order preservation or monotonicity with respect to resources, severity, or effectiveness. We check the consistency of individual choices with stated reasoning. The goals and allocation principles revealed show that the moral intuitions held by our experimental subjects are much more complex than the principles commonly applied in health economic theory. Especially, cost-utility principles are rarely applied, whereas the goal of equality of health gain is observed more often. The principle not to leave any patient untreated is very dominant. We also observe the degrees to which extent certain monotonicity principles, known from welfare economics, are followed. Subjects were able to describe their moral

  20. Grooming a CAT: customizing CAT administration rules to increase response efficiency in specific research and clinical settings.

    Kallen, Michael A; Cook, Karon F; Amtmann, Dagmar; Knowlton, Elizabeth; Gershon, Richard C

    2018-05-05

    To evaluate the degree to which applying alternative stopping rules would reduce response burden while maintaining score precision in the context of computer adaptive testing (CAT). Analyses were conducted on secondary data comprised of CATs administered in a clinical setting at multiple time points (baseline and up to two follow ups) to 417 study participants who had back pain (51.3%) and/or depression (47.0%). Participant mean age was 51.3 years (SD = 17.2) and ranged from 18 to 86. Participants tended to be white (84.7%), relatively well educated (77% with at least some college), female (63.9%), and married or living in a committed relationship (57.4%). The unit of analysis was individual assessment histories (i.e., CAT item response histories) from the parent study. Data were first aggregated across all individuals, domains, and time points in an omnibus dataset of assessment histories and then were disaggregated by measure for domain-specific analyses. Finally, assessment histories within a "clinically relevant range" (score ≥ 1 SD from the mean in direction of poorer health) were analyzed separately to explore score level-specific findings. Two different sets of CAT administration rules were compared. The original CAT (CAT ORIG ) rules required at least four and no more than 12 items be administered. If the score standard error (SE) reached a value CAT was stopped. We simulated applying alternative stopping rules (CAT ALT ), removing the requirement that a minimum four items be administered, and stopped a CAT if responses to the first two items were both associated with best health, if the SE was CAT ORIG and CAT ALT . CAT ORIG and CAT ALT scores varied little, especially within the clinically relevant range, and response burden was substantially lower under CAT ALT (e.g., 41.2% savings in omnibus dataset). Alternate stopping rules result in substantial reductions in response burden with minimal sacrifice in score precision.

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

    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

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

    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.

  3. A prediction rule for the development of delirium among patients in medical wards: Chi-Square Automatic Interaction Detector (CHAID) decision tree analysis model.

    Kobayashi, Daiki; Takahashi, Osamu; Arioka, Hiroko; Koga, Shinichiro; Fukui, Tsuguya

    2013-10-01

    To predict development of delirium among patients in medical wards by a Chi-Square Automatic Interaction Detector (CHAID) decision tree model. This was a retrospective cohort study of all adult patients admitted to medical wards at a large community hospital. The subject patients were randomly assigned to either a derivation or validation group (2:1) by computed random number generation. Baseline data and clinically relevant factors were collected from the electronic chart. Primary outcome was the development of delirium during hospitalization. All potential predictors were included in a forward stepwise logistic regression model. CHAID decision tree analysis was also performed to make another prediction model with the same group of patients. Receiver operating characteristic curves were drawn, and the area under the curves (AUCs) were calculated for both models. In the validation group, these receiver operating characteristic curves and AUCs were calculated based on the rules from derivation. A total of 3,570 patients were admitted: 2,400 patients assigned to the derivation group and 1,170 to the validation group. A total of 91 and 51 patients, respectively, developed delirium. Statistically significant predictors were delirium history, age, underlying malignancy, and activities of daily living impairment in CHAID decision tree model, resulting in six distinctive groups by the level of risk. AUC was 0.82 in derivation and 0.82 in validation with CHAID model and 0.78 in derivation and 0.79 in validation with logistic model. We propose a validated CHAID decision tree prediction model to predict the development of delirium among medical patients. Copyright © 2013 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  4. Intelligent Hypothermia Care System using Ant ‎Colony Optimization for Rules Prediction

    Hayder Naser Khraibet

    2017-12-01

    Full Text Available Intelligent Hypothermia Care System (IHCS is an intelligence system uses set of methodologies, algorithms, architectures and processes to determine where patients in a postoperative recovery area must be sent. Hypothermia is a significant concern after surgery. This paper utilizes the classification task in data mining to propose an intelligent technique to predict where to send a patient after surgery: intensive care unit, general floor or home. To achieve this goal, this paper evaluates the performance of decision tree algorithm, exemplifying the deterministic approach, against the AntMiner algorithm, exemplifying the heuristic approach, to choose the best approach in detecting the patient’s status. Results show the outperformance of the heuristic approach. The implication of this proposal will be twofold: in hypothermia treatment and in the application of ant colony optimization

  5. Do institutional logics predict interpretation of contract rules at the dental chair-side?

    Harris, Rebecca; Brown, Stephen; Holt, Robin; Perkins, Elizabeth

    2014-01-01

    In quasi-markets, contracts find purchasers influencing health care providers, although problems exist where providers use personal bias and heuristics to respond to written agreements, tending towards the moral hazard of opportunism. Previous research on quasi-market contracts typically understands opportunism as fully rational, individual responses selecting maximally efficient outcomes from a set of possibilities. We take a more emotive and collective view of contracting, exploring the influence of institutional logics in relation to the opportunistic behaviour of dentists. Following earlier qualitative work where we identified four institutional logics in English general dental practice, and six dental contract areas where there was scope for opportunism; in 2013 we surveyed 924 dentists to investigate these logics and whether they had predictive purchase over dentists' chair-side behaviour. Factor analysis involving 300 responses identified four logics entwined in (often technical) behaviour: entrepreneurial commercialism, duty to staff and patients, managerialism, public good. PMID:25441320

  6. Do Urinary Cystine Parameters Predict Clinical Stone Activity?

    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.

  7. Termination of Resuscitation Rules to Predict Neurological Outcomes in Out-of-Hospital Cardiac Arrest for an Intermediate Life Support Prehospital System.

    Cheong, Randy Wang Long; Li, Huihua; Doctor, Nausheen Edwin; Ng, Yih Yng; Goh, E Shaun; Leong, Benjamin Sieu-Hon; Gan, Han Nee; Foo, David; Tham, Lai Peng; Charles, Rabind; Ong, Marcus Eng Hock

    2016-01-01

    Futile resuscitation can lead to unnecessary transports for out-of-hospital cardiac arrest (OHCA). The Basic Life Support (BLS) and Advanced Life Support (ALS) termination of resuscitation (TOR) guidelines have been validated with good results in North America. This study aims to evaluate the performance of these two rules in predicting neurological outcomes of OHCA patients in Singapore, which has an intermediate life support Emergency Medical Services (EMS) system. A retrospective cohort study was carried out on Singapore OHCA data collected from April 2010 to May 2012 for the Pan-Asian Resuscitation Outcomes Study (PAROS). The outcomes of each rule were compared to the actual neurological outcomes of the patients. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and predicted transport rates of each test were evaluated. A total of 2,193 patients had cardiac arrest of presumed cardiac etiology. TOR was recommended for 1,411 patients with the BLS-TOR rule, with a specificity of 100% (91.9, 100.0) for predicting poor neurological outcomes, PPV 100% (99.7, 100.0), sensitivity 65.7% (63.6, 67.7), NPV 5.6% (4.1, 7.5), and transportation rate 35.6%. Using the ALS-TOR rule, TOR was recommended for 587 patients, specificity 100% (91.9, 100.0) for predicting poor neurological outcomes, PPV 100% (99.4, 100.0), sensitivity 27.3% (25.4, 29.3), NPV 2.7% (2.0, 3.7), and transportation rate 73.2%. BLS-TOR predicted survival (any neurological outcome) with specificity 93.4% (95% CI 85.3, 97.8) versus ALS-TOR 98.7% (95% CI 92.9, 99.8). Both the BLS and ALS-TOR rules had high specificities and PPV values in predicting neurological outcomes, the BLS-TOR rule had a lower predicted transport rate while the ALS-TOR rule was more accurate in predicting futility of resuscitation. Further research into unique local cultural issues would be useful to evaluate the feasibility of any system-wide implementation of TOR.

  8. Do institutional logics predict interpretation of contract rules at the dental chair-side?

    Harris, Rebecca; Brown, Stephen; Holt, Robin; Perkins, Elizabeth

    2014-12-01

    In quasi-markets, contracts find purchasers influencing health care providers, although problems exist where providers use personal bias and heuristics to respond to written agreements, tending towards the moral hazard of opportunism. Previous research on quasi-market contracts typically understands opportunism as fully rational, individual responses selecting maximally efficient outcomes from a set of possibilities. We take a more emotive and collective view of contracting, exploring the influence of institutional logics in relation to the opportunistic behaviour of dentists. Following earlier qualitative work where we identified four institutional logics in English general dental practice, and six dental contract areas where there was scope for opportunism; in 2013 we surveyed 924 dentists to investigate these logics and whether they had predictive purchase over dentists' chair-side behaviour. Factor analysis involving 300 responses identified four logics entwined in (often technical) behaviour: entrepreneurial commercialism, duty to staff and patients, managerialism, public good. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. On the Rule of Mixtures for Predicting Stress-Softening and Residual Strain Effects in Biological Tissues and Biocompatible Materials

    Alex Elías-Zúñiga

    2014-01-01

    Full Text Available In this work, we use the rule of mixtures to develop an equivalent material model in which the total strain energy density is split into the isotropic part related to the matrix component and the anisotropic energy contribution related to the fiber effects. For the isotropic energy part, we select the amended non-Gaussian strain energy density model, while the energy fiber effects are added by considering the equivalent anisotropic volumetric fraction contribution, as well as the isotropized representation form of the eight-chain energy model that accounts for the material anisotropic effects. Furthermore, our proposed material model uses a phenomenological non-monotonous softening function that predicts stress softening effects and has an energy term, derived from the pseudo-elasticity theory, that accounts for residual strain deformations. The model’s theoretical predictions are compared with experimental data collected from human vaginal tissues, mice skin, poly(glycolide-co-caprolactone (PGC25 3-0 and polypropylene suture materials and tracheal and brain human tissues. In all cases examined here, our equivalent material model closely follows stress-softening and residual strain effects exhibited by experimental data.

  10. On the Rule of Mixtures for Predicting Stress-Softening and Residual Strain Effects in Biological Tissues and Biocompatible Materials

    Elías-Zúñiga, Alex; Baylón, Karen; Ferrer, Inés; Serenó, Lídia; Garcia-Romeu, Maria Luisa; Bagudanch, Isabel; Grabalosa, Jordi; Pérez-Recio, Tania; Martínez-Romero, Oscar; Ortega-Lara, Wendy; Elizalde, Luis Ernesto

    2014-01-01

    In this work, we use the rule of mixtures to develop an equivalent material model in which the total strain energy density is split into the isotropic part related to the matrix component and the anisotropic energy contribution related to the fiber effects. For the isotropic energy part, we select the amended non-Gaussian strain energy density model, while the energy fiber effects are added by considering the equivalent anisotropic volumetric fraction contribution, as well as the isotropized representation form of the eight-chain energy model that accounts for the material anisotropic effects. Furthermore, our proposed material model uses a phenomenological non-monotonous softening function that predicts stress softening effects and has an energy term, derived from the pseudo-elasticity theory, that accounts for residual strain deformations. The model’s theoretical predictions are compared with experimental data collected from human vaginal tissues, mice skin, poly(glycolide-co-caprolactone) (PGC25 3-0) and polypropylene suture materials and tracheal and brain human tissues. In all cases examined here, our equivalent material model closely follows stress-softening and residual strain effects exhibited by experimental data. PMID:28788466

  11. KDIGO 2012 Clinical Practice Guideline CKD classification rules out creatinine clearance 24 hour urine collection?

    Ognibene, A; Grandi, G; Lorubbio, M; Rapi, S; Salvadori, B; Terreni, A; Veroni, F

    2016-01-01

    The recent guideline for the evaluation and management of Chronic Kidney Disease recommends assessing GFR employing equations based on serum creatinine; despite this, creatinine clearance 24-hour urine collection is used routinely in many settings. In this study we compared the classification assessed from CrCl (creatinine clearance 24h urine collection) and e-GFR calculated with CKD-EPI or MDRD formulas. In this retrospective study we analyze consecutive laboratory data: creatinine clearance 24h urine collection, serum creatinine and demographic data such as sex and age from 15,777 patients >18 years of age collected from 2011 to 2013 in our laboratory at Careggi Hospital. The results were then compared to the estimated GFR calculated with the equations according to the recent treatment guidelines. Consecutive and retrospective laboratory data (creatinine clearance 24h urine collection, serum creatinine and, demographic data such as sex and age) from 15,777 patients >18 years of age seen at Careggi Hospital were collected. Comparison between e-GFR calculated with CKD-EPI or MDRD formulas and GFR according CrCl determinations and bias [95% CI] were 11.34 [-47,4/70.1] and 11.4 [-50.2/73] respectively. The concordance for 18/65 years aged group when compared with e-GFR classification between MDRD vs CKDEPI, MDRD vs CrCl and CKD-EPI vs CrCl were 0.78, 0.34, and 0.41 respectively, while in the 65/110years aged group the concordance Kappas were 0.84, 0.38, and 0.36 respectively. The use of CrCl provides a different classification than the estimation of GFR using a prediction equation. The CrCl is unreliable when it is necessary to identify CKD subjects with decrease of GFR of 5ml/min/1.73m(2)/year. Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  12. Short Sleep Times Predict Obesity in Internal Medicine Clinic Patients

    Buscemi, Dolores; Kumar, Ashwani; Nugent, Rebecca; Nugent, Kenneth

    2007-01-01

    Study Objectives: Epidemiological studies have demonstrated an association between short sleep times and obesity as defined by body mass index (BMI). We wanted to determine whether this association occurs in patients with chronic medical diagnoses since the number of confounding factors is likely higher in patients than the general population. Methods: Two hundred patients attending internal medicine clinics completed a survey regarding sleep habits, lifestyle characteristics, and medical diagnoses. An independent surveyor collected the information on the questionnaires and reviewed the medical records. Height and weight were measured by clinic personnel. Data were analyzed with multivariate logistic regression. Results: Subjects with short sleep times (< 7 hours) had an increased likelihood of obesity as defined by a BMI ≥ 30 kg/m2 when compared to the reference group of (8, 9] hours (odds ratio 2.93; 95% confidence interval, 1.06–8.09). There was a U-shaped relationship between obesity and sleep time in women but not in men. Young age (18 to 49 years), not smoking, drinking alcohol, hypertension, diabetes, and sleep apnea were also associated with obesity in the overall model. Conclusions: This study demonstrates an association between short sleep times and obesity in undifferentiated patients attending an internal medicine clinic using models adjusting for age, lifestyle characteristics, and some medical diagnoses. The U-shaped relationship in women suggests that sleep patterns may have gender specific associations. These observations provide the background for therapeutic trials in weight loss in patients with established medical problems. Citation: Buscemi D; Kumar A; Nugent R; Nugent K. Short sleep times predict obesity in internal medicine clinic patients. J Clin Sleep Med 2007;3(7):681–688. PMID:18198800

  13. Clinical formulas, mother's opinion and ultrasound in predicting birth weight

    Maria Regina Torloni

    Full Text Available CONTEXT AND OBJECTIVE: Accurate fetal weight estimation is important for labor and delivery management. So far, there has not been any conclusive evidence to indicate that any technique for fetal weight estimation is superior to any other. Clinical formulas for fetal weight estimation are easy to use but have not been extensively studied in the literature. This study aimed to evaluate the accuracy of clinical formulas for fetal weight estimation compared to maternal and ultrasound estimates. DESIGN AND SETTING: Prospective study involving 100 full-term, cephalic, singleton pregnancies delivered within three days of fetal weight estimation. The setting was a tertiary public teaching hospital in São Paulo, Brazil. METHODS: Upon admission, the mother's opinion about fetal weight was recorded. Symphyseal-fundal height and abdominal girth were measured and two formulas were used to calculate fetal weight. An ultrasound scan was then performed by a specialist to estimate fetal weight. The four estimates were compared with the birth weight. The accuracy of the estimates was assessed by calculating the percentage that was within 10% of actual birth weight for each method. The chi-squared test was used for comparisons and p < 0.05 was considered significant. RESULTS: The birth weight was correctly estimated (± 10% in 59%, 57%, 61%, and 65% of the cases using the mother's estimate, two clinical formulas, and ultrasound estimate, respectively. The accuracy of the four methods did not differ significantly. CONCLUSION: Clinical formulas for fetal weight prediction are as accurate as maternal and ultrasound estimates.

  14. Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

    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. Review article. Predicting disease onset in clinically healthy people

    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.

  16. A clinical tool for predicting survival in ALS.

    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/.

  17. Clinical manifestations that predict abnormal brain computed tomography (CT in children with minor head injury

    Nesrin Alharthy

    2015-01-01

    Full Text Available Background: Computed tomography (CT used in pediatric pediatrics brain injury (TBI to ascertain neurological manifestations. Nevertheless, this practice is associated with adverse effects. Reports in the literature suggest incidents of morbidity and mortality in children due to exposure to radiation. Hence, it is found imperative to search for a reliable alternative. Objectives: The aim of this study is to find a reliable clinical alternative to detect an intracranial injury without resorting to the CT. Materials and Methods: Retrospective cross-sectional study was undertaken in patients (1-14 years with blunt head injury and having a Glasgow Coma Scale (GCS of 13-15 who had CT performed on them. Using statistical analysis, the correlation between clinical examination and positive CT manifestation is analyzed for different age-groups and various mechanisms of injury. Results: No statistically significant association between parameteres such as Loss of Consciousness, ′fall′ as mechanism of injury, motor vehicle accidents (MVA, more than two discrete episodes of vomiting and the CT finding of intracranial injury could be noted. Analyzed data have led to believe that GCS of 13 at presentation is the only important clinical predictor of intracranial injury. Conclusion: Retrospective data, small sample size and limited number of factors for assessing clinical manifestation might present constraints on the predictive rule that was derived from this review. Such limitations notwithstanding, the decision to determine which patients should undergo neuroimaging is encouraged to be based on clinical judgments. Further analysis with higher sample sizes may be required to authenticate and validate findings.

  18. Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth

    Thomas W. Frazier

    2014-03-01

    Full Text Available This report evaluates whether classification tree algorithms (CTA may improve the identification of individuals at risk for bipolar spectrum disorders (BPSD. Analyses used the Longitudinal Assessment of Manic Symptoms (LAMS cohort (629 youth, 148 with BPSD and 481 without BPSD. Parent ratings of mania symptoms, stressful life events, parenting stress, and parental history of mania were included as risk factors. Comparable overall accuracy was observed for CTA (75.4% relative to logistic regression (77.6%. However, CTA showed increased sensitivity (0.28 vs. 0.18 at the expense of slightly decreased specificity and positive predictive power. The advantage of CTA algorithms for clinical decision making is demonstrated by the combinations of predictors most useful for altering the probability of BPSD. The 24% sample probability of BPSD was substantially decreased in youth with low screening and baseline parent ratings of mania, negative parental history of mania, and low levels of stressful life events (2%. High screening plus high baseline parent-rated mania nearly doubled the BPSD probability (46%. Future work will benefit from examining additional, powerful predictors, such as alternative data sources (e.g., clinician ratings, neurocognitive test data; these may increase the clinical utility of CTA models further.

  19. Prediction of clinical course of glioblastomas by MRI during radiotherapy

    Leitzen, Christina; Schild, Hans H.; Bungart, Birgitta; Luetter, Christiana; Muedder, Thomas; Wilhelm-Buchstab, Timo; Schueller, Heinrich; Herrlinger, Ulrich

    2010-01-01

    Purpose: Determine the value of MR studies in patients undergoing radiotherapy for glioblastomas pre and during radiotherapy to predict the clinical course. Patients and Methods: MR follow-up studies were performed in 33 patients with glioblastomas before radiotherapy, after 30 Gy, after 60 Gy, and in the treatment follow-up. Findings on MR were categorized into: definite progress, questionable progress, status idem. Patients were followed clinically (median for 11 months). Results: After 30 Gy 23/33 (70%) of the MR examination showed status idem. 10/33 (30%) demonstrated definite (n = 6) or questionable (n = 4) progress. Further tumor progress was faster in these patients and patients succumb to their disease earlier (9 vs. 22 months). The 60 Gy study showed definite (n = 8) and questionable (n = 6) progress in 14/33 (42%) cases. All these tumors were progressing faster and were associated with a comparatively reduced life expectancy. Conclusion: MR follow-up studies after 30 Gy in patients undergoing radiotherapy for glioblastomas allow for prognostic appraisal, and potentially early modification of treatment. (orig.)

  20. A sharable cloud-based pancreaticoduodenectomy collaborative database for physicians: emphasis on security and clinical rule supporting.

    Yu, Hwan-Jeu; Lai, Hong-Shiee; Chen, Kuo-Hsin; Chou, Hsien-Cheng; Wu, Jin-Ming; Dorjgochoo, Sarangerel; Mendjargal, Adilsaikhan; Altangerel, Erdenebaatar; Tien, Yu-Wen; Hsueh, Chih-Wen; Lai, Feipei

    2013-08-01

    Pancreaticoduodenectomy (PD) is a major operation with high complication rate. Thereafter, patients may develop morbidity because of the complex reconstruction and loss of pancreatic parenchyma. A well-designed database is very important to address both the short-term and long-term outcomes after PD. The objective of this research was to build an international PD database implemented with security and clinical rule supporting functions, which made the data-sharing easier and improve the accuracy of data. The proposed system is a cloud-based application. To fulfill its requirements, the system comprises four subsystems: a data management subsystem, a clinical rule supporting subsystem, a short message notification subsystem, and an information security subsystem. After completing the surgery, the physicians input the data retrospectively, which are analyzed to study factors associated with post-PD common complications (delayed gastric emptying and pancreatic fistula) to validate the clinical value of this system. Currently, this database contains data from nearly 500 subjects. Five medical centers in Taiwan and two cancer centers in Mongolia are participating in this study. A data mining model of the decision tree analysis showed that elderly patients (>76 years) with pylorus-preserving PD (PPPD) have higher proportion of delayed gastric emptying. About the pancreatic fistula, the data mining model of the decision tree analysis revealed that cases with non-pancreaticogastrostomy (PG) reconstruction - body mass index (BMI)>29.65 or PG reconstruction - BMI>23.7 - non-classic PD have higher proportion of pancreatic fistula after PD. The proposed system allows medical staff to collect and store clinical data in a cloud, sharing the data with other physicians in a secure manner to achieve collaboration in research. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  1. Urate predicts rate of clinical decline in Parkinson disease

    Ascherio, Alberto; LeWitt, Peter A.; Xu, Kui; Eberly, Shirley; Watts, Arthur; Matson, Wayne R.; Marras, Connie; Kieburtz, Karl; Rudolph, Alice; Bogdanov, Mikhail B.; Schwid, Steven R.; Tennis, Marsha; Tanner, Caroline M.; Beal, M. Flint; Lang, Anthony E.; Oakes, David; Fahn, Stanley; Shoulson, Ira; Schwarzschild, Michael A.

    2009-01-01

    Context The risk of Parkinson disease (PD) and its rate of progression may decline with increasing blood urate, a major antioxidant. Objective To determine whether serum and cerebrospinal fluid (CSF) concentrations of urate predict clinical progression in patients with PD. Design, Setting, and Participants 800 subjects with early PD enrolled in the DATATOP trial. Pre-treatment urate was measured in serum for 774 subjects and in CSF for 713. Main Outcome Measures Treatment-, age- and sex-adjusted hazard ratios (HRs) for clinical disability requiring levodopa therapy, the pre-specified primary endpoint. Results The HR of progressing to endpoint decreased with increasing serum urate (HR for 1 standard deviation increase = 0.82; 95% CI = 0.73 to 0.93). In analyses stratified by α-tocopherol treatment (2,000 IU/day), a decrease in the HR for the primary endpoint was seen only among subjects not treated with α-tocopherol (HR = 0.75; 95% CI = 0.62 to 0.89, versus those treated HR = 0.90; 95% CI = 0.75 to 1.08). Results were similar for the rate of change in the United Parkinson Disease Rating Scale (UPDRS). CSF urate was also inversely related to both the primary endpoint (HR for highest versus lowest quintile = 0.65; 95% CI: 0.54 to 0.96) and to the rate of change in UPDRS. As with serum urate, these associations were present only among subjects not treated with α-tocopherol. Conclusion Higher serum and CSF urate at baseline were associated with slower rates of clinical decline. The findings strengthen the link between urate and PD and the rationale for considering CNS urate elevation as a potential strategy to slow PD progression. PMID:19822770

  2. Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study

    Nijman, Ruud G; Vergouwe, Yvonne; Thompson, Matthew; van Veen, Mirjam; van Meurs, Alfred H J; van der Lei, Johan; Steyerberg, Ewout W; Moll, Henriette A

    2013-01-01

    Objective To derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department. Design Prospective observational diagnostic study. Setting Three paediatric emergency care units: two in the Netherlands and one in the United Kingdom. Participants Children with fever, aged 1 month to 15 years, at three paediatric emergency care units: Rotterdam (n=1750) and the Hague (n=967), the Netherlands, and Coventry (n=487), United Kingdom. A prediction model was constructed using multivariable polytomous logistic regression analysis and included the predefined predictor variables age, duration of fever, tachycardia, temperature, tachypnoea, ill appearance, chest wall retractions, prolonged capillary refill time (>3 seconds), oxygen saturation rule out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the prediction of other SBIs was lower (0.69, 0.53 to 0.86). Conclusion A validated prediction model, including clinical signs, symptoms, and C reactive protein level, was useful for estimating the likelihood of pneumonia and other SBIs in children with fever, such as septicaemia/meningitis and urinary tract infections. PMID:23550046

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

    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…

  4. A NEW CLINICAL PREDICTION CRITERION ACCURATELY DETERMINES A SUBSET OF PATIENTS WITH BILATERAL PRIMARY ALDOSTERONISM BEFORE ADRENAL VENOUS SAMPLING.

    Kocjan, Tomaz; Janez, Andrej; Stankovic, Milenko; Vidmar, Gaj; Jensterle, Mojca

    2016-05-01

    Adrenal venous sampling (AVS) is the only available method to distinguish bilateral from unilateral primary aldosteronism (PA). AVS has several drawbacks, so it is reasonable to avoid this procedure when the results would not affect clinical management. Our objective was to identify a clinical criterion that can reliably predict nonlateralized AVS as a surrogate for bilateral PA that is not treated surgically. A retrospective diagnostic cross-sectional study conducted at Slovenian national endocrine referral center included 69 consecutive patients (mean age 56 ± 8 years, 21 females) with PA who underwent AVS. PA was confirmed with the saline infusion test (SIT). AVS was performed sequentially during continuous adrenocorticotrophic hormone (ACTH) infusion. The main outcome measures were variables associated with nonlateralized AVS to derive a clinical prediction rule. Sixty-seven (97%) patients had a successful AVS and were included in the statistical analysis. A total of 39 (58%) patients had nonlateralized AVS. The combined criterion of serum potassium ≥3.5 mmol/L, post-SIT aldosterone AVS. The best overall classification accuracy (50/67 = 75%) was achieved using the post-SIT aldosterone level AVS. Our clinical prediction criterion appears to accurately determine a subset of patients with bilateral PA who could avoid unnecessary AVS and immediately commence with medical treatment.

  5. A clinical tool to predict Plasmodium vivax recurrence in Malaysia.

    Mat Ariffin, Norliza; Islahudin, Farida; Kumolosasi, Endang; Makmor-Bakry, Mohd

    2017-12-08

    Recurrence rates of Plasmodium vivax infections differ across various geographic regions. Interestingly, South-East Asia and the Asia-Pacific region are documented to exhibit the most frequent recurrence incidences. Identifying patients at a higher risk for recurrences gives valuable information in strengthening the efforts to control P. vivax infections. The aim of the study was to develop a tool to identify P. vivax- infected patients that are at a higher risk of recurrence in Malaysia. Patient data was obtained retrospectively through the Ministry of Health, Malaysia, from 2011 to 2016. Patients with incomplete data were excluded. A total of 2044 clinical P. vivax malaria cases treated with primaquine were included. Data collected were patient, disease, and treatment characteristics. Two-thirds of the cases (n = 1362) were used to develop a clinical risk score, while the remaining third (n = 682) was used for validation. Using multivariate analysis, age (p = 0.03), gametocyte sexual count (p = 0.04), indigenous transmission (p = 0.04), type of treatment (p = 0.12), and incomplete primaquine treatment (p = 0.14) were found to be predictors of recurrence after controlling for other confounding factors; these predictors were then used in developing the final model. The beta-coefficient values were used to develop a clinical scoring tool to predict possible recurrence. The total scores ranged between 0 and 8. A higher score indicated a higher risk for recurrence (odds ratio [OR]: 1.971; 95% confidence interval [CI]: 1.562-2.487; p ≤ 0.001). The area under the receiver operating characteristic (ROC) curve of the developed (n = 1362) and validated model (n = 682) was of good accuracy (ROC: 0.728, 95% CI: 0.670-0.785, p value useful tool in targeting patients at a higher risk for recurrence for closer monitoring during follow-up, after treatment with primaquine.

  6. Can we Predict Disease Course with Clinical Factors?

    Vegh, Zsuzsanna; Kurti, Zsuzsanna; Golovics, Petra A; Lakatos, Peter L

    2018-01-01

    The disease phenotype at diagnosis and the disease course of Crohn's disease (CD) and ulcerative colitis (UC) show remarkable heterogeneity across patients. This review aims to summarize the currently available evidence on clinical and some environmental predictive factors, which clinicians should evaluate in the everyday practice together with other laboratory and imaging data to prevent disease progression, enable a more personalized therapy, and avoid negative disease outcomes. In recent population-based epidemiological and referral cohort studies, the evolution of disease phenotype of CD and UC varied significantly. Most CD and severe UC patients still require hospitalization or surgery/colectomy during follow-up. A change in the natural history of inflammatory bowel diseases (IBD) with improved outcomes in parallel with tailored positioning of aggressive immunomodulator and biological therapy has been suspected. According to the currently available literature, it is of major importance to refer IBD cases at risk for adverse disease outcomes as early during the disease course as possible. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  7. Clinical prediction and diagnosis of neurosyphilis in HIV-infected patients with early Syphilis.

    Dumaresq, Jeannot; Langevin, Stéphanie; Gagnon, Simon; Serhir, Bouchra; Deligne, Benoît; Tremblay, Cécile; Tsang, Raymond S W; Fortin, Claude; Coutlée, François; Roger, Michel

    2013-12-01

    The diagnosis of neurosyphilis (NS) is a challenge, especially in HIV-infected patients, and the criteria for deciding when to perform a lumbar puncture (LP) in HIV-infected patients with syphilis are controversial. We retrospectively reviewed demographic, clinical, and laboratory data from 122 cases of HIV-infected patients with documented early syphilis who underwent an LP to rule out NS, and we evaluated 3 laboratory-developed validated real-time PCR assays, the Treponema pallidum particle agglutination (TPPA) assay, the fluorescent treponemal antibody absorption (FTA-ABS) assay, and the line immunoassay INNO-LIA Syphilis, for the diagnosis of NS from cerebrospinal fluid (CSF) samples of these patients. NS was defined by a reactive CSF-VDRL test result and/or a CSF white blood cell (WBC) count of >20 cells/μl. Thirty of the 122 patients (24.6%) had early NS. Headache, visual symptoms, a CD4 cell count of FTA-ABS, TPPA, and INNO-LIA assays had sensitivities of 58%, 100%, 68%, and 100%, specificities of 67%, 12%, 49%, and 13%, and negative predictive values of 85%, 100%, 84%, and 100%, respectively. Visual disturbances, headache, uncontrolled HIV-1 viremia, and a CD4 cell count of <500 cells/μl were predictors of NS in HIV-infected patients with early syphilis, while blood serum RPR titers were not; therefore, RPR titers should not be used as the sole criterion for deciding whether to perform an LP in early syphilis. When applied to CSF samples, the INNO-LIA Syphilis assay easily helped rule out NS.

  8. Induction of labour: clinical predictive factors for success and failure.

    Batinelli, Laura; Serafini, Andrea; Nante, Nicola; Petraglia, Felice; Severi, Filiberto Maria; Messina, Gabriele

    2018-04-01

    literature that 'multiparity' and 'Bishop score' are linked with the success of IOL and adds that 'maternal age' and 'foetal birth weight' are significant risk factors for the population of uncomplicated post term pregnancies induced with prostaglandins. What the implications are of these findings for clinical practice and/or further research: Our results agreed with the existing literature regarding parity and Bishop score but not for maternal age and birth weight. This adds new precious data to the literature which could be used for systematic reviews and for implementing IOL guidelines and protocols, nationally and internationally. Our findings could be also used for guiding future research in this field. It will be interesting to investigate the existence of not just specific factors but also any combination of variables which could predict the success of the procedure. At the moment these information cannot be used in terms of decision making for healthcare professionals as no variable is 100% predictive but once further research will be added, we may be able to know when is best time to start the IOL, how to facilitate the success of the procedure and how to best support the woman throughout the whole experience.

  9. Towards automated processing of clinical Finnish: sublanguage analysis and a rule-based parser.

    Laippala, Veronika; Ginter, Filip; Pyysalo, Sampo; Salakoski, Tapio

    2009-12-01

    In this paper, we present steps taken towards more efficient automated processing of clinical Finnish, focusing on daily nursing notes in a Finnish Intensive Care Unit (ICU). First, we analyze ICU Finnish as a sublanguage, identifying its specific features facilitating, for example, the development of a specialized syntactic analyser. The identified features include frequent omission of finite verbs, limitations in allowed syntactic structures, and domain-specific vocabulary. Second, we develop a formal grammar and a parser for ICU Finnish, thus providing better tools for the development of further applications in the clinical domain. The grammar is implemented in the LKB system in a typed feature structure formalism. The lexicon is automatically generated based on the output of the FinTWOL morphological analyzer adapted to the clinical domain. As an additional experiment, we study the effect of using Finnish constraint grammar to reduce the size of the lexicon. The parser construction thus makes efficient use of existing resources for Finnish. The grammar currently covers 76.6% of ICU Finnish sentences, producing highly accurate best-parse analyzes with F-score of 91.1%. We find that building a parser for the highly specialized domain sublanguage is not only feasible, but also surprisingly efficient, given an existing morphological analyzer with broad vocabulary coverage. The resulting parser enables a deeper analysis of the text than was previously possible.

  10. A rule-based electronic phenotyping algorithm for detecting clinically relevant cardiovascular disease cases.

    Esteban, Santiago; Rodríguez Tablado, Manuel; Ricci, Ricardo Ignacio; Terrasa, Sergio; Kopitowski, Karin

    2017-07-14

    The implementation of electronic medical records (EMR) is becoming increasingly common. Error and data loss reduction, patient-care efficiency increase, decision-making assistance and facilitation of event surveillance, are some of the many processes that EMRs help improve. In addition, they show a lot of promise in terms of data collection to facilitate observational epidemiological studies and their use for this purpose has increased significantly over the recent years. Even though the quantity and availability of the data are clearly improved thanks to EMRs, still, the problem of the quality of the data remains. This is especially important when attempting to determine if an event has actually occurred or not. We sought to assess the sensitivity, specificity, and agreement level of a codes-based algorithm for the detection of clinically relevant cardiovascular (CaVD) and cerebrovascular (CeVD) disease cases, using data from EMRs. Three family physicians from the research group selected clinically relevant CaVD and CeVD terms from the international classification of primary care, Second Edition (ICPC-2), the ICD 10 version 2015 and SNOMED-CT 2015 Edition. These terms included both signs, symptoms, diagnoses and procedures associated with CaVD and CeVD. Terms not related to symptoms, signs, diagnoses or procedures of CaVD or CeVD and also those describing incidental findings without clinical relevance were excluded. The algorithm yielded a positive result if the patient had at least one of the selected terms in their medical records, as long as it was not recorded as an error. Else, if no terms were found, the patient was classified as negative. This algorithm was applied to a randomly selected sample of the active patients within the hospital's HMO by 1/1/2005 that were 40-79 years old, had at least one year of seniority in the HMO and at least one clinical encounter. Thus, patients were classified into four groups: (1) Negative patients (2) Patients with Ca

  11. ECG dispersion mapping predicts clinical deterioration, measured by increase in the Simple Clinical Score.

    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.

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

    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.

  13. Reliability of the American Academy of Sleep Medicine Rules for Assessing Sleep Depth in Clinical Practice.

    Younes, Magdy; Kuna, Samuel T; Pack, Allan I; Walsh, James K; Kushida, Clete A; Staley, Bethany; Pien, Grace W

    2018-02-15

    The American Academy of Sleep Medicine has published manuals for scoring polysomnograms that recommend time spent in non-rapid eye movement sleep stages (stage N1, N2, and N3 sleep) be reported. Given the well-established large interrater variability in scoring stage N1 and N3 sleep, we determined the range of time in stage N1 and N3 sleep scored by a large number of technologists when compared to reasonably estimated true values. Polysomnograms of 70 females were scored by 10 highly trained sleep technologists, two each from five different academic sleep laboratories. Range and confidence interval (CI = difference between the 5th and 95th percentiles) of the 10 times spent in stage N1 and N3 sleep assigned in each polysomnogram were determined. Average values of times spent in stage N1 and N3 sleep generated by the 10 technologists in each polysomnogram were considered representative of the true values for the individual polysomnogram. Accuracy of different technologists in estimating delta wave duration was determined by comparing their scores to digitally determined durations. The CI range of the ten N1 scores was 4 to 39 percent of total sleep time (% TST) in different polysomnograms (mean CI ± standard deviation = 11.1 ± 7.1 % TST). Corresponding range for N3 was 1 to 28 % TST (14.4 ± 6.1 % TST). For stage N1 and N3 sleep, very low or very high values were reported for virtually all polysomnograms by different technologists. Technologists varied widely in their assignment of stage N3 sleep, scoring that stage when the digitally determined time of delta waves ranged from 3 to 17 seconds. Manual scoring of non-rapid eye movement sleep stages is highly unreliable among highly trained, experienced technologists. Measures of sleep continuity and depth that are reliable and clinically relevant should be a focus of clinical research. © 2018 American Academy of Sleep Medicine

  14. Current good manufacturing practice and investigational new drugs intended for use in clinical trials. Final rule.

    2008-07-15

    The Food and Drug Administration (FDA) is amending the current good manufacturing practice (CGMP) regulations for human drugs, including biological products, to exempt most phase 1 investigational drugs from complying with the regulatory CGMP requirements. FDA will continue to exercise oversight of the manufacture of these drugs under FDA's general statutory CGMP authority and through review of the investigational new drug applications (IND). In addition, elsewhere in this issue of the Federal Register, FDA is announcing the availability of a guidance document entitled "Guidance for Industry: CGMP for Phase 1 Investigational Drugs" dated November 2007 (the companion guidance). This guidance document sets forth recommendations on approaches to compliance with statutory CGMP for the exempted phase 1 investigational drugs. FDA is taking this action to focus a manufacturer's effort on applying CGMP that is appropriate and meaningful for the manufacture of the earliest stage investigational drug products intended for use in phase 1 clinical trials while ensuring safety and quality. This action will also streamline and promote the drug development process.

  15. Adaptive clinical trial designs with pre-specified rules for modifying the sample size: understanding efficient types of adaptation.

    Levin, Gregory P; Emerson, Sarah C; Emerson, Scott S

    2013-04-15

    Adaptive clinical trial design has been proposed as a promising new approach that may improve the drug discovery process. Proponents of adaptive sample size re-estimation promote its ability to avoid 'up-front' commitment of resources, better address the complicated decisions faced by data monitoring committees, and minimize accrual to studies having delayed ascertainment of outcomes. We investigate aspects of adaptation rules, such as timing of the adaptation analysis and magnitude of sample size adjustment, that lead to greater or lesser statistical efficiency. Owing in part to the recent Food and Drug Administration guidance that promotes the use of pre-specified sampling plans, we evaluate alternative approaches in the context of well-defined, pre-specified adaptation. We quantify the relative costs and benefits of fixed sample, group sequential, and pre-specified adaptive designs with respect to standard operating characteristics such as type I error, maximal sample size, power, and expected sample size under a range of alternatives. Our results build on others' prior research by demonstrating in realistic settings that simple and easily implemented pre-specified adaptive designs provide only very small efficiency gains over group sequential designs with the same number of analyses. In addition, we describe optimal rules for modifying the sample size, providing efficient adaptation boundaries on a variety of scales for the interim test statistic for adaptation analyses occurring at several different stages of the trial. We thus provide insight into what are good and bad choices of adaptive sampling plans when the added flexibility of adaptive designs is desired. Copyright © 2012 John Wiley & Sons, Ltd.

  16. A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system

    Hamid Reza Marateb

    2015-01-01

    Full Text Available Background: Coronary heart diseases/coronary artery diseases (CHDs/CAD, the most common form of cardiovascular disease (CVD, are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. Materials and Methods: In this study, the Cleveland CAD dataset from the University of California UCI (Irvine was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS methods, multiple logistic regression (MLR and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. Results: In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard for 272 subjects (68% male were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results. Conclusion: The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.

  17. Clinical prediction of 5-year survival in systemic sclerosis

    Fransen, Julie Munk; Popa-Diaconu, D; Hesselstrand, R

    2011-01-01

    Systemic sclerosis (SSc) is associated with a significant reduction in life expectancy. A simple prognostic model to predict 5-year survival in SSc was developed in 1999 in 280 patients, but it has not been validated in other patients. The predictions of a prognostic model are usually less accura...

  18. Creep-fatigue life prediction for different heats of Type 304 stainless steel by linear-damage rule, strain-range partitioning method, and damage-rate approach

    Maiya, P.S.

    1978-07-01

    The creep-fatigue life results for five different heats of Type 304 stainless steel at 593 0 C (1100 0 F), generated under push-pull conditions in the axial strain-control mode, are presented. The life predictions for the various heats based on the linear-damage rule, strain-range partitioning method, and damage-rate approach are discussed. The appropriate material properties required for computation of fatigue life are also included

  19. A comparison of a new multinomial stopping rule with stopping rules of fleming and gehan in single arm phase II cancer clinical trials

    Tu Dongsheng

    2011-06-01

    Full Text Available Abstract Background Response rate (RR alone may be insensitive to drug activity in phase II trials. Early progressive disease (EPD could improve sensitivity as well as increase stage I stopping rates. This study compares the previously developed dual endpoint stopping rule (DESR, which incorporates both RR and EPD into a two-stage, phase II trial, with rules using only RR. Methods Stopping rules according to the DESR were compared with studies conducted under the Fleming (16 trials or Gehan (23 trials designs. The RR hypothesis for the DESR was consistent with the comparison studies (ralt = 0.2, rnul = 0.05. Two parameter sets were used for EPD rates of interest and disinterest respectively (epdalt, epdnul: (0.4, 0.6 and (0.3, 0.5. Results Compared with Fleming, the DESR was more likely to allow stage two of accrual and to reject the null hypothesis (Hnul after stage two, with rejection being more common with EPD parameters (0.4, 0.6 than (0.3, 0.5. Compared with Gehan, both DESR parameter sets accepted Hnul in 15 trials after stage I compared with 8 trials by Gehan, with consistent conclusions in all 23 trials after stage II. Conclusions The DESR may reject Hnul when EPD rates alone are low, and thereby may improve phase II trial sensitivity to active, cytostatic drugs having limited response rates. Conversely, the DESR may invoke early stopping when response rates are low and EPD rates are high, thus shortening trials when drug activity is unlikely. EPD parameters should be chosen specific to each trial.

  20. Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

    Voorhies, Coerte V.; Conrad, Joy

    1996-01-01

    The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

  1. Clinical predictive factors for endometriosis in a Portuguese infertile population

    Calhaz-Jorge, C.; Mol, Ben W.; Nunes, J.; Costa, A. P.

    2004-01-01

    BACKGROUND: Endometriosis is an important clinical situation associated with subfertility. It would be very useful to identify patients at increased risk for endometriosis prior to laparoscopy. In the present study, we evaluate the demographic and clinical characteristics in a cohort of Portuguese

  2. Validating the TeleStroke Mimic Score: A Prediction Rule for Identifying Stroke Mimics Evaluated Over Telestroke Networks.

    Ali, Syed F; Hubert, Gordian J; Switzer, Jeffrey A; Majersik, Jennifer J; Backhaus, Roland; Shepard, L Wylie; Vedala, Kishore; Schwamm, Lee H

    2018-03-01

    Up to 30% of acute stroke evaluations are deemed stroke mimics, and these are common in telestroke as well. We recently published a risk prediction score for use during telestroke encounters to differentiate stroke mimics from ischemic cerebrovascular disease derived and validated in the Partners TeleStroke Network. Using data from 3 distinct US and European telestroke networks, we sought to externally validate the TeleStroke Mimic (TM) score in a broader population. We evaluated the TM score in 1930 telestroke consults from the University of Utah, Georgia Regents University, and the German TeleMedical Project for Integrative Stroke Care Network. We report the area under the curve in receiver-operating characteristic curve analysis with 95% confidence interval for our previously derived TM score in which lower TM scores correspond with a higher likelihood of being a stroke mimic. Based on final diagnosis at the end of the telestroke consultation, there were 630 of 1930 (32.6%) stroke mimics in the external validation cohort. All 6 variables included in the score were significantly different between patients with ischemic cerebrovascular disease versus stroke mimics. The TM score performed well (area under curve, 0.72; 95% confidence interval, 0.70-0.73; P mimic during telestroke consultation in these diverse cohorts was similar to its performance in our original cohort. Predictive decision-support tools like the TM score may help highlight key clinical differences between mimics and patients with stroke during complex, time-critical telestroke evaluations. © 2018 American Heart Association, Inc.

  3. The Surgical Mortality Probability Model: derivation and validation of a simple risk prediction rule for noncardiac surgery.

    Glance, Laurent G; Lustik, Stewart J; Hannan, Edward L; Osler, Turner M; Mukamel, Dana B; Qian, Feng; Dick, Andrew W

    2012-04-01

    To develop a 30-day mortality risk index for noncardiac surgery that can be used to communicate risk information to patients and guide clinical management at the "point-of-care," and that can be used by surgeons and hospitals to internally audit their quality of care. Clinicians rely on the Revised Cardiac Risk Index to quantify the risk of cardiac complications in patients undergoing noncardiac surgery. Because mortality from noncardiac causes accounts for many perioperative deaths, there is also a need for a simple bedside risk index to predict 30-day all-cause mortality after noncardiac surgery. Retrospective cohort study of 298,772 patients undergoing noncardiac surgery during 2005 to 2007 using the American College of Surgeons National Surgical Quality Improvement Program database. The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index was derived empirically and includes three risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. Patients with risk scores less than 5 had a predicted risk of mortality less than 0.50%, whereas patients with a risk score of 5 to 6 had a risk of mortality between 1.5% and 4.0%. Patients with a risk score greater than 6 had risk of mortality more than 10%. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set. Thirty-day mortality after noncardiac surgery can be accurately predicted using a simple and accurate risk score based on information readily available at the bedside. This risk index may play a useful role in facilitating shared decision making, developing and implementing risk

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

    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.

  5. Predictions of the spontaneous symmetry-breaking theory for visual code completeness and spatial scaling in single-cell learning rules.

    Webber, C J

    2001-05-01

    This article shows analytically that single-cell learning rules that give rise to oriented and localized receptive fields, when their synaptic weights are randomly and independently initialized according to a plausible assumption of zero prior information, will generate visual codes that are invariant under two-dimensional translations, rotations, and scale magnifications, provided that the statistics of their training images are sufficiently invariant under these transformations. Such codes span different image locations, orientations, and size scales with equal economy. Thus, single-cell rules could account for the spatial scaling property of the cortical simple-cell code. This prediction is tested computationally by training with natural scenes; it is demonstrated that a single-cell learning rule can give rise to simple-cell receptive fields spanning the full range of orientations, image locations, and spatial frequencies (except at the extreme high and low frequencies at which the scale invariance of the statistics of digitally sampled images must ultimately break down, because of the image boundary and the finite pixel resolution). Thus, no constraint on completeness, or any other coupling between cells, is necessary to induce the visual code to span wide ranges of locations, orientations, and size scales. This prediction is made using the theory of spontaneous symmetry breaking, which we have previously shown can also explain the data-driven self-organization of a wide variety of transformation invariances in neurons' responses, such as the translation invariance of complex cell response.

  6. A new model and extension of Wong-Sandler mixing rule for prediction of (vapour + liquid) equilibrium of polymer solutions using EOS/GE

    Haghtalab, Ali; Espanani, Reza

    2004-01-01

    The cubic equation of state (CEOS) is a powerful method for calculation of (vapour + liquid) equilibrium (VLE) in polymer solutions. Using CEOS for both the vapour and liquid phases allows one to calculate the non-ideality of polymer solutions based on a single EOS approach. However, the traditional mixing rules are not appropriate to extend the CEOS to non-ideal mixtures such as polymer solutions. Several authors have applied the EOS/G E approach to predict (vapour + liquid) equilibria in polymer solutions, however, incorporating an appropriate excess Gibbs free energy for the new mixing rule is a major step. In this research, the NRTL-NRF model was extended in terms of volume fraction of polymer and solvent (instead of mole fraction), then equilibrium calculations were carried out using PRSV EOS and Wong-Sandler mixing rules. Using the adjustable parameters as a function of solution temperature, the NRTL-NRF model can be used as a predictive model. In comparison with NRTL model, the results of the new NRTL-NRF model show better accuracy

  7. Diagnostic accuracy of clinical decision rules to exclude fractures in acute ankle injuries : systematic review and meta-analysis

    Barelds, Ingrid; Krijnen, Wim P; van de Leur, Johannes P; van der Schans, Cees P; Goddard, Robert J

    BACKGROUND: Ankle decision rules are developed to expedite patient care and reduce the number of radiographs of the ankle and foot. Currently, only three systematic reviews have been conducted on the accuracy of the Ottawa Ankle and Foot Rules (OAFR) in adults and children. However, no systematic

  8. Analysis of Factors that Predict Clinical Performance in Medical School

    White, Casey B.; Dey, Eric L.; Fantone, Joseph C.

    2009-01-01

    Academic achievement indices including GPAs and MCAT scores are used to predict the spectrum of medical student academic performance types. However, use of these measures ignores two changes influencing medical school admissions: student diversity and affirmative action, and an increased focus on communication skills. To determine if GPA and MCAT…

  9. Predicting activities after stroke : what is clinically relevant?

    Kwakkel, G.; Kollen, B. J.

    Knowledge about factors that determine the final outcome after stroke is important for early stroke management, rehabilitation goals, and discharge planning. This narrative review provides an overview of current knowledge about the prediction of activities after stroke. We reviewed the pattern of

  10. predicted peak expiratory flow in human and the clinical implication ...

    DR. AMINU

    predicted PEF varied widely across formulae and choice of a particular formula may alter guideline- base care. This work has therefore accepted a recently published population-base equation proposed as the reference standard for future asthma guidelines. Keywords: Peak expiratory flow, Asthma, Practice guidelines, ...

  11. Assessing Discriminative Performance at External Validation of Clinical Prediction Models.

    Daan Nieboer

    Full Text Available External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting.We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1 the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2 the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury.The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples and heterogeneous in scenario 2 (in 17%-39% of simulated samples. Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2.The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.

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

    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

  13. Prediction of renal mass aggressiveness using clinical and radiographic features: a global, multicentre prospective study

    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

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

    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…

  15. Ruptured corpus luteal cyst: Prediction of clinical outcomes with CT

    Lee, Myoung Seok; Moon, Min Hoan; Woo, Hyun Sik; Sung, Chang Kyu; Jeon, Hye Won; Lee, Taek Sang [SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul (Korea, Republic of)

    2017-08-01

    To evaluate the determinant pretreatment CT findings that can predict surgical intervention for patients suffering from corpus luteal cyst rupture with hemoperitoneum. From January 2009 to December 2014, a total of 106 female patients (mean age, 26.1 years; range, 17–44 years) who visited the emergency room of our institute for acute abdominal pain and were subsequently diagnosed with ruptured corpus luteal cyst with hemoperitoneum were included in the retrospective study. The analysis of CT findings included cyst size, cyst shape, sentinel clot sign, ring of fire sign, hemoperitoneum depth, active bleeding in portal phase and attenuation of hemoperitoneum. The comparison of CT findings between the surgery and conservative management groups was performed with the Mann-Whitney U test or chi-square test. Logistic regression analysis was used to determine significant CT findings in predicting surgical intervention for a ruptured cyst. Comparative analysis revealed that the presence of active bleeding and the hemoperitoneum depth were significantly different between the surgery and conservative management groups and were confirmed as significant CT findings for predicting surgery, with adjusted odds ratio (ORs) of 3.773 and 1.318, respectively (p < 0.01). On the receiver-operating characteristic curve analysis for hemoperitoneum depth, the optimal cut-off value was 5.8 cm with 73.7% sensitivity and 58.6% specificity (Az = 0.711, p = 0.004). In cases with a hemoperitoneum depth > 5.8 cm and concurrent active bleeding, the OR for surgery increased to 5.786. The presence of active bleeding and the hemoperitoneum depth on a pretreatment CT scan can be predictive warning signs of surgery for a patient with a ruptured corpus luteal cyst with hemoperitoneum.

  16. Somatic cell count distributions during lactation predict clinical mastitis

    Green, M.J.; Green, L.E.; Schukken, Y.H.; Bradley, A.J.; Peeler, E.J.; Barkema, H.W.; Haas, de Y.; Collis, V.J.; Medley, G.F.

    2004-01-01

    This research investigated somatic cell count (SCC) records during lactation, with the purpose of identifying distribution characteristics (mean and measures of variation) that were most closely associated with clinical mastitis. Three separate data sets were used, one containing quarter SCC (n =

  17. Predicting malaria in an highly endemic country using clinical and ...

    Kate Zinszer

    evaluate statistical models that integrate environmental and clinical data to .... was to identify and assess forecasting methods used to forecast malaria, and ...... 3Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and ...... Sachs J, Malaney P. The economic and social burden of malaria.

  18. Predicting reattendance at a high-risk breast cancer clinic.

    Ormseth, Sarah R; Wellisch, David K; Aréchiga, Adam E; Draper, Taylor L

    2015-10-01

    The research about follow-up patterns of women attending high-risk breast-cancer clinics is sparse. This study sought to profile daughters of breast-cancer patients who are likely to return versus those unlikely to return for follow-up care in a high-risk clinic. Our investigation included 131 patients attending the UCLA Revlon Breast Center High Risk Clinic. Predictor variables included age, computed breast-cancer risk, participants' perceived personal risk, clinically significant depressive symptomatology (CES-D score ≥ 16), current level of anxiety (State-Trait Anxiety Inventory), and survival status of participants' mothers (survived or passed away from breast cancer). A greater likelihood of reattendance was associated with older age (adjusted odds ratio [AOR] = 1.07, p = 0.004), computed breast-cancer risk (AOR = 1.10, p = 0.017), absence of depressive symptomatology (AOR = 0.25, p = 0.009), past psychiatric diagnosis (AOR = 3.14, p = 0.029), and maternal loss to breast cancer (AOR = 2.59, p = 0.034). Also, an interaction was found between mother's survival and perceived risk (p = 0.019), such that reattendance was associated with higher perceived risk among participants whose mothers survived (AOR = 1.04, p = 0.002), but not those whose mothers died (AOR = 0.99, p = 0.685). Furthermore, a nonlinear inverted "U" relationship was observed between state anxiety and reattendance (p = 0.037); participants with moderate anxiety were more likely to reattend than those with low or high anxiety levels. Demographic, medical, and psychosocial factors were found to be independently associated with reattendance to a high-risk breast-cancer clinic. Explication of the profiles of women who may or may not reattend may serve to inform the development and implementation of interventions to increase the likelihood of follow-up care.

  19. 足踝关节骨折快速诊断规则的临床应用%Clinical application of Ottawa ankle rules in ankle fracture

    殷军明; 蒲祖辉; 陈伟南; 雷益

    2013-01-01

    目的 研究成人足踝扭伤是否有骨折的快速诊断规则(OAR)的准确性,探讨足踝关节外伤X线摄片合理选择的必要性.方法 总结736例足踝扭伤患者(年龄18~82岁),其中足扭伤342例,踝扭伤394例.在X线摄片前,由当班医师或当班技师按OAR进行临床检查,验证该诊断试验的准确性.结果 在394例踝扭伤患者中有136例骨折,OAR漏诊4例,对踝部骨折的诊断敏感性为97.1%,特异性为49.6%,阳性预测值为50.4%,阴性预测值为97.0%.在342例足扭伤患者中有117例骨折,无漏诊,OAR 对足部骨折的诊断敏感性为100%,特异性为75.6%,阳性预测值为68.0%,阴性预测值为100%.结论 OAR对排除足踝骨折具有很高的准确性,对诊断足踝扭伤骨折具有极高的敏感性和适度的特异性;OAR的应用有望节约时间,并降低医疗费用和减少射线照射.%Objective To evaluate the accuracy of adult Ottawa ankle rules for screening possible ankle fracture,and the necessity of X-ray examination. Methods 736 cases with ankle sprain (age 18 - 82 years old) were studied. The clinical examination according to the OAR were proceed before X-ray examination by the physician or technician on duty. The accuracy of diagnostic tests were verified by comparing the results of X-ray imaging. Results 136 out of 394 cases with ankle sprain were proved to be fractures. 4 cases were missed by OAR. The sensitivity of OAR was 97. 1% ,specificity was 49. 6%,positive predictive was 50. 4% and negative predictive was 97. 0%. There were 117 fractures in 342 cases with midfoot sprain. Without missing diagnosis, the sensitivity of OAR was 100% , specificity was 75. 6%,positive predictive was 68. 0% and negative predictive was 100%. Conclusion The OAR have a high accurate instrument in excluding fractures of the ankle and midfoot. The rules have a high sensitivity (almost 100%) and modest specificity. Using the OAR holds promise for saving time and reducing both costs and

  20. Occipital lobe and posterior cingulate perfusion in the prediction of dementia with Lewy body pathology in a clinical sample.

    Prosser, Angus M J; Tossici-Bolt, Livia; Kipps, Christopher M

    2017-12-01

    The aim of this study was to investigate the diagnostic value of occipital lobe and posterior cingulate perfusion in predicting dopamine transporter imaging outcome using a quantitative measure of analysis. In total, 99 patients with cognitive complaints who had undergone both technetium-99m-hexamethylpropyleneamine oxime single-photon emission computed tomography (Tc-HMPAO SPECT) and I ioflupane (I-FP-CIT also called DaTSCAN) imaging in a dementia diagnostic center were analyzed. Measures of perfusion were calculated from HMPAO SPECT images for the medial and lateral occipital lobe, the posterior cingulate cortex, precuneus and cuneus regions of interest using statistical parametric mapping 8. DaTSCAN images were quantified and specific binding ratios were calculated independent from HMPAO SPECT results. Statistical parametric mapping and tests of associations between perfusion and I-FP-CIT imaging were completed. Regions of interest on HMPAO yielded poor predictive values when used independently to predict I-FP-CIT status; however, the combination of normal posterior cingulate perfusion with medial and lateral occipital hypoperfusion was associated significantly with I-FP-CIT status, χ (1, N=99)=9.72, P=0.002. This combination also yielded a high positive likelihood ratio and specificity (11.1, 98%). Sensitivity was, however, low (22%). No significant perfusion differences were found when abnormal and normal I-FP-CIT groups were compared directly using voxel-based morphometry (Poccipital hypoperfusion with preserved posterior cingulate gyrus perfusion is highly specific for individuals with a positive I-FP-CIT scan in a clinical sample where diagnostic doubt exists. This regional combination, however, lacks sensitivity; therefore, absence of the sign cannot be used to rule out dementia with Lewy bodies. A positive finding provides strong evidence to rule in dementia with Lewy bodies.

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

    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.

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

    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.

  3. Locus heterogeneity for Waardenburg syndrome is predictive of clinical subtypes.

    Farrer, L A; Arnos, K S; Asher, J H; Baldwin, C T; Diehl, S R; Friedman, T B; Greenberg, J; Grundfast, K M; Hoth, C; Lalwani, A K

    1994-10-01

    Waardenburg syndrome (WS) is a dominantly inherited and clinically variable syndrome of deafness, pigmentary changes, and distinctive facial features. Clinically, WS type I (WS1) is differentiated from WS type II (WS2) by the high frequency of dystopia canthorum in the family. In some families, WS is caused by mutations in the PAX3 gene on chromosome 2q. We have typed microsatellite markers within and flanking PAX3 in 41 WS1 kindreds and 26 WS2 kindreds in order to estimate the proportion of families with probable mutations in PAX3 and to study the relationship between phenotypic and genotypic heterogeneity. Evaluation of heterogeneity in location scores obtained by multilocus analysis indicated that WS is linked to PAX3 in 60% of all WS families and in 100% of WS1 families. None of the WS2 families were linked. In those families in which equivocal lod scores (between -2 and +1) were found, PAX3 mutations have been identified in 5 of the 15 WS1 families but in none of the 4 WS2 families. Although preliminary studies do not suggest any association between the phenotype and the molecular pathology in 20 families with known PAX3 mutations and in four patients with chromosomal abnormalities in the vicinity of PAX3, the presence of dystopia in multiple family members is a reliable indicator for identifying families likely to have a defect in PAX3.

  4. Ethics and epistemology of accurate prediction in clinical research.

    Hey, Spencer Phillips

    2015-07-01

    All major research ethics policies assert that the ethical review of clinical trial protocols should include a systematic assessment of risks and benefits. But despite this policy, protocols do not typically contain explicit probability statements about the likely risks or benefits involved in the proposed research. In this essay, I articulate a range of ethical and epistemic advantages that explicit forecasting would offer to the health research enterprise. I then consider how some particular confidence levels may come into conflict with the principles of ethical research. 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.

  5. Termination of resuscitation in the prehospital setting: A comparison of decisions in clinical practice vs. recommendations of a termination rule.

    Verhaert, Dominique V M; Bonnes, Judith L; Nas, Joris; Keuper, Wessel; van Grunsven, Pierre M; Smeets, Joep L R M; de Boer, Menko Jan; Brouwer, Marc A

    2016-03-01

    Of the proposed algorithms that provide guidance for in-field termination of resuscitation (TOR) decisions, the guidelines for cardiopulmonary resuscitation (CPR) refer to the basic and advanced life support (ALS)-TOR rules. To assess the potential consequences of implementation of the ALS-TOR rule, we performed a case-by-case evaluation of our in-field termination decisions and assessed the corresponding recommendations of the ALS-TOR rule. Cohort of non-traumatic out-of-hospital cardiac arrest (OHCA)-patients who were resuscitated by the ALS-practising emergency medical service (EMS) in the Nijmegen area (2008-2011). The ALS-TOR rule recommends termination in case all following criteria are met: unwitnessed arrest, no bystander CPR, no shock delivery, no return of spontaneous circulation (ROSC). Of the 598 cases reviewed, resuscitative efforts were terminated in the field in 46% and 15% survived to discharge. The ALS-TOR rule would have recommended in-field termination in only 6% of patients, due to high percentages of witnessed arrests (73%) and bystander CPR (54%). In current practice, absence of ROSC was the most important determinant of termination [aOR 35.6 (95% CI 18.3-69.3)]. Weaker associations were found for: unwitnessed and non-public arrests, non-shockable initial rhythms and longer EMS-response times. While designed to optimise hospital transportations, application of the ALS-TOR rule would almost double our hospital transportation rate to over 90% of OHCA-cases due to the favourable arrest circumstances in our region. Prior to implementation of the ALS-TOR rule, local evaluation of the potential consequences for the efficiency of triage is to be recommended and initiatives to improve field-triage for ALS-based EMS-systems are eagerly awaited. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

    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.

  7. Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery.

    Parnell, S; Gottwald, T R; Cunniffe, N J; Alonso Chavez, V; van den Bosch, F

    2015-09-07

    Emerging plant pathogens are a significant problem for conservation and food security. Surveillance is often instigated in an attempt to detect an invading epidemic before it gets out of control. Yet in practice many epidemics are not discovered until already at a high prevalence, partly due to a lack of quantitative understanding of how surveillance effort and the dynamics of an invading epidemic relate. We test a simple rule of thumb to determine, for a surveillance programme taking a fixed number of samples at regular intervals, the distribution of the prevalence an epidemic will have reached on first discovery (discovery-prevalence) and its expectation E(q*). We show that E(q*) = r/(N/Δ), i.e. simply the rate of epidemic growth divided by the rate of sampling; where r is the epidemic growth rate, N is the sample size and Δ is the time between sampling rounds. We demonstrate the robustness of this rule of thumb using spatio-temporal epidemic models as well as data from real epidemics. Our work supports the view that, for the purposes of early detection surveillance, simple models can provide useful insights in apparently complex systems. The insight can inform decisions on surveillance resource allocation in plant health and has potential applicability to invasive species generally. © 2015 The Author(s).

  8. Mechanisms of rule acquisition and rule following in inductive reasoning.

    Crescentini, Cristiano; Seyed-Allaei, Shima; De Pisapia, Nicola; Jovicich, Jorge; Amati, Daniele; Shallice, Tim

    2011-05-25

    Despite the recent interest in the neuroanatomy of inductive reasoning processes, the regional specificity within prefrontal cortex (PFC) for the different mechanisms involved in induction tasks remains to be determined. In this study, we used fMRI to investigate the contribution of PFC regions to rule acquisition (rule search and rule discovery) and rule following. Twenty-six healthy young adult participants were presented with a series of images of cards, each consisting of a set of circles numbered in sequence with one colored blue. Participants had to predict the position of the blue circle on the next card. The rules that had to be acquired pertained to the relationship among succeeding stimuli. Responses given by subjects were categorized in a series of phases either tapping rule acquisition (responses given up to and including rule discovery) or rule following (correct responses after rule acquisition). Mid-dorsolateral PFC (mid-DLPFC) was active during rule search and remained active until successful rule acquisition. By contrast, rule following was associated with activation in temporal, motor, and medial/anterior prefrontal cortex. Moreover, frontopolar cortex (FPC) was active throughout the rule acquisition and rule following phases before a rule became familiar. We attributed activation in mid-DLPFC to hypothesis generation and in FPC to integration of multiple separate inferences. The present study provides evidence that brain activation during inductive reasoning involves a complex network of frontal processes and that different subregions respond during rule acquisition and rule following phases.

  9. Ecological niche modeling for visceral leishmaniasis in the state of Bahia, Brazil, using genetic algorithm for rule-set prediction and growing degree day-water budget analysis

    Prixia Nieto

    2006-11-01

    Full Text Available Two predictive models were developed within a geographic information system using Genetic Algorithm Rule-Set Prediction (GARP and the growing degree day (GDD-water budget (WB concept to predict the distribution and potential risk of visceral leishmaniasis (VL in the State of Bahia, Brazil. The objective was to define the environmental suitability of the disease as well as to obtain a deeper understanding of the eco-epidemiology of VL by associating environmental and climatic variables with disease prevalence. Both the GARP model and the GDDWB model, using different analysis approaches and with the same human prevalence database, predicted similar distribution and abundance patterns for the Lutzomyia longipalpis-Leishmania chagasi system in Bahia. High and moderate prevalence sites for VL were significantly related to areas of high and moderate risk prediction by: (i the area predicted by the GARP model, depending on the number of pixels that overlapped among eleven annual model years, and (ii the number of potential generations per year that could be completed by the Lu. longipalpis-L. chagasi system by GDD-WB analysis. When applied to the ecological zones of Bahia, both the GARP and the GDD-WB prediction models suggest that the highest VL risk is in the interior region of the state, characterized by a semi-arid and hot climate known as Caatinga, while the risk in the Bahia interior forest and the Cerrado ecological regions is lower. The Bahia coastal forest was predicted to be a low-risk area due to the unsuitable conditions for the vector and VL transmission.

  10. Clinical parameters predictive of malignancy of thyroid follicular neoplasms

    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

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

    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.

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

    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

  13. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma

    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. ...

  14. Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning Health System.

    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.

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

    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.

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

    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

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

    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.

  18. Termination of resuscitation in the prehospital setting: A comparison of decisions in clinical practice vs. recommendations of a termination rule

    Verhaert, D.V.; Bonnes, J.L.; Nas, J.; Keuper, W.; Grunsven, P.M. van; Smeets, J.L.; Boer, M.J. de; Brouwer, M.A.

    2016-01-01

    BACKGROUND: Of the proposed algorithms that provide guidance for in-field termination of resuscitation (TOR) decisions, the guidelines for cardiopulmonary resuscitation (CPR) refer to the basic and advanced life support (ALS)-TOR rules. To assess the potential consequences of implementation of the

  19. A Wear Rule and Cutter Life Prediction Model of a 20-in. TBM Cutter for Granite: A Case Study of a Water Conveyance Tunnel in China

    Liu, Quansheng; Liu, Jianping; Pan, Yucong; Zhang, Xiaoping; Peng, Xingxin; Gong, Qiuming; Du, Lijie

    2017-05-01

    Disc cutter wear is one of the comprehensive results of the rock-machine interaction in tunnel boring machine (TBM) tunneling. The replacement of the disc cutter is a time-consuming and costly activity that can significantly reduce the TBM utilization ( U) and advance rate (AR), and has a major effect on the total time and cost of TBM tunneling projects. Therefore, the importance of predicting the cutter life accurately can never be overemphasized. Most cutter wear prediction models are only suitable for 17-in. or smaller disc cutters. However, use of large-diameter disc cutters has been an irresistible trend for large-section hard rock TBMs. This study attempts to reveal the genuine wear rule of a 20-in. disc cutter and develop a new empirical model for predicting the cutter life in granite based on field data collected from a water conveyance tunnel constructed by the TBM tunneling method in China. The field data including the actual cutter wear and the geological parameters along the studied tunnel were compiled in a special database that was subjected to statistical analysis to reveal the genuine wear rule of a 20-in. disc cutter and develop the reasonable correlations between some common intact rock parameters and the disc cutter life. These equations were developed based on data from massive to very massive granite with a UCS range of 40-100 MPa, which can be applied for the assessment of the cutter life of a 20-in. disc cutter in similar hard rock projects with similar rock strengths and rock abrasivities.

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

    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.

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

    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

  2. Derivation and Validation of a Biomarker-Based Clinical Algorithm to Rule Out Sepsis From Noninfectious Systemic Inflammatory Response Syndrome at Emergency Department Admission: A Multicenter Prospective Study.

    Mearelli, Filippo; Fiotti, Nicola; Giansante, Carlo; Casarsa, Chiara; Orso, Daniele; De Helmersen, Marco; Altamura, Nicola; Ruscio, Maurizio; Castello, Luigi Mario; Colonetti, Efrem; Marino, Rossella; Barbati, Giulia; Bregnocchi, Andrea; Ronco, Claudio; Lupia, Enrico; Montrucchio, Giuseppe; Muiesan, Maria Lorenza; Di Somma, Salvatore; Avanzi, Gian Carlo; Biolo, Gianni

    2018-05-07

    To derive and validate a predictive algorithm integrating a nomogram-based prediction of the pretest probability of infection with a panel of serum biomarkers, which could robustly differentiate sepsis/septic shock from noninfectious systemic inflammatory response syndrome. Multicenter prospective study. At emergency department admission in five University hospitals. Nine-hundred forty-seven adults in inception cohort and 185 adults in validation cohort. None. A nomogram, including age, Sequential Organ Failure Assessment score, recent antimicrobial therapy, hyperthermia, leukocytosis, and high C-reactive protein values, was built in order to take data from 716 infected patients and 120 patients with noninfectious systemic inflammatory response syndrome to predict pretest probability of infection. Then, the best combination of procalcitonin, soluble phospholypase A2 group IIA, presepsin, soluble interleukin-2 receptor α, and soluble triggering receptor expressed on myeloid cell-1 was applied in order to categorize patients as "likely" or "unlikely" to be infected. The predictive algorithm required only procalcitonin backed up with soluble phospholypase A2 group IIA determined in 29% of the patients to rule out sepsis/septic shock with a negative predictive value of 93%. In a validation cohort of 158 patients, predictive algorithm reached 100% of negative predictive value requiring biomarker measurements in 18% of the population. We have developed and validated a high-performing, reproducible, and parsimonious algorithm to assist emergency department physicians in distinguishing sepsis/septic shock from noninfectious systemic inflammatory response syndrome.

  3. Phonological reduplication in sign language: rules rule

    Iris eBerent

    2014-06-01

    Full Text Available Productivity—the hallmark of linguistic competence—is typically attributed to algebraic rules that support broad generalizations. Past research on spoken language has documented such generalizations in both adults and infants. But whether algebraic rules form part of the linguistic competence of signers remains unknown. To address this question, here we gauge the generalization afforded by American Sign Language (ASL. As a case study, we examine reduplication (X→XX—a rule that, inter alia, generates ASL nouns from verbs. If signers encode this rule, then they should freely extend it to novel syllables, including ones with features that are unattested in ASL. And since reduplicated disyllables are preferred in ASL, such rule should favor novel reduplicated signs. Novel reduplicated signs should thus be preferred to nonreduplicative controls (in rating, and consequently, such stimuli should also be harder to classify as nonsigns (in the lexical decision task. The results of four experiments support this prediction. These findings suggest that the phonological knowledge of signers includes powerful algebraic rules. The convergence between these conclusions and previous evidence for phonological rules in spoken language suggests that the architecture of the phonological mind is partly amodal.

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

    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.

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

    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.

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

    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

  7. Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

    Meretoja, Tuomo J; Andersen, Kenneth Geving; Bruce, Julie

    2017-01-01

    are missing. The aim was to develop a clinically applicable risk prediction tool. Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity......), high body mass index ( P = .039), axillary lymph node dissection ( P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day ( P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC......-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively. Conclusion Our validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen...

  8. Specific clinical signs and symptoms are predictive of clinical course in sporadic Creutzfeldt-Jakob disease.

    Nakatani, E; Kanatani, Y; Kaneda, H; Nagai, Y; Teramukai, S; Nishimura, T; Zhou, B; Kojima, S; Kono, H; Fukushima, M; Kitamoto, T; Mizusawa, H

    2016-09-01

    Akinetic mutism is thought to be an appropriate therapeutic end-point in patients with sporadic Creutzfeldt-Jakob disease (sCJD). However, prognostic factors for akinetic mutism are unclear and clinical signs or symptoms that precede this condition have not been defined. The goal of this study was to identify prognostic factors for akinetic mutism and to clarify the order of clinical sign and symptom development prior to its onset. The cumulative incidence of akinetic mutism and other clinical signs and symptoms was estimated based on Japanese CJD surveillance data (455 cases) collected from 2003 to 2008. A proportional hazards model was used to identify prognostic factors for the time to onset of akinetic mutism and other clinical signs and symptoms. Periodic synchronous discharges on electroencephalography were present in the majority of cases (93.5%). The presence of psychiatric symptoms or cerebellar disturbance at sCJD diagnosis was associated with the development of akinetic mutism [hazard ratio (HR) 1.50, 95% confidence interval (CI) 1.14-1.99, and HR 2.15, 95% CI1.61-2.87, respectively]. The clinical course from cerebellar disturbance to myoclonus or akinetic mutism was classified into three types: (i) direct path, (ii) path via pyramidal or extrapyramidal dysfunction and (iii) path via psychiatric symptoms or visual disturbance. The presence of psychiatric symptoms or cerebellar disturbance increased the risk of akinetic mutism of sCJD cases with probable MM/MV subtypes. Also, there appear to be sequential associations in the development of certain clinical signs and symptoms of this disease. © 2016 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.

  9. A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

    Debangshu Dey

    2015-03-01

    Full Text Available Lean or ultralean combustion is one of the popular strategies to achieve very low emission levels. However, it is extremely susceptible to lean blow-out (LBO. The present work explores a Cross-wavelet transform (XWT aided rule based scheme for early prediction of lean blowout. XWT can be considered as an advancement of wavelet analysis which gives correlation between two waveforms in time-frequency space. In the present scheme a swirl-stabilized dump combustor is used as a laboratory-scale model of a generic gas turbine combustor with LPG as fuel. Various time series data of CH chemiluminescence signal are recorded for different flame conditions by varying equivalence ratio, flow rate and level of air-fuel premixing. Some features are extracted from the cross-wavelet spectrum of the recorded waveforms and a reference wave. The extracted features are observed to classify the flame condition into three major classes: near LBO, moderate and healthy. Moreover, a Rough Set based technique is also applied on the extracted features to generate a rule base so that it can be fed to a real time controller or expert system to take necessary control action to prevent LBO. Results show that the proposed methodology performs with an acceptable degree of accuracy.

  10. The predictive validity of a single leg bridge test for hamstring injuries in Australian Rules Football Players.

    Freckleton, Grant; Cook, Jill; Pizzari, Tania

    2014-04-01

    Hamstring muscle strain injuries (HMSI) are the greatest injury problem in kicking sports such as Australian Rules Football. Reduced hamstring muscle strength is commonly perceived to be a risk factor for hamstring injury; however, evidence is inconclusive. Testing hamstring strength with the hip and knee at functional angles and assessing endurance parameters may be more relevant for examining the risk of hamstring injury. The primary aim of this prospective study was to examine if reduced hamstring muscle strength assessed with the single leg hamstring bridge (SLHB) was a risk factor for hamstring injury. Hamstring muscle strength of 482 amateur and semielite players from 16 football clubs, mean age 20.7 (range 16-34 years), was tested during the 2011 preseason. Players were then monitored throughout the 2011 playing season for HMSI. A total of 28 hamstring injuries, 16 right and 12 left, were recorded. Players who sustained a right HMSI during the season had a significantly lower mean right SLHB score (p=0.029), were older (p=0.002) and were more likely to have sustained a past right hamstring injury (p=0.02) or right knee injury (p=0.035). For left-sided hamstring injury, the injured group was more likely to be left leg dominant (p=0.001), older athletes (p=0.002) and there was a trend towards a history of left hamstring injury (p=0.07). This study demonstrated a significant deficit in preseason SLHB scores on the right leg of players that subsequently sustained a right-sided hamstring injury. Age, previous knee injury and a history of hamstring injury were other risk factors supported in this study. Low hamstring strength appears to be a risk factor for hamstring injury; however, due to the confounding variables and low injury rate in this study, further studies are required.

  11. Thoracic injury rule out criteria and NEXUS chest in predicting the risk of traumatic intra-thoracic injuries: A diagnostic accuracy study.

    Safari, Saeed; Radfar, Fatemeh; Baratloo, Alireza

    2018-05-01

    This study aimed to compare the diagnostic accuracy of NEXUS chest and Thoracic Injury Rule out criteria (TIRC) models in predicting the risk of intra-thoracic injuries following blunt multiple trauma. In this diagnostic accuracy study, using the 2 mentioned models, blunt multiple trauma patients over the age of 15 years presenting to emergency department were screened regarding the presence of intra-thoracic injuries that are detectable via chest x-ray and screening performance characteristics of the models were compared. In this study, 3118 patients with the mean (SD) age of 37.4 (16.9) years were studied (57.4% male). Based on TIRC and NEXUS chest, respectively, 1340 (43%) and 1417 (45.4%) patients were deemed in need of radiography performance. Sensitivity, specificity, and positive and negative predictive values of TIRC were 98.95%, 62.70%, 21.19% and 99.83%. These values were 98.61%, 59.94%, 19.97% and 99.76%, for NEXUS chest, respectively. Accuracy of TIRC and NEXUS chest models were 66.04 (95% CI: 64.34-67.70) and 63.50 (95% CI: 61.78-65.19), respectively. TIRC and NEXUS chest models have proper and similar sensitivity in prediction of blunt traumatic intra-thoracic injuries that are detectable via chest x-ray. However, TIRC had a significantly higher specificity in this regard. Copyright © 2018 Elsevier Ltd. All rights reserved.

  12. Failure of the component additivity rule to predict gas yields of biomass in flash pyrolysis at 950 deg. C

    Couhert, Carole; Commandre, Jean-Michel; Salvador, Sylvain

    2009-01-01

    Ligno-cellulosic biomass from different sources presents variable composition. The main aim of this work was to develop a method to predict the gas yields after flash pyrolysis (and tar cracking) at 950 deg. C in an Entrained Flow Reactor of any biomass from its composition in the three main components - cellulose, hemicellulose and lignin. For this approach to be successful, three conditions need to be met: (C 1 )Pyrolytic behaviour of celluloses from different biomasses is similar, as is hemicellulose and lignin behaviour. (C 2 )There is no interaction between the components. (C 3 )Extractives and ashes have no impact on the pyrolysis process. Two approaches were chosen to investigate the condition C 1 : (i)Celluloses, hemicelluloses and lignins of various sources were pyrolysed. Results show that hemicelluloses and lignins from different sources do not form the same quantities of gases. (ii)An attempt was made to identify the gas yields of 'theoretical components' that are able to predict flash pyrolytic behaviour of any biomass. Results tend to show that this is not possible. The condition C 2 is investigated by comparing the gas yields of the components taken separately and the gas yields of mixes of the components. Two types of mixing were carried out: simple mixing and intimate mixing. Results show that interactions occur between the components during flash pyrolysis. The condition C 3 was not investigated here; it can nevertheless be concluded that the behaviour of a biomass during flash pyrolysis at high temperature cannot be predicted from its composition in cellulose, hemicellulose and lignin

  13. Rules for the certification of good practices in clinical laboratories. No regulation. 3-2009. Good Laboratory Practice

    2015-01-01

    Regulation for Certification of Good Practices in clinical laboratories, hereinafter Regulation establishes the methodology and procedures for clinical laboratories to demonstrate their state of compliance with good practices, according to Regulation 3-2009, and that the CECMED can verify.

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

    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

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

    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.

  16. Does GEM-Encoding Clinical Practice Guidelines Improve the Quality of Knowledge Bases? A Study with the Rule-Based Formalism

    Georg, Gersende; Séroussi, Brigitte; Bouaud, Jacques

    2003-01-01

    The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results. PMID:14728173

  17. Does GEM-encoding clinical practice guidelines improve the quality of knowledge bases? A study with the rule-based formalism.

    Georg, Georg; Séroussi, Brigitte; Bouaud, Jacques

    2003-01-01

    The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results.

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

    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.

  19. Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite

    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...

  20. Effect of the Pulmonary Embolism Rule-Out Criteria on Subsequent Thromboembolic Events Among Low-Risk Emergency Department Patients: The PROPER Randomized Clinical Trial.

    Freund, Yonathan; Cachanado, Marine; Aubry, Adeline; Orsini, Charlotte; Raynal, Pierre-Alexis; Féral-Pierssens, Anne-Laure; Charpentier, Sandrine; Dumas, Florence; Baarir, Nacera; Truchot, Jennifer; Desmettre, Thibaut; Tazarourte, Karim; Beaune, Sebastien; Leleu, Agathe; Khellaf, Mehdi; Wargon, Mathias; Bloom, Ben; Rousseau, Alexandra; Simon, Tabassome; Riou, Bruno

    2018-02-13

    The safety of the pulmonary embolism rule-out criteria (PERC), an 8-item block of clinical criteria aimed at ruling out pulmonary embolism (PE), has not been assessed in a randomized clinical trial. To prospectively validate the safety of a PERC-based strategy to rule out PE. A crossover cluster-randomized clinical noninferiority trial in 14 emergency departments in France. Patients with a low gestalt clinical probability of PE were included from August 2015 to September 2016, and followed up until December 2016. Each center was randomized for the sequence of intervention periods. In the PERC period, the diagnosis of PE was excluded with no further testing if all 8 items of the PERC rule were negative. The primary end point was the occurrence of a thromboembolic event during the 3-month follow-up period that was not initially diagnosed. The noninferiority margin was set at 1.5%. Secondary end points included the rate of computed tomographic pulmonary angiography (CTPA), median length of stay in the emergency department, and rate of hospital admission. Among 1916 patients who were cluster-randomized (mean age 44 years, 980 [51%] women), 962 were assigned to the PERC group and 954 were assigned to the control group. A total of 1749 patients completed the trial. A PE was diagnosed at initial presentation in 26 patients in the control group (2.7%) vs 14 (1.5%) in the PERC group (difference, 1.3% [95% CI, -0.1% to 2.7%]; P = .052). One PE (0.1%) was diagnosed during follow-up in the PERC group vs none in the control group (difference, 0.1% [95% CI, -∞ to 0.8%]). The proportion of patients undergoing CTPA in the PERC group vs control group was 13% vs 23% (difference, -10% [95% CI, -13% to -6%]; P < .001). In the PERC group, rates were significantly reduced for the median length of emergency department stay (mean reduction, 36 minutes [95% CI, 4 to 68]) and hospital admission (difference, 3.3% [95% CI, 0.1% to 6.6%]). Among very low-risk patients with suspected

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

    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.

  2. Artificial neural networks to predict presence of significant pathology in patients presenting to routine colorectal clinics.

    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.

  3. [Establishment of A Clinical Prediction Model of Prolonged Air Leak 
after Anatomic Lung Resection].

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

    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

  5. Compiling standardized information from clinical practice: using content analysis and ICF Linking Rules in a goal-oriented youth rehabilitation program.

    Lustenberger, Nadia A; Prodinger, Birgit; Dorjbal, Delgerjargal; Rubinelli, Sara; Schmitt, Klaus; Scheel-Sailer, Anke

    2017-09-23

    To illustrate how routinely written narrative admission and discharge reports of a rehabilitation program for eight youths with chronic neurological health conditions can be transformed to the International Classification of Functioning, Disability and Health. First, a qualitative content analysis was conducted by building meaningful units with text segments assigned of the reports to the five elements of the Rehab-Cycle ® : goal; assessment; assignment; intervention; evaluation. Second, the meaningful units were then linked to the ICF using the refined ICF Linking Rules. With the first step of transformation, the emphasis of the narrative reports changed to a process oriented interdisciplinary layout, revealing three thematic blocks of goals: mobility, self-care, mental, and social functions. The linked 95 unique ICF codes could be grouped in clinically meaningful goal-centered ICF codes. Between the two independent linkers, the agreement rate was improved after complementing the rules with additional agreements. The ICF Linking Rules can be used to compile standardized health information from narrative reports if prior structured. The process requires time and expertise. To implement the ICF into common practice, the findings provide the starting point for reporting rehabilitation that builds upon existing practice and adheres to international standards. Implications for Rehabilitation This study provides evidence that routinely collected health information from rehabilitation practice can be transformed to the International Classification of Functioning, Disability and Health by using the "ICF Linking Rules", however, this requires time and expertise. The Rehab-Cycle ® , including assessments, assignments, goal setting, interventions and goal evaluation, serves as feasible framework for structuring this rehabilitation program and ensures that the complexity of local practice is appropriately reflected. The refined "ICF Linking Rules" lead to a standardized

  6. Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.

    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.

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

    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.

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

    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.

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

    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.

  10. CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

    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

  11. Strategy as simple rules.

    Eisenhardt, K M; Sull, D N

    2001-01-01

    The success of Yahoo!, eBay, Enron, and other companies that have become adept at morphing to meet the demands of changing markets can't be explained using traditional thinking about competitive strategy. These companies have succeeded by pursuing constantly evolving strategies in market spaces that were considered unattractive according to traditional measures. In this article--the third in an HBR series by Kathleen Eisenhardt and Donald Sull on strategy in the new economy--the authors ask, what are the sources of competitive advantage in high-velocity markets? The secret, they say, is strategy as simple rules. The companies know that the greatest opportunities for competitive advantage lie in market confusion, but they recognize the need for a few crucial strategic processes and a few simple rules. In traditional strategy, advantage comes from exploiting resources or stable market positions. In strategy as simple rules, advantage comes from successfully seizing fleeting opportunities. Key strategic processes, such as product innovation, partnering, or spinout creation, place the company where the flow of opportunities is greatest. Simple rules then provide the guidelines within which managers can pursue such opportunities. Simple rules, which grow out of experience, fall into five broad categories: how- to rules, boundary conditions, priority rules, timing rules, and exit rules. Companies with simple-rules strategies must follow the rules religiously and avoid the temptation to change them too frequently. A consistent strategy helps managers sort through opportunities and gain short-term advantage by exploiting the attractive ones. In stable markets, managers rely on complicated strategies built on detailed predictions of the future. But when business is complicated, strategy should be simple.

  12. New prediction for the direct CP-violating parameter var-epsilon prime/var-epsilon and the ΔI=1/2 rule

    Wu, Yue-Liang

    2001-01-01

    The low-energy dynamics of QCD is investigated with special attention paid to the matching between QCD and chiral perturbation theory (ChPT), and also to some useful algebraic chiral operator relations which survive even when we include chiral loop corrections. It then allows us to evaluate the hadronic matrix elements below the energy scale Λ χ ≅1GeV. Based on the new analyses, we present a consistent prediction for both the direct CP-violating parameter var-epsilonprime/var-epsilon and the ΔI=1/2 rule in kaon decays. In the leading 1/N c approximation, the isospin amplitudes A 0 and A 2 are found to agree well with the data, and the direct CP-violating parameter var-epsilonprime/var-epsilon is predicted to be large, which also confirms our earlier conclusion. Its numerical value is var-epsilonprime/var-epsilon=23.6 -7.8 +12.4 x10 -4 (Imλ t /= 1.2x10 -4 ) which is no longer sensitive to the strange quark mass due to the matching conditions. Taking into account a simultaneous consistent analysis on the isospin amplitudes A 0 and A 2 , the ratio var-epsilonprime/var-epsilon is in favor of the values var-epsilonprime/var-epsilon=(20±9)x10 -4

  13. External validation of a multivariable claims-based rule for predicting in-hospital mortality and 30-day post-pulmonary embolism complications

    Craig I. Coleman

    2016-10-01

    Full Text Available Abstract Background Low-risk pulmonary embolism (PE patients may be candidates for outpatient treatment or abbreviated hospital stay. There is a need for a claims-based prediction rule that payers/hospitals can use to risk stratify PE patients. We sought to validate the In-hospital Mortality for PulmonAry embolism using Claims daTa (IMPACT prediction rule for in-hospital and 30-day outcomes. Methods We used the Optum Research Database from 1/2008-3/2015 and included adults hospitalized for PE (415.1x in the primary position or secondary position when accompanied by a primary code for a PE complication and having continuous medical and prescription coverage for ≥6-months prior and 3-months post-inclusion or until death. In-hospital and 30-day mortality and 30-day complications (recurrent venous thromboembolism, rehospitalization or death were assessed and prognostic accuracies of IMPACT with 95 % confidence intervals (CIs were calculated. Results In total, 47,531 PE patients were included. In-hospital and 30-day mortality occurred in 7.9 and 9.4 % of patients and 20.8 % experienced any complication within 30-days. Of the 19.5 % of patients classified as low-risk by IMPACT, 2.0 % died in-hospital, resulting in a sensitivity and specificity of 95.2 % (95 % CI, 94.4–95.8 and 20.7 % (95 % CI, 20.4–21.1. Only 1 additional low-risk patient died within 30-days of admission and 12.2 % experienced a complication, translating into a sensitivity and specificity of 95.9 % (95 % CI, 95.3–96.5 and 21.1 % (95 % CI, 20.7–21.5 for mortality and 88.5 % (95 % CI, 87.9–89.2 and 21.6 % (95 % CI, 21.2–22.0 for any complication. Conclusion IMPACT had acceptable sensitivity for predicting in-hospital and 30-day mortality or complications and may be valuable for retrospective risk stratification of PE patients.

  14. External validation of a multivariable claims-based rule for predicting in-hospital mortality and 30-day post-pulmonary embolism complications.

    Coleman, Craig I; Peacock, W Frank; Fermann, Gregory J; Crivera, Concetta; Weeda, Erin R; Hull, Michael; DuCharme, Mary; Becker, Laura; Schein, Jeff R

    2016-10-22

    Low-risk pulmonary embolism (PE) patients may be candidates for outpatient treatment or abbreviated hospital stay. There is a need for a claims-based prediction rule that payers/hospitals can use to risk stratify PE patients. We sought to validate the In-hospital Mortality for PulmonAry embolism using Claims daTa (IMPACT) prediction rule for in-hospital and 30-day outcomes. We used the Optum Research Database from 1/2008-3/2015 and included adults hospitalized for PE (415.1x in the primary position or secondary position when accompanied by a primary code for a PE complication) and having continuous medical and prescription coverage for ≥6-months prior and 3-months post-inclusion or until death. In-hospital and 30-day mortality and 30-day complications (recurrent venous thromboembolism, rehospitalization or death) were assessed and prognostic accuracies of IMPACT with 95 % confidence intervals (CIs) were calculated. In total, 47,531 PE patients were included. In-hospital and 30-day mortality occurred in 7.9 and 9.4 % of patients and 20.8 % experienced any complication within 30-days. Of the 19.5 % of patients classified as low-risk by IMPACT, 2.0 % died in-hospital, resulting in a sensitivity and specificity of 95.2 % (95 % CI, 94.4-95.8) and 20.7 % (95 % CI, 20.4-21.1). Only 1 additional low-risk patient died within 30-days of admission and 12.2 % experienced a complication, translating into a sensitivity and specificity of 95.9 % (95 % CI, 95.3-96.5) and 21.1 % (95 % CI, 20.7-21.5) for mortality and 88.5 % (95 % CI, 87.9-89.2) and 21.6 % (95 % CI, 21.2-22.0) for any complication. IMPACT had acceptable sensitivity for predicting in-hospital and 30-day mortality or complications and may be valuable for retrospective risk stratification of PE patients.

  15. Open Access Platforms in Spinal Cord Injury: Existing Clinical Trial Data to Predict and Improve Outcomes.

    Kramer, John L K; Geisler, Fred; Ramer, Leanne; Plunet, Ward; Cragg, Jacquelyn J

    2017-05-01

    Recovery from acute spinal cord injury (SCI) is characterized by extensive heterogeneity, resulting in uncertain prognosis. Reliable prediction of recovery in the acute phase benefits patients and their families directly, as well as improves the likelihood of detecting efficacy in clinical trials. This issue of heterogeneity is not unique to SCI. In fields such as traumatic brain injury, Parkinson's disease, and amyotrophic lateral sclerosis, one approach to understand variability in recovery has been to make clinical trial data widely available to the greater research community. We contend that the SCI community should adopt a similar approach in providing open access clinical trial data.

  16. Prediction of Driving Safety in Individuals with Homonymous Hemianopia and Quadrantanopia from Clinical Neuroimaging

    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.

  17. Prediction of driving safety in individuals with homonymous hemianopia and quadrantanopia from clinical neuroimaging.

    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.

  18. [Predictive methods versus clinical titration for the initiation of lithium therapy. A systematic review].

    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.

  19. Islet oxygen consumption rate (OCR) dose predicts insulin independence for first clinical islet allotransplants

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

    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.

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

    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.

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

    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. Clinical prediction of fall risk and white matter abnormalities: a diffusion tensor imaging study

    The Tinetti scale is a simple clinical tool designed to predict risk of falling by focusing on gait and stance impairment in elderly persons. Gait impairment is also associated with white matter (WM) abnormalities. Objective: To test the hypothesis that elderly subjects at risk for falling, as deter...

  4. Predictive Validity of DSM-IV Oppositional Defiant and Conduct Disorders in Clinically Referred Preschoolers

    Keenan, Kate; Boeldt, Debra; Chen, Diane; Coyne, Claire; Donald, Radiah; Duax, Jeanne; Hart, Katherine; Perrott, Jennifer; Strickland, Jennifer; Danis, Barbara; Hill, Carri; Davis, Shante; Kampani, Smita; Humphries, Marisha

    2011-01-01

    Background: Diagnostic validity of oppositional defiant and conduct disorders (ODD and CD) for preschoolers has been questioned based on concerns regarding the ability to differentiate normative, transient disruptive behavior from clinical symptoms. Data on concurrent validity have accumulated, but predictive validity is limited. Predictive…

  5. Clinical use of interface pressure to predict pressure ulcer development: A systematic review

    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.

  6. The predictive validity of the BioMedical Admissions Test for pre-clinical examination performance.

    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.

  7. A clinical prediction model to assess the risk of operative delivery

    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

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

    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.

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

    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

  10. Location of brain lesions predicts conversion of clinically isolated syndromes to multiple sclerosis

    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...

  11. A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

    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.

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

    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.

  13. Have the Findings from Clinical Risk Prediction and Trials Any Key Messages for Safety Pharmacology?

    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.

  14. MICE or NICE? An economic evaluation of clinical decision rules in the diagnosis of heart failure in primary care.

    Monahan, Mark; Barton, Pelham; Taylor, Clare J; Roalfe, Andrea K; Hobbs, F D Richard; Cowie, Martin; Davis, Russell; Deeks, Jon; Mant, Jonathan; McCahon, Deborah; McDonagh, Theresa; Sutton, George; Tait, Lynda

    2017-08-15

    Detection and treatment of heart failure (HF) can improve quality of life and reduce premature mortality. However, symptoms such as breathlessness are common in primary care, have a variety of causes and not all patients require cardiac imaging. In systems where healthcare resources are limited, ensuring those patients who are likely to have HF undergo appropriate and timely investigation is vital. A decision tree was developed to assess the cost-effectiveness of using the MICE (Male, Infarction, Crepitations, Edema) decision rule compared to other diagnostic strategies to identify HF patients presenting to primary care. Data from REFER (REFer for EchocaRdiogram), a HF diagnostic accuracy study, was used to determine which patients received the correct diagnosis decision. The model adopted a UK National Health Service (NHS) perspective. The current recommended National Institute for Health and Care Excellence (NICE) guidelines for identifying patients with HF was the most cost-effective option with a cost of £4400 per quality adjusted life year (QALY) gained compared to a "do nothing" strategy. That is, patients presenting with symptoms suggestive of HF should be referred straight for echocardiography if they had a history of myocardial infarction or if their NT-proBNP level was ≥400pg/ml. The MICE rule was more expensive and less effective than the other comparators. Base-case results were robust to sensitivity analyses. This represents the first cost-utility analysis comparing HF diagnostic strategies for symptomatic patients. Current guidelines in England were the most cost-effective option for identifying patients for confirmatory HF diagnosis. The low number of HF with Reduced Ejection Fraction patients (12%) in the REFER patient population limited the benefits of early detection. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

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

    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

  16. Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk.

    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

  17. Magnetic resonance imaging of injuries to the ankle joint: can it predict clinical outcome?

    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.

  18. Cosmic Sum Rules

    T. Frandsen, Mads; Masina, Isabella; Sannino, Francesco

    2011-01-01

    We introduce new sum rules allowing to determine universal properties of the unknown component of the cosmic rays and show how it can be used to predict the positron fraction at energies not yet explored by current experiments and to constrain specific models.......We introduce new sum rules allowing to determine universal properties of the unknown component of the cosmic rays and show how it can be used to predict the positron fraction at energies not yet explored by current experiments and to constrain specific models....

  19. The metabolic syndrome: validity and utility of clinical definitions for cardiovascular disease and diabetes risk prediction.

    Cameron, Adrian

    2010-02-01

    The purpose of clinical definitions of the metabolic syndrome is frequently misunderstood. While the metabolic syndrome as a physiological process describes a clustering of numerous age-related metabolic abnormalities that together increase the risk for cardiovascular disease and type 2 diabetes, clinical definitions include obesity which is thought to be a cause rather than a consequence of metabolic disturbance, and several elements that are routinely measured in clinical practice, including high blood pressure, high blood glucose and dyslipidaemia. Obesity is frequently a central player in the development of the metabolic syndrome and should be considered a key component of clinical definitions. Previous clinical definitions have differed in the priority given to obesity. Perhaps more importantly than its role in a clinical definition, however, is obesity in isolation before the hallmarks of metabolic dysfunction that typify the syndrome have developed. This should be treated seriously as an opportunity to prevent the consequences of the global diabetes epidemic now apparent. Clinical definitions were designed to identify a population at high lifetime CVD and type 2 diabetes risk, but in the absence of several major risk factors for each condition, are not optimal risk prediction devices for either. Despite this, the metabolic syndrome has several properties that make it a useful construct, in conjunction with short-term risk prediction algorithms and sound clinical judgement, for the identification of those at high lifetime risk of CVD and diabetes. A recently published consensus definition provides some much needed clarity about what a clinical definition entails. Even this, however, remains a work in progress until more evidence becomes available, particularly in the area of ethnicity-specific waist cut-points. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

  20. Predictive factors for the placebo effect in clinical trials for dry eye: a pooled analysis of three clinical trials.

    Imanaka, Takahiro; Sato, Izumi; Tanaka, Shiro; Kawakami, Koji

    2017-11-01

    Placebo effect is one of the methodological difficulties in dry eye clinical trials. If we could elucidate the tendencies of the placebo response and find predictors, we could reduce the placebo response in clinical trials for dry eye. In this study, we investigated the predictive factors for the placebo effect in dry eye clinical trials. A total of 205 patients with dry eye assigned to the placebo arms of three placebo-controlled randomised clinical trials were analysed by simple and multivariable regression analysis. The corneal fluorescein (FL) staining score and dry eye symptoms were studied at week 4. The variables of interest included gender, age, complications of Sjögren's syndrome, Schirmer's test I value, tear break-up time and conjunctival hyperaemia score. We also conducted a stratified analysis according to the patients' age. Among all the studied endpoints, the baseline scores were significantly related to the corresponding placebo response. In addition, for the FL score and the dryness score, age was a significant predictor of the placebo response (p=0.04 and p<0.0001, respectively). Stratified analysis by age showed that patients more than 40 years of age are more likely to have a stronger placebo response in the FL and dryness scores. The baseline scores and age were predictive factors of the placebo response in frequently used endpoints, such as FL score or dryness symptoms. These patient characteristics can be controlled by study design, and our findings enable the design of more efficient placebo-controlled studies with good statistical power. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  1. An IL28B genotype-based clinical prediction model for treatment of chronic hepatitis C.

    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.

  2. Islet Oxygen Consumption Rate (OCR) Dose Predicts Insulin Independence in Clinical Islet Autotransplantation.

    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.

  3. Islet Oxygen Consumption Rate (OCR Dose Predicts Insulin Independence in Clinical Islet Autotransplantation.

    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.

  4. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

    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.

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

    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.

  6. Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between?

    Niederer, Steven A; Smith, Nic P

    2016-12-01

    Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

  7. Return to work in a cohort of low back pain patients: development and validation of a clinical prediction rule

    Heymans, M.W.; Anema, J.R.; van Buuren, S.; Knol, D.L.; van Mechelen, W.; de Vet, H.C.W.

    2009-01-01

    BACKGROUND: From the viewpoint of cost prevention, it is necessary to identify patients that are of high risk for long-term work disability, production loss and sick-leave. METHODS: Secondary data analysis in a cohort of 628 workers on sick-leave between 3 and 6 weeks due to low back pain (LBP). The

  8. Return to work in a cohort of low back pain patients: Development and validation of a clinical prediction rule

    Heymans, M.W.; Anema, J.R.; Buuren, S. van; Knol, D.L.; Mechelen, W. van; Vet, H.C.W. de

    2009-01-01

    Background From the viewpoint of cost prevention, it is necessary to identify patients that are of high risk for long-term work disability, production loss and sick-leave. Methods Secondary data analysis in a cohort of 628 workers on sick-leave between 3 and 6 weeks due to low back pain (LBP). The

  9. Evidence That a Psychopathology Interactome Has Diagnostic Value, Predicting Clinical Needs: An Experience Sampling Study

    van Os, Jim; Lataster, Tineke; Delespaul, Philippe; Wichers, Marieke; Myin-Germeys, Inez

    2014-01-01

    Background For the purpose of diagnosis, psychopathology can be represented as categories of mental disorder, symptom dimensions or symptom networks. Also, psychopathology can be assessed at different levels of temporal resolution (monthly episodes, daily fluctuating symptoms, momentary fluctuating mental states). We tested the diagnostic value, in terms of prediction of treatment needs, of the combination of symptom networks and momentary assessment level. Method Fifty-seven patients with a psychotic disorder participated in an ESM study, capturing psychotic experiences, emotions and circumstances at 10 semi-random moments in the flow of daily life over a period of 6 days. Symptoms were assessed by interview with the Positive and Negative Syndrome Scale (PANSS); treatment needs were assessed using the Camberwell Assessment of Need (CAN). Results Psychotic symptoms assessed with the PANSS (Clinical Psychotic Symptoms) were strongly associated with psychotic experiences assessed with ESM (Momentary Psychotic Experiences). However, the degree to which Momentary Psychotic Experiences manifested as Clinical Psychotic Symptoms was determined by level of momentary negative affect (higher levels increasing probability of Momentary Psychotic Experiences manifesting as Clinical Psychotic Symptoms), momentary positive affect (higher levels decreasing probability of Clinical Psychotic Symptoms), greater persistence of Momentary Psychotic Experiences (persistence predicting increased probability of Clinical Psychotic Symptoms) and momentary environmental stress associated with events and activities (higher levels increasing probability of Clinical Psychotic Symptoms). Similarly, the degree to which momentary visual or auditory hallucinations manifested as Clinical Psychotic Symptoms was strongly contingent on the level of accompanying momentary paranoid delusional ideation. Momentary Psychotic Experiences were associated with CAN unmet treatment needs, over and above PANSS

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

    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

  11. Prediction of Clinically Relevant Safety Signals of Nephrotoxicity through Plasma Metabolite Profiling

    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.

  12. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

    Snowdon Stuart

    2009-07-01

    Full Text Available Abstract Background Metabolomics experiments using Mass Spectrometry (MS technology measure the mass to charge ratio (m/z and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50% of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. Conclusion We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data.

  13. Risk determination after an acute myocardial infarction: review of 3 clinical risk prediction tools.

    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

  14. The Diagnostic Accuracy of Clinical and External Pelvimetry in Prediction of Dystocia in Nulliparous Women

    R Alijahan

    2011-08-01

    Full Text Available Introduction: Clinical pelvimetry is very uncomfortable for the patient and is associated with subjective error, while external pelvimetry is a simple and acceptable method for patients. The objective of this study was to compare the diagnostic accuracy of clinical and external pelvimetry in prediction of dystocia in nulliparous women. Methods: In this study between December 2008 and January 2009, 447 nulliparous women with a single pregnancy in vertex presentation and gestational age 38-42 weeks referring to the Ommolbanin hospital of Mashhad were included. External pelvic dimensions were assessed at the time of admission and clinical pelvimetry was performed by another examiner. These measurements were not available to the clinician in charge of the delivery. Dystocia was defined as caesarean section and vacuum or forceps delivery for abnormal progress of labor ( active uterine contractions, arrest of cervical dilatation or cervical dilatation less than 1 cm /h in the active phase for 2 hours, prolongation of second stage beyond 2 hours or fetal head descent less than 1cm/h. Statistical tests included Fisher exact test and Chi- square test. Results: The highest sensitivity obtained from clinical pelvimetry was 33.3% and related to diagonal conjugate less than 11.5 cm. The sensitivity of external pelvic dimensions was higher than clinical pelvimetry that was highest for the Michaelis transverse diameter(60.72%. Conclusion: External pelvimetry in comparison to clinical pelvimetry is a better method for identifying dystocia in nulliparous women and can replace clinical pelvimetry in antenatal care programs.

  15. The Rules of Engagement: CTTI Recommendations for Successful Collaborations Between Sponsors and Patient Groups Around Clinical Trials.

    Bloom, Diane; Beetsch, Joel; Harker, Matthew; Hesterlee, Sharon; Moreira, Paulo; Patrick-Lake, Bray; Selig, Wendy; Sherman, Jeffrey; Smith, Sophia K; Valentine, James E; Roberts, Jamie N

    2018-03-01

    To identify the elements necessary for successful collaboration between patient groups and academic and industry sponsors of clinical trials, in order to develop recommendations for best practices for effective patient group engagement. In-depth interviews, informed by a previously reported survey, were conducted to identify the fundamentals of successful patient group engagement. Thirty-two respondents from 3 sectors participated: patient groups, academic researchers, and industry. The findings were presented to a multistakeholder group of experts in January 2015. The expert group came to consensus on a set of actionable recommendations for best practices for patient groups and research sponsors. Interview respondents acknowledged that not all patient groups are created equal in terms of what they can contribute to a clinical trial. The most important elements for effective patient group engagement include establishing meaningful partnerships, demonstrating mutual benefits, and collaborating as partners from the planning stage forward. Although there is a growing appreciation by sponsors about the benefits of patient group engagement, there remains some resistance and some uncertainty about how best to engage. Barriers include mismatched expectations and a perception that patient groups lack scientific sophistication and that "wishful thinking" may cloud their recommendations. Patient groups are developing diverse skillsets and acquiring assets to leverage in order to become collaborators with industry and academia on clinical trials. Growing numbers of research sponsors across the clinical trials enterprise are recognizing the benefits of continuous and meaningful patient group engagement, but there are still mindsets to change, and stakeholders need further guidance on operationalizing a new model of clinical trial conduct.

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

    Cai, John; Bresell,; Steinberg,; Silberg,; Furlong,Stephen

    2012-01-01

    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 a...

  17. Data Science Solution to Event Prediction in Outsourced Clinical Trial Models.

    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.

  18. Can machine-learning improve cardiovascular risk prediction using routine clinical data?

    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

  19. The presence, predictive utility, and clinical significance of body dysmorphic symptoms in women with eating disorders

    2013-01-01

    Background Both eating disorders (EDs) and body dysmorphic disorder (BDD) are disorders of body image. This study aimed to assess the presence, predictive utility, and impact of clinical features commonly associated with BDD in women with EDs. Methods Participants recruited from two non-clinical cohorts of women, symptomatic and asymptomatic of EDs, completed a survey on ED (EDE-Q) and BDD (BDDE-SR) psychopathology, psychological distress (K-10), and quality of life (SF-12). Results A strong correlation was observed between the total BDDE-SR and the global EDE-Q scores (r = 0.79, p 0.05) measured appearance checking, reassurance-seeking, camouflaging, comparison-making, and social avoidance. In addition to these behaviors, inspection of sensitivity (Se) and specificity (Sp) revealed that BDDE-SR items measuring preoccupation and dissatisfaction with appearance were most predictive of ED cases (Se and Sp > 0.60). Higher total BDDE-SR scores were associated with greater distress on the K-10 and poorer quality of life on the SF-12 (all p < 0.01). Conclusions Clinical features central to the model of BDD are common in, predictive of, and associated with impairment in women with EDs. Practice implications are that these features be included in the assessment and treatment of EDs. PMID:24999401

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

    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.

  1. Endovascular Treatment of Malignant Superior Vena Cava Syndrome: Results and Predictive Factors of Clinical Efficacy

    Fagedet, Dorothee, E-mail: DFagedet@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de medecine interne, Pole Pluridisciplinaire de Medecine (France); Thony, Frederic, E-mail: FThony@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Timsit, Jean-Francois, E-mail: JFTimsit@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de reanimation, Pole Medecine Aiguee Communautaire (France); Rodiere, Mathieu, E-mail: MRodiere@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Monnin-Bares, Valerie, E-mail: v-monnin@chu-montpellier.fr [CHRU Arnaud de Villeneuve, Imagerie Medicale Thoracique Cardiovasculaire (France); Ferretti, Gilbert R., E-mail: GFerretti@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Vesin, Aurelien; Moro-Sibilot, Denis, E-mail: DMoro.pneumo@chu-grenoble.fr [University Grenoble 1 e Albert Bonniot Institute, Inserm U823 (France)

    2013-02-15

    To demonstrate the effectiveness of endovascular treatment (EVT) with self-expandable bare stents for malignant superior vena cava syndrome (SVCS) and to analyze predictive factors of EVT efficacy. Retrospective review of the 164 patients with malignant SVCS treated with EVT in our hospital from August 1992 to December 2007 and followed until February 2009. Endovascular treatment includes angioplasty before and after stent placement. We used self-expandable bare stents. We studied results of this treatment and looked for predictive factors of clinical efficacy, recurrence, and complications by statistical analysis. Endovascular treatment was clinically successful in 95% of cases, with an acceptable rate of early mortality (2.4%). Thrombosis of the superior vena cava was the only independent factor for EVT failure. The use of stents over 16 mm in diameter was a predictive factor for complications (P = 0.008). Twenty-one complications (12.8%) occurred during the follow-up period. Relapse occurred in 36 patients (21.9%), with effective restenting in 75% of cases. Recurrence of SVCS was significantly increased in cases of occlusion (P = 0.01), initial associated thrombosis (P = 0.006), or use of steel stents (P = 0.004). Long-term anticoagulant therapy did not influence the risk of recurrence or complications. In malignancy, EVT with self-expandable bare stents is an effective SVCS therapy. These results prompt us to propose treatment with stents earlier in the clinical course of patients with SVCS and to avoid dilatation greater than 16 mm.

  2. Prediction of Lateral Ankle Sprains in Football Players Based on Clinical Tests and Body Mass Index.

    Gribble, Phillip A; Terada, Masafumi; Beard, Megan Q; Kosik, Kyle B; Lepley, Adam S; McCann, Ryan S; Pietrosimone, Brian G; Thomas, Abbey C

    2016-02-01

    The lateral ankle sprain (LAS) is the most common injury suffered in sports, especially in football. While suggested in some studies, a predictive role of clinical tests for LAS has not been established. To determine which clinical tests, focused on potentially modifiable factors of movement patterns and body mass index (BMI), could best demonstrate risk of LAS among high school and collegiate football players. Case-control study; Level of evidence, 3. A total of 539 high school and collegiate football players were evaluated during the preseason with the Star Excursion Balance Test (SEBT) and Functional Movement Screen as well as BMI. Results were compared between players who did and did not suffer an LAS during the season. Logistic regression analyses and calculated odds ratios were used to determine which measures predicted risk of LAS. The LAS group performed worse on the SEBT-anterior reaching direction (SEBT-ANT) and had higher BMI as compared with the noninjured group (P football players. BMI was also significantly higher in football players who sustained an LAS. Identifying clinical tools for successful LAS injury risk prediction will be a critical step toward the creation of effective prevention programs to reduce risk of sustaining an LAS during participation in football. © 2015 The Author(s).

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

    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.

  4. Endovascular Treatment of Malignant Superior Vena Cava Syndrome: Results and Predictive Factors of Clinical Efficacy

    Fagedet, Dorothée; Thony, Frederic; Timsit, Jean-François; Rodiere, Mathieu; Monnin-Bares, Valérie; Ferretti, Gilbert R.; Vesin, Aurélien; Moro-Sibilot, Denis

    2013-01-01

    To demonstrate the effectiveness of endovascular treatment (EVT) with self-expandable bare stents for malignant superior vena cava syndrome (SVCS) and to analyze predictive factors of EVT efficacy. Retrospective review of the 164 patients with malignant SVCS treated with EVT in our hospital from August 1992 to December 2007 and followed until February 2009. Endovascular treatment includes angioplasty before and after stent placement. We used self-expandable bare stents. We studied results of this treatment and looked for predictive factors of clinical efficacy, recurrence, and complications by statistical analysis. Endovascular treatment was clinically successful in 95% of cases, with an acceptable rate of early mortality (2.4%). Thrombosis of the superior vena cava was the only independent factor for EVT failure. The use of stents over 16 mm in diameter was a predictive factor for complications (P = 0.008). Twenty-one complications (12.8%) occurred during the follow-up period. Relapse occurred in 36 patients (21.9%), with effective restenting in 75% of cases. Recurrence of SVCS was significantly increased in cases of occlusion (P = 0.01), initial associated thrombosis (P = 0.006), or use of steel stents (P = 0.004). Long-term anticoagulant therapy did not influence the risk of recurrence or complications. In malignancy, EVT with self-expandable bare stents is an effective SVCS therapy. These results prompt us to propose treatment with stents earlier in the clinical course of patients with SVCS and to avoid dilatation greater than 16 mm.

  5. Clinical models are inaccurate in predicting bile duct stones in situ for patients with gallbladder.

    Topal, B; Fieuws, S; Tomczyk, K; Aerts, R; Van Steenbergen, W; Verslype, C; Penninckx, F

    2009-01-01

    The probability that a patient has common bile duct stones (CBDS) is a key factor in determining diagnostic and treatment strategies. This prospective cohort study evaluated the accuracy of clinical models in predicting CBDS for patients who will undergo cholecystectomy for lithiasis. From October 2005 until September 2006, 335 consecutive patients with symptoms of gallstone disease underwent cholecystectomy. Statistical analysis was performed on prospective patient data obtained at the time of first presentation to the hospital. Demonstrable CBDS at the time of endoscopic retrograde cholangiopancreatography (ERCP) or intraoperative cholangiography (IOC) was considered the gold standard for the presence of CBDS. Common bile duct stones were demonstrated in 53 patients. For 35 patients, ERCP was performed, with successful stone clearance in 24 of 30 patients who had proven CBDS. In 29 patients, IOC showed CBDS, which were managed successfully via laparoscopic common bile duct exploration, with stone extraction at the time of cholecystectomy. Prospective validation of the existing model for CBDS resulted in a predictive accuracy rate of 73%. The new model showed a predictive accuracy rate of 79%. Clinical models are inaccurate in predicting CBDS in patients with cholelithiasis. Management strategies should be based on the local availability of therapeutic expertise.

  6. Microsatellite Instability Predicts Clinical Outcome in Radiation-Treated Endometrioid Endometrial Cancer

    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.

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

    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.

  8. Personality and Defense Styles: Clinical Specificities and Predictive Factors of Alcohol Use Disorder in Women.

    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.

  9. A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic

    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

  10. Predicting dynamic knee joint load with clinical measures in people with medial knee osteoarthritis.

    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.

  11. Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

    Aurea Lima

    2015-06-01

    Full Text Available Background: Methotrexate (MTX is widely used for rheumatoid arthritis (RA treatment. Single nucleotide polymorphisms (SNPs could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment.

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

    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...

  13. A core competency-based objective structured clinical examination (OSCE) can predict future resident performance.

    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.

  14. Semi-empirical correlation for binary interaction parameters of the Peng–Robinson equation of state with the van der Waals mixing rules for the prediction of high-pressure vapor–liquid equilibrium

    Seif-Eddeen K. Fateen

    2013-03-01

    Full Text Available Peng–Robinson equation of state is widely used with the classical van der Waals mixing rules to predict vapor liquid equilibria for systems containing hydrocarbons and related compounds. This model requires good values of the binary interaction parameter kij. In this work, we developed a semi-empirical correlation for kij partly based on the Huron–Vidal mixing rules. We obtained values for the adjustable parameters of the developed formula for over 60 binary systems and over 10 categories of components. The predictions of the new equation system were slightly better than the constant-kij model in most cases, except for 10 systems whose predictions were considerably improved with the new correlation.

  15. Semi-empirical correlation for binary interaction parameters of the Peng-Robinson equation of state with the van der Waals mixing rules for the prediction of high-pressure vapor-liquid equilibrium.

    Fateen, Seif-Eddeen K; Khalil, Menna M; Elnabawy, Ahmed O

    2013-03-01

    Peng-Robinson equation of state is widely used with the classical van der Waals mixing rules to predict vapor liquid equilibria for systems containing hydrocarbons and related compounds. This model requires good values of the binary interaction parameter kij . In this work, we developed a semi-empirical correlation for kij partly based on the Huron-Vidal mixing rules. We obtained values for the adjustable parameters of the developed formula for over 60 binary systems and over 10 categories of components. The predictions of the new equation system were slightly better than the constant-kij model in most cases, except for 10 systems whose predictions were considerably improved with the new correlation.

  16. I. Photon transition amplitudes predicted by the transformation between current and constituent quarks. II. Saturation of the Drell--Hearn--Gerasimov sum rule

    Karliner, I.

    1975-01-01

    The SU(6)-W group structure appears in both current algebra and in the spectroscopy of hadrons. Recently, a considerable progress has taken place in relating these two SU(6)-W structures. The consequences of the proposed correspondence, as it applies to real photon transitions, are investigated in this work. The general structure of such transitions is shown, and a set of resulting selection rules is presented for the multipole character of the photon amplitudes. Many specific amplitudes for both mesons and baryons are worked out and their signs and magnitudes are compared with available experimental data. The saturation of the Drell-Hearn-Gerasimov sum rule for the forward spin-flip amplitude of nucleon Compton scattering was investigated. The sum rule saturation was studied using recent analyses of single pion photoproduction in the region up to photon laboratory energies of 1.2 GeV. The original sum rule is decomposed into separate sum rules originating from different isospin compnents of the electromagnetic current. All three sum rules receive important nonresonant as well as resonant contributions. The isovector-isovector sum rule, whose contributions are known best, is found to be nearly saturated, lending support to the assumptions underlying the sum rules. The failure of the isovector-isoscalar sumrule to be saturated is then presumably to be blamed on inadequate data for inelastic contributions. (Diss. Abs,r. Int., B)

  17. Extended prediction rule to optimise early detection of heart failure in older persons with non-acute shortness of breath : A cross-sectional study

    Van Riet, Evelien E S; Hoes, Arno W.; Limburg, Alexander; Landman, Marcel A J; Kemperman, Hans; Rutten, Frans H.

    2016-01-01

    Objectives: There is a need for a practical tool to aid general practitioners in early detection of heart failure in the elderly with shortness of breath. In this study, such a screening rule was developed based on an existing rule for detecting heart failure in older persons with a diagnosis of

  18. Magnetic resonance imaging and morphometric histologic analysis of prostate tissue composition in predicting the clinical outcome of terazosin therapy in benign prostatic hyperplasia

    Isen, K. [Karaelmas Univ., Zonguldak (Turkey). School of Medicine; Sinik, Z.; Alkibay, T.; Sezer, C.; Soezen, S.; Atilla, S.; Ataoglu, O.; Isik, S.

    2001-02-01

    The purpose of this study was to determine whether magnetic resonance imaging (MRI) or quantitative color-imaged morphometric analysis (MA) of the prostate gland are related to the clinical response to terazosin. Thirty-six male patients with symptomatic benign prostatic hyperplasia (BPH) with a serum prostate-specific antigen level of 4-10 ng/mL underwent MRI with body coil, transrectal prostate unltrasonography and biopsy prior to terazosin therapy. For MRI-determined stromal and non-stromal BPH, the ratio of the signal intensity of the inner gland to the obturator internus muscle was evaluated. Histologic sections were stained with hematoxylin and eosin. The MA of the specimens was performed by Samba 2000. Results of the two techniques were interpreted according to the terazosin therapy results. The mean stromal percentage was 60.5{+-}18.0%. No statistically significant relationship was found between the clinical outcome of terazosin and the MRI findings. The MA results showed a significant relationship between the percentage of stroma and the percent change of the peak urinary flow rate, but not with the percent change of the international prostate symptom score after terazosin therapy (P<0.05). Magnetic resonance imaging alone is not sufficient in predicting the response to terazosin therapy. Morphometric analysis of BPH tissue composition can be used in predicting the clinical outcome of terazosin therapy but it is suitable only in patients for whom prostatic biopsy is necessary in order to rule out prostate cancer. (author)

  19. Magnetic resonance imaging and morphometric histologic analysis of prostate tissue composition in predicting the clinical outcome of terazosin therapy in benign prostatic hyperplasia

    Isen, K.; Sinik, Z.; Alkibay, T.; Sezer, C.; Soezen, S.; Atilla, S.; Ataoglu, O.; Isik, S.

    2001-01-01

    The purpose of this study was to determine whether magnetic resonance imaging (MRI) or quantitative color-imaged morphometric analysis (MA) of the prostate gland are related to the clinical response to terazosin. Thirty-six male patients with symptomatic benign prostatic hyperplasia (BPH) with a serum prostate-specific antigen level of 4-10 ng/mL underwent MRI with body coil, transrectal prostate unltrasonography and biopsy prior to terazosin therapy. For MRI-determined stromal and non-stromal BPH, the ratio of the signal intensity of the inner gland to the obturator internus muscle was evaluated. Histologic sections were stained with hematoxylin and eosin. The MA of the specimens was performed by Samba 2000. Results of the two techniques were interpreted according to the terazosin therapy results. The mean stromal percentage was 60.5±18.0%. No statistically significant relationship was found between the clinical outcome of terazosin and the MRI findings. The MA results showed a significant relationship between the percentage of stroma and the percent change of the peak urinary flow rate, but not with the percent change of the international prostate symptom score after terazosin therapy (P<0.05). Magnetic resonance imaging alone is not sufficient in predicting the response to terazosin therapy. Morphometric analysis of BPH tissue composition can be used in predicting the clinical outcome of terazosin therapy but it is suitable only in patients for whom prostatic biopsy is necessary in order to rule out prostate cancer. (author)

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

    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.

  1. Highly accurate prediction of food challenge outcome using routinely available clinical data.

    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.

  2. Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models.

    Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O

    2012-06-01

    It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), psystems in any application domain. Copyright © 2012 Elsevier Inc. All rights reserved.

  3. Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

    Pereira J.C.R.

    2004-01-01

    Full Text Available The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA and by fuzzy max-min compositions (fuzzy, and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

  4. Clinical utility of polymorphisms in one-carbon metabolism for breast cancer risk prediction

    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.

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

    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. Predicting PTSD using the New York Risk Score with genotype data: potential clinical and research opportunities

    Boscarino JA

    2013-04-01

    Full Text Available Joseph A Boscarino,1,2 H Lester Kirchner,3,4 Stuart N Hoffman,5 Porat M Erlich1,4 1Center for Health Research, Geisinger Clinic, Danville, 2Department of Psychiatry, Temple University School of Medicine, Philadelphia, 3Division of Medicine, Geisinger Clinic, Danville, 4Department of Medicine, Temple University School of Medicine, Philadelphia, 5Department of Neurology, Geisinger Clinic, Danville, PA, USA Background: We previously developed a post-traumatic stress disorder (PTSD screening instrument, ie, the New York PTSD Risk Score (NYPRS, that was effective in predicting PTSD. In the present study, we assessed a version of this risk score that also included genetic information. Methods: Utilizing diagnostic testing methods, we hierarchically examined different prediction variables identified in previous NYPRS research, including genetic risk-allele information, to assess lifetime and current PTSD status among a population of trauma-exposed adults. Results: We found that, in predicting lifetime PTSD, the area under the receiver operating characteristic curve (AUC for the Primary Care PTSD Screen alone was 0.865. When we added psychosocial predictors from the original NYPRS to the model, including depression, sleep disturbance, and a measure of health care access, the AUC increased to 0.902, which was a significant improvement (P = 0.0021. When genetic information was added in the form of a count of PTSD risk alleles located within FKBP, COMT, CHRNA5, and CRHR1 genetic loci (coded 0–6, the AUC increased to 0.920, which was also a significant improvement (P = 0.0178. The results for current PTSD were similar. In the final model for current PTSD with the psychosocial risk factors included, genotype resulted in a prediction weight of 17 for each risk allele present, indicating that a person with six risk alleles or more would receive a PTSD risk score of 17 × 6 = 102, the highest risk score for any of the predictors studied. Conclusion: Genetic

  7. Predicting clinically unrecognized coronary artery disease: use of two- dimensional echocardiography

    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.

  8. Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review.

    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.

  9. Etiologies of Acute Undifferentiated Fever and Clinical Prediction of Scrub Typhus in a Non-Tropical Endemic Area

    Jung, Ho-Chul; Chon, Sung-Bin; Oh, Won Sup; Lee, Dong-Hyun; Lee, Ho-Jin

    2015-01-01

    Scrub typhus usually presents as acute undifferentiated fever. This cross-sectional study included adult patients presenting with acute undifferentiated fever defined as any febrile illness for ≤ 14 days without evidence of localized infection. Scrub typhus cases were defined by an antibody titer of a ≥ fourfold increase in paired sera, a ≥ 1:160 in a single serum using indirect immunofluorescence assay, or a positive result of the immunochromatographic test. Multiple regression analysis identified predictors associated with scrub typhus to develop a prediction rule. Of 250 cases with known etiology of acute undifferentiated fever, influenza (28.0%), hepatitis A (25.2%), and scrub typhus (16.4%) were major causes. A prediction rule for identifying suspected cases of scrub typhus consisted of age ≥ 65 years (two points), recent fieldwork/outdoor activities (one point), onset of illness during an outbreak period (two points), myalgia (one point), and eschar (two points). The c statistic was 0.977 (95% confidence interval = 0.960–0.994). At a cutoff value ≥ 4, the sensitivity and specificity were 92.7% (79.0–98.1%) and 90.9% (86.0–94.3%), respectively. Scrub typhus, the third leading cause of acute undifferentiated fever in our region, can be identified early using the prediction rule. PMID:25448236

  10. Clinical symptoms predict concurrent social and global functioning in an early psychosis sample.

    Cacciotti-Saija, Cristina; Langdon, Robyn; Ward, Philip B; Hickie, Ian B; Guastella, Adam J

    2018-04-01

    Although well established in chronic schizophrenia, the key determinants of functioning remain unknown during the early phase of a psychotic disorder. The aim of this study was to comprehensively examine the social cognitive, basic neurocognitive and clinical predictors of concurrent social functioning and global functioning in an early psychosis sample. This study examined the relationship between social cognition, basic neurocognition and clinical symptoms with concurrent functioning in 51 early psychosis individuals. Assessments included a range of self-report, observational and clinician-rated measures of cognitive, symptom severity and functioning domains. Results revealed a significant association between self-reported social function and lower levels of both social interaction anxiety and negative psychotic symptoms. A significant association was also observed between lower levels of negative psychotic symptoms and observed social functioning. Lastly, results demonstrated a significant association between reduced negative psychotic symptoms and clinician-rated global functioning. Clinical domains such as negative symptoms and social interaction anxiety significantly contribute to an optimal model predicting outcome during the early phase of a psychotic disorder. These clinical features may also provide useful markers of an individual's capacity for social participation. Clinical implications include the need for early targeted intervention to address social anxiety and negative psychotic symptoms to facilitate optimum patient outcome. © 2015 Wiley Publishing Asia Pty Ltd.

  11. Does vitamin D deficiency predict early conversion of clinically isolated syndrome? A preliminary Egyptian study.

    Shaheen, Hala A; Sayed, Sayed S; Daker, Lamiaa I; AbdelAziz, Hossam Eldin; Taha, Mohamed A

    2018-03-15

    It has been suggested that vitamin D influences the immunoregulation and subsequently affects the risk for conversion of clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (MS). There is little information regarding the relationship between levels of vitamin D and CIS conversion to MS in Egyptian patients. It is to study contribution of vitamin D deficiency to conversion of CIS to clinically definite multiple sclerosis (CDMS) and correlation of vitamin D level to cognitive and magnetic resonance imaging (MRI) results. A longitudinal prospective case control study was conducted on 43 Egyptian patients diagnosed as CIS according to McDonald criteria (2010). Clinical presentation, brain MRI and 25-hydroxyvitamin D levels were evaluated at baseline and after one-year follow-up. The CIS patients that converted to MS showed significant lower vitamin D level (p < 0.001) than the non-convertors. Multivariate logistic regression analysis revealed that the CIS patients with lower 25-hydroxyvitamin D level (p < 0.001) are at higher risk for early conversion to MS. There was a significant positive correlation between the vitamin D level and PASAT (r = 0.36, p = 0.02). It was found that there was a significant negative correlation between vitamin D level and MRI T 2 load (r = -0.38, p = 0.01). The low level of 25-hydroxyvitamin D may predict early conversion to clinically definite MS. Early vitamin D supplementation is recommended in patients with CIS.

  12. Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST

    Fenerty, Kathleen E.; Folio, Les R.; Patronas, Nicholas J.; Marté, Jennifer L.; Gulley, James L.; Heery, Christopher R.

    2016-01-01

    The Response Evaluation Criteria in Solid Tumors (RECIST) are the current standard for evaluating disease progression or therapy response in patients with solid tumors. RECIST 1.1 calls for axial, longest-diameter (or perpendicular short axis of lymph nodes) measurements of a maximum of five tumors, which limits clinicians’ ability to adequately measure disease burden, especially in patients with irregularly shaped tumors. This is especially problematic in chordoma, a disease for which RECIST does not always adequately capture disease burden because chordoma tumors are typically irregularly shaped and slow-growing. Furthermore, primary chordoma tumors tend to be adjacent to vital structures in the skull or sacrum that, when compressed, lead to significant clinical consequences. Volumetric segmentation is a newer technology that allows tumor burden to be measured in three dimensions on either MR or CT. Here, we compared the ability of RECIST measurements and tumor volumes to predict clinical outcomes in a cohort of 21 chordoma patients receiving immunotherapy. There was a significant difference in radiologic time to progression Kaplan-Meier curves between clinical outcome groups using volumetric segmentation (P = 0.012) but not RECIST (P = 0.38). In several cases, changes in volume were earlier and more sensitive reflections of clinical status. RECIST is a useful evaluation method when obvious changes are occurring in patients with chordoma. However, in many cases, RECIST does not detect small changes, and volumetric assessment was capable of detecting changes and predicting clinical outcome earlier than RECIST. Although this study was small and retrospective, we believe our results warrant further research in this area

  13. A prospective cohort study on the clinical utility of second trimester mean arterial blood pressure in the prediction of late-onset preeclampsia among Nigerian women.

    Udenze, I C; Arikawe, A P; Makwe, C C; Olowoselu, O F

    2017-06-01

    Early detection of preeclampsia will help reduce the morbidities and mortalities associated with the disorder. Late-onset preeclampsia was the predominant presentation in this cohort. The search for biomarkers for predicting preeclampsia is still ongoing. Mean arterial blood pressure (MABP), which has the advantage of presenting a single cutoff value compared with the use of systolic and diastolic blood pressure measurements, merits evaluation. The study aims to evaluate the clinical utility of second trimester MABP in the prediction of preeclampsia. This was a prospective cohort study of 155 normotensive, nonproteinuric pregnant women without prior history of gestational hypertension. The women were booked patients attending the antenatal clinic at the Lagos University Teaching Hospital and were all in their second trimesters of pregnancy. The outcome measures were systolic blood pressure, diastolic blood pressure, and MABP. The end point of the study was the development of preeclampsia. The diagnosis of preeclampsia was made by the attending obstetrician. The data were analyzed using the IBM SPSS statistical software. Statistical significance was set at P blood pressure, diastolic blood pressure, and MABP values in the group of women who later developed preeclampsia, P = 0.005, 0.001, and area under the receiver-operative characteristics curve (AUC) was 0.732 (95% confidence interval, 0.544-0.919, P = 0.011). The negative predictive value (NPV) was 88.88% and the positive predictive value (PPV) was 45.45%, P AUC of 0.732, MABP performed moderately (considering that excellent performance has an AUC of 1.0) in the prediction of late-onset preeclampsia in Nigerian women. Its high NPV suggests a strong ability to rule out preeclampsia and help to appropriate management.

  14. Clinical Prediction Models for Cardiovascular Disease: The Tufts PACE CPM Database

    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. The potential of predictive analytics to provide clinical decision support in depression treatment planning.

    Kessler, Ronald C

    2018-01-01

    To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

  16. The clinical factors′ prediction of increased intradialytic qt dispersion on the electrocardiograms of chronic hemodialysis patients

    Dina Oktavia

    2013-01-01

    Full Text Available Ventricular arrhythmias and sudden death are common in patients on maintenance hemodialysis (HD. The increase in QT dispersion (QTd on the electrocardiogram (ECG reflects increased tendency for ventricular repolarization that predisposes to arrhythmias. The purpose of the study was to identify the clinical factors that may predict the increased intradialytic QTd and to assess differences in QTd before and after HD. Each of 61 chronic HD patients underwent 12-lead ECG and blood pressure (BP measurement before and every 1 h during a single HD session. The QT intervals were corrected for heart rate using Bazett′s formula. Intradialytic QTd increased in 30 (49% patients. There was no correlation between the increased QTd and the clinical factors including hypertension, pulse pressure, intradialytic hypotension, left ventricular hypertrophy, old myocardial infarct, diabetes mellitus, and nutritional status. The means of QT interval and QTd increased after HD session (from 382 ± 29 to 444 ± 26 ms, P <0.05; and from 74 ± 21 to 114 ± 53 ms, respectively, P <0.05. We conclude that the increased intradialytic QTd could not be predicted by any of the clinical factors evaluated in this study. There was significant difference in the means of QTd before and after HD session.

  17. The prediction of output factors for spread-out proton Bragg peak fields in clinical practice

    Kooy, Hanne M; Rosenthal, Stanley J; Engelsman, Martijn; Mazal, Alejandro; Slopsema, Roelf L; Paganetti, Harald; Flanz, Jacob B

    2005-01-01

    The reliable prediction of output factors for spread-out proton Bragg peak (SOBP) fields in clinical practice remained unrealized due to a lack of a consistent theoretical framework and the great number of variables introduced by the mechanical devices necessary for the production of such fields. These limitations necessitated an almost exclusive reliance on manual calibration for individual fields and empirical, ad hoc, models. We recently reported on a theoretical framework for the prediction of output factors for such fields. In this work, we describe the implementation of this framework in our clinical practice. In our practice, we use a treatment delivery nozzle that uses a limited, and constant, set of mechanical devices to produce SOBP fields over the full extent of clinical penetration depths, or ranges, and modulation widths. This use of a limited set of mechanical devices allows us to unfold the physical effects that affect the output factor. We describe these effects and their incorporation into the theoretical framework. We describe the calibration and protocol for SOBP fields, the effects of apertures and range-compensators and the use of output factors in the treatment planning process

  18. Metallic ureteral stents in malignant ureteral obstruction: clinical factors predicting stent failure.

    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.

  19. Predictive value of clinical and laboratory variables for vesicoureteral reflux in children.

    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.

  20. A probabilistic model to predict clinical phenotypic traits from genome sequencing.

    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.

  1. Preoperative MRI findings predict two-year postoperative clinical outcome in lumbar spinal stenosis.

    Pekka Kuittinen

    Full Text Available To study the predictive value of preoperative magnetic resonance imaging (MRI findings for the two-year postoperative clinical outcome in lumbar spinal stenosis (LSS.84 patients (mean age 63±11 years, male 43% with symptoms severe enough to indicate LSS surgery were included in this prospective observational single-center study. Preoperative MRI of the lumbar spine was performed with a 1.5-T unit. The imaging protocol conformed to the requirements of the American College of Radiology for the performance of MRI of the adult spine. Visual and quantitative assessment of MRI was performed by one experienced neuroradiologist. At the two-year postoperative follow-up, functional ability was assessed with the Oswestry Disability Index (ODI 0-100% and treadmill test (0-1000 m, pain symptoms with the overall Visual Analogue Scale (VAS 0-100 mm, and specific low back pain (LBP and specific leg pain (LP separately with a numeric rating scale from 0-10 (NRS-11. Satisfaction with the surgical outcome was also assessed.Preoperative severe central stenosis predicted postoperatively lower LP, LBP, and VAS when compared in patients with moderate central stenosis (p<0.05. Moreover, severe stenosis predicted higher postoperative satisfaction (p = 0.029. Preoperative scoliosis predicted an impaired outcome in the ODI (p = 0.031 and lowered the walking distance in the treadmill test (p = 0.001. The preoperative finding of only one stenotic level in visual assessment predicted less postoperative LBP when compared with patients having 2 or more stenotic levels (p = 0.026. No significant differences were detected between quantitative measurements and the patient outcome.Routine preoperative lumbar spine MRI can predict the patient outcome in a two-year follow up in patients with LSS surgery. Severe central stenosis and one-level central stenosis are predictors of good outcome. Preoperative finding of scoliosis may indicate worse functional ability.

  2. Clinical Prediction Model for Time in Therapeutic Range While on Warfarin in Newly Diagnosed Atrial Fibrillation.

    Williams, Brent A; Evans, Michael A; Honushefsky, Ashley M; Berger, Peter B

    2017-10-12

    Though warfarin has historically been the primary oral anticoagulant for stroke prevention in newly diagnosed atrial fibrillation (AF), several new direct oral anticoagulants may be preferred when anticoagulation control with warfarin is expected to be poor. This study developed a prediction model for time in therapeutic range (TTR) among newly diagnosed AF patients on newly initiated warfarin as a tool to assist decision making between warfarin and direct oral anticoagulants. This electronic medical record-based, retrospective study included newly diagnosed, nonvalvular AF patients with no recent warfarin exposure receiving primary care services through a large healthcare system in rural Pennsylvania. TTR was estimated as the percentage of time international normalized ratio measurements were between 2.0 and 3.0 during the first year following warfarin initiation. Candidate predictors of TTR were chosen from data elements collected during usual clinical care. A TTR prediction model was developed and temporally validated and its predictive performance was compared with the SAMe-TT 2 R 2 score (sex, age, medical history, treatment, tobacco, race) using R 2 and c-statistics. A total of 7877 newly diagnosed AF patients met study inclusion criteria. Median (interquartile range) TTR within the first year of starting warfarin was 51% (32, 67). Of 85 candidate predictors evaluated, 15 were included in the final validated model with an R 2 of 15.4%. The proposed model showed better predictive performance than the SAMe-TT 2 R 2 score ( R 2 =3.0%). The proposed prediction model may assist decision making on the proper mode of oral anticoagulant among newly diagnosed AF patients. However, predicting TTR on warfarin remains challenging. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

  3. In 'big bang' major incidents do triage tools accurately predict clinical priority?: a systematic review of the literature.

    Kilner, T M; Brace, S J; Cooke, M W; Stallard, N; Bleetman, A; Perkins, G D

    2011-05-01

    The term "big bang" major incidents is used to describe sudden, usually traumatic,catastrophic events, involving relatively large numbers of injured individuals, where demands on clinical services rapidly outstrip the available resources. Triage tools support the pre-hospital provider to prioritise which patients to treat and/or transport first based upon clinical need. The aim of this review is to identify existing triage tools and to determine the extent to which their reliability and validity have been assessed. A systematic review of the literature was conducted to identify and evaluate published data validating the efficacy of the triage tools. Studies using data from trauma patients that report on the derivation, validation and/or reliability of the specific pre-hospital triage tools were eligible for inclusion.Purely descriptive studies, reviews, exercises or reports (without supporting data) were excluded. The search yielded 1982 papers. After initial scrutiny of title and abstract, 181 papers were deemed potentially applicable and from these 11 were identified as relevant to this review (in first figure). There were two level of evidence one studies, three level of evidence two studies and six level of evidence three studies. The two level of evidence one studies were prospective validations of Clinical Decision Rules (CDR's) in children in South Africa, all the other studies were retrospective CDR derivation, validation or cohort studies. The quality of the papers was rated as good (n=3), fair (n=7), poor (n=1). There is limited evidence for the validity of existing triage tools in big bang major incidents.Where evidence does exist it focuses on sensitivity and specificity in relation to prediction of trauma death or severity of injury based on data from single or small number patient incidents. The Sacco system is unique in combining survivability modelling with the degree by which the system is overwhelmed in the triage decision system. The

  4. Clinical history and biologic age predicted falls better than objective functional tests.

    Gerdhem, Paul; Ringsberg, Karin A M; Akesson, Kristina; Obrant, Karl J

    2005-03-01

    Fall risk assessment is important because the consequences, such as a fracture, may be devastating. The objective of this study was to find the test or tests that best predicted falls in a population-based sample of elderly women. The fall-predictive ability of a questionnaire, a subjective estimate of biologic age and objective functional tests (gait, balance [Romberg and sway test], thigh muscle strength, and visual acuity) were compared in 984 randomly selected women, all 75 years of age. A recalled fall was the most important predictor for future falls. Only recalled falls and intake of psycho-active drugs independently predicted future falls. Women with at least five of the most important fall predictors (previous falls, conditions affecting the balance, tendency to fall, intake of psychoactive medication, inability to stand on one leg, high biologic age) had an odds ratio of 11.27 (95% confidence interval 4.61-27.60) for a fall (sensitivity 70%, specificity 79%). The more time-consuming objective functional tests were of limited importance for fall prediction. A simple clinical history, the inability to stand on one leg, and a subjective estimate of biologic age were more important as part of the fall risk assessment.

  5. Predicting Causes of Data Quality Issues in a Clinical Data Research Network.

    Khare, Ritu; Ruth, Byron J; Miller, Matthew; Tucker, Joshua; Utidjian, Levon H; Razzaghi, Hanieh; Patibandla, Nandan; Burrows, Evanette K; Bailey, L Charles

    2018-01-01

    Clinical data research networks (CDRNs) invest substantially in identifying and investigating data quality problems. While identification is largely automated, the investigation and resolution are carried out manually at individual institutions. In the PEDSnet CDRN, we found that only approximately 35% of the identified data quality issues are resolvable as they are caused by errors in the extract-transform-load (ETL) code. Nonetheless, with no prior knowledge of issue causes, partner institutions end up spending significant time investigating issues that represent either inherent data characteristics or false alarms. This work investigates whether the causes (ETL, Characteristic, or False alarm) can be predicted before spending time investigating issues. We trained a classifier on the metadata from 10,281 real-world data quality issues, and achieved a cause prediction F1-measure of up to 90%. While initially tested on PEDSnet, the proposed methodology is applicable to other CDRNs facing similar bottlenecks in handling data quality results.

  6. Sensitivity, specificity and predictive value of blood cultures from cattle clinically suspected of bacterial endocarditis

    Houe, Hans; Eriksen, L.; Jungersen, Gregers

    1993-01-01

    This study investigated the number of blood culture-positive cattle among 215 animals clinically suspected of having bacterial endocarditis. For animals that were necropsied, the sensitivity, specificity and predictive value of the diagnosis of endocarditis were calculated on the basis...... of the isolation of the causative bacteria from blood. Furthermore, it was investigated whether the glutaraldehyde coagulation time, total leucocyte count, per cent neutrophil granulocytes, pulse rate and duration of disease could help to discriminate endocarditis from other diseases. Among 138 animals necropsied...... the sensitivity, specificity and predictive value of blood cultivation were 70.7 per cent, 93.8 per cent and 89.1 per cent, respectively. None of the other measurements could be used to discriminate between endocarditis and non-endocarditis cases....

  7. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

    Nelms, Benjamin E.; Zhen Heming; Tome, Wolfgang A. [Canis Lupus LLC and Department of Human Oncology, University of Wisconsin, Merrimac, Wisconsin 53561 (United States); Department of Medical Physics, University of Wisconsin, Madison, Wisconsin 53705 (United States); Departments of Human Oncology, Medical Physics, and Biomedical Engineering, University of Wisconsin, Madison, Wisconsin 53792 (United States)

    2011-02-15

    Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa

  8. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

    Nelms, Benjamin E.; Zhen Heming; Tome, Wolfgang A.

    2011-01-01

    Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa. Conclusions: There is a lack of correlation between

  9. Sputum biomarkers and the prediction of clinical outcomes in patients with cystic fibrosis.

    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

  10. From data to the decision: A software architecture to integrate predictive modelling in clinical settings.

    Martinez-Millana, A; Fernandez-Llatas, C; Sacchi, L; Segagni, D; Guillen, S; Bellazzi, R; Traver, V

    2015-08-01

    The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component (Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings.

  11. Predictive Factors of Gastrointestinal Caustic Injury According to Clinical and Endoscopic Findings

    Cherie Quingking

    2013-03-01

    Full Text Available Background: Ingestion of caustic substances is the main reason for referral to Philippines National Poison Management and Control Center among other causes of acute poisoning. Rapid assessment of severity of injury is important for treatment and prognosis of these cases. This study was aimed to investigate the correlation of clinical factors with severity of gastrointestinal (GI mucosal injury. Methods: In this retrospective study, a total of 105 patients were included. Patients were categorized into two groups including 35 patients with low grade and 70 patients with high grade GI injury to compare the predictive value of clinical findings. Results: Mean (SD age of patients was 27 (10 and 47% of patients were male. Oral burns (P

  12. Microdose clinical trial: quantitative determination of nicardipine and prediction of metabolites in human plasma.

    Yamane, Naoe; Takami, Tomonori; Tozuka, Zenzaburo; Sugiyama, Yuichi; Yamazaki, Akira; Kumagai, Yuji

    2009-01-01

    A sample treatment procedure and high-sensitive liquid chromatography/tandem mass spectrometry (LC/MS/MS) method for quantitative determination of nicardipine in human plasma were developed for a microdose clinical trial with nicardipine, a non-radioisotope labeled drug. The calibration curve was linear in the range of 1-500 pg/mL using 1 mL of plasma. Analytical method validation for the clinical dose, for which the calibration curve was linear in the range of 0.2-100 ng/mL using 20 microL of plasma, was also conducted. Each method was successfully applied to making determinations in plasma using LC/MS/MS after administration of a microdose (100 microg) and clinical dose (20 mg) to each of six healthy volunteers. We tested new approaches in the search for metabolites in plasma after microdosing. In vitro metabolites of nicardipine were characterized using linear ion trap-fourier transform ion cyclotron resonance mass spectrometry (LIT-FTICRMS) and the nine metabolites predicted to be in plasma were analyzed using LC/MS/MS. There is a strong possibility that analysis of metabolites by LC/MS/MS may advance to utilization in microdose clinical trials with non-radioisotope labeled drugs.

  13. Prediction of the Chemoreflex Gain by Common Clinical Variables in Heart Failure.

    Gianluca Mirizzi

    Full Text Available Peripheral and central chemoreflex sensitivity, assessed by the hypoxic or hypercapnic ventilatory response (HVR and HCVR, respectively, is enhanced in heart failure (HF patients, is involved in the pathophysiology of the disease, and is under investigation as a potential therapeutic target. Chemoreflex sensitivity assessment is however demanding and, therefore, not easily applicable in the clinical setting. We aimed at evaluating whether common clinical variables, broadly obtained by routine clinical and instrumental evaluation, could predict increased HVR and HCVR.191 patients with systolic HF (left ventricular ejection fraction--LVEF--<50% underwent chemoreflex assessment by rebreathing technique to assess HVR and HCVR. All patients underwent clinical and neurohormonal evaluation, comprising: echocardiogram, cardiopulmonary exercise test (CPET, daytime cardiorespiratory monitoring for breathing pattern evaluation. Regarding HVR, multivariate penalized logistic regression, Bayesian Model Averaging (BMA logistic regression and random forest analysis identified, as predictors, the presence of periodic breathing and increased slope of the relation between ventilation and carbon dioxide production (VE/VCO2 during exercise. Again, the above-mentioned statistical tools identified as HCVR predictors plasma levels of N-terminal fragment of proBNP and VE/VCO2 slope.In HF patients, the simple assessment of breathing pattern, alongside with ventilatory efficiency during exercise and natriuretic peptides levels identifies a subset of patients presenting with increased chemoreflex sensitivity to either hypoxia or hypercapnia.

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

    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.

  15. Acceptability of the Predicting Abusive Head Trauma (PredAHT) clinical prediction tool: A qualitative study with child protection professionals.

    Cowley, Laura E; Maguire, Sabine; Farewell, Daniel M; Quinn-Scoggins, Harriet D; Flynn, Matthew O; Kemp, Alison M

    2018-05-09

    The validated Predicting Abusive Head Trauma (PredAHT) tool estimates the probability of abusive head trauma (AHT) based on combinations of six clinical features: head/neck bruising; apnea; seizures; rib/long-bone fractures; retinal hemorrhages. We aimed to determine the acceptability of PredAHT to child protection professionals. We conducted qualitative semi-structured interviews with 56 participants: clinicians (25), child protection social workers (10), legal practitioners (9, including 4 judges), police officers (8), and pathologists (4), purposively sampled across southwest United Kingdom. Interviews were recorded, transcribed and imported into NVivo for thematic analysis (38% double-coded). We explored participants' evaluations of PredAHT, their opinions about the optimal way to present the calculated probabilities, and their interpretation of probabilities in the context of suspected AHT. Clinicians, child protection social workers and police thought PredAHT would be beneficial as an objective adjunct to their professional judgment, to give them greater confidence in their decisions. Lawyers and pathologists appreciated its value for prompting multidisciplinary investigations, but were uncertain of its usefulness in court. Perceived disadvantages included: possible over-reliance and false reassurance from a low score. Interpretations regarding which percentages equate to 'low', 'medium' or 'high' likelihood of AHT varied; participants preferred a precise % probability over these general terms. Participants would use PredAHT with provisos: if they received multi-agency training to define accepted risk thresholds for consistent interpretation; with knowledge of its development; if it was accepted by colleagues. PredAHT may therefore increase professionals' confidence in their decision-making when investigating suspected AHT, but may be of less value in court. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

    Gevaert, Olivier; De Smet, Frank; Van Gorp, Toon; Pochet, Nathalie; Engelen, Kristof; Amant, Frederic; De Moor, Bart; Timmerman, Dirk; Vergote, Ignace

    2008-01-01

    In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs). In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs. The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study – although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours – were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models. We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology

  17. Developing a clinical utility framework to evaluate prediction models in radiogenomics

    Wu, Yirong; Liu, Jie; Munoz del Rio, Alejandro; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

    2015-03-01

    Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based on utility analysis to assess prediction models for breast cancer. Our data comes from a retrospective case-control study, collecting Gail model risk factors, genetic variants (single nucleotide polymorphisms-SNPs), and mammographic features in Breast Imaging Reporting and Data System (BI-RADS) lexicon. We first constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail+SNP, and (3) Gail+SNP+BI-RADS. Then, we generated ROC curves for three models. After we assigned utility values for each category of findings (true negative, false positive, false negative and true positive), we pursued optimal operating points on ROC curves to achieve maximum expected utility (MEU) of breast cancer diagnosis. We used McNemar's test to compare the predictive performance of the three models. We found that SNPs and BI-RADS features augmented the baseline Gail model in terms of the area under ROC curve (AUC) and MEU. SNPs improved sensitivity of the Gail model (0.276 vs. 0.147) and reduced specificity (0.855 vs. 0.912). When additional mammographic features were added, sensitivity increased to 0.457 and specificity to 0.872. SNPs and mammographic features played a significant role in breast cancer risk estimation (p-value < 0.001). Our decision framework comprising utility analysis and McNemar's test provides a novel framework to evaluate prediction models in the realm of radiogenomics.

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

    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.

  19. Predicting the risk of suicide by analyzing the text of clinical notes.

    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.

  20. Psychological, interpersonal, and clinical factors predicting time spent on physical activity among Mexican patients with hypertension.

    Ybarra Sagarduy, José Luis; Camacho Mata, Dacia Yurima; Moral de la Rubia, José; Piña López, Julio Alfonso; Yunes Zárraga, José Luis Masud

    2018-01-01

    It is widely known that physical activity is the key to the optimal management and clinical control of hypertension. This research was conducted to identify factors that can predict the time spent on physical activity among Mexican adults with hypertension. This cross-sectional study was conducted among 182 Mexican patients with hypertension, who completed a set of self-administered questionnaires related to personality, social support, and medical adherence and health care behaviors, body mass index, and time since the disease diagnosis. Several path analyses were performed in order to test the predictors of the study behavior. Lower tolerance to frustration, more tolerance to ambiguity, more effective social support, and less time since the disease diagnosis predicted more time spent on physical activity, accounting for 13.3% of the total variance. The final model shows a good fit to the sample data ( p BS =0.235, χ 2 / gl =1.519, Jöreskog and Sörbom's Goodness of Fit Index =0.987, adjusted modality =0.962, Bollen's Incremental Fit Index =0.981, Bentler-Bonett Normed Fit Index =0.946, standardized root mean square residual =0.053). The performance of physical activity in patients with hypertension depends on a complex set of interactions between personal, interpersonal, and clinical variables. Understanding how these factors interact might enhance the design of interdisciplinary intervention programs so that quality of life of patients with hypertension improves and they might be able to manage and control their disease well.

  1. Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

    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

  2. Life fraction rules

    Maile, K.

    1989-01-01

    Evaluations for lifetime estimation of high temperature loaded HTR-components under creep fatigue load had been performed. The evaluations were carried out on the basis of experimental data of strain controlled fatigue tests with respectively without hold times performed on material NiCr 22 Co 12 Mo (Inconel 617). Life prediction was made by means of the linear damage accumulation rule. Due to the high temperatures no realistic estimates of creep damage can be obtained with this rule. Therefore the rule was modified. The modifications consist in a different analysis of the relaxation curve including different calculation of the creep damage estimate resp. in an extended rule, taking into consideration the interaction between creep and fatigue. In order to reach a better result transparency and to reduce data set dependent result scattering a round robin with a given data set was carried out. The round robin yielded that for a given test temperature of T = 950deg C realistic estimate of damage can be obtained with each modification. Furthermore a reduction of resulting scatterbands in the interaction diagram can be observed, i.e. the practicability of the rule has been increased. (orig.)

  3. Factors predicting suicidal ideation in the preceding 12 months among patients attending a community psychiatric outpatient clinic.

    Anyansi, Tochukwu E

    2013-06-01

    Predictive factors are used to alert the clinician to the necessity of carrying out a suicide risk assessment in those patients whose demographic and clinical characteristics suggest the possibility of suicide.

  4. A comparative study: classification vs. user-based collaborative filtering for clinical prediction

    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

  5. A comparative study: classification vs. user-based collaborative filtering for clinical prediction.

    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

  6. Strategies to design clinical studies to identify predictive biomarkers in cancer research.

    Perez-Gracia, Jose Luis; Sanmamed, Miguel F; Bosch, Ana; Patiño-Garcia, Ana; Schalper, Kurt A; Segura, Victor; Bellmunt, Joaquim; Tabernero, Josep; Sweeney, Christopher J; Choueiri, Toni K; Martín, Miguel; Fusco, Juan Pablo; Rodriguez-Ruiz, Maria Esperanza; Calvo, Alfonso; Prior, Celia; Paz-Ares, Luis; Pio, Ruben; Gonzalez-Billalabeitia, Enrique; Gonzalez Hernandez, Alvaro; Páez, David; Piulats, Jose María; Gurpide, Alfonso; Andueza, Mapi; de Velasco, Guillermo; Pazo, Roberto; Grande, Enrique; Nicolas, Pilar; Abad-Santos, Francisco; Garcia-Donas, Jesus; Castellano, Daniel; Pajares, María J; Suarez, Cristina; Colomer, Ramon; Montuenga, Luis M; Melero, Ignacio

    2017-02-01

    The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  7. A Clinical Indications Prediction Scale Based on TWIST1 for Human Mesenchymal Stem Cells

    Siddaraju V. Boregowda

    2016-02-01

    Full Text Available In addition to their stem/progenitor properties, mesenchymal stem cells (MSCs also exhibit potent effector (angiogenic, antiinflammatory, immuno-modulatory functions that are largely paracrine in nature. It is widely believed that effector functions underlie most of the therapeutic potential of MSCs and are independent of their stem/progenitor properties. Here we demonstrate that stem/progenitor and effector functions are coordinately regulated at the cellular level by the transcription factor Twist1 and specified within populations according to a hierarchical model. We further show that manipulation of Twist1 levels by genetic approaches or by exposure to widely used culture supplements including fibroblast growth factor 2 (Ffg2 and interferon gamma (IFN-gamma alters MSC efficacy in cell-based and in vivo assays in a predictable manner. Thus, by mechanistically linking stem/progenitor and effector functions our studies provide a unifying framework in the form of an MSC hierarchy that models the functional complexity of populations. Using this framework, we developed a CLinical Indications Prediction (CLIP scale that predicts how donor-to-donor heterogeneity and culture conditions impact the therapeutic efficacy of MSC populations for different disease indications.

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

    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

  9. Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

    Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

    2016-01-01

    One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

  10. Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset

    Yung-Fu Chen

    2018-01-01

    Full Text Available More than 1 billion people suffer from chronic respiratory diseases worldwide, accounting for more than 4 million deaths annually. Inhaled corticosteroid is a popular medication for treating chronic respiratory diseases. Its side effects include decreased bone mineral density and osteoporosis. The aims of this study are to investigate the association of inhaled corticosteroids and fracture and to design a clinical support system for fracture prediction. The data of patients aged 20 years and older, who had visited healthcare centers and been prescribed with inhaled corticosteroids within 2002–2010, were retrieved from the National Health Insurance Research Database (NHIRD. After excluding patients diagnosed with hip fracture or vertebrate fractures before using inhaled corticosteroid, a total of 11645 patients receiving inhaled corticosteroid therapy were included for this study. Among them, 1134 (9.7% were diagnosed with hip fracture or vertebrate fracture. The statistical results showed that demographic information, chronic respiratory diseases and comorbidities, and corticosteroid-related variables (cumulative dose, mean exposed daily dose, follow-up duration, and exposed duration were significantly different between fracture and nonfracture patients. The clinical decision support systems (CDSSs were designed with integrated genetic algorithm (GA and support vector machine (SVM by training and validating the models with balanced training sets obtained by random and cluster-based undersampling methods and testing with the imbalanced NHIRD dataset. Two different objective functions were adopted for obtaining optimal models with best predictive performance. The predictive performance of the CDSSs exhibits a sensitivity of 69.84–77.00% and an AUC of 0.7495–0.7590. It was concluded that long-term use of inhaled corticosteroids may induce osteoporosis and exhibit higher incidence of hip or vertebrate fractures. The accumulated dose of ICS and

  11. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

    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.

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

    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.

  13. Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score.

    Tabak, Ying P; Sun, Xiaowu; Nunez, Carlos M; Gupta, Vikas; Johannes, Richard S

    2017-03-01

    Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission. We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative data-enhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model. There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n=142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722. Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data-enhanced model may be used for hospital comparison and outcome research.

  14. Prediction of clinical factors associated with pandemic influenza A (H1N1 2009 in Pakistan.

    Nadia Nisar

    Full Text Available BACKGROUND: Influenza is a viral infection that can lead to serious complications and death(s in vulnerable groups if not diagnosed and managed in a timely manner. This study was conducted to improve the accuracy of predicting influenza through various clinical and statistical models. METHODOLOGY: A retrospective cross sectional analysis was done on demographic and epidemiological data collected from March 2009 to March 2010. Patients were classified as ILI or SARI using WHO case definitions. Respiratory specimens were tested by RT-PCR. Clinical symptoms and co-morbid conditions were analyzed using binary logistic regression models. RESULTS: In the first approach, analysis compared children (≤12 and adults (>12. Of 1,243 cases, 262 (21% tested positive for A(H1N1pdm09 and the proportion of children (≤12 and adults (>12 were 27% and 73% respectively. Four symptoms predicted influenza in children: fever (OR 2.849, 95% CI 1.931-8.722, cough (OR 1.99, 95% CI 1.512-3.643, diarrhea (OR 2.100, 95% CI 2.040-3.25 and respiratory disease (OR 3.269, 95% CI 2.128-12.624. In adults, the strongest clinical predictor was fever (OR 2.80, 95% CI 1.025-3.135 followed by cough (OR 1.431, 95% CI 1.032-2.815. In the second instance, patients were separated into two groups: SARI 326 (26% and ILI 917 (74% cases. Male to female ratio was 1.41∶1.12 for SARI and 2∶1.5 for ILI cases. Chi-square test showed that fever, cough and sore throat were significant factors for A(H1N1pdm09 infections (p = 0.008. CONCLUSION: Studies in a primary care setting should be encouraged focused on patients with influenza-like illness to develop sensitive clinical case definition that will help to improve accuracy of detecting influenza infections. Formulation of a standard "one size fits all" case definition that best correlates with influenza infections can help guide decisions for additional diagnostic testing and also discourage unjustified antibiotic prescription and usage

  15. A Clinical Prediction Algorithm to Stratify Pediatric Musculoskeletal Infection by Severity

    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

  16. Predictive models of pain following root canal treatment: a prospective clinical study.

    Arias, A; de la Macorra, J C; Hidalgo, J J; Azabal, M

    2013-08-01

    To determine the probability of the incidence, intensity, duration and triggering of post-endodontic pain, considering factors related to the patient (age, gender, medical evaluation) and to the affected tooth (group, location, number of canals, pulp vitality, preoperative pain, periapical radiolucencies, previous emergency access, presence of occlusal contacts with antagonist). A total of 500 one-visit root canal treatments (RCTs) were performed on patients referred to an endodontist. Shaping of root canals was performed manually with Gates-Glidden drills and K-Flexofiles, and apical patency was maintained with a size 10 file. A 5% NaOCl solution was used for irrigation, and canals were filled with lateral compaction and AH-Plus sealer. Independent factors were recorded during the treatment, and characteristics of post-endodontic pain (incidence, intensity, type and duration) were later surveyed through questionnaires. Of the 500 questionnaires, 374 were properly returned and split in two groups for two different statistical purposes: 316 cases were used to adjust the logistic regression models to predict each characteristic of post-endodontic pain using predictive factors, and the remaining 58 cases were used to test the validity of each model. The predictive models showed that the incidence of post-endodontic pain was significantly lower when the treated tooth was not a molar (P = 0.003), demonstrated periapical radiolucencies (P = 0.003), had no history of previous pain (P = 0.006) or emergency endodontic treatment (P = 0.045) and had no occlusal contact (P endodontic pain were generated and validated taking account of the interrelation of multiple concomitant clinical factors. A predictive model for triggering post-endodontic pain could not be established. © 2013 International Endodontic Journal. Published by John Wiley & Sons Ltd.

  17. Clinical impact of predictive assays for acute and late radiation morbidity

    Budach, W.; Classen, J.; Belka, C.; Bamberg, M.

    1998-01-01

    Background: Clinically reliable predictive assays for normal tissue radiation sensitivity would help to avoid severe radiation induced morbidity and result in individualized dose prescriptions. Profound differences of individual fibroblast and lymphocyte radiation sensitivity in vitro have been documented in patients with certain genetic syndromes but also in patients without known genetic disorders. The following review evaluates whether fibroblast or lymphocyte radiation sensitivity measured in vitro correlates with the degree of acute and late radiation induced morbidity. Results: Acute radiation side effects and lymphocyte sensitivity has been investigated in 2 studies. One of them reported an insecure correlation, the other no correlation at all. Fibroblast radiation sensitivity and the extent of acute radiation induced side effects on skin and mucosal sites has been compared in a total of 5 studies. None of these studies found a consistent significant correlation. Lymphocyte radiation sensitivity and late effects have been studied by 2 institutions. Late radiation induced skin and mucosal changes did not correlate with lymphocyte sensitivity in head and neck cancer patients, whereas in breast cancer patients a weak (R 2 =0.06) correlation between the degree of late skin reactions and lymphocyte sensitivity was observed. Late skin or mucosal radiation reactions and fibroblast sensitivity were examined by 5 research groups. Data analysis revealed significant correlations or at least a trend towards a significant correlation in all studies. The quality of the reported correlations expressed as R 2 ranged from 0.13 to 0.60, indicating a low predictive value. Conclusions: Lymphocyte radiation sensitivity as measured by currently available assays does not or only poorly correlate with acute and late effects of radiation in patients, precluding predictive tests based on lymphocyte sensitivity. Fibroblast radiation sensitivity does not correlate with acute but

  18. Usefulness of two clinical chorioamnionitis definitions in predicting neonatal infectious outcomes: a systematic review.

    Avila, Cecilia; Willins, Jennifer L; Jackson, Matthew; Mathai, Jacob; Jabsky, Marina; Kong, Alex; Callaghan, Fiona; Ishkin, Selda; Shroyer, A Laurie W

    2015-09-01

    To assess the usefulness of two definitions of acute clinical chorioamnionitis (ACCA) in predicting risk of neonatal infectious outcomes (NIO) and mortality, the first definition requiring maternal fever alone (Fever), and the second requiring ≥ 1 Gibbs criterion besides fever (Fever + 1). PubMed, Web of Science, and the Cochrane Database of Systematic Reviews were searched from January 1, 1979 to April 9, 2013. Twelve studies were reviewed (of 316 articles identified): three studies with term patients, four with preterm premature rupture of membranes (PPROM) patients, and five mixed studies with mixed gestational ages and/or membrane status (intact and/or ruptured). Both definitions demonstrated an increased NIO risk for ACCA versus non-ACCA patients, with an odds ratio increase for the Fever + 1 definition that was about twofold larger than the Fever definition. As the Fever definition demonstrated increased NIO risk for ACCA versus non-ACCA patients, the Fever alone ACCA definition should be used to trigger future clinical treatment in many clinical situations. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

  19. Prolonged PR interval predicts clinical recurrence of atrial fibrillation after catheter ablation.

    Park, Junbeom; Kim, Tae-Hoon; Lee, Jihei Sara; Park, Jin Kyu; Uhm, Jae Sun; Joung, Boyoung; Lee, Moon Hyoung; Pak, Hui-Nam

    2014-10-07

    A prolonged PR interval is known to be a poor prognostic factor in cardiovascular disease. The aim of this study was to investigate the association between PR interval and clinical outcome in patients undergoing radiofrequency catheter ablation (RFCA) of atrial fibrillation (AF). We prospectively included 576 patients with AF (75.5% male, 57.8±11.6 years old, 68.8% paroxysmal AF) who underwent RFCA. We analyzed preprocedural sinus rhythm ECGs obtained in the absence of antiarrhythmic drug, and all enrolled patients were categorized into 4 groups based on the quartile values of the PR interval (166, 182, and 202 ms), and were analyzed according to the left atrium (LA) volume (CT; Computed tomography), LA voltage (NavX), and clinical outcome of AF ablation. Based on quartile value of PR interval, the highest quartile of PR interval (Q4; PR ≥202 ms) was oldest (PPR interval was a significant predictor of AF recurrence after RFCA of AF (HR=1.969, 95% CI 1.343 to 2.886, P=0.001). The PR interval was closely associated with advanced LA remodeling due to AF, and had a noninvasive significant predictive value of clinical recurrence of AF after RFCA. © 2014 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  20. Thrombus length discrepancy on dual-phase CT can predict clinical outcome in acute ischemic stroke

    Park, Mina; Kim, Kyung-eun; Lee, Seung-Koo; Shin, Na-Young; Lim, Soo Mee; Song, Dongbeom; Heo, Ji Hoe; Kim, Jin Woo; Oh, Se Won

    2016-01-01

    The thrombus length may be overestimated on early arterial computed tomography angiography (CTA) depending on the collateral status. We evaluated the value of a grading system based on the thrombus length discrepancy on dual-phase CT in outcome prediction. Forty-eight acute ischemic stroke patients with M1 occlusion were included. Dual-phase CT protocol encompassed non-contrast enhanced CT, CTA with a bolus tracking technique, and delayed contrast enhanced CT (CECT) performed 40s after contrast injection. The thrombus length discrepancy between CTA and CECT was graded by using a three-point scale: G0 = no difference; G1 = no difference in thrombus length, but in attenuation distal to thrombus; G2 = difference in thrombus length. Univariate and multivariate analyses were performed to define independent predictors of poor clinical outcome at 3 months. The thrombus discrepancy grade showed significant linear relationships with both the collateral status (P = 0.008) and the presence of antegrade flow on DSA (P = 0.010) with good interobserver agreement (κ = 0.868). In a multivariate model, the presence of thrombus length discrepancy (G2) was an independent predictor of poor clinical outcome [odds ratio = 11.474 (1.350-97.547); P =0.025]. The presence of thrombus length discrepancy on dual-phase CT may be a useful predictor of unfavourable clinical outcome in acute M1 occlusion patients. (orig.)

  1. Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment.

    Levey, D F; Niculescu, E M; Le-Niculescu, H; Dainton, H L; Phalen, P L; Ladd, T B; Weber, H; Belanger, E; Graham, D L; Khan, F N; Vanipenta, N P; Stage, E C; Ballew, A; Yard, M; Gelbart, T; Shekhar, A; Schork, N J; Kurian, S M; Sandusky, G E; Salomon, D R; Niculescu, A B

    2016-06-01

    Women are under-represented in research on suicidality to date. Although women have a lower rate of suicide completion than men, due in part to the less-violent methods used, they have a higher rate of suicide attempts. Our group has previously identified genomic (blood gene expression biomarkers) and clinical information (apps) predictors for suicidality in men. We now describe pilot studies in women. We used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation (no SI) and high suicidal ideation (high SI) states (n=12 participants out of a cohort of 51 women psychiatric participants followed longitudinally, with diagnoses of bipolar disorder, depression, schizoaffective disorder and schizophrenia). We then used a Convergent Functional Genomics (CFG) approach to prioritize the candidate biomarkers identified in the discovery step by using all the prior evidence in the field. Next, we validated for suicidal behavior the top-ranked biomarkers for SI, in a demographically matched cohort of women suicide completers from the coroner's office (n=6), by assessing which markers were stepwise changed from no SI to high SI to suicide completers. We then tested the 50 biomarkers that survived Bonferroni correction in the validation step, as well as top increased and decreased biomarkers from the discovery and prioritization steps, in a completely independent test cohort of women psychiatric disorder participants for prediction of SI (n=33) and in a future follow-up cohort of psychiatric disorder participants for prediction of psychiatric hospitalizations due to suicidality (n=24). Additionally, we examined how two clinical instruments in the form of apps, Convergent Functional Information for Suicidality (CFI-S) and Simplified Affective State Scale (SASS), previously tested in men, perform in women. The top CFI-S item distinguishing high SI from no SI states was the chronic stress of social isolation. We

  2. "Send & Hold" Clinical Decision Support Rules improvement to reduce unnecessary testing of vitamins A, E, K, B1, B2, B3, B6 and C.

    Rodriguez-Borja, Enrique; Corchon-Peyrallo, Africa; Barba-Serrano, Esther; Villalba Martínez, Celia; Carratala Calvo, Arturo

    2018-02-03

    We assessed the impact of several "send & hold" clinical decision support rules (CDSRs) within the electronical request system for vitamins A, E, K, B1, B2, B3, B6 and C for all outpatients at a large health department. When ordered through electronical request, providers (except for all our primary care physicians who worked as a non-intervention control group) were always asked to answer several compulsory questions regarding main indication, symptomatology, suspected diagnosis, vitamin active treatments, etc., for each vitamin test using a drop-down list format. After samples arrival, tests were later put on hold internally by our laboratory information system (LIS) until review for their appropriateness was made by two staff pathologists according to the provided answers and LIS records (i.e. "send & hold"). The number of tests for each analyte was compared between the 10-month period before and after CDSRs implementation in both groups. After implementation, vitamins test volumes decreased by 40% for vitamin A, 29% for vitamin E, 42% for vitamin K, 37% for vitamin B1, 85% for vitamin B2, 68% for vitamin B3, 65% for vitamin B6 and 59% for vitamin C (all p values 0.03 or lower except for vitamin B3), whereas in control group, the majority increased or remained stable. In patients with rejected vitamins, no new requests and/or adverse clinical outcome comments due to this fact were identified. "Send & hold" CDSRs are a promising informatics tool that can support in utilization management and enhance the pathologist's leadership role as tests specialist.

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

    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

  4. Outcome prediction in mild traumatic brain injury: age and clinical variables are stronger predictors than CT abnormalities.

    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)

  5. Clinical findings just after return to play predict hamstring re-injury, but baseline MRI findings do not

    de Vos, Robert-Jan; Reurink, Gustaaf; Goudswaard, Gert-Jan; Moen, Maarten H.; Weir, Adam; Tol, Johannes L.

    2014-01-01

    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. We obtained baseline data (clinical and MRI findings) of athletes who sustained an acute hamstring

  6. Clinical findings just after return to play predict hamstring re-injury, but baseline MRI findings do not

    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

  7. Prediction of clinical response based on pharmacokinetic/pharmacodynamic models of 5-hydroxytryptamine reuptake inhibitors in mice

    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...

  8. CD117 expression in operable oesophageal squamous cell carcinomas predicts worse clinical outcome

    Fan, Huijie; Yuan, Yuan; Wang, Junsheng; Zhou, Fuyou; Zhang, Mingzhi; Giercksky, Karl-Erik; Nesland, Jahn M; Suo, Zhenhe

    2013-01-01

    Aims To investigate the aberrant expression of CD117 in oesophageal squamous cell carcinoma (SCC) and its prognostic significance. Methods and results Immunohistochemical staining for CD117 was performed on tissue microarray and routine tissue sections from 157 oesophageal SCC patients and 10 normal oesophageal epithelia adjacent to tumour. The positive rate of CD117 expression was 29.9% in oesophageal SCC tissues, whereas no CD117 expression was detected in the 10 normal oesophageal epithelia. CD117 expression was significantly associated with T stage (P < 0.001), distant metastasis (P = 0.015), lymph node metastasis (P = 0.019), and clinical stage (P = 0.021). Progression-free survival in the patients with CD117-positive tumours was shorter than that in the patients with CD117-negative tumours (P = 0.010). In univariate analyses, CD117 expression was the most significant factor for overall survival of oesophageal SCC patients (P < 0.001), followed by lymph node metastasis (P = 0.001), T stage (P = 0.002), clinical stage (P = 0.006), distant metastasis (P = 0.020), and histological grade (P = 0.027). Multivariate analyses verified that CD117 expression was an independent prognostic marker for oesophageal SCC patients (P = 0.002). In addition, CD117 expression predicted poorer survival in patients without distant metastases. Conclusions CD117 expression in operable oesophageal SCC may be a valuable prognostic marker, and detection of its expression in clinical samples may be useful in defining a subclass of oesophageal SCCs with extremely poor clinical outcome, which may require a specially targeted treatment modality. PMID:23570416

  9. Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite

    Schlaudraff KU

    2014-05-01

    Full Text Available 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 predicted by the patient's individual cellulite grade at baseline, individual patient age, body mass index (BMI, weight, and/or height.Methods: Fourteen Caucasian females with cellulite were enrolled in a prospective, single-center, randomized, open-label Phase II study. The mean (± standard error of the mean cellulite grade at baseline was 2.5±0.09 and mean BMI was 22.8±1.17. All patients were treated with radial extracorporeal shock waves using the Swiss DolorClast® device (Electro Medical Systems, S.A., Nyon, Switzerland. Patients were treated unilaterally with 2 weekly treatments for 4 weeks on a randomly selected side (left or right, totaling eight treatments on the selected side. Treatment was performed at 3.5–4.0 bar, with 15,000 impulses per session applied at 15 Hz. Impulses were homogeneously distributed over the posterior thigh and buttock area (resulting in 7,500 impulses per area. Treatment success was evaluated after the last treatment and 4 weeks later by clinical examination, photographic documentation, contact thermography, and patient satisfaction questionnaires.Results: The mean cellulite grade improved from 2.5±0.09 at baseline to 1.57±0.18 after the last treatment (ie, mean δ-1 was 0.93 cellulite grades and 1.68±0.16 at follow-up (ie, mean δ-2 was 0.82 cellulite grades. Compared with baseline, no patient's condition worsened, the treatment was well tolerated, and no unwanted side effects were observed. No statistically significant (ie, P<0

  10. Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease

    Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G

    2009-01-01

    This article extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515-527) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination,...

  11. Structured prediction models for RNN based sequence labeling in clinical text.

    Jagannatha, Abhyuday N; Yu, Hong

    2016-11-01

    Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies for structured prediction in order to improve the exact phrase detection of various medical entities.

  12. Predictive value of eosinophils and neutrophils on clinical effects of ICS in COPD

    Hartjes, Floor J; Vonk, Judith M; Faiz, Alen

    2018-01-01

    BACKGROUND AND OBJECTIVE: Inflammation is present to a variable degree and composition in patients with COPD. This study investigates associations between both eosinophils and neutrophils in blood, sputum, airway wall biopsies and bronchoalveolar lavage (BAL) and their potential use as biomarkers...... and BAL were evaluated at baseline. In addition, at baseline, 6 and 30 months, forced expiratory flow in 1 s (FEV1 ), residual volume/total lung capacity (hyperinflation) and Clinical COPD Questionnaire were evaluated. RESULTS: Cross-sectional analyses at baseline showed that higher blood eosinophils were...... significantly associated with higher eosinophil counts in sputum, biopsies and BAL. However, blood neutrophils did not significantly correlate with neutrophil counts in the other compartments. Baseline eosinophils and neutrophils, in whichever compartment measured, did not predict longitudinal FEV1 changes...

  13. External validation of a clinical scoring system for the risk of gestational diabetes mellitus

    van Leeuwen, M.; Opmeer, B. C.; Zweers, E. J. K.; van Ballegooie, E.; ter Brugge, H. G.; de Valk, H. W.; Visser, G. H. A.; Mol, B. W. J.

    Aim: A prediction rule for gestational diabetes mellitus (GDM) could be helpful in early detection and increased efficiency of screening. A prediction rule by means of a clinical scoring system is available, but has never been validated externally. The aim of this study was to validate the scoring

  14. Clinical audit in gynecological cancer surgery: development of a risk scoring system to predict adverse events.

    Kondalsamy-Chennakesavan, Srinivas; Bouman, Chantal; De Jong, Suzanne; Sanday, Karen; Nicklin, Jim; Land, Russell; Obermair, Andreas

    2009-12-01

    Advanced gynecological surgery undertaken in a specialized gynecologic oncology unit may be associated with significant perioperative morbidity. Validated risk prediction models are available for general surgical specialties but currently not for gynecological cancer surgery. The objective of this study was to evaluate risk factors for adverse events (AEs) of patients treated for suspected or proven gynecological cancer and to develop a clinical risk score (RS) to predict such AEs. AEs were prospectively recorded and matched with demographical, clinical and histopathological data on 369 patients who had an abdominal or laparoscopic procedure for proven or suspected gynecological cancer at a tertiary gynecological cancer center. Stepwise multiple logistic regression was used to determine the best predictors of AEs. For the risk score (RS), the coefficients from the model were scaled using a factor of 2 and rounded to the nearest integer to derive the risk points. Sum of all the risk points form the RS. Ninety-five patients (25.8%) had at least one AE. Twenty-nine (7.9%) and 77 (20.9%) patients experienced intra- and postoperative AEs respectively with 11 patients (3.0%) experiencing both. The independent predictors for any AE were complexity of the surgical procedure, elevated SGOT (serum glutamic oxaloacetic transaminase, > or /=35 U/L), higher ASA scores and overweight. The risk score can vary from 0 to 14. The risk for developing any AE is described by the formula 100 / (1 + e((3.697 - (RS /2)))). RS allows for quantification of the risk for AEs. Risk factors are generally not modifiable with the possible exception of obesity.

  15. Empirically derived personality subtyping for predicting clinical symptoms and treatment response in bulimia nervosa.

    Haynos, Ann F; Pearson, Carolyn M; Utzinger, Linsey M; Wonderlich, Stephen A; Crosby, Ross D; Mitchell, James E; Crow, Scott J; Peterson, Carol B

    2017-05-01

    Evidence suggests that eating disorder subtypes reflecting under-controlled, over-controlled, and low psychopathology personality traits constitute reliable phenotypes that differentiate treatment response. This study is the first to use statistical analyses to identify these subtypes within treatment-seeking individuals with bulimia nervosa (BN) and to use these statistically derived clusters to predict clinical outcomes. Using variables from the Dimensional Assessment of Personality Pathology-Basic Questionnaire, K-means cluster analyses identified under-controlled, over-controlled, and low psychopathology subtypes within BN patients (n = 80) enrolled in a treatment trial. Generalized linear models examined the impact of personality subtypes on Eating Disorder Examination global score, binge eating frequency, and purging frequency cross-sectionally at baseline and longitudinally at end of treatment (EOT) and follow-up. In the longitudinal models, secondary analyses were conducted to examine personality subtype as a potential moderator of response to Cognitive Behavioral Therapy-Enhanced (CBT-E) or Integrative Cognitive-Affective Therapy for BN (ICAT-BN). There were no baseline clinical differences between groups. In the longitudinal models, personality subtype predicted binge eating (p = 0.03) and purging (p = 0.01) frequency at EOT and binge eating frequency at follow-up (p = 0.045). The over-controlled group demonstrated the best outcomes on these variables. In secondary analyses, there was a treatment by subtype interaction for purging at follow-up (p = 0.04), which indicated a superiority of CBT-E over ICAT-BN for reducing purging among the over-controlled group. Empirically derived personality subtyping appears to be a valid classification system with potential to guide eating disorder treatment decisions. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:506-514). © 2016 Wiley Periodicals, Inc.

  16. How Accurately Do Consecutive Cohort Audits Predict Phase III Multisite Clinical Trial Recruitment in Palliative Care?

    McCaffrey, Nikki; Fazekas, Belinda; Cutri, Natalie; Currow, David C

    2016-04-01

    Audits have been proposed for estimating possible recruitment rates to randomized controlled trials (RCTs), but few studies have compared audit data with subsequent recruitment rates. To compare the accuracy of estimates of potential recruitment from a retrospective consecutive cohort audit of actual participating sites and recruitment to four Phase III multisite clinical RCTs. The proportion of potentially eligible study participants estimated from an inpatient chart review of people with life-limiting illnesses referred to six Australian specialist palliative care services was compared with recruitment data extracted from study prescreening information from three sites that participated fully in four Palliative Care Clinical Studies Collaborative RCTs. The predominant reasons for ineligibility in the audit and RCTs were analyzed. The audit overestimated the proportion of people referred to the palliative care services who could participate in the RCTs (pain 17.7% vs. 1.2%, delirium 5.8% vs. 0.6%, anorexia 5.1% vs. 0.8%, and bowel obstruction 2.8% vs. 0.5%). Approximately 2% of the referral base was potentially eligible for these effectiveness studies. Ineligibility for general criteria (language, cognition, and geographic proximity) varied between studies, whereas the reasons for exclusion were similar between the audit and pain and anorexia studies but not for delirium or bowel obstruction. The retrospective consecutive case note audit in participating sites did not predict realistic recruitment rates, mostly underestimating the impact of study-specific inclusion criteria. These findings have implications for the applicability of the results of RCTs. Prospective pilot studies are more likely to predict actual recruitment. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

  17. Pulmonary embolism in intensive care unit: Predictive factors, clinical manifestations and outcome

    Bahloul Mabrouk

    2010-01-01

    Full Text Available Objective : To determine predictive factors, clinical and demographics characteristics of patients with pulmonary embolism (PE in ICU, and to identify factors associated with poor outcome in the hospital and in the ICU. Methods : During a four-year prospective study, a medical committee of six ICU physicians prospectively examined all available data for each patient in order to classify patients according to the level of clinical suspicion of pulmonary thromboembolism. During the study periods, all patients admitted to our ICU were classified into four groups. The first group includes all patients with confirmed PE; the second group includes some patients without clinical manifestations of PE; the third group includes patients with suspected and not confirmed PE and the fourth group includes all patients with only deep vein thromboses (DVTs without suspicion of PE. The diagnosis of PE was confirmed either by a high-probability ventilation/perfusion (V/Q scan or by a spiral computed tomography (CT scan showing one or more filling defects in the pulmonary artery or in its branches. The diagnosis was also confirmed by echocardiography when a thrombus in the pulmonary artery was observed. Results : During the study periods, 4408 patients were admitted in our ICU. The diagnosis of PE was confirmed in 87 patients (1.9%. The mean delay of development of PE was 7.8 ± 9.5 days. On the day of PE diagnosis, clinical examination showed that 50 patients (57.5% were hypotensive, 63 (72.4% have SIRS, 15 (17.2% have clinical manifestations of DVT and 71 (81.6% have respiratory distress requiring mechanical ventilation. In our study, intravenous unfractionated heparin was used in 81 cases (93.1% and low molecular weight heparins were used in 4 cases (4.6%. The mean ICU stay was 20.2 ± 25.3 days and the mean hospital stay was 25.5 ± 25 days. The mortality rate in ICU was 47.1% and the in-hospital mortality rate was 52.9%. Multivariate analysis showed that

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

    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.)

  19. Clinical usefulness of the clock drawing test applying rasch analysis in predicting of cognitive impairment.

    Yoo, Doo Han; Lee, Jae Shin

    2016-07-01

    [Purpose] This study examined the clinical usefulness of the clock drawing test applying Rasch analysis for predicting the level of cognitive impairment. [Subjects and Methods] A total of 187 stroke patients with cognitive impairment were enrolled in this study. The 187 patients were evaluated by the clock drawing test developed through Rasch analysis along with the mini-mental state examination of cognitive evaluation tool. An analysis of the variance was performed to examine the significance of the mini-mental state examination and the clock drawing test according to the general characteristics of the subjects. Receiver operating characteristic analysis was performed to determine the cutoff point for cognitive impairment and to calculate the sensitivity and specificity values. [Results] The results of comparison of the clock drawing test with the mini-mental state showed significant differences in according to gender, age, education, and affected side. A total CDT of 10.5, which was selected as the cutoff point to identify cognitive impairement, showed a sensitivity, specificity, Youden index, positive predictive, and negative predicive values of 86.4%, 91.5%, 0.8, 95%, and 88.2%. [Conclusion] The clock drawing test is believed to be useful in assessments and interventions based on its excellent ability to identify cognitive disorders.

  20. Gaussian processes with optimal kernel construction for neuro-degenerative clinical onset prediction

    Canas, Liane S.; Yvernault, Benjamin; Cash, David M.; Molteni, Erika; Veale, Tom; Benzinger, Tammie; Ourselin, Sébastien; Mead, Simon; Modat, Marc

    2018-02-01

    Gaussian Processes (GP) are a powerful tool to capture the complex time-variations of a dataset. In the context of medical imaging analysis, they allow a robust modelling even in case of highly uncertain or incomplete datasets. Predictions from GP are dependent of the covariance kernel function selected to explain the data variance. To overcome this limitation, we propose a framework to identify the optimal covariance kernel function to model the data.The optimal kernel is defined as a composition of base kernel functions used to identify correlation patterns between data points. Our approach includes a modified version of the Compositional Kernel Learning (CKL) algorithm, in which we score the kernel families using a new energy function that depends both the Bayesian Information Criterion (BIC) and the explained variance score. We applied the proposed framework to model the progression of neurodegenerative diseases over time, in particular the progression of autosomal dominantly-inherited Alzheimer's disease, and use it to predict the time to clinical onset of subjects carrying genetic mutation.

  1. Timing of delivery in a high-risk obstetric population: a clinical prediction model.

    De Silva, Dane A; Lisonkova, Sarka; von Dadelszen, Peter; Synnes, Anne R; Magee, Laura A

    2017-06-29

    The efficacy of antenatal corticosteroid treatment for women with threatened preterm birth depends on timely administration within 7 days before delivery. We modelled the probability of delivery within 7 days of admission to hospital among women presenting with threatened preterm birth, using routinely collected clinical characteristics. Data from the Canadian Perinatal Network (CPN) were used, 2005-11, including women admitted to hospital with preterm labour, preterm pre-labour rupture of membranes, short cervix without contractions, or dilated cervix or prolapsed membranes without contractions at preterm gestation. Women with fetal anomaly, intrauterine fetal demise, twin-to-twin transfusion syndrome, and quadruplets were excluded. Logistic regression was undertaken to create a predictive model that was assessed for its calibration capacity, stratification ability, and classification accuracy (ROC curve). We included 3012 women admitted at 24-28 weeks gestation, or readmitted at up to 34 weeks gestation, to 16 tertiary-care CPN hospitals. Of these, 1473 (48.9%) delivered within 7 days of admission. Significant predictors of early delivery included maternal age, parity, gestational age at admission, smoking, preterm labour, prolapsed membranes, preterm pre-labour rupture of membranes, and antepartum haemorrhage. The area under the ROC curve was 0.724 (95% CI 0.706-0.742). We propose a useful tool to improve prediction of delivery within 7 days after admission among women with threatened preterm birth. This information is important for optimal corticosteroid treatment.

  2. Collaboration rules.

    Evans, Philip; Wolf, Bob

    2005-01-01

    Corporate leaders seeking to boost growth, learning, and innovation may find the answer in a surprising place: the Linux open-source software community. Linux is developed by an essentially volunteer, self-organizing community of thousands of programmers. Most leaders would sell their grandmothers for workforces that collaborate as efficiently, frictionlessly, and creatively as the self-styled Linux hackers. But Linux is software, and software is hardly a model for mainstream business. The authors have, nonetheless, found surprising parallels between the anarchistic, caffeinated, hirsute world of Linux hackers and the disciplined, tea-sipping, clean-cut world of Toyota engineering. Specifically, Toyota and Linux operate by rules that blend the self-organizing advantages of markets with the low transaction costs of hierarchies. In place of markets' cash and contracts and hierarchies' authority are rules about how individuals and groups work together (with rigorous discipline); how they communicate (widely and with granularity); and how leaders guide them toward a common goal (through example). Those rules, augmented by simple communication technologies and a lack of legal barriers to sharing information, create rich common knowledge, the ability to organize teams modularly, extraordinary motivation, and high levels of trust, which radically lowers transaction costs. Low transaction costs, in turn, make it profitable for organizations to perform more and smaller transactions--and so increase the pace and flexibility typical of high-performance organizations. Once the system achieves critical mass, it feeds on itself. The larger the system, the more broadly shared the knowledge, language, and work style. The greater individuals' reputational capital, the louder the applause and the stronger the motivation. The success of Linux is evidence of the power of that virtuous circle. Toyota's success is evidence that it is also powerful in conventional companies.

  3. Clinical prediction in defined populations: a simulation study investigating when and how to aggregate existing models

    Glen P. Martin

    2017-01-01

    Full Text Available Abstract Background Clinical prediction models (CPMs are increasingly deployed to support healthcare decisions but they are derived inconsistently, in part due to limited data. An emerging alternative is to aggregate existing CPMs developed for similar settings and outcomes. This simulation study aimed to investigate the impact of between-population-heterogeneity and sample size on aggregating existing CPMs in a defined population, compared with developing a model de novo. Methods Simulations were designed to mimic a scenario in which multiple CPMs for a binary outcome had been derived in distinct, heterogeneous populations, with potentially different predictors available in each. We then generated a new ‘local’ population and compared the performance of CPMs developed for this population by aggregation, using stacked regression, principal component analysis or partial least squares, with redevelopment from scratch using backwards selection and penalised regression. Results While redevelopment approaches resulted in models that were miscalibrated for local datasets of less than 500 observations, model aggregation methods were well calibrated across all simulation scenarios. When the size of local data was less than 1000 observations and between-population-heterogeneity was small, aggregating existing CPMs gave better discrimination and had the lowest mean square error in the predicted risks compared with deriving a new model. Conversely, given greater than 1000 observations and significant between-population-heterogeneity, then redevelopment outperformed the aggregation approaches. In all other scenarios, both aggregation and de novo derivation resulted in similar predictive performance. Conclusion This study demonstrates a pragmatic approach to contextualising CPMs to defined populations. When aiming to develop models in defined populations, modellers should consider existing CPMs, with aggregation approaches being a suitable modelling

  4. Fall prediction according to nurses' clinical judgment: differences between medical, surgical, and geriatric wards.

    Milisen, Koen; Coussement, Joke; Flamaing, Johan; Vlaeyen, Ellen; Schwendimann, René; Dejaeger, Eddy; Surmont, Kurt; Boonen, Steven

    2012-06-01

    To assess the value of nurses' clinical judgment (NCJ) in predicting hospital inpatient falls. Prospective multicenter study. Six Belgian hospitals. Two thousand four hundred seventy participants (mean age 67.6 ± 18.3; female, 55.7%) on four surgical (n = 812, 32.9%), eight geriatric (n = 666, 27.0%), and four general medical wards (n = 992, 40.1%) were included upon admission. All participants were hospitalized for at least 48 hours. Within 24 hours after admission, nurses gave their judgment on the question "Do you think your patient is at high risk for falling?" Nurses were not trained in assessing fall risk. Falls were documented on a standardized incident report form. During hospitalization, 143 (5.8%) participants experienced one or more falls, accounting for 202 falls and corresponding to an overall rate of 7.9 falls per 1,000 patient days. NCJ of participant's risk of falling had high sensitivity (78-92%) with high negative predictive value (94-100%) but low positive predictive value (4-17%). Although false-negative rates were low (8-22%) for all departments and age groups, false-positive rates were high (55-74%), except on surgical and general medical wards and in participants younger than 75. This analysis, based on multicenter data and a large sample size, suggests that NCJ can be recommended on surgical and general medical wards and in individuals younger than 75, but on geriatric wards and in participants aged 75 and older, NCJ overestimates risk of falling and is thus not recommended because expensive comprehensive fall-prevention measures would be implemented in a large number of individuals who do not need it. © 2012, Copyright the Authors Journal compilation © 2012, The American Geriatrics Society.

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

    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.

  6. Predicting clinical concussion measures at baseline based on motivation and academic profile.

    Trinidad, Katrina J; Schmidt, Julianne D; Register-Mihalik, Johna K; Groff, Diane; Goto, Shiho; Guskiewicz, Kevin M

    2013-11-01

    The purpose of this study was to predict baseline neurocognitive and postural control performance using a measure of motivation, high school grade point average (hsGPA), and Scholastic Aptitude Test (SAT) score. Cross-sectional. Clinical research center. Eighty-eight National Collegiate Athletic Association Division I incoming student-athletes (freshman and transfers). Participants completed baseline clinical concussion measures, including a neurocognitive test battery (CNS Vital Signs), a balance assessment [Sensory Organization Test (SOT)], and motivation testing (Rey Dot Counting). Participants granted permission to access hsGPA and SAT total score. Standard scores for each CNS Vital Signs domain and SOT composite score. Baseline motivation, hsGPA, and SAT explained a small percentage of the variance of complex attention (11%), processing speed (12%), and composite SOT score (20%). Motivation, hsGPA, and total SAT score do not explain a significant amount of the variance in neurocognitive and postural control measures but may still be valuable to consider when interpreting neurocognitive and postural control measures.

  7. Immunological monitoring for prediction of clinical response to antitumor vaccine therapy.

    Mikhaylova, Irina N; Shubina, Irina Zh; Chkadua, George Z; Petenko, Natalia N; Morozova, Lidia F; Burova, Olga S; Beabelashvili, Robert Sh; Parsunkova, Kermen A; Balatskaya, Natalia V; Chebanov, Dmitrii K; Pospelov, Vadim I; Nazarova, Valeria V; Vihrova, Anastasia S; Cheremushkin, Evgeny A; Molodyk, Alvina A; Kiselevsky, Mikhail V; Demidov, Lev V

    2018-05-11

    Immunotherapy has shown promising results in a variety of cancers, including melanoma. However, the responses to therapy are usually heterogeneous, and understanding the factors affecting clinical outcome is still not achieved. Here, we show that immunological monitoring of the vaccine therapy for melanoma patients may help to predict the clinical course of the disease. We studied cytokine profile of cellular Th1 (IL-2, IL-12, IFN-γ) and humoral Th2 (IL-4, IL-10) immune response, vascular endothelial growth factor (VEGFA), transforming growth factor-β 2 (TGF-β 2), S100 protein (S100A1B and S100BB), adhesion molecule CD44 and serum cytokines β2-microglobulin to analyze different peripheral blood mononuclear cell subpopuations of patients treated with dendritic vaccines and/or cyclophosphamide in melanoma patients in the course of adjuvant treatment. The obtained data indicate predominance of cellular immunity in the first adjuvant group of patients with durable time to progression and shift to humoral with low cellular immunity in patients with short-term period to progression (increased levels of IL-4 and IL- 10). Beta-2 microglobulin was differentially expressed in adjuvant subgroups: its higher levels correlated with shorter progression-free survival and the total follow-up time. Immunoregulatory index was overall higher in patients with disease progression compared to the group of patients with no signs of disease progression.

  8. Predicting loss of employment over three years in multiple sclerosis: clinically meaningful cognitive decline.

    Morrow, Sarah A; Drake, Allison; Zivadinov, Robert; Munschauer, Frederick; Weinstock-Guttman, Bianca; Benedict, Ralph H B

    2010-10-01

    Cognitive dysfunction is common in multiple sclerosis (MS), yet the magnitude of change on objective neuropsychological (NP) tests that is clinically meaningful is unclear. We endeavored to determine NP markers of the transition from employment to work disability in MS, as indicated by degree of decline on individual tests. Participants were 97 employed MS patients followed over 41.3 ± 17.6 months with a NP battery covering six domains of cognitive function. Deterioration at follow-up was designated as documented and paid disability benefits (conservative definition) or a reduction in hours/work responsibilities (liberal definition). Using the conservative definition, 28.9% reported deteriorated employment status and for the liberal definition, 45.4%. The Symbol Digit Modalities Test (SDMT) and California Verbal Learning Test, Total Learning (CVLT2-TL) measures distinguished employed and disabled patients at follow-up. Controlling for demographic and MS characteristics, the odds ratio of a deterioration based on a change of 2.0 on the CVLT2-TL was 3.7 (95% CI 1.2-11.4 and SDMT by 4.0 was 4.2 (95% CI 1.2-14.8), accounting for 86.7% of the area under the ROC curve. We conclude that decline on NP testing over time is predictive of deterioration in vocational status, establishing a magnitude of decline on NP tests that is clinically meaningful.

  9. Clinical use of the hyperthermia treatment planning system HyperPlan to predict effectiveness and toxicity

    Sreenivasa, Geetha; Gellermann, Johanna; Rau, Beate; Nadobny, Jacek; Schlag, Peter; Deuflhard, Peter; Felix, Roland; Wust, Peter

    2003-01-01

    Purpose: The main aim is to prove the clinical practicability of the hyperthermia treatment planning system HyperPlan on a β-test level. Data and observations obtained from clinical hyperthermia are compared with the numeric methods FE (finite element) and FDTD (finite difference time domain), respectively. Methods and Materials: The planning system HyperPlan is built on top of the modular, object-oriented platform for visualization and model generation AMIRA. This system already contains powerful algorithms for image processing, geometric modeling, and three-dimensional graphics display. A number of hyperthermia-specific modules are provided, enabling the creation of three-dimensional tetrahedral patient models suitable for treatment planning. Two numeric methods, FE and FDTD, are implemented in HyperPlan for solving Maxwell's equations. Both methods base their calculations on segmented (contour based) CT or MR image data. A tetrahedral grid is generated from the segmented tissue boundaries, consisting of approximately 80,000 tetrahedrons per patient. The FE method necessitates, primarily, this tetrahedral grid for the calculation of the E-field. The FDTD method, on the other hand, calculates the E-field on a cubical grid, but also requires a tetrahedral grid for correction at electrical interfaces. In both methods, temperature distributions are calculated on the tetrahedral grid by solving the bioheat transfer equation with the FE method. Segmentation, grid generation, E-field, and temperature calculation can be carried out in clinical practice at an acceptable time expenditure of about 1-2 days. Results: All 30 patients we analyzed with cervical, rectal, and prostate carcinoma exhibit a good correlation between the model calculations and the attained clinical data regarding acute toxicity (hot spots), prediction of easy-to-heat or difficult-to-heat patients, and the dependency on various other individual parameters. We could show sufficient agreement between

  10. Predicting the performance of a strategic alliance: an analysis of the Community Clinical Oncology Program.

    Kaluzny, A D; Lacey, L M; Warnecke, R; Hynes, D M; Morrissey, J; Ford, L; Sondik, E

    1993-06-01

    This study is designed to examine the effects of environment and structure of the Community Clinical Oncology Program (CCOP) on performance as measured by patient accrual to National Cancer Institute (NCI)-approved treatment protocols. Data and analysis are part of a larger evaluation of the NCI Community Clinical Oncology Program during its second funding cycle, June 1987-May 1990. Data, taken from primary and secondary sources, included a survey of selected informants in CCOPs and research bases, CCOP grant applications, CCOP annual progress reports, and site visits to a subsample of CCOPs (N = 20) and research bases (N = 5). Accrual data were obtained from NCI records. Analysis involved three complementary sets of factors: the local health care resources environment available to the CCOP, the larger policy environment as reflected by the relationship of the CCOP to selected research bases and the NCI, and the operational structure of the CCOP itself. A hierarchical model examined the separate and cumulative effects of local and policy environment and structure on performance. Other things equal, the primary predictors of treatment accrual were: (1) the larger policy environment, as measured by the attendance of nurses at research base meetings; and (2) operational structure, as measured by the number and character of components within participating CCOPs and the number of hours per week worked by data managers. These factors explained 73 percent of the total variance in accrual performance. Findings suggest criteria for selecting the types of organizations to participate in the alliance, as well as for establishing guidelines for managing such alliances. A future challenge is to determine the extent to which factors predicting accrual to cancer treatment clinical trials are equally important as predictors of accrual to cancer prevention and control trials.

  11. Immunologic prediction of relapse in patients with pemphigus vulgaris (PV) in clinical remission.

    Daneshpazhooh, Maryam; Zafarmand Sedigh, Vahid; Balighi, Kamran; Hosseini, S Hamed; Ramezani, Ali; Kalantari, Mohammad-Sadegh; Ghandi, Narges; Ghiasi, Maryam; Nikoo, Azita; Chams-Davatchi, Cheyda

    2016-06-01

    Pemphigus vulgaris (PV) is characterized by multiple relapses, occurring especially in patients on minimal therapy or off therapy. To identify immunologic predictors (anti-desmoglein [Dsg] 1 and 3 antibodies; direct immunofluorescence [DIF]) for relapse in PV patients. Eighty-nine patients in complete clinical remission for at least 6 months and receiving less than or equal to 10 mg prednisolone daily and no immunosuppressive drugs were evaluated using DIF (n=89) and Dsg ELISA (n=46). They were followed until relapse or for at least 18 months. DIF was positive in 44 of 89 patients (49.5%); anti-Dsg 3 antibodies were detected in 18 of 46 patients (39.1%) and anti-Dsg 1 antibodies were detected in 4 of 46 patients (8.7%). Relapse occurred in 38 patients (42.7%). Mean relapse-free time was significantly shorter in anti-Dsg 3-positive patients compared to anti-Dsg 3- negative patients (P = .015) and in DIF-positive patients compared to DIF-negative patients (P = .047), but not in anti-Dsg 1- positive patients compared to anti-Dsg 1-negative patients (P = .501). Sensitivity and predictive values of neither of these tests were high. Small number of anti-Dsg 1-positive patients and use of conventional ELISA. Positive anti-Dsg 3 ELISA and, to a lesser degree, positive DIF are predictors of relapse in PV patients in clinical remission. Decision on discontinuing treatment should be based on the results of these tests as well as on clinical findings. Copyright © 2015 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  12. Psychological, interpersonal, and clinical factors predicting time spent on physical activity among Mexican patients with hypertension

    Ybarra Sagarduy JL

    2018-01-01

    Full Text Available José Luis Ybarra Sagarduy,1 Dacia Yurima Camacho Mata,1 José Moral de la Rubia,2 Julio Alfonso Piña López,3 José Luis Masud Yunes Zárraga4 1Unit of Social Work and Human Development, Autonomous University of Tamaulipas, Ciudad Victoria, 2School of Psychology, Autonomous University of Nuevo Leon, Monterrey, 3Independent Researcher, Hermosillo, 4Institute of Health and Safety Services for State Workers, Clinic for the Study and Prevention of the Chilhood Obesity, Ciudad Victoria, Mexico Background: It is widely known that physical activity is the key to the optimal management and clinical control of hypertension.Purpose: This research was conducted to identify factors that can predict the time spent on physical activity among Mexican adults with hypertension.Methods: This cross-sectional study was conducted among 182 Mexican patients with hypertension, who completed a set of self-administered questionnaires related to personality, social support, and medical adherence and health care behaviors, body mass index, and time since the disease diagnosis. Several path analyses were performed in order to test the predictors of the study behavior.Results: Lower tolerance to frustration, more tolerance to ambiguity, more effective social support, and less time since the disease diagnosis predicted more time spent on physical activity, accounting for 13.3% of the total variance. The final model shows a good fit to the sample data (pBS =0.235, χ2/gl =1.519, Jöreskog and Sörbom’s Goodness of Fit Index =0.987, adjusted modality =0.962, Bollen’s Incremental Fit Index =0.981, Bentler-Bonett Normed Fit Index =0.946, standardized root mean square residual =0.053.Conclusion: The performance of physical activity in patients with hypertension depends on a complex set of interactions between personal, interpersonal, and clinical variables. Understanding how these factors interact might enhance the design of interdisciplinary intervention programs so

  13. Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite.

    Schlaudraff, Kai-Uwe; Kiessling, Maren C; Császár, Nikolaus Bm; Schmitz, Christoph

    2014-01-01

    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 predicted by the patient's individual cellulite grade at baseline, individual patient age, body mass index (BMI), weight, and/or height. Fourteen Caucasian females with cellulite were enrolled in a prospective, single-center, randomized, open-label Phase II study. The mean (± standard error of the mean) cellulite grade at baseline was 2.5±0.09 and mean BMI was 22.8±1.17. All patients were treated with radial extracorporeal shock waves using the Swiss DolorClast(®) device (Electro Medical Systems, S.A., Nyon, Switzerland). Patients were treated unilaterally with 2 weekly treatments for 4 weeks on a randomly selected side (left or right), totaling eight treatments on the selected side. Treatment was performed at 3.5-4.0 bar, with 15,000 impulses per session applied at 15 Hz. Impulses were homogeneously distributed over the posterior thigh and buttock area (resulting in 7,500 impulses per area). Treatment success was evaluated after the last treatment and 4 weeks later by clinical examination, photographic documentation, contact thermography, and patient satisfaction questionnaires. The mean cellulite grade improved from 2.5±0.09 at baseline to 1.57±0.18 after the last treatment (ie, mean δ-1 was 0.93 cellulite grades) and 1.68±0.16 at follow-up (ie, mean δ-2 was 0.82 cellulite grades). Compared with baseline, no patient's condition worsened, the treatment was well tolerated, and no unwanted side effects were observed. No statistically significant (ie, Pcellulite grade at baseline, BMI, weight, height, or age. Radial shock wave therapy is a safe and effective treatment option for cellulite. The individual clinical outcome cannot be predicted by the patient's individual cellulite grade at baseline, BMI, weight

  14. Development and Validation of a Clinically Based Risk Calculator for the Transdiagnostic Prediction of Psychosis

    Rutigliano, Grazia; Stahl, Daniel; Davies, Cathy; Bonoldi, Ilaria; Reilly, Thomas; McGuire, Philip

    2017-01-01

    Importance The overall effect of At Risk Mental State (ARMS) services for the detection of individuals who will develop psychosis in secondary mental health care is undetermined. Objective To measure the proportion of individuals with a first episode of psychosis detected by ARMS services in secondary mental health services, and to develop and externally validate a practical web-based individualized risk calculator tool for the transdiagnostic prediction of psychosis in secondary mental health care. Design, Setting, and Participants Clinical register-based cohort study. Patients were drawn from electronic, real-world, real-time clinical records relating to 2008 to 2015 routine secondary mental health care in the South London and the Maudsley National Health Service Foundation Trust. The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and the Maudsley National Health Service Foundation Trust in the period between January 1, 2008, and December 31, 2015. Data analysis began on September 1, 2016. Main Outcomes and Measures Risk of development of nonorganic International Statistical Classification of Diseases and Related Health Problems, Tenth Revision psychotic disorders. Results A total of 91 199 patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within South London and the Maudsley National Health Service Foundation Trust were included in the derivation (n = 33 820) or external validation (n = 54 716) data sets. The mean age was 32.97 years, 50.88% were men, and 61.05% were white race/ethnicity. The mean follow-up was 1588 days. The overall 6-year risk of psychosis in secondary mental health care was 3.02 (95% CI, 2.88-3.15), which is higher than the 6-year risk in the local general population (0.62). Compared with the ARMS designation, all of the International Statistical Classification of Diseases and Related Health Problems

  15. Validation of Clinical Prediction Models: Theory and Applications in Testicular Germ Cell Cancer

    Y. Vergouwe (Yvonne)

    2003-01-01

    textabstractlinical prediction models combine patient characteristics to predict the probability of having a certain disease (diagnosis) or the probability that a particular disease state will occur (prognosis). The predicted probability of the diagnostic or prognostic outcome may assist the

  16. Prediction consistency and clinical presentations of breast cancer molecular subtypes for Han Chinese population

    Huang Chi-Cheng

    2012-09-01

    Full Text Available Abstract Background Breast cancer is a heterogeneous disease in terms of transcriptional aberrations; moreover, microarray gene expression profiles had defined 5 molecular subtypes based on certain intrinsic genes. This study aimed to evaluate the prediction consistency of breast cancer molecular subtypes from 3 distinct intrinsic gene sets (Sørlie 500, Hu 306 and PAM50 as well as clinical presentations of each molecualr subtype in Han Chinese population. Methods In all, 169 breast cancer samples (44 from Taiwan and 125 from China of Han Chinese population were gathered, and the gene expression features corresponding to 3 distinct intrinsic gene sets (Sørlie 500, Hu 306 and PAM50 were retrieved for molecular subtype prediction. Results For Sørlie 500 and Hu 306 intrinsic gene set, mean-centring of genes and distance-weighted discrimination (DWD remarkably reduced the number of unclassified cases. Regarding pairwise agreement, the highest predictive consistency was found between Hu 306 and PAM50. In all, 150 and 126 samples were assigned into identical subtypes by both Hu 306 and PAM50 genes, under mean-centring and DWD. Luminal B tended to show a higher nuclear grade and have more HER2 over-expression status than luminal A did. No basal-like breast tumours were ER positive, and most HER2-enriched breast tumours showed HER2 over-expression, whereas, only two-thirds of ER negativity/HER2 over-expression tumros were predicted as HER2-enriched molecular subtype. For 44 Taiwanese breast cancers with survival data, a better prognosis of luminal A than luminal B subtype in ER-postive breast cancers and a better prognosis of basal-like than HER2-enriched subtype in ER-negative breast cancers was observed. Conclusions We suggest that the intrinsic signature Hu 306 or PAM50 be used for breast cancers in the Han Chinese population during molecular subtyping. For the prognostic value and decision making based on intrinsic subtypes, further prospective

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

    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.

  18. Clinical Nomogram for Predicting Survival Outcomes in Early Mucinous Breast Cancer.

    Jianfei Fu

    Full Text Available The features related to the prognosis of patients with mucinous breast cancer (MBC remain controversial. We aimed to explore the prognostic factors of MBC and develop a nomogram for predicting survival outcomes.The Surveillance, Epidemiology, and End Results (SEER database was searched to identify 139611 women with resectable breast cancer from 1990 to 2007. Survival curves were generated using Kaplan-Meier methods. The 5-year and 10-year cancer-specific survival (CSS rates were calculated using the Life-Table method. Based on Cox models, a nomogram was constructed to predict the probabilities of CSS for an individual patient. The competing risk regression model was used to analyse the specific survival of patients with MBC.There were 136569 (97.82% infiltrative ductal cancer (IDC patients and 3042 (2.18% MBC patients. Patients with MBC had less lymph node involvement, a higher frequency of well-differentiated lesions, and more estrogen receptor (ER-positive tumors. Patients with MBC had significantly higher 5 and10-year CSS rates (98.23 and 96.03%, respectively than patients with IDC (91.44 and 85.48%, respectively. Univariate and multivariate analyses showed that MBC was an independent factor for better prognosis. As for patients with MBC, the event of death caused by another disease exceeded the event of death caused by breast cancer. A competing risk regression model further showed that lymph node involvement, poorly differentiated grade and advanced T-classification were independent factors of poor prognosis in patients with MBC. The Nomogram can accurately predict CSS with a high C-index (0.816. Risk scores developed from the nomogram can more accurately predict the prognosis of patients with MBC (C-index = 0.789 than the traditional TNM system (C-index = 0.704, P< 0.001.Patients with MBC have a better prognosis than patients with IDC. Nomograms could help clinicians make more informed decisions in clinical practice. The competing risk

  19. Society for immunotherapy of cancer (SITC) statement on the proposed changes to the common rule.

    Kaufman, Howard L; Butterfield, Lisa H; Coulie, Pierre G; Demaria, Sandra; Ferris, Robert L; Galon, Jérôme; Khleif, Samir N; Mellman, Ira; Ohashi, Pamela S; Overwijk, Willem W; Topalian, Suzanne L; Marincola, Francesco M

    2016-01-01

    The Common Rule is a set of ethical principles that provide guidance on the management of human subjects taking part in biomedical and behavioral research in the United States. The elements of the Common Rule were initially developed in 1981 following a revision of the Declaration of Helsinki in 1975. Most academic facilities follow the Common Rule in the regulation of clinical trials research. Recently, the government has suggested a revision of the Common Rule to include more contemporary and streamlined oversight of clinical research. In this commentary, the leadership of the Society for Immunotherapy of Cancer (SITC) provides their opinion on this plan. While the Society recognizes the considerable contribution of clinical research in supporting progress in tumor immunotherapy and supports the need for revisions to the Common Rule, there is also some concern over certain elements which may restrict access to biospecimens and clinical data at a time when high throughput technologies, computational biology and assay standardization is allowing major advances in understanding cancer biology and providing potential predictive biomarkers of immunotherapy response. The Society values its professional commitment to patients for improving clinical outcomes with tumor immunotherapy and supports continued discussion with all stakeholders before implementing changes to the Common Rule in order to ensure maximal patient protections while promoting continued clinical research at this historic time in cancer research.

  20. Australian road rules

    2009-02-01

    *These are national-level rules. Australian Road Rules - 2009 Version, Part 18, Division 1, Rule 300 "Use of Mobile Phones" describes restrictions of mobile phone use while driving. The rule basically states that drivers cannot make or receive calls ...

  1. Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

    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.

  2. Development, external validation and clinical usefulness of a practical prediction model for radiation-induced dysphagia in lung cancer patients

    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.

  3. Rule Induction-Based Knowledge Discovery for Energy Efficiency

    Chen, Qipeng; Fan, Zhong; Kaleshi, Dritan; Armour, Simon M D

    2015-01-01

    Rule induction is a practical approach to knowledge discovery. Provided that a problem is developed, rule induction is able to return the knowledge that addresses the goal of this problem as if-then rules. The primary goals of knowledge discovery are for prediction and description. The rule format knowledge representation is easily understandable so as to enable users to make decisions. This paper presents the potential of rule induction for energy efficiency. In particular, three rule induct...

  4. Pretreatment clinical findings predict outcome for patients receiving preoperative radiation for rectal cancer

    Myerson, Robert J.; Singh, Anurag; Birnbaum, Elisa H.; Fry, Robert D.; Fleshman, James W.; Kodner, Ira J.; Lockett, Mary Ann; Picus, Joel; Walz, Bruce J.; Read, Thomas E.

    2001-01-01

    adverse clinical factors were present: 0 for none, 1 for one or two, 2 for three or four. This sorted outcome highly significantly (p≤0.002, Tarone Ware), with 5-year LC/FFR of 98%/85% (score 0), 90%/72% (score 1), and 74%/58% (score 2). The scoring system sorts the data for both subgroups of surgeons; however, there are substantial differences in LC on the basis of the surgeon's experience. For colorectal specialists (251 cases), the 5-year LC is 100%, 94%, and 78% for scores of 0, 1, and 2, respectively (p=0.004). For the more mixed group of nonspecialist surgeons (133 cases), LC is 98%, 80%, and 65% for scores of 0, 1, and 2 (p=0.008). In multivariate analysis, the clinical score and surgeon's background retained independent predictive value, even when pathologic stage was included. Conclusions: For many patients with rectal cancer, adjuvant treatment can be administered in a well-tolerated sequential fashion--moderate doses of preoperative radiation followed by surgery followed by postoperative chemotherapy to address the risk of occult metastatic disease. A clinical scoring system has been presented here that would suggest that the local control is excellent for lesions with a score of 0 or (if the surgeon is experienced) 1, and therefore sequential treatment could be considered. Cases with a clinical score of 2 should be strongly considered for protocols evaluating more aggressive preoperative treatment, such as combined modality preoperative treatment

  5. Predictive validity of measurements of clinical competence using the team objective structured bedside assessment (TOSBA): assessing the clinical competence of final year medical students.

    Meagher, Frances M

    2009-11-01

    The importance of valid and reliable assessment of student competence and performance is gaining increased recognition. Provision of valid patient-based formative assessment is an increasing challenge for clinical teachers in a busy hospital setting. A formative assessment tool that reliably predicts performance in the summative setting would be of value to both students and teachers.

  6. Clinical Relevance and Predictive Value of Damage Biomarkers of Drug-Induced Kidney Injury.

    Kane-Gill, Sandra L; Smithburger, Pamela L; Kashani, Kianoush; Kellum, John A; Frazee, Erin

    2017-11-01

    Nephrotoxin exposure accounts for up to one-fourth of acute kidney injury episodes in hospitalized patients, and the associated consequences are as severe as acute kidney injury due to other etiologies. As the use of nephrotoxic agents represents one of the few modifiable risk factors for acute kidney injury, clinicians must be able to identify patients at high risk for drug-induced kidney injury rapidly. Recently, significant advancements have been made in the field of biomarker utilization for the prediction and detection of acute kidney injury. Such biomarkers may have a role both for detection of drug-induced kidney disease and implementation of preventative and therapeutic strategies designed to mitigate injury. In this article, basic principles of renal biomarker use in practice are summarized, and the existing evidence for six markers specifically used to detect drug-induced kidney injury are outlined, including liver-type fatty acid binding protein, neutrophil gelatinase-associated lipocalin, tissue inhibitor of metalloproteinase-2 times insulin-like growth factor-binding protein 7 ([TIMP-2]·[IGFBP7]), kidney injury molecule-1 and N-acetyl-β-D-glucosaminidase. The results of the literature search for these six kidney damage biomarkers identified 29 unique articles with none detected for liver-type fatty acid binding protein and [TIMP-2]·[IGFBP7]. For three biomarkers, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin and N-acetyl-β-D-glucosaminidase, the majority of the studies suggest utility in clinical practice. While many questions need to be answered to clearly articulate the use of biomarkers to predict drug-induced kidney disease, current data are promising.

  7. Incidence, predictive factors, and clinical outcomes of acute kidney injury after gastric surgery for gastric cancer.

    Chang Seong Kim

    Full Text Available BACKGROUND: Postoperative acute kidney injury (AKI, a serious surgical complication, is common after cardiac surgery; however, reports on AKI after noncardiac surgery are limited. We sought to determine the incidence and predictive factors of AKI after gastric surgery for gastric cancer and its effects on the clinical outcomes. METHODS: We conducted a retrospective study of 4718 patients with normal renal function who underwent partial or total gastrectomy for gastric cancer between June 2002 and December 2011. Postoperative AKI was defined by serum creatinine change, as per the Kidney Disease Improving Global Outcomes guideline. RESULTS: Of the 4718 patients, 679 (14.4% developed AKI. Length of hospital stay, intensive care unit admission rates, and in-hospital mortality rate (3.5% versus 0.2% were significantly higher in patients with AKI than in those without. AKI was also associated with requirement of renal replacement therapy. Multivariate analysis revealed that male gender; hypertension; chronic obstructive pulmonary disease; hypoalbuminemia (<4 g/dl; use of diuretics, vasopressors, and contrast agents; and packed red blood cell transfusion were independent predictors for AKI after gastric surgery. Postoperative AKI and vasopressor use entailed a high risk of 3-month mortality after multiple adjustments. CONCLUSIONS: AKI was common after gastric surgery for gastric cancer and associated with adverse outcomes. We identified several factors associated with postoperative AKI; recognition of these predictive factors may help reduce the incidence of AKI after gastric surgery. Furthermore, postoperative AKI in patients with gastric cancer is an important risk factor for short-term mortality.

  8. Pharmacogenetic markers to predict the clinical response to methotrexate in south Indian Tamil patients with psoriasis.

    Indhumathi, S; Rajappa, Medha; Chandrashekar, Laxmisha; Ananthanarayanan, P H; Thappa, D M; Negi, V S

    2017-08-01

    Despite the advent of several new systemic therapies, methotrexate remains the gold standard for the treatment of moderate to severe psoriasis. However, there exists a significant heterogeneity in individual response to methotrexate. There are no consistently reliable markers to predict methotrexate treatment response till date. We aimed to demonstrate the association of certain genetic variants in the HLA (HLA-A2, HLA-B17, and HLA-Cw6) and the non-HLA genes including T-helper (Th)-1, Th-2, Th-17 cytokine genes (IFN-γ, IL-2, IL-4, IL-10, IL-12B, and IL-23R), and T-regulatory gene (FOXP3) with the methotrexate treatment response in South Indian Tamil patients with psoriasis. Of the 360 patients recruited, 189 patients with moderate to severe psoriasis were treated with methotrexate. Of the 189 patients, 132 patients responded to methotrexate and the remaining 57 patients were non-responders. We analyzed the association of aforesaid polymorphisms with the methotrexate treatment outcome using binary logistic regression. We observed that there were significant differences between genotype frequencies of HLA-Cw6 and FOXP3 (rs3761548) among the responders compared to non-responders, with conservative estimation. We observed that pro-inflammatory cytokines such as IFN-γ, IL-2, IL-12, and IL-23 were markedly reduced with the use of methotrexate, in comparison to the baseline levels, while the plasma IL-4 levels were increased posttreatment. Our results serve as preliminary evidence for the clinical use of genetic markers as predictors of response to methotrexate in psoriasis. This might aid in the future in the development of a point-of-care testing (POCT) gene chip, to predict optimal treatment response in patients with psoriasis, based on their individual genotypic profile.

  9. Comparison of COPD Assessment Test and Clinical COPD Questionnaire to predict the risk of exacerbation

    Jo YS

    2017-12-01

    Full Text Available Yong Suk Jo,1 Ho Il Yoon,2 Deog Kyeom Kim,3 Chul-Gyu Yoo,1 Chang-Hoon Lee1 1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, 2Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, 3Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea Background and objective: Guidelines recommend the use of simple but comprehensive tools such as COPD Assessment Test (CAT and Clinical COPD Questionnaire (CCQ to assess health status in COPD patients. We aimed to compare the ability of CAT and CCQ to predict exacerbation in COPD patients. Methods: We organized a multicenter prospective cohort study that included COPD patients. The relationships between CAT, CCQ, and other clinical measurements were analyzed by correlation analysis, and the impact of CAT and CCQ scores on exacerbation was analyzed by logistic regression analyses and receiver operating characteristic curve. Results: Among 121 COPD patients, CAT and CCQ score correlated with other symptom measures, lung function and exercise capacity as well. Compared with patients who did not experience exacerbation, those who experienced exacerbation (n=45; 38.2% exhibited more severe airflow limitation, were more likely to have a history of exacerbation in the year prior to enrollment, and demonstrated higher CAT scores. CCQ scores were not significantly associated with exacerbations. A CAT score of ≥15 was an independent risk factor for exacerbation (adjusted odds ratio [aOR], 2.40; 95% CI, 1.03–6.50; P=0.04. Furthermore, CAT scores of ≥15 demonstrated an increased predictive ability for exacerbation compared with currently accepted guidelines for the use of CAT (≥10 and CCQ (≥1 in the assessment of COPD

  10. Optimized outcome prediction in breast cancer by combining the 70-gene signature with clinical risk prediction algorithms

    Drukker, C.A.; Nijenhuis, M.V.; Bueno de Mesquita, J.M.; Retel, V.P.; Retel, Valesca; van Harten, Willem H.; van Tinteren, H.; Wesseling, J.; Schmidt, M.K.; van 't Veer, L.J.; Sonke, G.S.; Rutgers, E.J.T.; van de Vijver, M.J.; Linn, S.C.

    2014-01-01

    Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether

  11. Accuracy of Clinicians in Predicting the Bacterial Cause of Clinical Bovine Mastitis

    White, Maurice E.; Glickman, Lawrence T.; Barnes-Pallesen, Frances D.; Stem, Edgar S.; Dinsmore, Page; Powers, Michael S.; Powers, Pamela; Smith, Mary C.; Jasko, David

    1986-01-01

    We examined the ability of clinicians to predict the causative organism of bovine mastitis in our practice. We obtained 118 milk culture results from 112 mastitic cows and compared the culture results to the predictions of clinicians at the time of milk sample collection. Sixty of 118 culture results were accurately predicted. The positive predictive value for coliform mastitis was 42% and the negative predictive value was 79% in a study population with a 31% prevalence of coliform mastitis. ...

  12. Predicting United States Medical Licensure Examination Step 2 clinical knowledge scores from previous academic indicators

    Monteiro KA

    2017-06-01

    Full Text Available Kristina A Monteiro, Paul George, Richard Dollase, Luba Dumenco Office of Medical Education, The Warren Alpert Medical School of Brown University, Providence, RI, USA Abstract: The use of multiple academic indicators to identify students at risk of experiencing difficulty completing licensure requirements provides an opportunity to increase support services prior to high-stakes licensure examinations, including the United States Medical Licensure Examination (USMLE Step 2 clinical knowledge (CK. Step 2 CK is becoming increasingly important in decision-making by residency directors because of increasing undergraduate medical enrollment and limited available residency vacancies. We created and validated a regression equation to predict students’ Step 2 CK scores from previous academic indicators to identify students at risk, with sufficient time to intervene with additional support services as necessary. Data from three cohorts of students (N=218 with preclinical mean course exam score, National Board of Medical Examination subject examinations, and USMLE Step 1 and Step 2 CK between 2011 and 2013 were used in analyses. The authors created models capable of predicting Step 2 CK scores from academic indicators to identify at-risk students. In model 1, preclinical mean course exam score and Step 1 score accounted for 56% of the variance in Step 2 CK score. The second series of models included mean preclinical course exam score, Step 1 score, and scores on three NBME subject exams, and accounted for 67%–69% of the variance in Step 2 CK score. The authors validated the findings on the most recent cohort of graduating students (N=89 and predicted Step 2 CK score within a mean of four points (SD=8. The authors suggest using the first model as a needs assessment to gauge the level of future support required after completion of preclinical course requirements, and rescreening after three of six clerkships to identify students who might benefit from

  13. Clinical and pathologic factors predictive of biochemical control following post-prostatectomy irradiation

    Stromberg, Jannifer S.; Ziaja, Ellen L.; Horwitz, Eric M.; Vicini, Frank A.; Brabbins, Donald S.; Dmuchowski, Carl F.; Gonzalez, Jose; Martinez, Alvaro A.

    1996-01-01

    Purpose/Objective: Indications for post-prostatectomy radiation therapy are not well defined. We reviewed our experience treating post-prostatectomy patients with external beam irradiation to assess clinical and pathologic factors predictive of biochemical control. Materials and Methods: Between 1/87 and 3/93, 61 patients received post-operative tumor bed irradiation with a median dose of 59.4 Gy (50.4 - 68 Gy). Median follow-up was 4.1 years (7.6 months - 8.3 years) from irradiation. Patients were treated for the following reasons: 1) adjuvantly, within 6 months of surgery for extracapsular extension, seminal vesicle involvement, or positive surgical margins (n=38); 2) persistently elevated PSA post-operatively (n=2); 3) rising PSA >6 months after surgery (n=9); and 4) biopsy proven local recurrence (n=12). No patients had known nodal or metastatic disease. All patients had post-radiation PSA data available. Biochemical control was the endpoint studied using Kaplan-Meier life table analysis. Biochemical control was defined as the ability to maintain an undetectable PSA ( 4 and ≤1 0, >10 and ≤20, and > 20 ng/ml. The 3 year actuarial rates of biochemical control were 100% for group 1, 66.7% for group 2, 61.5% for group 3, and 28.6% for group 4. Pre-RT PSA values were also evaluated. Univariate Cox models indicated lower presurgical and pre-RT PSA values were predictive of biochemical control (p=0.017, p 6 months after surgery (group 3), the 3 year actuarial rate of biochemical control was 55.6%. The 3 year actuarial rate of biochemical control for patients treated for a biopsy proven recurrence (group 4) was 8.3%. By pair-wise log rank test, the rates of biochemical control were significantly different between groups 1 and 3 (p=0.036), groups 1 and 4 (p<0.001), and groups 3 and 4 (p=0.009). Conclusion: Biochemical control was achieved in approximately half of the patients treated with post-operative prostatic fossa irradiation. Elevated presurgical and pre

  14. Development of a Simple Clinical Risk Score for Early Prediction of Severe Dengue in Adult Patients.

    Ing-Kit Lee

    , irrespective of the day of illness onset, suggesting that our simple risk score can be easily implemented in resource-limited countries for early prediction of dengue patients at risk of SD provided that they have rapid dengue confirmed tests. For patients with other acute febrile illnesses or bacterial infections usually have SD risk score of >1. Thus, these scoring algorithms cannot totally replace good clinical judgement of the physician, and most importantly, early differentiating dengue from other febrile illnesses is critical for appropriate monitoring and management.

  15. Diagnosing unilateral primary aldosteronism - comparison of a clinical prediction score, computed tomography and adrenal venous sampling.

    Sze, W C Candy; Soh, Lip Min; Lau, Jeshen H; Reznek, Rodney; Sahdev, Anju; Matson, Matthew; Riddoch, Fiona; Carpenter, Robert; Berney, Dan; Grossman, Ashley B; Chew, Shern L; Akker, Scott A; Druce, Maralyn R; Waterhouse, Mona; Monson, John P; Drake, William M

    2014-07-01

    In patients with primary aldosteronism (PA), adrenalectomy is potentially curative for those correctly identified as having unilateral excessive aldosterone production. It has been suggested that a recently developed and published clinical prediction score (CPS) may correctly identify some patients as having unilateral disease, without recourse to adrenal venous sampling. We have applied the CPS to a large cohort of PA patients with defined and documented outcomes. We also incorporated a minor modification to the CPS and a radiological grading score (RGS) into our analysis to assess whether its performance could be augmented. A total of 75 patients with a robust diagnosis following bilateral adrenal venous cannulation and/or strictly defined surgical outcome were analysed. Applying the CPS to this group of patients produced a sensitivity of 38·8% and a specificity of 88·5% of correctly identifying unilateral aldosterone production. Using a suggested modification to the CPS, in which different levels of hypokalaemia were given different weightings, the sensitivity rose to 40·8%, with an identical specificity. Using the RGS alone improved sensitivity to 91·7%, but specificity was reduced to 62·5%. Applying the recently developed CPS to this cohort of patients, it was not possible to reproduce the 100% specificity reported in the original publication. Using the modified score or incorporating the RGS did not improve its performance. In this cohort, we were unable to show superiority of the CPS over an imaging-based strategy. CPS may have a role in guiding clinical decision-making, especially in those whose adrenal venous sampling (AVS) has been unsuccessful. © 2013 John Wiley & Sons Ltd.

  16. Usefulness of optic nerve ultrasound to predict clinical progression in multiple sclerosis.

    Pérez Sánchez, S; Eichau Madueño, S; Rus Hidalgo, M; Domínguez Mayoral, A M; Vilches-Arenas, A; Navarro Mascarell, G; Izquierdo, G

    2018-03-21

    Progressive neuronal and axonal loss are considered the main causes of disability in patients with multiple sclerosis (MS). The disease frequently involves the visual system; the accessibility of the system for several functional and structural tests has made it a model for the in vivo study of MS pathogenesis. Orbital ultrasound is a non-invasive technique that enables various structures of the orbit, including the optic nerve, to be evaluated in real time. We conducted an observational, ambispective study of MS patients. Disease progression data were collected. Orbital ultrasound was performed on all patients, with power set according to the 'as low as reasonably achievable' (ALARA) principle. Optical coherence tomography (OCT) data were also collected for those patients who underwent the procedure. Statistical analysis was conducted using SPSS version 22.0. Disease progression was significantly correlated with ultrasound findings (P=.041 for the right eye and P=.037 for the left eye) and with Expanded Disability Status Scale (EDSS) score at the end of the follow-up period (P=.07 for the right eye and P=.043 for the left eye). No statistically significant differences were found with relation to relapses or other clinical variables. Ultrasound measurement of optic nerve diameter constitutes a useful, predictive factor for the evaluation of patients with MS. Smaller diameters are associated with poor clinical progression and greater disability (measured by EDSS). Copyright © 2018 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

  17. The importance of histopathological and clinical variables in predicting the evolution of colon cancer.

    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.

  18. Prediction of myotonic dystrophy clinical severity based on the number of intragenic [CTG]{sub n} trinucleotide repeats

    Gennarelli, M.; Dallapiccola, B. [Universita Tor Vergata, Rome (Italy); Novelli, G. [Universita Cattolica del Sacro Cuore, Rome (Italy)] [and others

    1996-11-11

    We carried out a genotype-phenotype correlation study, based on clinical findings in 465 patients with myotonic dystrophy (DM), in order to assess [CTG] repeat number as a predictive test of disease severity. Our analysis showed that the DM subtypes defined by strict clinical criteria fall into three different classes with a log-normal distribution. This distribution is useful in predicting the probability of specific DM phenotypes based on triplet [CTG] number. This study demonstrates that measurement of triplet expansions in patients` lymphocyte DNA is highly valuable and accurate for prognostic assessment. 45 refs., 1 fig., 2 tabs.