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Sample records for evaluate risk prediction

  1. Crash Prediction and Risk Evaluation Based on Traffic Analysis Zones

    Directory of Open Access Journals (Sweden)

    Cuiping Zhang

    2014-01-01

    Full Text Available Traffic safety evaluation for traffic analysis zones (TAZs plays an important role in transportation safety planning and long-range transportation plan development. This paper aims to present a comprehensive analysis of zonal safety evaluation. First, several criteria are proposed to measure the crash risk at zonal level. Then these criteria are integrated into one measure-average hazard index (AHI, which is used to identify unsafe zones. In addition, the study develops a negative binomial regression model to statistically estimate significant factors for the unsafe zones. The model results indicate that the zonal crash frequency can be associated with several social-economic, demographic, and transportation system factors. The impact of these significant factors on zonal crash is also discussed. The finding of this study suggests that safety evaluation and estimation might benefit engineers and decision makers in identifying high crash locations for potential safety improvements.

  2. Evaluating a new marker for risk prediction: decision analysis to the rescue.

    Science.gov (United States)

    Baker, Stuart G; Kramer, Barnett S

    2012-09-01

    In many areas of medicine risk prediction models are used to identify high-risk persons to receive treatment, with the goal of maximizing the ratio of benefits to harms. Thus there is considerable interest in evaluating markers to improve risk prediction. Many measures to evaluate a new marker for risk prediction are based solely on predictive accuracy including the odds ratio, change in the area under the receiver operating characteristic curve, and net reclassification improvement. However, predictive accuracy measures do not capture important clinical implications. Decision analysis comes to the rescue by including the ratio of the anticipated harm ("cost") of a false positive to the anticipated benefit of a true positive, which is transformed into a risk threshold (T) of indifference between treatment and no treatment. A decision-analytic measure of the "value" of a new marker is the number needed to test at a particular risk threshold, denoted NNTest(T), the minimum number of marker tests per true positive needed for risk prediction to be worthwhile. If NNTest(T) is acceptable given the invasiveness and adverse consequences of the test for the new marker, the new marker is recommended for inclusion in risk prediction. We provide a simple review of the derivation and computation of NNTest(T) from risk stratification tables and compare the minimum of NNTest(T), over risk thresholds, with measures of predictive accuracy in six studies. The results illustrate the advantages of this decision-analytic approach for evaluating a new marker for risk prediction.

  3. Predicting risk of atrial fibrillation after heart valve surgery: evaluation of a Brazilian risk score.

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    Sá, Michel Pompeu Barros de Oliveira; Sá, Marcus Villander Barros de Oliveira; Albuquerque, Ana Carla Lopes de; Silva, Belisa Barreto Gomes da; Siqueira, José Williams Muniz de; Brito, Phabllo Rodrigo Santos de; Ferraz, Paulo Ernando; Lima, Ricardo de Carvalho

    2012-01-01

    The aim of this study is to evaluate the applicability of a Brazilian score for predicting atrial fibrillation (AF) in patients undergoing heart valve surgery in the Division of Cardiovascular Surgery of Pronto Socorro Cardiológico de Pernambuco - PROCAPE (Recife, PE, Brazil). Retrospective study involving 491 consecutive patients operated between May/2007 and December/2010. The registers contained all the information used to calculate the score. The outcome of interest was AF. We calculated association of model factors with AF (univariate analysis and multivariate logistic regression analysis), and association of risk score classes with AF. The incidence of AF was 31.2%. In multivariate analysis, the four variables of the score were predictors of postoperative AF: age >70 years (OR 6.82; 95%CI 3.34-14.10; P 1500 ml at first 24 hours (OR 1.92; 95%CI 1.28-2.88; P=0.002). We observed that the higher the risk class of the patient (low, medium, high, very high), the greater is the incidence of postoperative AF (4.2%; 18.1%; 30.8%; 49.2%), showing that the model seems to be a good predictor of risk of postoperative AF, in a statistically significant association (P<0.001). The Brazilian score proved to be a simple and objective index, revealing a satisfactory predictor of development of postoperative AF in patients undergoing heart valve surgery at our institution.

  4. Liver function tests and risk prediction of incident type 2 diabetes : evaluation in two independent cohorts

    NARCIS (Netherlands)

    Abbasi, Ali; Bakker, Stephan J. L.; Corpeleijn, Eva; van der A, Daphne L.; Gansevoort, Ron T.; Gans, Rijk O. B.; Peelen, Linda M.; van der Schouw, Yvonne T.; Stolk, Ronald P.; Navis, Gerjan; Spijkerman, Annemieke M. W.; Beulens, Joline W. J.

    2012-01-01

    Background: Liver function tests might predict the risk of type 2 diabetes. An independent study evaluating utility of these markers compared with an existing prediction model is yet lacking. Methods and Findings: We performed a case-cohort study, including random subcohort (6.5%) from 38,379

  5. Evaluating a new marker for risk prediction using the test tradeoff: an update.

    Science.gov (United States)

    Baker, Stuart G; Van Calster, Ben; Steyerberg, Ewout W

    2012-03-22

    Most of the methodological literature on evaluating an additional marker for risk prediction involves purely statistical measures of classification performance. A disadvantage of a purely statistical measure is the difficulty in deciding the improvement in the measure that would make inclusion of the additional marker worthwhile. In contrast, a medical decision making approach can weigh the cost or harm of ascertaining an additional marker against the benefit of a higher true positive rate for a given false positive rate that may be associated with risk prediction involving the additional marker. An appealing form of the medical decision making approach involves the risk threshold, which is the risk at which the expected utility of treatment and no treatment is the same. In this framework, a readily interpretable evaluation of the net benefit of an additional marker is the test tradeoff corresponding to the risk threshold. The test tradeoff is the minimum number of tests for a new marker that need to be traded for a true positive to yield an increase in the net benefit of risk prediction with the additional marker. For a sensitivity analysis the test tradeoff is computed over multiple risk thresholds. This article updates the theory and estimation of the test tradeoff. An example is provided.

  6. Nutritional risk and time to death; predictive validity of SCREEN (Seniors in the Community Risk Evaluation for Eating and Nutrition).

    Science.gov (United States)

    Keller, H H; Østbye, T

    2003-01-01

    Undernutrition in community-living seniors is common and has the potential to adversely influence health outcomes. Nutritional risk screening tools can help identify seniors at risk, but few have predicted health outcomes. Seniors were recruited from 23 community service providers. The 8-item abbreviated version SCREEN (Seniors in the Community Risk Evaluation for Eating and Nutrition) was used to identify nutritional risk in 367 seniors; demographics, health, activities of daily living, and psychosocial variables were included in a baseline assessment. The seniors were followed-up by telephone for 18 months to determine the occurrence of health outcomes, including death. Cox regression was used to identify predictors of survival time. During the 18-month follow-up there were 27 deaths (approximately 7%). Using the abbreviated tool, nutritional risk was common (42.2%). This low rate of death limited the modeling to only a few key covariates, which were based on bivariate analyses. Nutritional risk was significantly associated with time to death. Gender was also associated with time to death, with men more likely to die sooner than women. Increasing age was also significantly associated with shorter survival times. Nutritional risk as measured by SCREEN was predictive of time to death. This simple tool may be useful for future epidemiological research on health outcomes of seniors. Further work should confirm these results, as the low event rate influenced the modeling strategy.

  7. Extensions of criteria for evaluating risk prediction models for public health applications

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    Pfeiffer, Ruth M.

    2013-01-01

    We recently proposed two novel criteria to assess the usefulness of risk prediction models for public health applications. The proportion of cases followed, PCF(p), is the proportion of individuals who will develop disease who are included in the proportion p of individuals in the population at highest risk. The proportion needed to follow-up, PNF(q), is the proportion of the general population at highest risk that one needs to follow in order that a proportion q of those destined to become cases will be followed (Pfeiffer, R.M. and Gail, M.H., 2011. Two criteria for evaluating risk prediction models. Biometrics 67, 1057–1065). Here, we extend these criteria in two ways. First, we introduce two new criteria by integrating PCF and PNF over a range of values of q or p to obtain iPCF, the integrated PCF, and iPNF, the integrated PNF. A key assumption in the previous work was that the risk model is well calibrated. This assumption also underlies novel estimates of iPCF and iPNF based on observed risks in a population alone. The second extension is to propose and study estimates of PCF, PNF, iPCF, and iPNF that are consistent even if the risk models are not well calibrated. These new estimates are obtained from case–control data when the outcome prevalence in the population is known, and from cohort data, with baseline covariates and observed health outcomes. We study the efficiency of the various estimates and propose and compare tests for comparing two risk models, both of which were evaluated in the same validation data. PMID:23087412

  8. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers

    Science.gov (United States)

    Kuchenbaecker, Karoline B.; McGuffog, Lesley; Barrowdale, Daniel; Lee, Andrew; Soucy, Penny; Healey, Sue; Dennis, Joe; Lush, Michael; Robson, Mark; Spurdle, Amanda B.; Ramus, Susan J.; Mavaddat, Nasim; Terry, Mary Beth; Neuhausen, Susan L.; Hamann, Ute; Southey, Melissa; John, Esther M.; Chung, Wendy K.; Daly, Mary B.; Buys, Saundra S.; Goldgar, David E.; Dorfling, Cecilia M.; van Rensburg, Elizabeth J.; Ding, Yuan Chun; Ejlertsen, Bent; Gerdes, Anne-Marie; Hansen, Thomas V. O.; Slager, Susan; Hallberg, Emily; Benitez, Javier; Osorio, Ana; Cohen, Nancy; Lawler, William; Weitzel, Jeffrey N.; Peterlongo, Paolo; Pensotti, Valeria; Dolcetti, Riccardo; Barile, Monica; Bonanni, Bernardo; Azzollini, Jacopo; Manoukian, Siranoush; Peissel, Bernard; Radice, Paolo; Savarese, Antonella; Papi, Laura; Giannini, Giuseppe; Fostira, Florentia; Konstantopoulou, Irene; Adlard, Julian; Brewer, Carole; Cook, Jackie; Davidson, Rosemarie; Eccles, Diana; Eeles, Ros; Ellis, Steve; Frost, Debra; Hodgson, Shirley; Izatt, Louise; Lalloo, Fiona; Ong, Kai-ren; Godwin, Andrew K.; Arnold, Norbert; Dworniczak, Bernd; Engel, Christoph; Gehrig, Andrea; Hahnen, Eric; Hauke, Jan; Kast, Karin; Meindl, Alfons; Niederacher, Dieter; Schmutzler, Rita Katharina; Varon-Mateeva, Raymonda; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Barjhoux, Laure; Collonge-Rame, Marie-Agnès; Elan, Camille; Golmard, Lisa; Barouk-Simonet, Emmanuelle; Lesueur, Fabienne; Mazoyer, Sylvie; Sokolowska, Joanna; Stoppa-Lyonnet, Dominique; Isaacs, Claudine; Claes, Kathleen B. M.; Poppe, Bruce; de la Hoya, Miguel; Garcia-Barberan, Vanesa; Aittomäki, Kristiina; Nevanlinna, Heli; Ausems, Margreet G. E. M.; de Lange, J. L.; Gómez Garcia, Encarna B.; Hogervorst, Frans B. L.; Kets, Carolien M.; Meijers-Heijboer, Hanne E. J.; Oosterwijk, Jan C.; Rookus, Matti A.; van Asperen, Christi J.; van den Ouweland, Ans M. W.; van Doorn, Helena C.; van Os, Theo A. M.; Kwong, Ava; Olah, Edith; Diez, Orland; Brunet, Joan; Lazaro, Conxi; Teulé, Alex; Gronwald, Jacek; Jakubowska, Anna; Kaczmarek, Katarzyna; Lubinski, Jan; Sukiennicki, Grzegorz; Barkardottir, Rosa B.; Chiquette, Jocelyne; Agata, Simona; Montagna, Marco; Teixeira, Manuel R.; Park, Sue Kyung; Olswold, Curtis; Tischkowitz, Marc; Foretova, Lenka; Gaddam, Pragna; Vijai, Joseph; Pfeiler, Georg; Rappaport-Fuerhauser, Christine; Singer, Christian F.; Tea, Muy-Kheng M.; Greene, Mark H.; Loud, Jennifer T.; Rennert, Gad; Imyanitov, Evgeny N.; Hulick, Peter J.; Hays, John L.; Piedmonte, Marion; Rodriguez, Gustavo C.; Martyn, Julie; Glendon, Gord; Mulligan, Anna Marie; Andrulis, Irene L.; Toland, Amanda Ewart; Jensen, Uffe Birk; Kruse, Torben A.; Pedersen, Inge Sokilde; Thomassen, Mads; Caligo, Maria A.; Teo, Soo-Hwang; Berger, Raanan; Friedman, Eitan; Laitman, Yael; Arver, Brita; Borg, Ake; Ehrencrona, Hans; Rantala, Johanna; Olopade, Olufunmilayo I.; Ganz, Patricia A.; Nussbaum, Robert L.; Bradbury, Angela R.; Domchek, Susan M.; Nathanson, Katherine L.; Arun, Banu K.; James, Paul; Karlan, Beth Y.; Lester, Jenny; Simard, Jacques; Pharoah, Paul D. P.; Offit, Kenneth; Couch, Fergus J.; Chenevix-Trench, Georgia; Easton, Douglas F.

    2017-01-01

    Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]–positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2×10−53). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2×10−20). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management. PMID

  9. Predicting malignancy in adrenal incidentaloma and evaluation of a novel risk stratification algorithm.

    Science.gov (United States)

    Foo, Elizabeth; Turner, Robin; Wang, Kuan-Chi; Aniss, Adam; Gill, Anthony J; Sidhu, Stanley; Clifton-Bligh, Roderick; Sywak, Mark

    2017-01-24

    Incidentally discovered adrenal lesions known as adrenal incidentalomas (AI) are being encountered with increasing frequency due to the widespread use of abdominal computed tomography (CT). The aim of this study was to identify the clinical predictors of malignancy in AI and to evaluate the accuracy of a recently proposed risk stratification algorithm. A retrospective analysis of 96 patients presenting with AI between 2004 and 2014 was undertaken; 66 patients underwent adrenalectomy, and 30 were managed non-operatively. Univariate analysis including patient demographics, CT features of tumour size, density and heterogeneity was performed. Hormonal parameters including 24-h urinary-free cortisol and serum dehydroepiandrosterone sulphate (DHEAS) were also included. A Cleveland Clinic risk stratification model utilizing adrenal size and density was evaluated. The overall rate of malignancy was 8%. On univariate analysis, the following preoperative variables were predictive of malignancy - tumour size on pathology (P = 0.0031) and CT (P = 0.0016), heterogeneity on CT imaging (P = 0.0036), a relative percentage washout of less than 40% (P = 0.0178), elevated 24-h urinary-free cortisol levels (P = 0.0176), elevated DHEAs (P = 0.0061) and younger age at presentation (P < 0.0001). Evaluation of the Cleveland Clinic algorithm found an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.52-1.00). CT characteristics of tumour size, density and heterogeneity are significantly associated with malignancy in AI and applied together reliably exclude malignancy. The risk stratification algorithm utilizing size and density alone may fail to identify some smaller adrenal cancers. © 2017 Royal Australasian College of Surgeons.

  10. Evaluation of in vitro models for predicting acidosis risk of barley grain in finishing beef cattle.

    Science.gov (United States)

    Anele, U Y; Swift, M-L; McAllister, T A; Galyean, M L; Yang, W Z

    2015-10-01

    Our objective was to develop a model to predict the acidosis potential of barley based on the in vitro batch culture incubation of 50 samples varying in bulk density, starch content, processing method, growing location, and agronomic practices. The model was an adaptation of the acidosis index (calculated from a combination of in situ and in vitro analyses and from several components of grain chemical composition) developed in Australia for use in the feed industry to estimate the potential for grains to increase the risk of ruminal acidosis. Of the independent variables considered, DM disappearance at 6 h of incubation (DMD6) using reduced-strength (20%) buffer in the batch culture accounted for 90.5% of the variation in the acidosis index with a root mean square error (RMSE) of 4.46%. To evaluate our model using independent datasets (derived from previous batch culture studies using full-strength [100%] buffer), we performed another batch culture study using full-strength buffer. The full-strength buffer model using in vitro DMD6 (DMD6-FS) accounted for 66.5% of the variation in the acidosis index with an RMSE of 8.30%. When the new full-strength buffer model was applied to 3 independent datasets to predict acidosis, it accounted for 20.1, 28.5, and 30.2% of the variation in the calculated acidosis index. Significant ( < 0.001) mean bias was evident in 2 of the datasets, for which the DMD6 model underpredicted the acidosis index by 46.9 and 5.73%. Ranking of samples from the most diverse independent dataset using the DMD6-FS model and the Black (2008) model (calculated using in situ starch degradation) indicated the relationship between the rankings using Spearman's rank correlation was negative (ρ = -0.30; = 0.059). When the reduced-strength buffer model was used, however, there were similarities in the acidosis index ranking of barley samples by the models as shown by the result of a correlation analysis between calculated (using the Australian model) and

  11. [Evaluation of carcinogenic risk assessment of metallurgic copper production based on mortality studies and predictive risk values].

    Science.gov (United States)

    Russkikh, K Iu; Adrianovskiĭ, V I; Kuz'mina, E A

    2014-01-01

    Comparative evaluation covered carcinogenic jeopardy at metallurgic copper production through studies of the workers' mortality with malignancies and calculation of individual carcinogenic risks. Findings are that the individual carcinogenic risks calucations correspond to the data obtained in epidemiologic study of the mortality with malignancies and could be used for evaluation of carcinogenic jeopardy.

  12. Stochastic rainfall-runoff forecasting: parameter estimation, multi-step prediction, and evaluation of overflow risk

    DEFF Research Database (Denmark)

    Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik

    2014-01-01

    Probabilistic runoff forecasts generated by stochastic greybox models can be notably useful for the improvement of the decision-making process in real-time control setups for urban drainage systems because the prediction risk relationships in these systems are often highly nonlinear. To date...

  13. [Establishment of risk evaluation model of peritoneal metastasis in gastric cancer and its predictive value].

    Science.gov (United States)

    Zhao, Junjie; Zhou, Rongjian; Zhang, Qi; Shu, Ping; Li, Haojie; Wang, Xuefei; Shen, Zhenbin; Liu, Fenglin; Chen, Weidong; Qin, Jing; Sun, Yihong

    2017-01-25

    To establish an evaluation model of peritoneal metastasis in gastric cancer, and to assess its clinical significance. Clinical and pathologic data of the consecutive cases of gastric cancer admitted between April 2015 and December 2015 in Department of General Surgery, Zhongshan Hospital of Fudan University were analyzed retrospectively. A total of 710 patients were enrolled in the study after 18 patients with other distant metastasis were excluded. The correlations between peritoneal metastasis and different factors were studied through univariate (Pearson's test or Fisher's exact test) and multivariate analyses (Binary Logistic regression). Independent predictable factors for peritoneal metastasis were combined to establish a risk evaluation model (nomogram). The nomogram was created with R software using the 'rms' package. In the nomogram, each factor had different scores, and every patient could have a total score by adding all the scores of each factor. A higher total score represented higher risk of peritoneal metastasis. Receiver operating characteristic (ROC) curve analysis was used to compare the sensitivity and specificity of the established nomogram. Delong. Delong. Clarke-Pearson test was used to compare the difference of the area under the curve (AUC). The cut-off value was determined by the AUC, when the ROC curve had the biggest AUC, the model had the best sensitivity and specificity. Among 710 patients, 47 patients had peritoneal metastasis (6.6%), including 30 male (30/506, 5.9%) and 17 female (17/204, 8.3%); 31 were ≥ 60 years old (31/429, 7.2%); 38 had tumor ≥ 3 cm(38/461, 8.2%). Lauren classification indicated that 2 patients were intestinal type(2/245, 0.8%), 8 patients were mixed type(8/208, 3.8%), 11 patients were diffuse type(11/142, 7.7%), and others had no associated data. CA19-9 of 13 patients was ≥ 37 kU/L(13/61, 21.3%); CA125 of 11 patients was ≥ 35 kU/L(11/36, 30.6%); CA72-4 of 11 patients was ≥ 10 kU/L(11/39, 28

  14. Ability of physical activity to predict cardiovascular disease beyond commonly evaluated cardiometabolic risk factors.

    Science.gov (United States)

    McGuire, K Ashlee; Janssen, Ian; Ross, Robert

    2009-12-01

    It is well-established that increasing physical activity (PA) is important for the prevention and management of cardiovascular disease (CVD). Although it has been demonstrated that PA predicts CVD independent of commonly measured cardiometabolic risk factors in women, it is unclear whether this association is true in men. The study participants consisted of 5,882 adults (age >or=18 years) from the 1999 to 2004 United States National Health and Nutrition Examination Survey. Blood pressure, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, glucose, and waist circumference were categorized using standard clinical thresholds. The participants were divided into the following groups according to the volume of their moderate-to-vigorous intensity PA: active (>or=150 min/wk), somewhat active (30 to 149 min/wk), and inactive (active participants to have CVD. Additional adjustment for cardiometabolic risk factors did not change the odds ratio for CVD in the inactive group. To further delineate the effects of PA on CVD, the participants were cross-classified according to their PA level and their number of cardiometabolic risk factors. Both PA and cardiometabolic risk factors were independent predictors of CVD (P(trend) <0.0001). The results were not modified by gender. In conclusion, PA was associated with CVD, independent of the common cardiometabolic risk factors, in men and women. The association between PA and CVD risk was not mediated by the measured cardiometabolic risk factors.

  15. Cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Graversen, Peter; Abildstrøm, Steen Z.; Jespersen, Lasse

    2016-01-01

    Aim European society of cardiology (ESC) guidelines recommend that cardiovascular disease (CVD) risk stratification in asymptomatic individuals is based on the Systematic Coronary Risk Evaluation (SCORE) algorithm, which estimates individual 10-year risk of death from CVD. We assessed the potential...

  16. Evaluation of easily measured risk factors in the prediction of osteoporotic fractures

    Directory of Open Access Journals (Sweden)

    Brown Jacques P

    2005-09-01

    Full Text Available Abstract Background Fracture represents the single most important clinical event in patients with osteoporosis, yet remains under-predicted. As few premonitory symptoms for fracture exist, it is of critical importance that physicians effectively and efficiently identify individuals at increased fracture risk. Methods Of 3426 postmenopausal women in CANDOO, 40, 158, 99, and 64 women developed a new hip, vertebral, wrist or rib fracture, respectively. Seven easily measured risk factors predictive of fracture in research trials were examined in clinical practice including: age (, 65–69, 70–74, 75–79, 80+ years, rising from a chair with arms (yes, no, weight (≥ 57kg, maternal history of hip facture (yes, no, prior fracture after age 50 (yes, no, hip T-score (>-1, -1 to >-2.5, ≤-2.5, and current smoking status (yes, no. Multivariable logistic regression analysis was conducted. Results The inability to rise from a chair without the use of arms (3.58; 95% CI: 1.17, 10.93 was the most significant risk factor for new hip fracture. Notable risk factors for predicting new vertebral fractures were: low body weight (1.57; 95% CI: 1.04, 2.37, current smoking (1.95; 95% CI: 1.20, 3.18 and age between 75–79 years (1.96; 95% CI: 1.10, 3.51. New wrist fractures were significantly identified by low body weight (1.71, 95% CI: 1.01, 2.90 and prior fracture after 50 years (1.96; 95% CI: 1.19, 3.22. Predictors of new rib fractures include a maternal history of a hip facture (2.89; 95% CI: 1.04, 8.08 and a prior fracture after 50 years (2.16; 95% CI: 1.20, 3.87. Conclusion This study has shown that there exists a variety of predictors of future fracture, besides BMD, that can be easily assessed by a physician. The significance of each variable depends on the site of incident fracture. Of greatest interest is that an inability to rise from a chair is perhaps the most readily identifiable significant risk factor for hip fracture and can be easily incorporated

  17. Uptake of Predictive Genetic Testing and Cardiac Evaluation for Children at Risk for an Inherited Arrhythmia or Cardiomyopathy.

    Science.gov (United States)

    Christian, Susan; Atallah, Joseph; Clegg, Robin; Giuffre, Michael; Huculak, Cathleen; Dzwiniel, Tara; Parboosingh, Jillian; Taylor, Sherryl; Somerville, Martin

    2017-07-11

    Predictive genetic testing in minors should be considered when clinical intervention is available. Children who carry a pathogenic variant for an inherited arrhythmia or cardiomyopathy require regular cardiac screening and may be prescribed medication and/or be told to modify their physical activity. Medical genetics and pediatric cardiology charts were reviewed to identify factors associated with uptake of genetic testing and cardiac evaluation for children at risk for long QT syndrome, hypertrophic cardiomyopathy or arrhythmogenic right ventricular cardiomyopathy. The data collected included genetic diagnosis, clinical symptoms in the carrier parent, number of children under 18 years of age, age of children, family history of sudden cardiac arrest/death, uptake of cardiac evaluation and if evaluated, phenotype for each child. We identified 97 at risk children from 58 families found to carry a pathogenic variant for one of these conditions. Sixty six percent of the families pursued genetic testing and 73% underwent cardiac screening when it was recommended. Declining predictive genetic testing was significantly associated with genetic specialist recommendation (p testing (p = 0.007). This study provides a greater understanding of factors associated with uptake of genetic testing and cardiac evaluation in children at risk of an inherited arrhythmia or cardiomyopathy. It also identifies a need to educate families about the importance of cardiac evaluation even in the absence of genetic testing.

  18. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers

    DEFF Research Database (Denmark)

    Kuchenbaecker, Karoline B; McGuffog, Lesley; Barrowdale, Daniel

    2017-01-01

    mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk...

  19. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  20. Comparative Evaluation of Four Risk Scores for Predicting Mortality in Patients With Implantable Cardioverter-defibrillator for Primary Prevention.

    Science.gov (United States)

    Rodríguez-Mañero, Moisés; Abu Assi, Emad; Sánchez-Gómez, Juan Miguel; Fernández-Armenta, Juan; Díaz-Infante, Ernesto; García-Bolao, Ignacio; Benezet-Mazuecos, Juan; Andrés Lahuerta, Ana; Expósito-García, Víctor; Bertomeu-González, Vicente; Arce-León, Álvaro; Barrio-López, María Teresa; Peinado, Rafael; Martínez-Sande, Luis; Arias, Miguel A

    2016-11-01

    Several clinical risk scores have been developed to identify patients at high risk of all-cause mortality despite implantation of an implantable cardioverter-defibrillator. We aimed to examine and compare the predictive capacity of 4 simple scoring systems (MADIT-II, FADES, PACE and SHOCKED) for predicting mortality after defibrillator implantation for primary prevention of sudden cardiac death in a Mediterranean country. A multicenter retrospective study was performed in 15 Spanish hospitals. Consecutive patients referred for defibrillator implantation between January 2010 and December 2011 were included. A total of 916 patients with ischemic and nonischemic heart disease were included (mean age, 62 ± 11 years, 81.4% male). Over 33.4 ± 12.9 months, 113 (12.3%) patients died (cardiovascular origin in 86 [9.4%] patients). At 12, 24, 36, and 48 months, mortality rates were 4.5%, 7.6%, 10.8%, and 12.3% respectively. All the risk scores showed a stepwise increase in the risk of death throughout the scoring system of each of the scores and all 4 scores identified patients at greater risk of mortality. The scores were significantly associated with all-cause mortality throughout the follow-up period. PACE displayed the lowest c-index value regardless of whether the population had heart disease of ischemic (c-statistic = 0.61) or nonischemic origin (c-statistic = 0.61), whereas MADIT-II (c-statistic = 0.67 and 0.65 in ischemic and nonischemic cardiomyopathy, respectively), SHOCKED (c-statistic = 0.68 and 0.66, respectively), and FADES (c-statistic = 0.66 and 0.60) provided similar c-statistic values (P ≥ .09). In this nontrial-based cohort of Mediterranean patients, the 4 evaluated risk scores showed a significant stepwise increase in the risk of death. Among the currently available risk scores, MADIT-II, FADES, and SHOCKED provide slightly better performance than PACE. Copyright © 2016 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All

  1. Combining psychosocial data to improve prediction of cardiovascular disease risk factors and events:: The NHLBI-Sponsored Women's Ischemia Syndrome Evaluation (WISE) Study

    Science.gov (United States)

    Whittaker, Kerry S.; Krantz, David S.; Rutledge, Thomas; Johnson, B. Delia; Wawrzyniak, Andrew J.; Bittner, Vera; Eastwood, Jo-Ann; Eteiba, Wafia; Cornell, Carol E.; Pepine, Carl J.; Vido, Diane A.; Handberg, Eileen; Merz, C. Noel Bairey

    2012-01-01

    Background There is overlap among psychosocial variables associated with cardiovascular disease (CVD), and utility of combining psychosocial variables as risk markers for understanding CVD is largely unknown. Methods Women (n=493) in the NHLBI Women's Ischemia Syndrome Evaluation (WISE) Study were evaluated. The predictive value for CVD events was determined for multivariate combination of Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), Social Network Index (SNI), and Cook-Medley hostility (Ho) scales. Principal components analysis of psychosocial scales revealed composite psychosocial risk markers, and their relationships to CVD events and risk factors were assessed. Results In a multivariate model, the block of SNI, Hostile Affect, STAI and BDI predicted CVD events (χ2[6]=27.8, ppsychosocial variables. Multivariate combination of psychosocial risk markers predicts CVD events; derived psychosocial factors were associated with CVD risk factors but not events. Measuring common and unique variance among psychosocial variables may be useful for understanding and predicting CVD. PMID:22434916

  2. Evaluation of the predictive performance of bleeding risk scores in patients with non-valvular atrial fibrillation on oral anticoagulants.

    Science.gov (United States)

    Beshir, S A; Aziz, Z; Yap, L B; Chee, K H; Lo, Y L

    2017-10-13

    Bleeding risk scores (BRSs) aid in the assessment of oral anticoagulant-related bleeding risk in patients with atrial fibrillation. Ideally, the applicability of a BRS needs to be assessed, prior to its routine use in a population other than the original derivation cohort. Therefore, we evaluated the performance of 6 established BRSs to predict major or clinically relevant bleeding (CRB) events associated with the use of oral anticoagulant (OAC) among Malaysian patients. The pharmacy supply database and the medical records of patients with non-valvular atrial fibrillation (NVAF) receiving warfarin, dabigatran or rivaroxaban at two tertiary hospitals were reviewed. Patients who experienced an OAC-associated major or CRB event within 12 months of follow-up, or who have received OAC therapy for at least 1 year, were identified. The BRSs were fitted separately into patient data. The discrimination and the calibration of these BRSs as well as the factors associated with bleeding events were then assessed. A total of 1017 patients with at least 1-year follow-up period, or those who developed a bleeding event within 1 year of OAC use, were recruited. Of which, 23 patients experienced a first major bleeding event, whereas 76 patients, a first CRB event. Multivariate logistic regression results show that age of 75 or older, prior bleeding and male gender are associated with major bleeding events. On the other hand, prior gastrointestinal bleeding, a haematocrit value of less than 30% and renal impairment are independent predictors of CRB events. All the BRSs show a satisfactory calibration for major and CRB events. Among these BRSs, only HEMORR2 HAGES (C-statistic = 0.71, 95% CI 0.60-0.82, P performance for major bleeding events. All the 6 BRSs, however, lack acceptable predictive performance for CRB events. To the best of our knowledge, this is the first evaluation study of the predictive performance of these 6 BRSs on clinically relevant bleeding events applied to

  3. Predicting Academics via Behavior within an Elementary Sample: An Evaluation of the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS)

    Science.gov (United States)

    Kilgus, Stephen P.; Bowman, Nicollette A.; Christ, Theodore J.; Taylor, Crystal N.

    2017-01-01

    This study examined the extent to which teacher ratings of student behavior via the "Social, Academic, and Emotional Behavior Risk Screener" (SAEBRS) predicted academic achievement in math and reading. A secondary purpose was to compare the predictive capacity of three SAEBRS subscales corresponding to social, academic, or emotional…

  4. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  5. Cardiovascular risk prediction in the Netherlands

    NARCIS (Netherlands)

    Dis, van S.J.

    2011-01-01

    Background: In clinical practice, Systematic COronary Risk Evaluation (SCORE) risk prediction functions and charts are used to identify persons at high risk for cardiovascular diseases (CVD), who are considered eligible for drug treatment of elevated blood pressure and serum cholesterol. These

  6. Evaluation of New Zealand's high-seas bottom trawl closures using predictive habitat models and quantitative risk assessment.

    Directory of Open Access Journals (Sweden)

    Andrew J Penney

    Full Text Available United Nations General Assembly Resolution 61/105 on sustainable fisheries (UNGA 2007 establishes three difficult questions for participants in high-seas bottom fisheries to answer: 1 Where are vulnerable marine systems (VMEs likely to occur?; 2 What is the likelihood of fisheries interaction with these VMEs?; and 3 What might qualify as adequate conservation and management measures to prevent significant adverse impacts? This paper develops an approach to answering these questions for bottom trawling activities in the Convention Area of the South Pacific Regional Fisheries Management Organisation (SPRFMO within a quantitative risk assessment and cost : benefit analysis framework. The predicted distribution of deep-sea corals from habitat suitability models is used to answer the first question. Distribution of historical bottom trawl effort is used to answer the second, with estimates of seabed areas swept by bottom trawlers being used to develop discounting factors for reduced biodiversity in previously fished areas. These are used in a quantitative ecological risk assessment approach to guide spatial protection planning to address the third question. The coral VME likelihood (average, discounted, predicted coral habitat suitability of existing spatial closures implemented by New Zealand within the SPRFMO area is evaluated. Historical catch is used as a measure of cost to industry in a cost : benefit analysis of alternative spatial closure scenarios. Results indicate that current closures within the New Zealand SPRFMO area bottom trawl footprint are suboptimal for protection of VMEs. Examples of alternative trawl closure scenarios are provided to illustrate how the approach could be used to optimise protection of VMEs under chosen management objectives, balancing protection of VMEs against economic loss to commercial fishers from closure of historically fished areas.

  7. Evaluating the Joint Effect of Sea Level and Wind Waves to Predict Extreme Coastal Flooding Risks in the Future Climate

    Science.gov (United States)

    Leijala, U.; Bjorkqvist, J. V.; Kahma, K. K.; Pellikka, H.; Johansson, M. M.; Särkkä, J.

    2016-12-01

    The evaluation of coastal flooding risks has a crucial role in supporting secure planning, building and operation of densely populated and vulnerable coastal areas. Global mean sea level rise predictions together with past short-term sea level variability form the basis of the sea level projections and flooding probabilities for the future. However, the coastal effect of the sea level is also affected by the wave conditions. Coastal wave height may vary significantly locally as the wave field is affected by the islands, shape of the shoreline and topography of the seabed. In this study, we present a method to combine sea level events with wind-generated waves by using a method based on location-specific probability distributions. Our outcome gives an estimate for the maximum wave crest elevation at a steep shore during a storm surge. Simply summing the maximum sea level and maximum wave height components together might result in an overestimation of the joint effect, thus a method based on probability distributions is sensible. The wave statistics of our study are constructed of individual wave buoy measurements conducted during 2012-2014 at multiple sites outside Helsinki, the capital of Finland, which is located on the coast of the Gulf of Finland in the Baltic Sea. An estimate of short-term sea level variability is based on the last 30 years (1986-2015) of hourly data from the Helsinki tide gauge. Predictions for the future long-term mean sea level changes at Helsinki are based on scenarios including the most recent knowledge of the global mean sea level rise, local land uplift, and changes in the total water amount in the Baltic Sea. The method developed in this study gives a new tool for evaluating utmost coastal flood events by combining sea level with the wave height information. The method can be used to evaluate risk levels of different coastal infrastructure and can be applied to any coastal areas where adequate sea level and wave data are available.

  8. Incremental value of anemia in cardiac surgical risk prediction with the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II model.

    Science.gov (United States)

    Scrascia, Giuseppe; Guida, Pietro; Caparrotti, Sergio Maria; Capone, Giuseppe; Contini, Marco; Cassese, Mauro; Fanelli, Vitantonio; Martinelli, Gianluca; Mazzei, Valerio; Zaccaria, Salvatore; Paparella, Domenico

    2014-09-01

    Anemia is a risk factor for adverse events after cardiac operations. We evaluated the incremental value of preoperative anemia over the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II to predict hospital death after cardiac operations. Data for 4,594 consecutive adults (1,548 women [33.7%]), aged 67 ± 11 years, who underwent cardiac operations from January 2011 to July 2013 were extracted from the Regional Cardiac Surgery Registry of Puglia. The last preoperative hemoglobin value was used, according to World Health Organization criteria, to classify anemia as mild (hemoglobin 11.0 to 12.9 g/dL in men and 11.0 to 11.9 g/dL in women) in 1,021 patients (22.2%) and as moderate to severe (hemoglobin anemia, with model discrimination quantified by C statistic and risk classification by the use of net reclassification improvement (NRI). Overall expected and observed mortality rates were 4.4% and 5.9%. Anemia was significantly associated with a mortality rate of 3.4% in patients without anemia, 7.7% in mild anemia, and 15.7% in moderate to severe anemia (p anemia was analyzed with EuroSCORE II, the model improved in discrimination (C statistic = 0.852 vs 0.860; p = 0.007) and reclassification (category free-NRI, 0.592; p anemia has strong association with operative death in cardiac surgical patients. Anemia provides significant incremental value over the EuroSCORE II and should be considered for assessment of cardiac surgical risk. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  9. Airframe noise prediction evaluation

    Science.gov (United States)

    Yamamoto, Kingo J.; Donelson, Michael J.; Huang, Shumei C.; Joshi, Mahendra C.

    1995-01-01

    The objective of this study is to evaluate the accuracy and adequacy of current airframe noise prediction methods using available airframe noise measurements from tests of a narrow body transport (DC-9) and a wide body transport (DC-10) in addition to scale model test data. General features of the airframe noise from these aircraft and models are outlined. The results of the assessment of two airframe prediction methods, Fink's and Munson's methods, against flight test data of these aircraft and scale model wind tunnel test data are presented. These methods were extensively evaluated against measured data from several configurations including clean, slat deployed, landing gear-deployed, flap deployed, and landing configurations of both DC-9 and DC-10. They were also assessed against a limited number of configurations of scale models. The evaluation was conducted in terms of overall sound pressure level (OASPL), tone corrected perceived noise level (PNLT), and one-third-octave band sound pressure level (SPL).

  10. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Cancer Risk Prediction and Assessment

    Science.gov (United States)

    Cancer prediction models provide an important approach to assessing risk and prognosis by identifying individuals at high risk, facilitating the design and planning of clinical cancer trials, fostering the development of benefit-risk indices, and enabling estimates of the population burden and cost of cancer.

  12. Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

    Directory of Open Access Journals (Sweden)

    Brenton A

    2017-05-01

    Full Text Available Ashley Brenton,1 Steven Richeimer,2,3 Maneesh Sharma,4 Chee Lee,1 Svetlana Kantorovich,1 John Blanchard,1 Brian Meshkin1 1Proove Biosciences, Irvine, CA, 2Keck school of Medicine, University of Southern California, Los Angeles, CA, 3Departments of Anesthesiology and Psychiatry, University of Southern California, Los Angeles, CA, 4Interventional Pain Institute, Baltimore, MD, USA Background: Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999. Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs. Patients and methods: The Proove Opioid Risk (POR algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%. Conclusion: The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes. Keywords: opioid use disorder, addiction, personalized medicine, pharmacogenetics, genetic testing, predictive algorithm

  13. Predicting rib fracture risk with whole-body finite element models: development and preliminary evaluation of a probabilistic analytical framework.

    Science.gov (United States)

    Forman, Jason L; Kent, Richard W; Mroz, Krystoffer; Pipkorn, Bengt; Bostrom, Ola; Segui-Gomez, Maria

    2012-01-01

    This study sought to develop a strain-based probabilistic method to predict rib fracture risk with whole-body finite element (FE) models, and to describe a method to combine the results with collision exposure information to predict injury risk and potential intervention effectiveness in the field. An age-adjusted ultimate strain distribution was used to estimate local rib fracture probabilities within an FE model. These local probabilities were combined to predict injury risk and severity within the whole ribcage. The ultimate strain distribution was developed from a literature dataset of 133 tests. Frontal collision simulations were performed with the THUMS (Total HUman Model for Safety) model with four levels of delta-V and two restraints: a standard 3-point belt and a progressive 3.5-7 kN force-limited, pretensioned (FL+PT) belt. The results of three simulations (29 km/h standard, 48 km/h standard, and 48 km/h FL+PT) were compared to matched cadaver sled tests. The numbers of fractures predicted for the comparison cases were consistent with those observed experimentally. Combining these results with field exposure informantion (ΔV, NASS-CDS 1992-2002) suggests a 8.9% probability of incurring AIS3+ rib fractures for a 60 year-old restrained by a standard belt in a tow-away frontal collision with this restraint, vehicle, and occupant configuration, compared to 4.6% for the FL+PT belt. This is the first study to describe a probabilistic framework to predict rib fracture risk based on strains observed in human-body FE models. Using this analytical framework, future efforts may incorporate additional subject or collision factors for multi-variable probabilistic injury prediction.

  14. Novel and convenient method to evaluate the character of solitary pulmonary nodule-comparison of three mathematical prediction models and further stratification of risk factors.

    Science.gov (United States)

    Xiao, Fei; Liu, Deruo; Guo, Yongqing; Shi, Bin; Song, Zhiyi; Tian, Yanchu; Liang, Chaoyang

    2013-01-01

    To study risk factors that affect the evaluation of malignancy in patients with solitary pulmonary nodules (SPN) and verify different predictive models for malignant probability of SPN. Retrospectively analyzed 107 cases of SPN with definite post-operative histological diagnosis whom underwent surgical procedures in China-Japan Friendship Hospital from November of 2010 to February of 2013. Age, gender, smoking history, malignancy history of patients, imaging features of the nodule including maximum diameter, position, spiculation, lobulation, calcification and serum level of CEA and Cyfra21-1 were assessed as potential risk factors. Univariate analysis model was used to establish statistical correlation between risk factors and post-operative histological diagnosis. Receiver operating characteristic (ROC) curves were drawn using different predictive models for malignant probability of SPN to get areas under the curves (AUC values), sensitivity, specificity, positive predictive values, negative predictive values for each model, respectively. The predictive effectiveness of each model was statistically assessed subsequently. In 107 patients, 78 cases were malignant (72.9%), 29 cases were benign (27.1%). Statistical significant difference was found between benign and malignant group in age, maximum diameter, serum level of Cyfra21-1, spiculation, lobulation and calcification of the nodules. The AUC values were 0.786±0.053 (Mayo model), 0.682±0.060 (VA model) and 0.810±0.051 (Peking University People's Hospital model), respectively. Serum level of Cyfra21-1, patient's age, maximum diameter of the nodule, spiculation, lobulation and calcification of the nodule are independent risk factors associated with the malignant probability of SPN. Peking University People's Hospital model is of high accuracy and clinical value for patients with SPN. Adding serum index (e.g. Cyfra21-1) into the prediction models as a new risk factor and adjusting the weight of age in the models

  15. Developmental dyslexia: predicting individual risk.

    Science.gov (United States)

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-09-01

    Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

  16. Evaluation of FOCUS surface water pesticide concentration predictions and risk assessment of field-measured pesticide mixtures-a crop-based approach under Mediterranean conditions.

    Science.gov (United States)

    Pereira, Ana Santos; Daam, Michiel A; Cerejeira, Maria José

    2017-07-01

    FOCUS models are used in the European regulatory risk assessment (RA) to predict individual pesticide concentrations in edge-of-field surface waters. The scenarios used in higher tier FOCUS simulations were mainly based on Central/North European, and work is needed to underpin the validity of simulated exposure profiles for Mediterranean agroecosystems. In addition, the RA of chemicals are traditionally evaluated on the basis of single substances although freshwater life is generally exposed to a multitude of pesticides. In the present study, we monitored 19 pesticides in surface waters of five locations in the Portuguese 'Lezíria do Tejo' agricultural area. FOCUS step 3 simulations were performed for the South European scenarios to estimate predicted environmental concentrations (PECs). We verified that 44% of the PECs underestimated the measured environmental concentrations (MEC) of the pesticides, showing a non-compliance with the field data. Risk was assessed by comparing the environmental quality standards (EQS) and regulatory acceptable concentrations with their respective MECs. Risk of mixtures was demonstrated in 100% of the samples with insecticides accounting for 60% of the total risk identified. The overall link between the RA and the actual situation in the field must be considerably strengthened, and field studies on pesticide exposure and effects should be carried out to assist the improvement of predictive approaches used for regulatory purposes.

  17. PERFORMANCE EVALUATION AND DISTRESS PREDICTION FOR EFFECTIVE RISK MANAGEMENT IN FINANCE SECTOR: AN INTEGRATED DECISION MAKING PROCEDURE

    Directory of Open Access Journals (Sweden)

    Hasan SELİM

    2017-04-01

    Full Text Available Considering its important role in the socio-economic status of the developing countries, finance sector, which is one of the core components of the service sector, is the focus of this study. The main drivers of this study are, to explore the most significant factors influencing the performance of the financial institutions in a risky environment, to evaluate the economic and financial performances using the selected factors and predict the future distress/bankruptcy possibility of the institutions by a comparative analysis employing a quantitative three-step decision making procedure. To explore the viability of the proposed approach, an up-to-date and comprehensive application on commercial banks operating in Turkish Banking sector is presented by using a wide range of financial ratios. To this aim, 44 commercial banks operating in Turkish financial sector are assessed as healthy and non-healthy by using 57 selected fundamental financial ratios to provide a comprehensive insight to the bank managers, investors, government units and rating agencies to predict the financial performances of banks and make related decisions when a risky socio-economic environment is a matter of a country.

  18. Prediction Model of Drug-Induced Liver Injury in Patients with Pulmonary Tuberculosis: Evaluation of the Incidence and Risk Factors

    Directory of Open Access Journals (Sweden)

    Farzaneh Dastan

    2017-03-01

    Full Text Available Introduction and objectives:Tuberculosis (TB still remains a major health concern both in developing and developed countries. The rate of the liver injury due to anti-TB drugs in developed countries has been reported up to 4%. The goal of this study is to assess the rate and risk factors for anti-tuberculosis drug-induced liver injury (DILI. Also, a model has been designed to predict DILI in patients with pulmonary tuberculosis.Methods:We conducted an observational study. The investigation was carried out in the National Research Institute of Tuberculosis and Lung Disease, Tehran, Iran. Anti-tuberculosis drug treatment course and patients’ demographic data, medical and drug history, and social habits were extracted from their medical records. DILI was defined as an increase in serum alanine aminotransfrase (ALT or aspartate aminotransfrase (AST greater than three times of the upper limit of normal (ULN, with symptoms of liver injury, or five times of the ULN without symptoms.Results:In this study, 87 patients (33 male, 54 female, mean age 54.29±21.79 years with tuberculosis diagnosis were followed. Anti-tuberculosis induced liver injury was detected in 14 (16.1% patients. Concomitant use of hepatotoxic drugs (Isoniazid, Rifampin and Pyrazinamide and the abnormal baseline serum liver enzyme levels before the initiation of therapy were found as risk factors for anti-tuberculosis induced liver injury.Conclusion:Anti-tuberculosis induced liver injury is a major problem in tuberculosis patients which lead to treatment interruption in 14 (16.1% patients. Due to the lack of evidence regarding the mechanism of this side effect, we recommend to monitor anti-tuberculosis drug levels in order to study their probable correlations with DILI.   

  19. Credit Risk Evaluation

    Directory of Open Access Journals (Sweden)

    Nora Mihail

    2007-04-01

    Full Text Available In the environment in which a bank functions there are many risk sources that determine the reduction of the profitability. These risk sources must be attentively identified, measured and taken into consideration for the elaboration of a bank’s general strategy of monitoring and disproof of the risks. The risk is generally defined as: the adverse effect that certain distinct incertitude sources exert over the profitability. The measurement of the risk requires that both the incertitude and the potential adverse effect over the profitability be surprised and evaluated.

  20. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  1. Predicting Risk of Violence in Mental Disorders

    Directory of Open Access Journals (Sweden)

    Miguel Talina

    2014-10-01

    Full Text Available The most recent research on psychosis and violence shows a significant positive association between both, although the risk of violence on psychosis is much lower than the risk of violence in substance abuse or personality disorders; and, in a general way, the predicting fac- tors for violence in patients are the same than in individuals without mental disorders. Psychiatrists and clinical or forensic psycholo- gists frequently have to predict violent behaviour. Since the 90s, instruments that evaluate the risk of violence have been developed, based on statistical methods to improve the efficacy of the evaluation. The best known are Psychology Checklist-Revised, Historical Risk Management-20 and Violence Risk Appraisal Guide. Several investigators consider these instruments essential for more rigorous predictions, as they are superior to clinical methods; other investigators state that the main advantage of these instruments is the fact that they sumarise the most recent advances in these areas, so that clinicians can make evidence based decisions.

  2. Genetic risk prediction for common diseases : methodology and applications

    NARCIS (Netherlands)

    R. Mihaescu (Raluca)

    2013-01-01

    textabstractThis thesis describes methodological and empirical studies of genetic risk prediction of common diseases. The methodological studies involved the evaluation of traditional and new methods of model performance, the evaluation of rare variants for risk prediction of common diseases, the

  3. Advantages and limitations of chemical extraction tests to predict mercury soil-plant transfer in soil risk evaluations.

    Science.gov (United States)

    Monteiro, R J R; Rodrigues, S M; Cruz, N; Henriques, B; Duarte, A C; Römkens, P F A M; Pereira, E

    2016-07-01

    In this study, we compared the size of the mobile Hg pool in soil to those obtained by extractions using 2 M HNO3, 5 M HNO3, and 2 M HCl. This was done to evaluate their suitability to be used as proxies in view of Hg uptake by ryegrass. Total levels of Hg in soil ranged from 0.66 to 70 mg kg(-1) (median 17 mg kg(-1)), and concentrations of Hg extracted increased in the order: mobile Hg extracted relative to total Hg in soil varied from 0.13 to 0.79 % (for the mobile pool) to 4.8-82 % (for 2 M HCl). Levels of Hg in ryegrass ranged from 0.060 to 36 mg kg(-1) (median 0.65 mg kg(-1), in roots) and from 0.040 to 5.4 mg kg(-1) (median 0.34 mg kg(-1), in shoots). Although results from the 2 M HNO3 extraction appeared to the most comparable to the actual total Hg levels measured in plants, the 2 M HCl extraction better expressed the variation in plant pools. In general, soil tests explained between 66 and 86 % of the variability of Hg contents in ryegrass shoots. Results indicated that all methods tested here can be used to estimate the plant total Hg pool at contaminated areas and can be used in first tier soil risk evaluations. This study also indicates that a relevant part of Hg in plants is from deposition of soil particles and that splashing of soil can be more significant for plant contamination than actual uptake processes. Graphical Abstract Illustration of potential mercury soil-plant transfer routes.

  4. A new pre-employment functional capacity evaluation predicts longer-term risk of musculoskeletal injury in healthy workers: a prospective cohort study.

    Science.gov (United States)

    Legge, Jennifer; Burgess-Limerick, Robin; Peeters, Geeske

    2013-12-01

    Prospective cohort study. To determine if a job-specific pre-employment functional assessment (PEFA) predicts musculoskeletal injury risk in healthy mineworkers. Traditional methods of pre-employment screening, including radiography and medical screenings, are not valid predictors of occupational musculoskeletal injury risk. Short-form job-specific functional capacity evaluations are increasing in popularity, despite limited evidence of their ability to predict injury risk in healthy workers. Participants were recruited from an Australian coal mine between 2002 and 2009 as part of the hiring process. At baseline, participants were screened with the JobFit System PEFA, and classified as PEFA 1 if they met job demands and PEFA>1, if not. Males who completed the PEFA and were employed were included. Injury data from company records were coded for body part, mechanism, and severity. The relationship between PEFA classification and time to first injury was analyzed using Cox proportional hazards regression with adjustments for department and post hoc stratification for time (0-1.3 yr, 1.3-6 yr). Of the 600 participants (median age, 37 yr, range, 17.0-62.6 yr), 427 scored PEFA 1. One hundred ninety-six sprain/strain injuries were reported by 121 workers, including 35 back injuries from manual handling. Significant differences between PEFA groups were found in time to first injury for all injury types during the long term (any injury: adjusted hazard ratio [HR] = 2.3, 95% confidence interval [CI] = 1.4-3.9; manual handling injury: HR = 3.3, CI = 1.6-7.2; any back injury: HR = 3.3, CI = 1.6-6.6; back injuries from manual handling HR = 5.8, CI = 2.0-16.7), but not during the short term. An area under the receiver operator curve value of 0.73 (CI = 0.61-0.86) demonstrated acceptable predictive ability for back injuries from manual handling during the long term. JobFit System PEFAs predict musculoskeletal injury risk in healthy mineworkers after 1.3 years of employment

  5. The importance of evaluating metal exposure and predicting human health risks in urban-periurban environments influenced by emerging industry.

    Science.gov (United States)

    Yousaf, Balal; Amina; Liu, Guijian; Wang, Ruwei; Imtiaz, Muhammad; Rizwan, Muhammad Shahid; Zia-Ur-Rehman, Muhammad; Qadir, Abdul; Si, Youbin

    2016-05-01

    The human population boom, urbanization and rapid industrialization have either directly or indirectly resulted in the serious environmental toxification of the soil-food web by metal exposure from anthropogenic sources in most of the developing industrialized world. The present study was conducted to analyze concentrations of Cd, Cr, Cu, Mn, Ni, Pb, and Zn in soil and vegetables in the urban-periurban areas influenced by emerging industry. Vegetables and their corresponding soil samples were collected and analyzed for heavy metals contents from six random sites. According to the results, the potential health risks from metals to the local communities were assessed by following the methodology described by the US-EPA. In general, the total non-carcinogenic risks were shown to be less than the limits set by the US-EPA. However, the potential risk of developing carcinogenicity in humans over a lifetime of exposure could be increased through the dietary intake of Cd, Cr and Ni. In some cases, Pb was also marginally higher than the safe level. It was concluded that some effective remedial approaches should be adopted to mitigate the risks of Cd, Cr, Ni and Pb in the study area because these metal levels have exceeded the safe limits for human health. However, new studies on gastrointestinal bioaccessibility in human are required to heighten our understanding about metals exposure and health risk assessment. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Glycated albumin predicts the risk of mortality in type 2 diabetic patients on hemodialysis: evaluation of a target level for improving survival.

    Science.gov (United States)

    Isshiki, Keiji; Nishio, Toshiki; Isono, Motohide; Makiishi, Tetsuya; Shikano, Tsutomu; Tomita, Koubin; Nishio, Toshiji; Kanasaki, Masami; Maegawa, Hiroshi; Uzu, Takashi

    2014-10-01

    Glycated albumin (GA) is considered a more reliable marker than glycated hemoglobin (HbA1c) for monitoring glycemic control, particularly in diabetic hemodialysis patients. We investigated the associations of GA, HbA1c, and random serum glucose levels with survival, and evaluated possible targets for improving survival in diabetic hemodialysis patients. In this prospective, longitudinal, observational study, we enrolled 90 diabetic hemodialysis patients across six dialysis centers in Japan. The median duration of follow-up was 36.0 months (mean follow-up, 29.8 months; range, 3-36 months). There were 11 deaths during the observation period. GA was a significant predictor for mortality (hazard ratio, 1.143 per 1% increase in GA; 95% confidence interval, 1.011-1.292; P = 0.033), whereas HbA1c and random glucose levels were not predictors for mortality. Receiver operating characteristics curve analysis showed that the cutoff value of GA for predicting the risk of mortality was 25%. In the Kaplan-Meier analysis, the cumulative survival rate was significantly greater in patients with GA ≤ 25% than in patients with GA >25%. GA predicted the risk of all-cause and cardiovascular mortality in diabetic hemodialysis patients. Our results suggest that GA ≤ 25% is an appropriate target for improving survival in diabetic hemodialysis patients. © 2013 The Authors. Therapeutic Apheresis and Dialysis © 2013 International Society for Apheresis.

  7. Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT).

    Science.gov (United States)

    Hsu, Amy T; Manuel, Douglas G; Taljaard, Monica; Chalifoux, Mathieu; Bennett, Carol; Costa, Andrew P; Bronskill, Susan; Kobewka, Daniel; Tanuseputro, Peter

    2016-12-01

    Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning. To predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers. Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013. The derivation cohort will consist of ∼437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ∼122 000 older adults with an RAI-HC assessment between 1 January and 31 December 2013. Predicted survival from the time of an RAI-HC assessment. All deaths (n≈245 000) will be ascertained through linkage to a population-based registry that is maintained by the Ministry of Health in Ontario. Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data. Research ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals. NCT02779309, Pre-results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

  8. [The evaluation of color vision and its diagnostic value in predicting the risk of diabetic retinopathy in patients with glucose metabolism disorders].

    Science.gov (United States)

    Jończyk-Skórka, Katarzyna; Kowalski, Jan

    2017-07-21

    The aim of the study was to evaluate color vision and its diagnostic value in predicting the risk of diabetic retinopathy in patients with glucose metabolism disorders. The study involved 197 people, 92 women and 105 men aged 63.21 ± 8.74 years. In order to assess glucose metabolism disorders, patients were divided into three groups. The first group (DM) consisted of 60 people (16 women and 44 men aged 61.92 ± 8.46 years). These were people with type 2 diabetes. Second group (IFG IGT) consisted of 67 people (35 women and 32 men aged 65 ± 8.5 years). These were people who were diagnosed with impaired fasting glucose or impaired glucose tolerance. The third group, the control one (K) consisted of 70 people (41 women and 29 men aged 62.6 ± 9.06 years). They were healthy individuals. In order to assess diabetic retinopathy study population was divided into two groups. The first group (BZ) consisted of 177 patients (84 women and 93 men aged 62.9 ± 8.78 years) without diabetic retinopathy. The second group (NPDR) consisted of 20 patients (8 women and 12 men aged 65.95 ± 8.17 years) with diabetic retinopathy. Glucose metabolism disorders were diagnosed with glucose tolerance test (OGTT). Evaluation of retinopathy was based on eye examination. All patients underwent binocular Farnsworth-Munsell 100 Hue color vision test (test result is a Total Error Score - TES). In the healthy control group (K) there were less patients with diabetic retinopathy (p = 0,0101), and less patients with abnormal color vision test (p = 0,0001) than in other groups. Majority of patients in K group had generalized abnormalities of color vision while other groups demonstrated tritanomalią (p = 0,0018). It was discovered that sTES value adequately distinguishes group K from group IFG, IGT, DM (AUC = 0,673), group K from group DM (AUC = 0,701), and group K from group IFG IGT (AUC = 0,648) sTES does not differentiate groups IGT, IFG and DM (AUC = 0,563). It was shown that in IGT, IFG group s

  9. Advantages and limitations of chemical extraction tests to predict mercury soil-plant transfer in soil risk evaluations

    NARCIS (Netherlands)

    Monteiro, R.J.R.; Rodrigues, S.M.; Cruz, N.; Henriques, B.; Duarte, A.C.; Römkens, P.F.A.M.; Pereira, E.

    2016-01-01

    In this study, we compared the size of the mobile Hg pool in soil to those obtained by extractions using 2 M HNO3, 5 M HNO3, and 2 M HCl. This was done to evaluate their suitability to be used as proxies in view of Hg uptake by ryegrass. Total levels of Hg in soil ranged

  10. Excess pressure integral predicts cardiovascular events independent of other risk factors in the conduit artery functional evaluation substudy of Anglo-Scandinavian Cardiac Outcomes Trial.

    Science.gov (United States)

    Davies, Justin E; Lacy, Peter; Tillin, Therese; Collier, David; Cruickshank, J Kennedy; Francis, Darrel P; Malaweera, Anura; Mayet, Jamil; Stanton, Alice; Williams, Bryan; Parker, Kim H; McG Thom, Simon A; Hughes, Alun D

    2014-07-01

    Excess pressure integral (XSPI), a new index of surplus work performed by the left ventricle, can be calculated from blood pressure waveforms and may indicate circulatory dysfunction. We investigated whether XSPI predicted future cardiovascular events and target organ damage in treated hypertensive individuals. Radial blood pressure waveforms were acquired by tonometry in 2069 individuals (aged, 63±8 years) in the Conduit Artery Functional Evaluation (CAFE) substudy of the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT). Measurements of left ventricular mass index (n=862) and common carotid artery intima media thickness (n=923) were also performed. XSPI and the integral of reservoir pressure were lower in people treated with amlodipine±perindopril than in those treated with atenolol±bendroflumethiazide, although brachial systolic blood pressure was similar. A total of 134 cardiovascular events accrued during a median 3.4 years of follow-up; XSPI was a significant predictor of cardiovascular events after adjustment for age and sex, and this relationship was unaffected by adjustment for conventional cardiovascular risk factors or Framingham risk score. XSPI, central systolic blood pressure, central augmentation pressure, central pulse pressure, and integral of reservoir pressure were correlated with left ventricular mass index, but only XSPI, augmentation pressure, and central pulse pressure were associated positively with carotid artery intima media thickness. Associations between left ventricular mass index, XSPI, and integral of reservoir pressure and carotid artery intima media thickness and XSPI were unaffected by multivariable adjustment for other covariates. XSPI is a novel indicator of cardiovascular dysfunction and independently predicts cardiovascular events and targets organ damage in a prospective clinical trial. © 2014 American Heart Association, Inc.

  11. Which are the most useful scales for predicting repeat self-harm? A systematic review evaluating risk scales using measures of diagnostic accuracy

    Science.gov (United States)

    Quinlivan, L; Cooper, J; Davies, L; Hawton, K; Gunnell, D; Kapur, N

    2016-01-01

    Objectives The aims of this review were to calculate the diagnostic accuracy statistics of risk scales following self-harm and consider which might be the most useful scales in clinical practice. Design Systematic review. Methods We based our search terms on those used in the systematic reviews carried out for the National Institute for Health and Care Excellence self-harm guidelines (2012) and evidence update (2013), and updated the searches through to February 2015 (CINAHL, EMBASE, MEDLINE, and PsychINFO). Methodological quality was assessed and three reviewers extracted data independently. We limited our analysis to cohort studies in adults using the outcome of repeat self-harm or attempted suicide. We calculated diagnostic accuracy statistics including measures of global accuracy. Statistical pooling was not possible due to heterogeneity. Results The eight papers included in the final analysis varied widely according to methodological quality and the content of scales employed. Overall, sensitivity of scales ranged from 6% (95% CI 5% to 6%) to 97% (CI 95% 94% to 98%). The positive predictive value (PPV) ranged from 5% (95% CI 3% to 9%) to 84% (95% CI 80% to 87%). The diagnostic OR ranged from 1.01 (95% CI 0.434 to 2.5) to 16.3 (95%CI 12.5 to 21.4). Scales with high sensitivity tended to have low PPVs. Conclusions It is difficult to be certain which, if any, are the most useful scales for self-harm risk assessment. No scales perform sufficiently well so as to be recommended for routine clinical use. Further robust prospective studies are warranted to evaluate risk scales following an episode of self-harm. Diagnostic accuracy statistics should be considered in relation to the specific service needs, and scales should only be used as an adjunct to assessment. PMID:26873046

  12. Lipoprotein metabolism indicators improve cardiovascular risk prediction.

    Directory of Open Access Journals (Sweden)

    Daniël B van Schalkwijk

    Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

  13. Nottingham knee osteoarthritis risk prediction models.

    Science.gov (United States)

    Zhang, Weiya; McWilliams, Daniel F; Ingham, Sarah L; Doherty, Sally A; Muthuri, Stella; Muir, Kenneth R; Doherty, Michael

    2011-09-01

    (1) To develop risk prediction models for knee osteoarthritis (OA) and (2) to estimate the risk reduction that results from modification of potential risk factors. This was a 12-year retrospective cohort study undertaken in the general population in Nottingham, UK. Baseline risk factors were collected by questionnaire. Incident radiographic knee OA was defined by Kellgren and Lawrence (KL) score ≥2. Incident symptomatic knee OA was defined by KL ≥2 plus knee pain. Progression of knee OA was defined by KL ≥1 grade increase from baseline. A logistic regression model was used for prediction. Calibration and discrimination of the models were tested in the Osteoarthritis Initiative (OAI) population and Genetics of Osteoarthritis and Lifestyle (GOAL) population. ORs of the models were compared with those obtained from meta-analysis of existing literature. From a community sample of 424 people aged over 40, 3 risk prediction models were developed. These included incidence of radiographic knee OA, incidence of symptomatic knee OA and progression of knee OA. All models had good calibration and moderate discrimination power in OAI and GOAL. The ORs lied within the 95% CIs of the published studies. The risk reduction due to modifying obesity at the individual and the population levels were demonstrated. Risk prediction of knee OA based on the well established, common modifiable risk factors has been established. The models may be used to predict the risk of knee OA, and risk reduction due to preventing a specific risk factor.

  14. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Developmental Dyslexia: Predicting Individual Risk

    Science.gov (United States)

    Thompson, Paul A.; Hulme, Charles; Nash, Hannah M.; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J.

    2015-01-01

    Background: Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods: The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6…

  16. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Prediction of eyespot infection risks

    Directory of Open Access Journals (Sweden)

    M. Váòová

    2012-12-01

    Full Text Available The objective of the study was to design a prediction model for eyespot (Tapesia yallundae infection based on climatic factors (temperature, precipitation, air humidity. Data from experiment years 1994-2002 were used to study correlations between the eyespot infection index and individual weather characteristics. The model of prediction was constructed using multiple regression when a separate parameter is assigned to each factor, i.e. the frequency of days with optimum temperatures, humidity, and precipitation. The correlation between relative air humidity and precipitation and the infection index is significant.

  1. Predictive validity of the Hand Arm Risk assessment Method (HARM)

    NARCIS (Netherlands)

    Douwes, M.; Boocock, M.; Coenen, P.; Heuvel, S. van den; Bosch, T.

    2014-01-01

    The Hand Arm Risk assessment Method (HARM) is a simplified risk assessment method for determining musculoskeletal symptoms to the arm, neck and/or shoulder posed by hand-arm tasks of the upper body. The purpose of this study was to evaluate the predictive validity of HARM using data collected from a

  2. TIMI Risk Score predicts early readmission.

    Science.gov (United States)

    Soiza, Roy L; Hughes, Niall J; Leslie, Stephen J; Peden, Norman R; Hargreaves, Allister D

    2006-08-10

    To assess if the TIMI Risk Score could predict early readmission. 869 consecutive admissions to a Scottish district general hospital with suspected acute coronary syndrome. A computerised clinical information system was interrogated to verify readmission. Area under the receiver operator characteristic curve and chi-square test for trend between TIMI Risk Score and readmission rate were calculated. Median follow up was 73 days. There was a strong association between TIMI Risk Score and readmission rate (chi-square test for trend, pTIMI Risk Score can predict readmission. This study reinforces its utility as a tool for identifying patients more likely to benefit from aggressive intervention.

  3. Limiting overdiagnosis of low-risk prostate cancer through an evaluation of the predictive value of transrectal and power Doppler ultrasonography.

    Science.gov (United States)

    Sauvain, Jean Luc; Sauvain, Elise; Papavero, Roger; Louis, Didier; Rohmer, Paul

    2016-12-01

    Overdiagnosis induced by prostate cancer screening makes necessary a better selection of candidate patients for prostate biopsy. The objective of our study is to assess the probability of having a high- or low-risk lesion that could require active surveillance (AS) after biopsies and a normal or abnormal examination, including transrectal and power Doppler ultrasonography (TRUS-PDS). Four hundred and twenty-nine consecutive patients with a PSA level risk of a biological recurrence and Dall'Era's criteria to assess possible AS. The TRUS-PDS was considered positive if one biopsy was positive in the same sextant as the suspect image. One hundred and seventy-seven out of 429 (41 %) T1c cancers were diagnosed; 131 out of 177 (74 %) could be qualified as low risk, and 119 out of 177 (67 %) could require AS. The TRUS-PDS was normal in 285 of 429 patients (66 %). With a normal TRUS-PDS, the probability of not having cancer with a high or intermediate risk was 96 % (negative predictive value). With an abnormal TRUS-PDS, the probability of having a positive biopsy was 59 %, and the probability of having a significant cancer was 30 %, according to the Dall'Era criteria. When TRUS-PDS was normal, these probabilities significantly decreased to 32 and 5 %, respectively ( p  risk of high- or intermediate-risk cancer.

  4. Genetically Predicted Body Mass Index and Breast Cancer Risk

    DEFF Research Database (Denmark)

    Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou

    2016-01-01

    is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons......BACKGROUND: Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic...... or environmental factors. METHODS: We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated...

  5. Subclinical organ damage and cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Sehestedt, Thomas; Olsen, Michael H

    2010-01-01

    Traditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided...... by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii...... for risk discrimination, calibration and reclassification; and (ii) the economic costs and health benefits associated with measuring markers of subclinical organ damage....

  6. Evaluation of BRCA1 and BRCA2 mutation prevalence, risk prediction models and a multistep testing approach in French‐Canadian families with high risk of breast and ovarian cancer

    Science.gov (United States)

    Simard, Jacques; Dumont, Martine; Moisan, Anne‐Marie; Gaborieau, Valérie; Vézina, Hélène; Durocher, Francine; Chiquette, Jocelyne; Plante, Marie; Avard, Denise; Bessette, Paul; Brousseau, Claire; Dorval, Michel; Godard, Béatrice; Houde, Louis; Joly, Yann; Lajoie, Marie‐Andrée; Leblanc, Gilles; Lépine, Jean; Lespérance, Bernard; Malouin, Hélène; Parboosingh, Jillian; Pichette, Roxane; Provencher, Louise; Rhéaume, Josée; Sinnett, Daniel; Samson, Carolle; Simard, Jean‐Claude; Tranchant, Martine; Voyer, Patricia; BRCAs, INHERIT; Easton, Douglas; Tavtigian, Sean V; Knoppers, Bartha‐Maria; Laframboise, Rachel; Bridge, Peter; Goldgar, David

    2007-01-01

    Background and objective In clinical settings with fixed resources allocated to predictive genetic testing for high‐risk cancer predisposition genes, optimal strategies for mutation screening programmes are critically important. These depend on the mutation spectrum found in the population under consideration and the frequency of mutations detected as a function of the personal and family history of cancer, which are both affected by the presence of founder mutations and demographic characteristics of the underlying population. The results of multistep genetic testing for mutations in BRCA1 or BRCA2 in a large series of families with breast cancer in the French‐Canadian population of Quebec, Canada are reported. Methods A total of 256 high‐risk families were ascertained from regional familial cancer clinics throughout the province of Quebec. Initially, families were tested for a panel of specific mutations known to occur in this population. Families in which no mutation was identified were then comprehensively tested. Three algorithms to predict the presence of mutations were evaluated, including the prevalence tables provided by Myriad Genetics Laboratories, the Manchester Scoring System and a logistic regression approach based on the data from this study. Results 8 of the 15 distinct mutations found in 62 BRCA1/BRCA2‐positive families had never been previously reported in this population, whereas 82% carried 1 of the 4 mutations currently observed in ⩾2 families. In the subset of 191 families in which at least 1 affected individual was tested, 29% carried a mutation. Of these 27 BRCA1‐positive and 29 BRCA2‐positive families, 48 (86%) were found to harbour a mutation detected by the initial test. Among the remaining 143 inconclusive families, all 8 families found to have a mutation after complete sequencing had Manchester Scores ⩾18. The logistic regression and Manchester Scores provided equal predictive power, and both were significantly better

  7. Evaluation of Cardiovascular Risk Scores Applied to NASA's Astronant Corps

    Science.gov (United States)

    Jain, I.; Charvat, J. M.; VanBaalen, M.; Lee, L.; Wear, M. L.

    2014-01-01

    In an effort to improve cardiovascular disease (CVD) risk prediction, this analysis evaluates and compares the applicability of multiple CVD risk scores to the NASA Astronaut Corps which is extremely healthy at selection.

  8. Subclinical organ damage and cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Sehestedt, Thomas; Olsen, Michael H

    2010-01-01

    Traditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided...... by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii......) investigated which and how many markers to measure and (iv) finally discussed whether measuring subclinical organ damage provided benefits beyond risk prediction. In conclusion, more studies and if possible randomized studies are needed to investigate (i) the importance of markers of subclinical organ damage...

  9. Evaluation of the Finnish Diabetes Risk Score to predict type 2 diabetes mellitus in a Colombian population: A longitudinal observational study.

    Science.gov (United States)

    Gomez-Arbelaez, Diego; Alvarado-Jurado, Laura; Ayala-Castillo, Miguel; Forero-Naranjo, Leonardo; Camacho, Paul Anthony; Lopez-Jaramillo, Patricio

    2015-12-10

    To assess the performance of the Finnish Diabetes Risk Score (FINDRISC) questionnaire for detecting and predicting type 2 diabetes mellitus (DM2) in a Colombian population. This is a longitudinal observational study conducted in Floridablanca, Colombia. Adult subjects (age ≥ 35 years) without known diabetes, were included. A modified version of FINDRISC was completed, and the glycemia values from all the subjects were collected from the hospital's database. Firstly, a cross-sectional analysis was performed and then, the subsample of prediabetic participants was followed for diabetes incidence. A total of 772 subjects were suitable for the study. The overall prevalence of undiagnosed DM2 was 2.59%, and the incidence of DM2 among the prediabetic participants was 7.5 per 100 person-years after a total of 265257 person-years follow-up. The FINDRISC at baseline was significantly associated with undiagnosed and incident DM2. The area under receiver operating characteristics curve of the FINDRISC score for detecting undiagnosed DM2 in both men and women was 0.7477 and 0.7175, respectively; and for predicting the incidence of DM2 among prediabetics was 71.99% in men and 67.74% in women. The FINDRISC questionnaire is a useful screening tool to identify cross-sectionally unknown DM2 and to predict the incidence of DM2 among prediabetics in the Colombian population.

  10. Credit Risk Prediction Using Fuzzy Immune Learning

    Directory of Open Access Journals (Sweden)

    Ehsan Kamalloo

    2014-01-01

    Full Text Available The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.

  11. Dementia risk prediction in the population: are screening models accurate?

    Science.gov (United States)

    Stephan, Blossom C M; Kurth, Tobias; Matthews, Fiona E; Brayne, Carol; Dufouil, Carole

    2010-06-01

    Early identification of individuals at risk of dementia will become crucial when effective preventative strategies for this condition are developed. Various dementia prediction models have been proposed, including clinic-based criteria for mild cognitive impairment, and more-broadly constructed algorithms, which synthesize information from known dementia risk factors, such as poor cognition and health. Knowledge of the predictive accuracy of such models will be important if they are to be used in daily clinical practice or to screen the entire older population (individuals aged >or=65 years). This article presents an overview of recent progress in the development of dementia prediction models for use in population screening. In total, 25 articles relating to dementia risk screening met our inclusion criteria for review. Our evaluation of the predictive accuracy of each model shows that most are poor at discriminating at-risk individuals from not-at-risk cases. The best models incorporate diverse sources of information across multiple risk factors. Typically, poor accuracy is associated with single-factor models, long follow-up intervals and the outcome measure of all-cause dementia. A parsimonious and cost-effective consensus model needs to be developed that accurately identifies individuals with a high risk of future dementia.

  12. Predictive lethal proarrhythmic risk evaluation using a closed-loop-circuit cell network with human induced pluripotent stem cells derived cardiomyocytes

    Science.gov (United States)

    Nomura, Fumimasa; Hattori, Akihiro; Terazono, Hideyuki; Kim, Hyonchol; Odaka, Masao; Sugio, Yoshihiro; Yasuda, Kenji

    2016-06-01

    For the prediction of lethal arrhythmia occurrence caused by abnormality of cell-to-cell conduction, we have developed a next-generation in vitro cell-to-cell conduction assay, i.e., a quasi in vivo assay, in which the change in spatial cell-to-cell conduction is quantitatively evaluated from the change in waveforms of the convoluted electrophysiological signals from lined-up cardiomyocytes on a single closed loop of a microelectrode of 1 mm diameter and 20 µm width in a cultivation chip. To evaluate the importance of the closed-loop arrangement of cardiomyocytes for prediction, we compared the change in waveforms of convoluted signals of the responses in the closed-loop circuit arrangement with that of the response of cardiomyocyte clusters using a typical human ether a go-go related gene (hERG) ion channel blocker, E-4031. The results showed that (1) waveform prolongation and fluctuation both in the closed loops and clusters increased depending on the E-4031 concentration increase. However, (2) only the waveform signals in closed loops showed an apparent temporal change in waveforms from ventricular tachycardia (VT) to ventricular fibrillation (VF), which is similar to the most typical cell-to-cell conductance abnormality. The results indicated the usefulness of convoluted waveform signals of a closed-loop cell network for acquiring reproducible results acquisition and more detailed temporal information on cell-to-cell conduction.

  13. Risk prediction of hepatotoxicity in paracetamol poisoning.

    Science.gov (United States)

    Wong, Anselm; Graudins, Andis

    2017-09-01

    Paracetamol (acetaminophen) poisoning is the most common cause of acute liver failure in the developed world. A paracetamol treatment nomogram has been used for over four decades to help determine whether patients will develop hepatotoxicity without acetylcysteine treatment, and thus indicates those needing treatment. Despite this, a small proportion of patients still develop hepatotoxicity. More accurate risk predictors would be useful to increase the early detection of patients with the potential to develop hepatotoxicity despite acetylcysteine treatment. Similarly, there would be benefit in early identification of those with a low likelihood of developing hepatotoxicity, as this group may be safely treated with an abbreviated acetylcysteine regimen. To review the current literature related to risk prediction tools that can be used to identify patients at increased risk of hepatotoxicity. A systematic literature review was conducted using the search terms: "paracetamol" OR "acetaminophen" AND "overdose" OR "toxicity" OR "risk prediction rules" OR "hepatotoxicity" OR "psi parameter" OR "multiplication product" OR "half-life" OR "prothrombin time" OR "AST/ALT (aspartate transaminase/alanine transaminase)" OR "dose" OR "biomarkers" OR "nomogram". The search was limited to human studies without language restrictions, of Medline (1946 to May 2016), PubMed and EMBASE. Original articles pertaining to the theme were identified from January 1974 to May 2016. Of the 13,975 articles identified, 60 were relevant to the review. Paracetamol treatment nomograms: Paracetamol treatment nomograms have been used for decades to help decide the need for acetylcysteine, but rarely used to determine the risk of hepatotoxicity with treatment. Reported paracetamol dose and concentration: A dose ingestion >12 g or serum paracetamol concentration above the treatment thresholds on the paracetamol nomogram are associated with a greater risk of hepatotoxicity. Paracetamol elimination half

  14. Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Perandini, Simone; Soardi, G.A.; Signorini, M.; Motton, M.; Montemezzi, S. [Azienda Ospedaliera Universitaria Integrata di Verona, UOC Radiologia, Ospedale Maggiore di Borgo Trento, Verona (Italy); Larici, A.R.; Del Ciello, A. [Universita Cattolica del Sacro Cuore, Dipartimento di Scienze Radiologiche, Roma (Italy); Rizzardi, G. [Ospedale Humanitas Gavazzeni, UO Chirurgia Toracica, Bergamo (Italy); Solazzo, A. [Ospedale Humanitas Gavazzeni, UO Radiologia, Bergamo (Italy); Mancino, L.; Zeraj, F. [Ospedale dell' Angelo di Mestre, UO Pneumologia, Venezia (Italy); Bernhart, M. [Ospedale dell' Angelo di Mestre, UO Radiologia, Venezia (Italy)

    2017-05-15

    To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models. Two hundred and fifty-nine solitary pulmonary nodules (SPNs) collected from four major hospitals which underwent 18-FDG-PET characterization were included in this multicentre retrospective study. The Herder model was tested on all available lesions (group A). A subgroup of 180 SPNs (group B) was used to provide unbiased comparison between the Herder and BIMC models. Receiver operating characteristic (ROC) area under the curve (AUC) analysis was performed to assess diagnostic accuracy. Decision analysis was performed by adopting the risk threshold stated in British Thoracic Society (BTS) guidelines. Unbiased comparison performed In Group B showed a ROC AUC for the Herder model of 0.807 (95 % CI 0.742-0.862) and for the BIMC model of 0.822 (95 % CI 0.758-0.875). Both the Herder and the BIMC models were proven to accurately predict the risk of malignancy when tested on a large multicentre external case series. The BIMC model seems advantageous on the basis of a more favourable decision analysis. (orig.)

  15. Online Risk Prediction for Indoor Moving Objects

    DEFF Research Database (Denmark)

    Ahmed, Tanvir; Pedersen, Torben Bach; Calders, Toon

    2016-01-01

    or inaccuracy at any step can make the bag risky, i.e., the bag may be delayed at the airport or sent to a wrong airport. In this paper, we propose a novel probabilistic approach for predicting the risk of an indoor moving object in real-time. We propose a probabilistic flow graph (PFG) and an aggregated......Technologies such as RFID and Bluetooth have received considerable attention for tracking indoor moving objects. In a time-critical indoor tracking scenario such as airport baggage handling, a bag has to move through a sequence of locations until it is loaded into the aircraft. Inefficiency...... probabilistic flow graph (APFG) that capture the historical object transitions and the durations of the transitions. In the graphs, the probabilistic information is stored in a set of histograms. Then we use the flow graphs for obtaining a risk score of an online object and use it for predicting its riskiness...

  16. Working postures: prediction and evaluation

    NARCIS (Netherlands)

    Delleman, N.J.

    1999-01-01

    To date, workstation designers cannot see the effects of a design on working posture before a mock-up/prototype is available. At that moment, usually the margin for creating the conditions required for adopting favourable working postures is still very limited. Posture prediction at an early design

  17. Extreme lipoprotein(a) levels and improved cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Kamstrup, Pia R; Tybjærg-Hansen, Anne; Nordestgaard, Børge G

    2013-01-01

    The study tested whether extreme lipoprotein(a) levels and/or corresponding LPA risk genotypes improve myocardial infarction (MI) and coronary heart disease (CHD) risk prediction beyond conventional risk factors.......The study tested whether extreme lipoprotein(a) levels and/or corresponding LPA risk genotypes improve myocardial infarction (MI) and coronary heart disease (CHD) risk prediction beyond conventional risk factors....

  18. The prediction of the bankruptcy risk

    Directory of Open Access Journals (Sweden)

    Gheorghe DUMITRESCU

    2010-04-01

    Full Text Available The study research results of the bankruptcy risk in the actual economic crisis are very weak. This issue is very important for the economy of every country, no matter what their actual development level.The necessity of bankruptcy risk prediction appears in every company,but also in the related institutions like financial companies, investors, suppliers, customers.The bankruptcy risk made and makes the object of many studies of research that want to identify: the moment of the appearance of the bankruptcy, the factors that compete at the reach of this state, the indicators that express the best this orientation (to the bankruptcy.The threats to the firms impose the knowledge by the managers,permanently of the economic-financial situations, of the vulnerable areas and of those with potential of development. Thus, these must identify and gesture the threats that would stop the fulfillment of the established purposes.

  19. Predicting neutropenia risk in patients with cancer using electronic data.

    Science.gov (United States)

    Pawloski, Pamala A; Thomas, Avis J; Kane, Sheryl; Vazquez-Benitez, Gabriela; Shapiro, Gary R; Lyman, Gary H

    2017-04-01

    Clinical guidelines recommending the use of myeloid growth factors are largely based on the prescribed chemotherapy regimen. The guidelines suggest that oncologists consider patient-specific characteristics when prescribing granulocyte-colony stimulating factor (G-CSF) prophylaxis; however, a mechanism to quantify individual patient risk is lacking. Readily available electronic health record (EHR) data can provide patient-specific information needed for individualized neutropenia risk estimation. An evidence-based, individualized neutropenia risk estimation algorithm has been developed. This study evaluated the automated extraction of EHR chemotherapy treatment data and externally validated the neutropenia risk prediction model. A retrospective cohort of adult patients with newly diagnosed breast, colorectal, lung, lymphoid, or ovarian cancer who received the first cycle of a cytotoxic chemotherapy regimen from 2008 to 2013 were recruited from a single cancer clinic. Electronically extracted EHR chemotherapy treatment data were validated by chart review. Neutropenia risk stratification was conducted and risk model performance was assessed using calibration and discrimination. Chemotherapy treatment data electronically extracted from the EHR were verified by chart review. The neutropenia risk prediction tool classified 126 patients (57%) as being low risk for febrile neutropenia, 44 (20%) as intermediate risk, and 51 (23%) as high risk. The model was well calibrated (Hosmer-Lemeshow goodness-of-fit test = 0.24). Discrimination was adequate and slightly less than in the original internal validation (c-statistic 0.75 vs 0.81). Chemotherapy treatment data were electronically extracted from the EHR successfully. The individualized neutropenia risk prediction model performed well in our retrospective external cohort.

  20. Risk assessment strategy for prediction of pathological hyperbilirubinemia in neonates.

    Science.gov (United States)

    Chawla, Deepak; Jain, Suksham; Dhir, Shashikant; Rani, Shikha

    2012-02-01

    To evaluate combined ability of clinical risk factors and transcutaneous bilirubin (TcB) in predicting pathological hyperbilirubinemia (PHB) needing treatment during first week of life in healthy term and late preterm neonates. This prospective cohort study included healthy neonates with gestation ≥35 wk and birth weight ≥2000 g. TcB was measured with a multi-wavelength transcutaneous bilirubinometer (Bilichek®) at 30 ± 12 h of postnatal age. Follow-up was conducted as per American Academy of Pediatrics guidelines. For diagnosis of PHB, TcB was measured at each follow-up visit. Serum bilirubin was measured if TcB was >15 mg/dL or within 2 mg/dL of phototherapy cut-off. Among 462 neonates [birth weight (g; mean ± SD): 2711 ± 431, gestation (wk; median, IQR): 38 (37-39), male: 52%] enrolled in the study, 392 (84.9%) completed followup and PHB was observed in 65 (16.6%) neonates. Discriminant ability of risk model, including both clinical risk factors and TcB, was better than the risk models with clinical risk factors or TcB alone (c-statistic: 0.86 vs. 0.74 vs. 0.77). On logistic regression analysis risk factors found significant were TcB (OR: 1.65, 95% CI: 1.4-1.9), gestation at birth (OR: 0.6, 95% CI: 0.50-0.77) and primiparity (OR: 2.1, 95% CI: 1.1-3.9). A risk prediction score was developed with these three risk factors as ordinal/dichotomous variables. Negative and positive predictive values for score 12 were 97% and 46%, respectively. Risk score consisting of TcB, gestation at birth and parity status was able to accurately predict pathological hyperbilirubinemia in derivation cohort of healthy term and late preterm north Indian neonates.

  1. [Predict value of clinical risk score, thrombolysis in myocardial infarction flow grade and combined clinical risk score plus TIMI flow on outcome evaluation of patients with acute coronary syndrome].

    Science.gov (United States)

    Zhong, Bin; Liu, Zeng-Zhang; Su, Li; Lan, Xian-Bin; Chen, Yun-Qing; Ling, Zhi-Yu; Yin, Yue-Hui

    2008-01-01

    To compare the prognostic value of clinical risk score and thrombolysis in myocardial infarction (TIMI) flow grade alone or combined on outcome of acute coronary syndrome (ACS). A total of 206 eligible patients [135 males, mean age (67.57 +/- 9.88) years] were enrolled. The primary endpoints included cardiac death and non-cardiac death. The secondary endpoints included non-fatal stroke, reinfarction, heart failure and recurrent angina. Receiver operating characteristic curve (ROC) established by using different endpoints and clinical risk score, TIMI flow grade or combined risk scores. The prognostic value for different endpoint expressed as the area under the curve (AUC). Eleven patients lost during the (11.41 +/- 5.33) months follow up and data were available for 195 patients, 8 patients reached the primary endpoints, and 17 patients reached the secondary end-points at the end of follow up. The AUC was 0.67 (95% CI = 0.557 approximately 0.786), P = 0.006; 0.68 (95% CI = 0.557 approximately 0.786), P = 0.004 and 0.730 (95% CI = 0.691 approximately 0.815), P risk score, TIMI flow grade and the combined risk score respectively. There were no significant differences among clinical risk score, TIMI flow grade and combined risk score (all P > 0.05) for AUC and for primary end point and the secondary end point. The result from this study suggests that the efficacy of predicting the total events based on clinical risk score, TIMI flow grade and combined risk score was similar.

  2. Predicting risk of violence through a self-appraisal questionnaire

    Directory of Open Access Journals (Sweden)

    José Manuel Andreu-Rodríguez

    2016-07-01

    Full Text Available The Self-Appraisal Questionnaire (SAQ is a self-report that predicts the risk of violence and recidivism and provides relevant information about treatment needs for incarcerated populations. The objective of the present study was to evaluate the concurrent and predictive validity of this self-report in Spanish offenders. The SAQ was administered to 276 offenders recruited from several prisons in Madrid (Spain. SAQ total scores presented high levels of internal consistency (alpha = .92. Correlations of the instrument with violence risk instruments were statistically significant and showed a moderate magnitude, indicating a reasonable degree of concurrent validity. The ROC analysis carried out on the SAQ total score revealed an AUC of .80, showing acceptable accuracy discriminating between violent and nonviolent recidivist groups. It is concluded that the SAQ total score is a reliable and valid measure to estimate violence and recidivism risk in Spanish offenders.

  3. Advanced technology wind shear prediction system evaluation

    Science.gov (United States)

    Gering, Greg

    1992-01-01

    The program overviews: (1) American Airline (AA)/Turbulence Prediction Systems (TPS), which have installed forward looking infrared predictive windshear system on 3 MD-80 aircraft; (2) AA/TPS AWAS III evaluation, which is a joint effort and is installed in the noise landing gear (NLG) area and a data recorder installed in the E/E compartment.

  4. [Mortality in patients with potentially severe trauma in a tertiary care hospital emergency department and evaluation of risk prediction with the GAP prognostic scale].

    Science.gov (United States)

    Martín Quirós, Alejandro; Borobia Pérez, Alberto; Pertejo Fernández, Ana; Pérez Perilla, Patricia; Rivera Núñez, Angélica; Martínez Virto, Ana María; Quintana Díaz, Manuel

    2015-01-01

    To assess mortality in patients with potentially severe injuries and explore the correlation between mortality and the score on the GAP scale (Glasgow Coma Scale, age, and systolic blood pressure). Retrospective observational study of all patients with potentially severe injuries treated in an emergency department (ED) over a period of 15 months. We recorded epidemiologic variables, cause of injury, type of transport, need for prehospital orotracheal intubation, substance abuse, Charlson Comorbidity Index (CCI), variables for the GAP prognostic score, destination on discharge from the ED and at the end of the episode, and mortality. Data for 864 patients entered the final analysis. Mortality was higher in older patients (mean [SD] age, 57.9 [26.6] vs 41.1 [17.4], P<.05) and those with a higher mean CCI (3.3 [2.9] vs 0.9 [1.7]). Accident type was a precipitating factor associated with mortality (P<.001), but substance abuse was unrelated. Patients who died had lower mean Glasgow scores (9.1 [5.3] vs 14.8 [1.2], P<.001) and lower mean systolic and diastolic pressures (respectively, 113.8 [19.8] vs 131.3 [20.7] mm Hg, P=.012, and 60.1 [16.8] vs 77.7 [11.7] mm Hg, P=.002). Patients who died also had lower mean GAP scores than survivors (15.1 [4.8] vs 22.6 [1.7], P<.001). Risk factors that remained significant in the multivariate analysis were CCI (odds ratio [OR], 0.704; 95% CI, 0.52-0.96) and GAP score (OR, 1.8; 95% CI, 1.45-2.20). Mortality in our patient series was lower than rates in previously published reports. The GAP score was a useful tool for predicting mortality in the series we studied.

  5. Troponin I and cardiovascular risk prediction in the general population

    DEFF Research Database (Denmark)

    Blankenberg, Stefan; Salomaa, Veikko; Makarova, Nataliya

    2016-01-01

    Aims: Our aims were to evaluate the distribution of troponin I concentrations in population cohorts across Europe, to characterize the association with cardiovascular outcomes, to determine the predictive value beyond the variables used in the ESC SCORE, to test a potentially clinically relevant...... population-based studies including 74 738 participants. We investigated the value of adding troponin I levels to conventional risk factors for prediction of cardiovascular disease by calculating measures of discrimination (C-index) and net reclassification improvement (NRI). We further tested the clinical...

  6. Providing access to risk prediction tools via the HL7 XML-formatted risk web service.

    Science.gov (United States)

    Chipman, Jonathan; Drohan, Brian; Blackford, Amanda; Parmigiani, Giovanni; Hughes, Kevin; Bosinoff, Phil

    2013-07-01

    Cancer risk prediction tools provide valuable information to clinicians but remain computationally challenging. Many clinics find that CaGene or HughesRiskApps fit their needs for easy- and ready-to-use software to obtain cancer risks; however, these resources may not fit all clinics' needs. The HughesRiskApps Group and BayesMendel Lab therefore developed a web service, called "Risk Service", which may be integrated into any client software to quickly obtain standardized and up-to-date risk predictions for BayesMendel tools (BRCAPRO, MMRpro, PancPRO, and MelaPRO), the Tyrer-Cuzick IBIS Breast Cancer Risk Evaluation Tool, and the Colorectal Cancer Risk Assessment Tool. Software clients that can convert their local structured data into the HL7 XML-formatted family and clinical patient history (Pedigree model) may integrate with the Risk Service. The Risk Service uses Apache Tomcat and Apache Axis2 technologies to provide an all Java web service. The software client sends HL7 XML information containing anonymized family and clinical history to a Dana-Farber Cancer Institute (DFCI) server, where it is parsed, interpreted, and processed by multiple risk tools. The Risk Service then formats the results into an HL7 style message and returns the risk predictions to the originating software client. Upon consent, users may allow DFCI to maintain the data for future research. The Risk Service implementation is exemplified through HughesRiskApps. The Risk Service broadens the availability of valuable, up-to-date cancer risk tools and allows clinics and researchers to integrate risk prediction tools into their own software interface designed for their needs. Each software package can collect risk data using its own interface, and display the results using its own interface, while using a central, up-to-date risk calculator. This allows users to choose from multiple interfaces while always getting the latest risk calculations. Consenting users contribute their data for future

  7. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  8. Usefulness of a decision tree model for the analysis of adverse drug reactions: Evaluation of a risk prediction model of vancomycin-associated nephrotoxicity constructed using a data mining procedure.

    Science.gov (United States)

    Imai, Shungo; Yamada, Takehiro; Kasashi, Kumiko; Kobayashi, Masaki; Iseki, Ken

    2017-12-01

    Several publications concerning decision tree (DT) analysis in medical fields have recently demonstrated its usefulness for defining prognostic factors in various diseases. However, there are minimal reports on the predictors of adverse drug reactions. We attempted to use DT analysis to discover combinations of multiple risk factors that would increase the risk of nephrotoxicity associated with vancomycin (VCM). To demonstrate the usefulness of DT analysis, we compared its predictive performance with that of multiple logistic regression analysis. A single-centre, retrospective study was conducted at Hokkaido University Hospital. A total of 592 patients, who received intravenous administrations of VCM between November 2011 and April 2016, were enrolled. Nephrotoxicity was defined as an increase in serum creatinine of ≥0.5 mg/dL or a ≥50% increase in serum creatinine from the baseline. Risk factors for VCM nephrotoxicity were extracted from previous reports. In the DT analysis, a chi-squared automatic interaction detection algorithm was constructed. For evaluating the established algorithms, a 10-fold cross validation method was adopted to calculate the misclassification risk of the model. Moreover, to compare the accuracy of the DT analysis, multiple logistic regression analysis was conducted. Eighty-seven (14.7%) patients developed nephrotoxicity. A VCM trough concentration of ≥15.0 mg/L, concomitant medication (vasopressor drugs and furosemide), and a duration of therapy ≥14 days were extracted to build the DT model, in which the patients were divided into 6 subgroups based on variable rates of nephrotoxicity, ranging from 4.6 to 69.6%. The predictive accuracies of the DT and logistic regression models were similar (87.3%, respectively), indicating that they were accurate. This study suggests the usefulness of DT models for the evaluation of adverse drug reactions. © 2017 John Wiley & Sons, Ltd.

  9. Performance of risk assessment instruments for predicting osteoporotic fracture risk: a systematic review.

    Science.gov (United States)

    Nayak, S; Edwards, D L; Saleh, A A; Greenspan, S L

    2014-01-01

    We systematically reviewed the literature on the performance of osteoporosis absolute fracture risk assessment instruments. Relatively few studies have evaluated the calibration of instruments in populations separate from their development cohorts, and findings are mixed. Many studies had methodological limitations making susceptibility to bias a concern. The aim of this study was to systematically review the literature on the performance of osteoporosis clinical fracture risk assessment instruments for predicting absolute fracture risk, or calibration, in populations other than their derivation cohorts. We performed a systematic review, and MEDLINE, Embase, Cochrane Library, and multiple other literature sources were searched. Inclusion and exclusion criteria were applied and data extracted, including information about study participants, study design, potential sources of bias, and predicted and observed fracture probabilities. A total of 19,949 unique records were identified for review. Fourteen studies met inclusion criteria. There was substantial heterogeneity among included studies. Six studies assessed the WHO's Fracture Risk Assessment (FRAX) instrument in five separate cohorts, and a variety of risk assessment instruments were evaluated in the remainder of the studies. Approximately half found good instrument calibration, with observed fracture probabilities being close to predicted probabilities for different risk categories. Studies that assessed the calibration of FRAX found mixed performance in different populations. A similar proportion of studies that evaluated simple risk assessment instruments (≤5 variables) found good calibration when compared with studies that assessed complex instruments (>5 variables). Many studies had methodological features making them susceptible to bias. Few studies have evaluated the performance or calibration of osteoporosis fracture risk assessment instruments in populations separate from their development cohorts

  10. Cardiovascular risk prediction in chronic kidney disease patients.

    Science.gov (United States)

    Cedeño Mora, Santiago; Goicoechea, Marian; Torres, Esther; Verdalles, Úrsula; Pérez de José, Ana; Verde, Eduardo; García de Vinuesa, Soledad; Luño, José

    Scores underestimate the prediction of cardiovascular risk (CVR) as they are not validated in patients with chronic kidney disease (CKD). Two of the most commonly used scores are the Framingham Risk Score (FRS-CVD) and the ASCVD (AHA/ACC 2013). The aim of this study is to evaluate the predictive ability of experiencing a cardiovascular event (CVE) via these 2scores in the CKD population. Prospective, observational study of 400 prevalent patients with CKD (stages 4 and 5 according the KDOQI; not on dialysis). Cardiovascular risk was calculated according to the 2scores and the predictive capacity of cardiovascular events (atherosclerotic events: myocardial infarction, ischaemic and haemorrhagic stroke, peripheral vascular disease; and non-atherosclerotic events: heart failure) was analysed. Forty-nine atherosclerotic cardiovascular events occurred in 40.3±6.6 months of follow-up. Most of the patients were classified as high CVR by both scores (59% by the FRS-CVD and 75% by the ASCVD). All cardiovascular events occurred in the high CVR patients and both scores (FRS-CVD log-rank 12.2, Prenal function, albuminuria and previous cardiovascular events. The cardiovascular risk scores (FRS-CVD and ASCVD [AHA/ACC 2013]) can estimate the probability of atherosclerotic cardiovascular events in patients with CKD regardless of renal function, albuminuria and previous cardiovascular events. Copyright © 2016 Sociedad Española de Nefrología. Published by Elsevier España, S.L.U. All rights reserved.

  11. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    2016-01-01

    This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for investors. We propose a novel estimation strategy for affine term structure models that jointly fits yields and bond excess returns, thereby capturing predictive information...... otherwise hidden to standard estimations. The model predicts excess returns with high regression R2s and high forecast accuracy but cannot outperform the expectations hypothesis out-of-sample in terms of economic value, showing a general contrast between statistical and economic metrics of forecast...... evaluation. More specifically, the model mostly generates positive (negative) economic value during times of high (low) macroeconomic uncertainty. Overall, the expectations hypothesis remains a useful benchmark for investment decisions in bond markets, especially in low uncertainty states....

  12. Adult body size, sexual history and adolescent sexual development, may predict risk of developing prostate cancer: Results from the New South Wales Lifestyle and Evaluation of Risk Study (CLEAR).

    Science.gov (United States)

    Nair-Shalliker, Visalini; Yap, Sarsha; Nunez, Carlos; Egger, Sam; Rodger, Jennifer; Patel, Manish I; O'Connell, Dianne L; Sitas, Freddy; Armstrong, Bruce K; Smith, David P

    2017-02-01

    Prostate cancer (PC) is the most common non-cutaneous cancer in men worldwide. The relationships between PC and possible risk factors for PC cases (n = 1,181) and male controls (n = 875) from the New South Wales (NSW) Cancer, Lifestyle and Evaluation of Risk Study (CLEAR) were examined in this study. The associations between PC risk and paternal history of PC, body mass index (BMI), medical conditions, sexual behaviour, balding pattern and puberty, after adjusting for age, income, region of birth, place of residence, and PSA testing, were examined. Adjusted risk of PC was higher for men with a paternal history of PC (OR = 2.31; 95%CI: 1.70-3.14), personal history of prostatitis (OR = 2.30; 95%CI: 1.44-3.70), benign prostatic hyperplasia (OR = 2.29; 95%CI: 1.79-2.93), being overweight (vs. normal; OR = 1.24; 95%CI: 0.99-1.55) or obese (vs. normal; OR = 1.44; 95%CI: 1.09-1.89), having reported more than seven sexual partners in a lifetime (vs. partners; OR = 2.00; 95%CI: 1.49-2.68), and having reported more than 5 orgasms a month prior to PC diagnosis (vs. ≤3 orgasms; OR = 1.59; 95%CI: 1.18-2.15). PC risk was lower for men whose timing of puberty was later than their peers (vs. same as peers; OR = 0.75; 95%CI: 0.59-0.97), and a smaller risk reduction of was observed in men whose timing of puberty was earlier than their peers (vs. same as peers; OR = 0.85; 95%CI: 0.61-1.17). No associations were found between PC risk and vertex balding, erectile function, acne, circumcision, vasectomy, asthma or diabetes. These results support a role for adult body size, sexual activity, and adolescent sexual development in PC development. © 2016 UICC.

  13. EXCESS PRESSURE INTEGRAL PREDICTS CARDIOVASCULAR EVENTS INDEPENDENT OF OTHER RISK FACTORS IN THE CONDUIT ARTERY FUNCTIONAL EVALUATION (CAFE) SUB-STUDY OF ANGLO-SCANDINAVIAN CARDIAC OUTCOMES TRIAL (ASCOT)

    Science.gov (United States)

    Davies, Justin E; Lacy, Peter; Tillin, Therese; Collier, David; Cruickshank, J Kennedy; Francis, Darrel P; Malaweera, Anura; Mayet, Jamil; Stanton, Alice; Williams, Bryan; Parker, Kim H; McG Thom, Simon A; Hughes, Alun D

    2014-01-01

    Excess pressure integral (XSPI), a new index of surplus work performed by the left ventricle, can be calculated from blood pressure (BP) waveforms and may indicate circulatory dysfunction. We investigated whether XSPI predicted future cardiovascular (CV) events and target organ damage in treated hypertensive individuals. Radial BP waveforms were acquired by tonometry in 2069 individuals (63±8y) in the Conduit Artery Functional Evaluation sub-study of the Anglo-Scandinavian Cardiac Outcomes trial. Measurements of left ventricular mass index (LVMI; n = 862) and common carotid artery intima media thickness (cIMT; n = 923) were also performed. XSPI and the integral of reservoir pressure (PRI) were lower in people treated with amlodipine ± perindopril than atenolol ± bendroflumethiazide, although brachial systolic BP was similar. A total of 134 CV events accrued over a median 3.4 years of follow-up; XSPI was a significant predictor of CV events after adjustment for age and sex and this relationship was unaffected by adjustment for conventional CV risk factors or Framingham risk score. XSPI, central systolic BP, central augmentation pressure (AP), central pulse pressure (cPP) and PRI were correlated with LVMI, but only XSPI, AP and cPP were positively associated with cIMT. Associations between LVMI and XSPI and PRI, and cIMT and XSPI were unaffected by multivariable adjustment for other covariates. XSPI is a novel indicator of CV dysfunction and independently predicts CV events and target organ damage in a prospective clinical trial. PMID:24821941

  14. Childhood BMI Trajectories Predicting Cardiovascular Risk in Adolescence

    Science.gov (United States)

    Boyer, Brittany P.; Nelson, Jackie A.; Holub, Shayla C.

    2015-01-01

    Objective The current study compared growth parameters of girls’ and boys’ BMI trajectories from infancy to middle childhood, and evaluated these parameters as predictors of cardiovascular disease (CVD) risk in adolescence. Methods Using 657 children from the NICHD Study of Early Child Care and Youth Development (SECCYD), quadratic growth curve analyses were conducted to establish growth parameters (intercept, slope, quadratic term) for girls and boys from 15 months to age 10 ½. Parameters were compared across gender and evaluated as predictors of a CVD risk index at age 15, controlling for characteristics of the adiposity rebound (AR) including age at which it occurred and children’s BMI at the rebound. Results Boys had more extreme trajectories of growth compared to girls with higher initial BMI at 15 months (intercept), more rapid declines in BMI before the AR (slope), and sharper rebound growth in BMI after the rebound (quadratic term). For boys and girls, higher intercept, slope, and quadratic term values predicted higher CVD risk at age 15, controlling for characteristics of the AR. Conclusions Findings suggest that individuals at risk for developing CVD later in life may be identified before the AR by elevated BMI at 15 months and slow BMI declines. Due to the importance of early intervention in altering lifelong health trajectories, consistent BMI monitoring is essential in identifying high-risk children. PMID:25746172

  15. Childhood body mass index trajectories predicting cardiovascular risk in adolescence.

    Science.gov (United States)

    Boyer, Brittany P; Nelson, Jackie A; Holub, Shayla C

    2015-06-01

    The present study compared growth parameters of girls' and boys' body mass index (BMI) trajectories from infancy to middle childhood and evaluated these parameters as predictors of cardiovascular disease (CVD) risk in adolescence. Using 657 children from the NICHD Study of Early Child Care and Youth Development, quadratic growth curve analyses were conducted to establish growth parameters (intercept, slope, and quadratic term) for girls and boys from age 15 months to 10.5 years. Parameters were compared across gender and evaluated as predictors of a CVD risk index at the age of 15 years, controlling for characteristics of the adiposity rebound (AR) including age at which it occurred and children's BMI at the rebound. Boys had more extreme trajectories of growth than girls with higher initial BMI at age 15 months (intercept), more rapid declines in BMI before the AR (slope), and sharper rebound growth in BMI after the rebound (quadratic term). For boys and girls, higher intercept, slope, and quadratic term values predicted higher CVD risk at the age of 15 years, controlling for characteristics of the AR. Findings suggest that individuals at risk for developing CVD later in life may be identified before the AR by elevated BMI at 15 months and slow BMI declines. Because of the importance of early intervention in altering lifelong health trajectories, consistent BMI monitoring is essential in identifying high-risk children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  16. Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

    Science.gov (United States)

    Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

    2015-01-01

    Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

  17. Biological cluster evaluation for gene function prediction.

    Science.gov (United States)

    Klie, Sebastian; Nikoloski, Zoran; Selbig, Joachim

    2014-06-01

    Recent advances in high-throughput omics techniques render it possible to decode the function of genes by using the "guilt-by-association" principle on biologically meaningful clusters of gene expression data. However, the existing frameworks for biological evaluation of gene clusters are hindered by two bottleneck issues: (1) the choice for the number of clusters, and (2) the external measures which do not take in consideration the structure of the analyzed data and the ontology of the existing biological knowledge. Here, we address the identified bottlenecks by developing a novel framework that allows not only for biological evaluation of gene expression clusters based on existing structured knowledge, but also for prediction of putative gene functions. The proposed framework facilitates propagation of statistical significance at each of the following steps: (1) estimating the number of clusters, (2) evaluating the clusters in terms of novel external structural measures, (3) selecting an optimal clustering algorithm, and (4) predicting gene functions. The framework also includes a method for evaluation of gene clusters based on the structure of the employed ontology. Moreover, our method for obtaining a probabilistic range for the number of clusters is demonstrated valid on synthetic data and available gene expression profiles from Saccharomyces cerevisiae. Finally, we propose a network-based approach for gene function prediction which relies on the clustering of optimal score and the employed ontology. Our approach effectively predicts gene function on the Saccharomyces cerevisiae data set and is also employed to obtain putative gene functions for an Arabidopsis thaliana data set.

  18. Evaluation of disorder predictions in CASP9

    KAUST Repository

    Monastyrskyy, Bohdan

    2011-01-01

    Lack of stable three-dimensional structure, or intrinsic disorder, is a common phenomenon in proteins. Naturally, unstructured regions are proven to be essential for carrying function by many proteins, and therefore identification of such regions is an important issue. CASP has been assessing the state of the art in predicting disorder regions from amino acid sequence since 2002. Here, we present the results of the evaluation of the disorder predictions submitted to CASP9. The assessment is based on the evaluation measures and procedures used in previous CASPs. The balanced accuracy and the Matthews correlation coefficient were chosen as basic measures for evaluating the correctness of binary classifications. The area under the receiver operating characteristic curve was the measure of choice for evaluating probability-based predictions of disorder. The CASP9 methods are shown to perform slightly better than the CASP7 methods but not better than the methods in CASP8. It was also shown that capability of most CASP9 methods to predict disorder decreases with increasing minimum disorder segment length.

  19. Evaluation of Hs-CRP levels and interleukin 18 (-137G/C) promoter polymorphism in risk prediction of coronary artery disease in first degree relatives.

    Science.gov (United States)

    G, Rajesh Kumar; K, Mrudula Spurthi; G, Kishore Kumar; Kurapati, Mohanalatha; M, Saraswati; T, Mohini Aiyengar; P, Chiranjeevi; G, Srilatha Reddy; S, Nivas; P, Kaushik; K, Sanjib Sahu; H, Surekha Rani

    2015-01-01

    Coronary Artery Disease (CAD) is clearly a multifactorial disease that develops from childhood and ultimately leads to death. Several reports revealed having a First Degree Relatives (FDRS) with premature CAD is a significant autonomous risk factor for CAD development. C - reactive protein (CRP) is a member of the pentraxin family and is the most widely studied proinflammatory biomarker. IL-18 is a pleiotrophic and proinflammatory cytokine which is produced mainly by macrophages and plays an important role in the inflammatory cascade. Hs-CRP levels were estimated by ELISA and Genotyping of IL-18 gene variant located on promoter -137 (G/C) by Allele specific PCR in blood samples of 300 CAD patients and 300 controls and 100 FDRS. Promoter Binding sites and Protein interacting partners were identified by Alibaba 2.1 and Genemania online tools respectively. Hs-CRP levels were significantly high in CAD patients followed by FDRS when compared to controls. In IL-18 -137 (G/C) polymorphism homozygous GG is significantly associated with occurrence of CAD and Hs-CRP levels were significantly higher in GG genotype subjects when compared to GC and CC. IL-18 was found to be interacting with 100 protein interactants. Our results indicate that Hs-CRP levels and IL-18-137(G/C) polymorphism may help to identify risk of future events of CAD in asymptomatic healthy FDRS.

  20. Evaluation of Hs-CRP levels and interleukin 18 (-137G/C promoter polymorphism in risk prediction of coronary artery disease in first degree relatives.

    Directory of Open Access Journals (Sweden)

    Rajesh Kumar G

    Full Text Available Coronary Artery Disease (CAD is clearly a multifactorial disease that develops from childhood and ultimately leads to death. Several reports revealed having a First Degree Relatives (FDRS with premature CAD is a significant autonomous risk factor for CAD development. C - reactive protein (CRP is a member of the pentraxin family and is the most widely studied proinflammatory biomarker. IL-18 is a pleiotrophic and proinflammatory cytokine which is produced mainly by macrophages and plays an important role in the inflammatory cascade.Hs-CRP levels were estimated by ELISA and Genotyping of IL-18 gene variant located on promoter -137 (G/C by Allele specific PCR in blood samples of 300 CAD patients and 300 controls and 100 FDRS. Promoter Binding sites and Protein interacting partners were identified by Alibaba 2.1 and Genemania online tools respectively. Hs-CRP levels were significantly high in CAD patients followed by FDRS when compared to controls. In IL-18 -137 (G/C polymorphism homozygous GG is significantly associated with occurrence of CAD and Hs-CRP levels were significantly higher in GG genotype subjects when compared to GC and CC. IL-18 was found to be interacting with 100 protein interactants.Our results indicate that Hs-CRP levels and IL-18-137(G/C polymorphism may help to identify risk of future events of CAD in asymptomatic healthy FDRS.

  1. Implicit self-evaluations predict changes in implicit partner evaluations.

    Science.gov (United States)

    McNulty, James K; Baker, Levi R; Olson, Michael A

    2014-08-01

    Do people who feel good about themselves have better relations with others? Although the notion that they do is central to both classic and modern theories, there is little strong evidence to support it. We argue that one reason for the lack of evidence is that prior research has relied exclusively on explicit measures of self- and relationship evaluation. The current longitudinal study of newlywed couples used implicit measures of self- and partner evaluation, as well as explicit measures of self-, relationship, and partner evaluation, to examine the link between self-evaluations and changes in relationship evaluations over the first 3 years of marriage. Whereas explicit self-evaluations were unrelated to changes in all interpersonal measures, implicit self-evaluations positively predicted changes in implicit partner evaluations. This finding adds to previous research by highlighting the importance of automatic processes and implicit measures in the study of close interpersonal relationships. © The Author(s) 2014.

  2. Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

    Science.gov (United States)

    Coiera, Enrico; Wang, Ying; Magrabi, Farah; Concha, Oscar Perez; Gallego, Blanca; Runciman, William

    2014-05-21

    Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to

  3. Elderly fall risk prediction using static posturography.

    Directory of Open Access Journals (Sweden)

    Jennifer Howcroft

    Full Text Available Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP and medial-lateral (ML center of pressure (CoP motion; AP and ML CoP root mean square distance from mean (RMS; and AP, ML, and vector sum magnitude (VSM CoP velocity were calculated. Romberg Quotients (RQ were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24, prospective all fallers (42, prospective fallers (22 single, 6 multiple, and prospective non-fallers (47. Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for

  4. Utility and applicability of the "Childhood Obesity Risk Evaluation" (CORE)-index in predicting obesity in childhood and adolescence in Greece from early life: the "National Action Plan for Public Health".

    Science.gov (United States)

    Manios, Yannis; Vlachopapadopoulou, Elpis; Moschonis, George; Karachaliou, Feneli; Psaltopoulou, Theodora; Koutsouki, Dimitra; Bogdanis, Gregory; Carayanni, Vilelmine; Hatzakis, Angelos; Michalacos, Stefanos

    2016-12-01

    Early identification of infants being at high risk to become obese at their later childhood or adolescence can be of vital importance in any obesity prevention initiative. The aim of the present study was to examine the utility and applicability of the "Childhood Obesity Risk Evaluation (CORE)" index as a screening tool for the early prediction of obesity in childhood and adolescence. Anthropometric, socio-demographic data were collected cross-sectionally and retrospectively from a representative sample of 5946 children, and adolescents and were combined for calculating the CORE-index score. Logistic regression analyses were performed to examine the associations of the CORE-index score with obesity by gender and age group, and cut-off point analysis was also applied to identify the optimal value of the CORE-index score that differentiates obese from non-obese children. Mean CORE-index score in the total sample was 3.06 (sd 1.92) units (range 0-11 units). Each unit increase in the CORE-index score was found to be associated with a 30 % (95 % C.I. 1.24-1.36) increased likelihood for obesity in childhood or adolescence, while the optimal cut-off value of the CORE-index score that predicted obesity with the highest possible sensitivity and specificity was found to be 3.5. The present study supports the utility and applicability of the CORE-index as a screening tool for the early identification of infants that are potentially at a higher risk for becoming obese at their childhood and adolescence. This tool could be routinely used by health professionals to identify infants at high risk and provide appropriate counselling to their parents and caregivers so as to maximize the effectiveness of early obesity prevention initiatives. What is known? • Childhood obesity has reached epidemic proportions worldwide. • Certain perinatal and socio-demographic indices that were previously identified as correlates of childhood obesity in children were combined to develop the

  5. Environmental risk: perception and target with local versus global evaluation.

    Science.gov (United States)

    Fleury-Bahi, Ghozlane

    2008-02-01

    This research addressed environmental risk perception depending on the target evaluated and on the category of hazard (technological and chemical hazards, climate change, loss of biodiversity). Correlations between environmental risk assessment and pro-environmental behavioural intentions were also tested. In a sample of 113 French adults, 15 different environmental risks were evaluated for four different risk targets (oneself, the inhabitants of the town, the inhabitants of the country, and humanity). As expected, environmental hazards were perceived as a greater risk for larger areas. Moreover, risks difficult to conceptualise, which contain both high uncertainty and long-term consequences (climate change, loss of biodiversity) are perceived as less risk to oneself and to the inhabitants of the town and the country of residence than more concrete and immediate risks (technological and chemical). Only the technological and chemical hazards significantly predict pro-environmental behavioural intentions.

  6. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  7. Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium.

    Science.gov (United States)

    Fischer, Christine; Kuchenbäcker, Karoline; Engel, Christoph; Zachariae, Silke; Rhiem, Kerstin; Meindl, Alfons; Rahner, Nils; Dikow, Nicola; Plendl, Hansjörg; Debatin, Irmgard; Grimm, Tiemo; Gadzicki, Dorothea; Flöttmann, Ricarda; Horvath, Judit; Schröck, Evelin; Stock, Friedrich; Schäfer, Dieter; Schwaab, Ira; Kartsonaki, Christiana; Mavaddat, Nasim; Schlegelberger, Brigitte; Antoniou, Antonis C; Schmutzler, Rita

    2013-06-01

    Risk prediction models are widely used in clinical genetic counselling. Despite their frequent use, the genetic risk models BOADICEA, BRCAPRO, IBIS and extended Claus model (eCLAUS), used to estimate BRCA1/2 mutation carrier probabilities, have never been comparatively evaluated in a large sample from central Europe. Additionally, a novel version of BOADICEA that incorporates tumour pathology information has not yet been validated. Using data from 7352 German families we estimated BRCA1/2 carrier probabilities under each model and compared their discrimination and calibration. The incremental value of using pathology information in BOADICEA was assessed in a subsample of 4928 pedigrees with available data on breast tumour molecular markers oestrogen receptor, progesterone receptor and human epidermal growth factor 2. BRCAPRO (area under receiver operating characteristic curve (AUC)=0.80 (95% CI 0.78 to 0.81)) and BOADICEA (AUC=0.79 (0.78-0.80)), had significantly higher diagnostic accuracy than IBIS and eCLAUS (p<0.001). The AUC increased when pathology information was used in BOADICEA: AUC=0.81 (95% CI 0.80 to 0.83, p<0.001). At carrier thresholds of 10% and 15%, the net reclassification index was +3.9% and +5.4%, respectively, when pathology was included in the model. Overall, calibration was best for BOADICEA and worst for eCLAUS. With eCLAUS, twice as many mutation carriers were predicted than observed. Our results support the use of BRCAPRO and BOADICEA for decision making regarding genetic testing for BRCA1/2 mutations. However, model calibration has to be improved for this population. eCLAUS should not be used for estimating mutation carrier probabilities in clinical settings. Whenever possible, breast tumour molecular marker information should be taken into account.

  8. Evaluating Grayware Characteristics and Risks

    Directory of Open Access Journals (Sweden)

    Zhongqiang Chen

    2011-01-01

    Full Text Available Grayware encyclopedias collect known species to provide information for incident analysis, however, the lack of categorization and generalization capability renders them ineffective in the development of defense strategies against clustered strains. A grayware categorization framework is therefore proposed here to not only classify grayware according to diverse taxonomic features but also facilitate evaluations on grayware risk to cyberspace. Armed with Support Vector Machines, the framework builds learning models based on training data extracted automatically from grayware encyclopedias and visualizes categorization results with Self-Organizing Maps. The features used in learning models are selected with information gain and the high dimensionality of feature space is reduced by word stemming and stopword removal process. The grayware categorizations on diversified features reveal that grayware typically attempts to improve its penetration rate by resorting to multiple installation mechanisms and reduced code footprints. The framework also shows that grayware evades detection by attacking victims' security applications and resists being removed by enhancing its clotting capability with infected hosts. Our analysis further points out that species in categories Spyware and Adware continue to dominate the grayware landscape and impose extremely critical threats to the Internet ecosystem.

  9. Predictive risk factors for persistent postherniotomy pain

    DEFF Research Database (Denmark)

    Aasvang, Eske K; Gmaehle, Eliza; Hansen, Jeanette B

    2010-01-01

    BACKGROUND: Persistent postherniotomy pain (PPP) affects everyday activities in 5-10% of patients. Identification of predisposing factors may help to identify the risk groups and guide anesthetic or surgical procedures in reducing risk for PPP. METHODS: A prospective study was conducted in 464 pa...... to a standardized heat stimulus may preferably be treated using an operative technique with lowest risk for nerve damage.......BACKGROUND: Persistent postherniotomy pain (PPP) affects everyday activities in 5-10% of patients. Identification of predisposing factors may help to identify the risk groups and guide anesthetic or surgical procedures in reducing risk for PPP. METHODS: A prospective study was conducted in 464...... patients undergoing open or laparoscopic transabdominal preperitoneal elective groin hernia repair. Primary outcome was identification of risk factors for substantial pain-related functional impairment at 6 months postoperatively assessed by the validated Activity Assessment Scale (AAS). Data on potential...

  10. Judging risk behaviour and risk preference: the role of the evaluative connotation of risk terms.

    NARCIS (Netherlands)

    van Schie, E.C.M.; van der Pligt, J.; van Baaren, K.

    1993-01-01

    Two experiments investigated the impact of the evaluative connotation of risk terms on the judgment of risk behavior and on risk preference. Exp 1 focused on the evaluation congruence of the risk terms with a general risk norm and with Ss' individual risk preference, and its effects on the extremity

  11. Poor predictive ability of the risk chart SCORE in a Danish population

    DEFF Research Database (Denmark)

    Saidj, Madina; Jørgensen, Torben; Prescott, Eva

    2013-01-01

    In Denmark, the European risk chart Systematic COronary Risk Evaluation (SCORE) from the European Society of Cardiology is recommended for use in cardiovascular prevention. Nevertheless, its predictive ability in a Danish population has never been investigated. The purpose of this study was there......In Denmark, the European risk chart Systematic COronary Risk Evaluation (SCORE) from the European Society of Cardiology is recommended for use in cardiovascular prevention. Nevertheless, its predictive ability in a Danish population has never been investigated. The purpose of this study...... was therefore to assess the predictive ability of the SCORE risk chart with regard to fatal cardiovascular risk according to the socio-demographic factors of age, sex, income and education in a Danish population....

  12. Global Variance Risk Premium and Forex Return Predictability

    OpenAIRE

    Aloosh, Arash

    2014-01-01

    In a long-run risk model with stochastic volatility and frictionless markets, I express expected forex returns as a function of consumption growth variances and stock variance risk premiums (VRPs)—the difference between the risk-neutral and statistical expectations of market return variation. This provides a motivation for using the forward-looking information available in stock market volatility indices to predict forex returns. Empirically, I find that stock VRPs predict forex returns at a ...

  13. Genetic Risk Prediction of Atrial Fibrillation

    NARCIS (Netherlands)

    Lubitz, Steven A; Yin, Xiaoyan; Lin, Henry; Kolek, Matthew; Smith, J Gustav; Trompet, Stella; Rienstra, Michiel; Rost, Natalia S; Teixeira, Pedro; Almgren, Peter; Anderson, Christopher D; Chen, Lin Y; Engström, Gunnar; Ford, Ian; Furie, Karen L; Guo, Xiuqing; Larson, Martin G; Lunetta, Kathryn; Macfarlane, Peter W; Psaty, Bruce M; Soliman, Elsayed Z; Sotoodehnia, Nona; Stott, David J; Taylor, Kent D; Weng, Lu-Chen; Yao, Jie; Geelhoed, Bastiaan; Verweij, Niek; Siland, Joylene E; Kathiresan, Sekar; Roselli, Carolina; Roden, Dan M; van der Harst, Pim; Darbar, Dawood; Jukema, J Wouter; Melander, Olle; Rosand, Jonathan; Rotter, Jerome I; Heckbert, Susan R; Ellinor, Patrick T; Alonso, Alvaro; Benjamin, Emelia J

    2017-01-01

    BACKGROUND: Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. METHODS: To determine whether genetic data can stratify risk for development of AF, we examined associations between AF

  14. Predicting risk of cancer during HIV infection

    DEFF Research Database (Denmark)

    Borges, Álvaro H; Silverberg, Michael J; Wentworth, Deborah

    2013-01-01

    To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection.......To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection....

  15. Alternative Testing Methods for Predicting Health Risk from Environmental Exposures

    Directory of Open Access Journals (Sweden)

    Annamaria Colacci

    2014-08-01

    Full Text Available Alternative methods to animal testing are considered as promising tools to support the prediction of toxicological risks from environmental exposure. Among the alternative testing methods, the cell transformation assay (CTA appears to be one of the most appropriate approaches to predict the carcinogenic properties of single chemicals, complex mixtures and environmental pollutants. The BALB/c 3T3 CTA shows a good degree of concordance with the in vivo rodent carcinogenesis tests. Whole-genome transcriptomic profiling is performed to identify genes that are transcriptionally regulated by different kinds of exposures. Its use in cell models representative of target organs may help in understanding the mode of action and predicting the risk for human health. Aiming at associating the environmental exposure to health-adverse outcomes, we used an integrated approach including the 3T3 CTA and transcriptomics on target cells, in order to evaluate the effects of airborne particulate matter (PM on toxicological complex endpoints. Organic extracts obtained from PM2.5 and PM1 samples were evaluated in the 3T3 CTA in order to identify effects possibly associated with different aerodynamic diameters or airborne chemical components. The effects of the PM2.5 extracts on human health were assessed by using whole-genome 44 K oligo-microarray slides. Statistical analysis by GeneSpring GX identified genes whose expression was modulated in response to the cell treatment. Then, modulated genes were associated with pathways, biological processes and diseases through an extensive biological analysis. Data derived from in vitro methods and omics techniques could be valuable for monitoring the exposure to toxicants, understanding the modes of action via exposure-associated gene expression patterns and to highlight the role of genes in key events related to adversity.

  16. Prostate Specific Antigen (PSA as Predicting Marker for Clinical Outcome and Evaluation of Early Toxicity Rate after High-Dose Rate Brachytherapy (HDR-BT in Combination with Additional External Beam Radiation Therapy (EBRT for High Risk Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Thorsten H. Ecke

    2016-11-01

    Full Text Available High-dose-rate brachytherapy (HDR-BT with external beam radiation therapy (EBRT is a common treatment option for locally advanced prostate cancer (PCa. Seventy-nine male patients (median age 71 years, range 50 to 79 with high-risk PCa underwent HDR-BT following EBRT between December 2009 and January 2016 with a median follow-up of 21 months. HDR-BT was administered in two treatment sessions (one week interval with 9 Gy per fraction using a planning system and the Ir192 treatment unit GammaMed Plus iX. EBRT was performed with CT-based 3D-conformal treatment planning with a total dose administration of 50.4 Gy with 1.8 Gy per fraction and five fractions per week. Follow-up for all patients was organized one, three, and five years after radiation therapy to evaluate early and late toxicity side effects, metastases, local recurrence, and prostate-specific antigen (PSA value measured in ng/mL. The evaluated data included age, PSA at time of diagnosis, PSA density, BMI (body mass index, Gleason score, D’Amico risk classification for PCa, digital rectal examination (DRE, PSA value after one/three/five year(s follow-up (FU, time of follow-up, TNM classification, prostate volume, and early toxicity rates. Early toxicity rates were 8.86% for gastrointestinal, and 6.33% for genitourinary side effects. Of all treated patients, 84.81% had no side effects. All reported complications in early toxicity were grade 1. PSA density at time of diagnosis (p = 0.009, PSA on date of first HDR-BT (p = 0.033, and PSA on date of first follow-up after one year (p = 0.025 have statistical significance on a higher risk to get a local recurrence during follow-up. HDR-BT in combination with additional EBRT in the presented design for high-risk PCa results in high biochemical control rates with minimal side-effects. PSA is a negative predictive biomarker for local recurrence during follow-up. A longer follow-up is needed to assess long-term outcome and toxicities.

  17. Prostate Specific Antigen (PSA) as Predicting Marker for Clinical Outcome and Evaluation of Early Toxicity Rate after High-Dose Rate Brachytherapy (HDR-BT) in Combination with Additional External Beam Radiation Therapy (EBRT) for High Risk Prostate Cancer.

    Science.gov (United States)

    Ecke, Thorsten H; Huang-Tiel, Hui-Juan; Golka, Klaus; Selinski, Silvia; Geis, Berit Christine; Koswig, Stephan; Bathe, Katrin; Hallmann, Steffen; Gerullis, Holger

    2016-11-10

    High-dose-rate brachytherapy (HDR-BT) with external beam radiation therapy (EBRT) is a common treatment option for locally advanced prostate cancer (PCa). Seventy-nine male patients (median age 71 years, range 50 to 79) with high-risk PCa underwent HDR-BT following EBRT between December 2009 and January 2016 with a median follow-up of 21 months. HDR-BT was administered in two treatment sessions (one week interval) with 9 Gy per fraction using a planning system and the Ir192 treatment unit GammaMed Plus iX. EBRT was performed with CT-based 3D-conformal treatment planning with a total dose administration of 50.4 Gy with 1.8 Gy per fraction and five fractions per week. Follow-up for all patients was organized one, three, and five years after radiation therapy to evaluate early and late toxicity side effects, metastases, local recurrence, and prostate-specific antigen (PSA) value measured in ng/mL. The evaluated data included age, PSA at time of diagnosis, PSA density, BMI (body mass index), Gleason score, D'Amico risk classification for PCa, digital rectal examination (DRE), PSA value after one/three/five year(s) follow-up (FU), time of follow-up, TNM classification, prostate volume, and early toxicity rates. Early toxicity rates were 8.86% for gastrointestinal, and 6.33% for genitourinary side effects. Of all treated patients, 84.81% had no side effects. All reported complications in early toxicity were grade 1. PSA density at time of diagnosis (p = 0.009), PSA on date of first HDR-BT (p = 0.033), and PSA on date of first follow-up after one year (p = 0.025) have statistical significance on a higher risk to get a local recurrence during follow-up. HDR-BT in combination with additional EBRT in the presented design for high-risk PCa results in high biochemical control rates with minimal side-effects. PSA is a negative predictive biomarker for local recurrence during follow-up. A longer follow-up is needed to assess long-term outcome and toxicities.

  18. Tryptophan Predicts the Risk for Future Type 2 Diabetes.

    Science.gov (United States)

    Chen, Tianlu; Zheng, Xiaojiao; Ma, Xiaojing; Bao, Yuqian; Ni, Yan; Hu, Cheng; Rajani, Cynthia; Huang, Fengjie; Zhao, Aihua; Jia, Weiping; Jia, Wei

    2016-01-01

    Recently, 5 amino acids were identified and verified as important metabolites highly associated with type 2 diabetes (T2D) development. This report aims to assess the association of tryptophan with the development of T2D and to evaluate its performance with existing amino acid markers. A total of 213 participants selected from a ten-year longitudinal Shanghai Diabetes Study (SHDS) were examined in two ways: 1) 51 subjects who developed diabetes and 162 individuals who remained metabolically healthy in 10 years; 2) the same 51 future diabetes and 23 strictly matched ones selected from the 162 healthy individuals. Baseline fasting serum tryptophan concentrations were quantitatively measured using ultra-performance liquid chromatography triple quadruple mass spectrometry. First, serum tryptophan level was found significantly higher in future T2D and was positively and independently associated with diabetes onset risk. Patients with higher tryptophan level tended to present higher degree of insulin resistance and secretion, triglyceride and blood pressure. Second, the prediction potential of tryptophan is non-inferior to the 5 existing amino acids. The predictive performance of the combined score improved after taking tryptophan into account. Our findings unveiled the potential of tryptophan as a new marker associated with diabetes risk in Chinese populations. The addition of tryptophan provided complementary value to the existing amino acid predictors.

  19. Measuring fall risk and predicting who will fall: clinimetric properties of four fall risk assessment tools for residential aged care.

    Science.gov (United States)

    Barker, Anna L; Nitz, Jennifer C; Low Choy, Nancy L; Haines, Terry

    2009-08-01

    The purpose of this prospective cohort study was to describe the clinimetric evaluation of four fall risk assessment tools (FRATs) recommended in best practice guidelines for use in residential aged care (RAC). Eighty-seven residents, mean age 81.59 years (SD +/-10.69), participated. The Falls Assessment Risk and Management Tool (FARAM), Peninsula Health Fall Risk Assessment Tool (PHFRAT), Queensland Fall Risk Assessment Tool (QFRAT), and Melbourne Fall Risk Assessment Tool (MFRAT) were completed at baseline, and 2 and 4 months, and falls occurring in the 6 months after the baseline assessment were recorded. Interrater agreement (kappa), predictive accuracy (survival analysis and Youden Index), and fit to the Rasch model were examined. Twelve-month fall history formed the predictive accuracy reference. Interrater risk classification agreement was high for the PHFRAT (small ka, Cyrillic = .84) and FARAM (small ka, Cyrillic = .81), and low for the QFRAT (small ka, Cyrillic = .51) and MFRAT (small ka, Cyrillic = .21). Survival analysis identified that 43%-66% of risk factors on each tool had no (p > .10) association with falls. No tool had higher predictive accuracy (Youden index) than the question, "has the resident fallen in past 12 months?" (p > .05). All tools did not exhibit fit to the Rasch model, invalidating summing of risk factor scores to provide an overall risk score. The studied tools have poor clinimetric properties, casting doubt about their usefulness for identifying fall risk factors for those most at risk for falling and measuring fall risk in RAC.

  20. Predicting Autism in High-Risk Infants

    Science.gov (United States)

    ... main content NIH MedlinePlus the Magazine NIH MedlinePlus Salud Download the Current Issue PDF [2.68 mb] ... developing the disorder,” said Joshua Gordon, M.D., Ph.D., NIMH director. One study analysis predicted each ...

  1. LSST Painting Risk Evaluation Memo

    Energy Technology Data Exchange (ETDEWEB)

    Wolfe, Justin E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2016-11-10

    The optics subsystem is required to paint the edges of optics black where possible. Due to the risks in applying the paint LSST requests a review of the impact of removing this requirement for the filters and L3.

  2. Predicting risk and the emergence of schizophrenia.

    LENUS (Irish Health Repository)

    Clarke, Mary C

    2012-09-01

    This article gives an overview of genetic and environmental risk factors for schizophrenia. The presence of certain molecular, biological, and psychosocial factors at certain points in the life span, has been linked to later development of schizophrenia. All need to be considered in the context of schizophrenia as a lifelong brain disorder. Research interest in schizophrenia is shifting to late childhood\\/early adolescence for screening and preventative measures. This article discusses those environmental risk factors for schizophrenia for which there is the largest evidence base.

  3. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  4. Evaluation of forest management systems under risk of wildfire

    Science.gov (United States)

    Kari Hyytiainen; Robert G. Haight

    2010-01-01

    We evaluate the economic efficiency of even- and uneven-aged management systems under risk of wildfire. The management problems are formulated for a mixed-conifer stand and approximations of the optimal solutions are obtained using simulation optimization. The Northern Idaho variant of the Forest Vegetation Simulator and its Fire and Fuels Extension is used to predict...

  5. Evaluating Mediterranean Soil Contamination Risks in Selected Hydrological Scenarios.

    NARCIS (Netherlands)

    Rosa, de la D.; Crompvoets, J.

    1997-01-01

    This paper reports an attempt of predicting the contamination risk of soils and water as they respond to hydrological changes in the agricultural lands of Sevilla province, Spain. Based on land evaluation methodologies, a semi-empirical model (named Pantanal, as module of the integrated package

  6. Predicting risk of venous thromboembolism in hospitalized cancer patients: Utility of a risk assessment tool.

    Science.gov (United States)

    Patell, Rushad; Rybicki, Lisa; McCrae, Keith R; Khorana, Alok A

    2017-06-01

    Inpatient venous thromboembolism (VTE) is a priority preventable illness; risk in cancer varies and prophylaxis is inconsistently used. A previously validated tool (Khorana Score, [KS]) identifies VTE risk in cancer outpatients with 5 easily available variables but has not been studied in the inpatient setting. We evaluated the validity of KS in predicting VTE risk in hospitalized cancer patients. We conducted a retrospective cohort study of consecutive oncology inpatients at the Cleveland Clinic from 11/2012 to 12/2014 (n = 3531). Patients were excluded for VTE on admission (n = 304), incomplete KS data (n = 439) or other reasons (n = 8). Data collected included demographics, cancer type, length of stay (LOS), anticoagulant use, and laboratory values. Multivariate risk factors were identified with stepwise logistic regression, confirmed with bootstrap analysis. Of 2780 patients included, 106 (3.8%) developed VTE during hospitalization. Median age was 62 (range, 19-98) years and 56% were male. Median LOS was 5 (range, 0-152) days. High risk KS (≥ 3) was significantly associated with VTE in uni- and multivariate analyses (OR 2.5, 95% [confidence interval] CI 1.3-4.9). Other significant variables included male gender (OR 1.67, 1.1-2.53), older age (OR 0.86, 0.75-0.99) and use of anticoagulants (OR 0.57, 0.39-0.85). Recursive partitioning analysis suggested optimal cut point for KS is 2 (OR 1.82, 1.23-2.69). This is the first report validating KS as a risk tool to predict VTE in hospitalized cancer patients. Using this tool could lead to more consistent and successful application of inpatient thromboprophylaxis. © 2017 Wiley Periodicals, Inc.

  7. Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

    Science.gov (United States)

    van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

    2017-11-01

    Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Predicting tick presence by environmental risk mapping

    Directory of Open Access Journals (Sweden)

    Arno eSwart

    2014-11-01

    Full Text Available Public health statistics recorded an increasing trend in the incidence of tick bites and erythema migrans in the Netherlands. We investigated whether the disease incidence could be predicted by a spatially explicit categorization model, based on environmental factors and a training set of tick absence-presence data. Presence and absence of Ixodes ricinus were determined by the blanket-dragging method at numerous sites spread over the Netherlands. The probability of tick presence on a 1 km by 1 km square grid was estimated from the field data using a satellite-based methodology. Expert elicitation was conducted to provide a Bayesian prior per landscape type. We applied a linear model to test a correlation between incidence of erythema migrans consultations by general practitioners in the Netherlands and the estimated probability of tick presence. Ticks were present at 252 distinct sampling coordinates and absent at 425. Tick presence was estimated for 54% percent of the total land cover. Our model has predictive power for tick presence in the Netherlands, tick bite incidence per municipality correlated significantly with the average probability of tick presence per grid. The estimated intercept of the linear model was positive and significant. This indicates that a significant fraction of the tick bite consultations could be attributed to the Ixodes ricinus population outside the resident municipality.

  9. Risk prediction models for incident primary cutaneous melanoma: a systematic review.

    Science.gov (United States)

    Vuong, Kylie; McGeechan, Kevin; Armstrong, Bruce K; Cust, Anne E

    2014-04-01

    Currently, there is no comprehensive assessment of melanoma risk prediction models. To systematically review published studies reporting multivariable risk prediction models for incident primary cutaneous melanoma for adults. EMBASE, MEDLINE, PREMEDLINE, and Cochrane databases were searched to April 30, 2013. Eligible studies were hand searched and citation tracked. Two independent reviewers extracted information. Nineteen studies reporting 28 melanoma prediction models were included. The number of predictors in the final models ranged from 2 to 13; the most common were nevi, skin type, freckle density, age, hair color, and sunburn history. There was limited reporting and substantial variation among the studies in model development and performance. Discrimination (the ability of the model to differentiate between patients with and without melanoma) was reported in 9 studies and ranged from fair to very good (area under the receiver operating characteristic curve, 0.62-0.86). Few studies assessed internal or external validity of the models or their use in clinical and public health practice. Of the published melanoma risk prediction models, the risk prediction tool developed by Fears and colleagues, which was designed for the US population, appears to be the most clinically useful and may also assist in identifying high-risk groups for melanoma prevention strategies. Few melanoma risk prediction models have been comprehensively developed and assessed. More external validation and prospective evaluation will help translate melanoma risk prediction models into useful tools for clinical and public health practice.

  10. Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences

    OpenAIRE

    Kastens, Terry L.; Featherstone, Allen M.

    1996-01-01

    An out-of-sample prediction of Kansas farmers' responses to five surveyed questions involving risk is used to compare ordered multinomial logistic regression models with feedforward backpropagation neural network models. Although the logistic models often predict more accurately than the neural network models in a mean-squared error sense, the neural network models are shown to be more accommodating of loss functions associated with a desire to predict certain combinations of categorical resp...

  11. Integrative genetic risk prediction using nonparametric empirical Bayes classification

    OpenAIRE

    Zhao, Sihai Dave

    2016-01-01

    Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorl...

  12. Comparative analyses of genetic risk prediction methods reveal ...

    Indian Academy of Sciences (India)

    2015-03-12

    Mar 12, 2015 ... ethnic, linguistic and geographic diversity of India. In the present study we aimed to find out whether the. Indian populations differ in risk allele frequencies (RAFs) at. NAFLD-associated candidate SNPs, and also to predict the genetic risk score of NAFLD in different Indian populations, as well as to compare ...

  13. Insider Risk Evaluation and Audit

    Science.gov (United States)

    2009-08-01

    exerting stress on the organization that may translate into increased insider risk (See Table A-1 in Appendix A). Differences in ethical assumptions...organizational events (i.e., layoffs , mergers, pay reductions, outsourcing, and technological changes) can cause increases in employee stress and...They saw no ethical or business conflict in this activity, popularly referred to as the “third shift” since this above-quota production typically

  14. Predicting Mild Traumatic Brain Injury with Injury Risk Functions

    OpenAIRE

    Young, Tyler

    2013-01-01

    To assess the safety of various products, equipment, and vehicles during traumatic events injury risk curves have been developed correlate measurable parameters with risk of injury. The first risk curves to predict head injuries focused on severe head injuries such as skull fractures. These curves were generated by impacting cadaver heads. To understand the biomechanics of mild traumatic brain injuries, cadaver heads have also been used to monitor pressure and strain in the brain during impac...

  15. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk)

    DEFF Research Database (Denmark)

    Hajifathalian, Kaveh; Ueda, Peter; Lu, Yuan

    2015-01-01

    BACKGROUND: Treatment of cardiovascular risk factors based on disease risk depends on valid risk prediction equations. We aimed to develop, and apply in example countries, a risk prediction equation for cardiovascular disease (consisting here of coronary heart disease and stroke) that can...... be recalibrated and updated for application in different countries with routinely available information. METHODS: We used data from eight prospective cohort studies to estimate coefficients of the risk equation with proportional hazard regressions. The risk prediction equation included smoking, blood pressure......, diabetes, and total cholesterol, and allowed the effects of sex and age on cardiovascular disease to vary between cohorts or countries. We developed risk equations for fatal cardiovascular disease and for fatal plus non-fatal cardiovascular disease. We validated the risk equations internally and also using...

  16. Predicting the risk of bone metastasis in prostate cancer.

    Science.gov (United States)

    Briganti, Alberto; Suardi, Nazareno; Gallina, Andrea; Abdollah, Firas; Novara, Giacomo; Ficarra, Vincenzo; Montorsi, Francesco

    2014-02-01

    The ability to identify prostate cancer patients at 'high risk' for bone metastasis development could allow early selection of those most likely to benefit from interventions to prevent or delay bone metastasis. This review is aimed to identify potential predictors of risk for bone metastasis in newly diagnosed patients and in those who have already received treatment. At diagnosis, established predictors of prostate cancer aggressiveness (e.g. PSA level, clinical stage, Gleason score) can identify patients at risk for bone metastasis. Following treatment of the disease, increasing evidence suggests that absolute PSA levels and other measures of PSA kinetics are useful to aid prediction of bone metastasis risk in patients both with and without a history of ADT. However, which PSA parameter most accurately predicts risk and the cut-off values that should be employed are unclear. Inclusion of PSA parameters to identify a high risk population may be beneficial in whom bone-modifying treatments are being considered. Other novel (but unvalidated) biomarkers that potentially predict the development of bone metastases have been identified, although it is unclear whether they will have value as independent markers or when combined with other parameters (e.g. measures of PSA kinetics). Further prospective studies of PSA kinetics and other predictive markers are, therefore, required to define the optimal criteria for identifying patients at high risk of bone metastases and those who are most likely to benefit from intensive monitoring and therapeutic intervention. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Predicting Risk-Mitigating Behaviors From Indecisiveness and Trait Anxiety

    DEFF Research Database (Denmark)

    Mcneill, Ilona M.; Dunlop, Patrick D.; Skinner, Timothy C.

    2016-01-01

    Past research suggests that indecisiveness and trait anxiety may both decrease the likelihood of performing risk-mitigating preparatory behaviors (e.g., preparing for natural hazards) and suggests two cognitive processes (perceived control and worrying) as potential mediators. However, no single...... control over wildfire-related outcomes. Trait anxiety did not uniquely predict preparedness or perceived control, but it did uniquely predict worry, with higher trait anxiety predicting more worrying. Also, worry trended toward uniquely predicting preparedness, albeit in an unpredicted positive direction...

  18. Ethical and affective evaluation of environmental risks

    Energy Technology Data Exchange (ETDEWEB)

    Bohm, G.; Pfister, H.R. [Bremen Univ. (Germany)

    1998-07-01

    Full text of publication follows: the present paper will be concerned with environmental risk perception, with special emphasis on those environmental risks that pertain to global change phenomena, such as climate change and ozone depletion. Two determinants of risk judgments are investigated that seem particularly relevant to environmental risks: ethical and affective evaluations. It is assumed that the focus of risk evaluation can be on one of two aspects: (a) on an evaluation of potential losses, or (b) on ethical considerations. We assume that both, potential loss and violation of ethical principles elicit emotional evaluations, but that these two judgmental aspects are associated with different specific emotions. Following cognitive emotion theories, we distinguish loss-based emotions, such as worry and fear, from ethical emotions, e.g., guilt and anger. A study is presented that investigates the role of ethical and affective evaluations in risk judgments. Various environmental risks were presented to subjects, e.g., air pollution, ozone depletion, climate change and destruction of ecological balance. For each environmental risk, subjects indicated in free-response format as well as on rating scales the extent to which ethical principles were violated, and the intensity of both loss-based and ethical emotions. The correlational structure of the emotion ratings confirms the distinction between loss-based and ethical emotions. Risk judgments co-vary with the strength of ethical evaluation and with the intensity of loss-based emotions, but are independent of ethical emotions. The implications of these findings for the risk appraisal process are discussed. (authors)

  19. Approved Risk Evaluation and Mitigation Strategies

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Food and Drug Administration Amendments Act of 2007 gave FDA the authority to require a Risk Evaluation and Mitigation Strategy (REMS) from manufacturers to...

  20. Evaluation of edge detectors using average risk

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan; van der Heijden, Ferdinand

    1992-01-01

    A new method for evaluation of edge detectors, based on the average risk of a decision, is discussed. The average risk is a performance measure well-known in Bayesian decision theory. Since edge detection can be regarded as a compound decision making process, the performance of an edge detector is

  1. The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes

    DEFF Research Database (Denmark)

    Alssema, M; Vistisen, D; Heymans, M W

    2011-01-01

    AIMS/HYPOTHESIS: The Finnish diabetes risk questionnaire is a widely used, simple tool for identification of those at risk for drug-treated type 2 diabetes. We updated the risk questionnaire by using clinically diagnosed and screen-detected type 2 diabetes instead of drug-treated diabetes...... as an endpoint and by considering additional predictors. METHODS: Data from 18,301 participants in studies of the Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) project with baseline and follow-up information on oral glucose tolerance status...... of the original Finnish risk questionnaire could be improved by adding information on sex, smoking and family history of diabetes. The DETECT-2 update of the Finnish diabetes risk questionnaire is an adequate and robust predictor for future screen-detected and clinically diagnosed type 2 diabetes in Europid...

  2. Evaluation of Newer Risk Markers for Coronary Heart Disease Risk Classification A Cohort Study

    NARCIS (Netherlands)

    Kavousi, Maryam; Elias-Smale, Suzette; Rutten, Joost H. W.; Leening, Maarten J. G.; Vliegenthart, Rozemarijn; Verwoert, Germaine C.; Krestin, Gabriel P.; Oudkerk, Matthijs; de Maat, Moniek P. M.; Leebeek, Frank W. G.; Mattace-Raso, Francesco U. S.; Lindemans, Jan; Hofman, Albert; Steyerberg, Ewout W.; van der Lugt, Aad; van den Meiracker, Anton H.; Witteman, Jacqueline C. M.

    2012-01-01

    Background: Whether newer risk markers for coronary heart disease (CHD) improve CHD risk prediction remains unclear. Objective: To assess whether newer risk markers for CHD risk prediction and stratification improve Framingham risk score (FRS) predictions. Design: Prospective population-based study.

  3. Risk factors that predict future onset of each DSM-5 eating disorder: Predictive specificity in high-risk adolescent females.

    Science.gov (United States)

    Stice, Eric; Gau, Jeff M; Rohde, Paul; Shaw, Heather

    2017-01-01

    Because no single report has examined risk factors that predict future onset each type of eating disorder and core symptom dimensions that crosscut disorders, we addressed these aims to advance knowledge regarding risk factor specificity. Data from 3 prevention trials that targeted young women with body dissatisfaction (N = 1,272; Mage = 18.5, SD = 4.2) and collected annual diagnostic interview data over 3-year follow-up were combined to identify predictors of subthreshold/threshold anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), and purging disorder (PD). Negative affect and functional impairment predicted onset of all eating disorders. Thin-ideal internalization, body dissatisfaction, dieting, overeating, and mental health care predicted onset of subthreshold/threshold BN, BED, and PD; positive thinness expectations, denial of cost of pursuing the thin ideal, and fasting predicted onset of 2 of these 3 disorders. Similar risk factors predicted core eating disorder symptom onset. Low BMI and dieting specifically predicted onset of subthreshold/threshold AN or low BMI. Only a subset of factors showed unique predictive effects in multivariate models, likely due to moderate correlations between the risk factors (M r = .14). Results provide support for the theory that pursuit of the thin ideal and the resulting body dissatisfaction, dieting, and unhealthy weight control behaviors increase risk for binge/purge spectrum eating disorders, but suggest that youth who are inherently lean, rather than purposely pursuing the thin ideal, are at risk for AN. Impaired interpersonal functioning and negative affect are transdiagnostic risk factors, suggesting these factors should be targeted in prevention programs. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  4. 76 FR 19123 - National Earthquake Prediction Evaluation Council (NEPEC)

    Science.gov (United States)

    2011-04-06

    ....S. Geological Survey National Earthquake Prediction Evaluation Council (NEPEC) AGENCY: U.S... Earthquake Prediction Evaluation Council (NEPEC) will hold a 1-day meeting on April 16, 2011. The meeting... the Director of the U.S. Geological Survey on proposed earthquake predictions, on the completeness and...

  5. Explained Variation and Predictive Accuracy with an Extension to the Competing Risks Model

    DEFF Research Database (Denmark)

    Rosthøj, Susanne; Keiding, Niels

    2003-01-01

    Competing risks; efficiency; explained variation; misspecification; predictive accuracy; survival analysis......Competing risks; efficiency; explained variation; misspecification; predictive accuracy; survival analysis...

  6. Risk models to predict hypertension: a systematic review.

    Directory of Open Access Journals (Sweden)

    Justin B Echouffo-Tcheugui

    Full Text Available BACKGROUND: As well as being a risk factor for cardiovascular disease, hypertension is also a health condition in its own right. Risk prediction models may be of value in identifying those individuals at risk of developing hypertension who are likely to benefit most from interventions. METHODS AND FINDINGS: To synthesize existing evidence on the performance of these models, we searched MEDLINE and EMBASE; examined bibliographies of retrieved articles; contacted experts in the field; and searched our own files. Dual review of identified studies was conducted. Included studies had to report on the development, validation, or impact analysis of a hypertension risk prediction model. For each publication, information was extracted on study design and characteristics, predictors, model discrimination, calibration and reclassification ability, validation and impact analysis. Eleven studies reporting on 15 different hypertension prediction risk models were identified. Age, sex, body mass index, diabetes status, and blood pressure variables were the most common predictor variables included in models. Most risk models had acceptable-to-good discriminatory ability (C-statistic>0.70 in the derivation sample. Calibration was less commonly assessed, but overall acceptable. Two hypertension risk models, the Framingham and Hopkins, have been externally validated, displaying acceptable-to-good discrimination, and C-statistic ranging from 0.71 to 0.81. Lack of individual-level data precluded analyses of the risk models in subgroups. CONCLUSIONS: The discrimination ability of existing hypertension risk prediction tools is acceptable, but the impact of using these tools on prescriptions and outcomes of hypertension prevention is unclear.

  7. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    the expectations hypothesis (EH) out-ofsample: the forecasts do not add economic value compared to using the average historical excess return as an EH-consistent estimate of constant risk premia. We show that in general statistical signicance does not necessarily translate into economic signicance because EH...... deviations mainly matter at short horizons and standard predictability metrics are not compatible with common measures of economic value. Overall, the EH remains the benchmark for investment decisions and should be considered an economic prior in models of bond risk premia.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...

  8. Risk prediction of pulmonary tuberculosis using genetic and conventional risk factors in adult Korean population.

    Science.gov (United States)

    Hong, Eun Pyo; Go, Min Jin; Kim, Hyung-Lae; Park, Ji Wan

    2017-01-01

    A complex interplay among host, pathogen, and environmental factors is believed to contribute to the risk of developing pulmonary tuberculosis (PTB). The lack of replication of published genome-wide association study (GWAS) findings limits the clinical utility of reported single nucleotide polymorphisms (SNPs). We conducted a GWAS using 467 PTB cases and 1,313 healthy controls obtained from two community-based cohorts in Korea. We evaluated the performance of PTB risk models based on different combinations of genetic and nongenetic factors and validated the results in an independent Korean population comprised of 179 PTB cases and 500 healthy controls. We demonstrated the polygenic nature of PTB and nongenetic factors such as age, sex, and body mass index (BMI) were strongly associated with PTB risk. None of the SNPs achieved genome-wide significance; instead, we were able to replicate the associations between PTB and ten SNPs near or in the genes, CDCA7, GBE1, GADL1, SPATA16, C6orf118, KIAA1432, DMRT2, CTR9, CCDC67, and CDH13, which may play roles in the immune and inflammatory pathways. Among the replicated SNPs, an intergenic SNP, rs9365798, located downstream of the C6orf118 gene showed the most significant association under the dominant model (OR = 1.59, 95% CI 1.32-1.92, P = 2.1×10-6). The performance of a risk model combining the effects of ten replicated SNPs and six nongenetic factors (i.e., age, sex, BMI, cigarette smoking, systolic blood pressure, and hemoglobin) were validated in the replication set (AUC = 0.80, 95% CI 0.76-0.84). The strategy of combining genetic and nongenetic risk factors ultimately resulted in better risk prediction for PTB in the adult Korean population.

  9. Evaluation Method of Collision Risk by Using True Motion

    Directory of Open Access Journals (Sweden)

    Hayama Imazu

    2017-03-01

    Full Text Available It is necessary to develop a useful application to use big data like as AIS for safety and efficiency of ship operation. AIS is very useful system to collect targets information, but this information is not effective use yet. The evaluation method of collision risk is one of the cause disturb. Usually the collision risk of ship is evaluated by the value of the Closest Point of Approach (CPA which is related to a relative motion. So, it becomes difficult to find out a safety pass in a congested water. Here, Line of Predicted Collision (LOPC and Obstacle Zone by Target (OZT for evaluation of collision risk are introduced, these values are related to a true motion and it became visible of dangerous place, so it will make easy to find out a safety pass in a congested water.

  10. Predicting perceived risk of crime: a multilevel study.

    Science.gov (United States)

    Russo, Silvia; Roccato, Michele; Vieno, Alessio

    2011-12-01

    With a sample of Italians selected from 71 Italian counties (N = 1,868), we performed two multilevel analyses aimed at predicting the perceived risk of crime at local (i.e., in the participants' county of residence) and at societal (i.e., in the context of Italian society) levels. A significant proportion of the variation in local risk perception was at the county level. The following individual variables predicted higher levels of this variable: indirect victimization, the perception of social and physical disorder, being a woman, being poorly educated, and being an older person. Among the ecological predictors, the crime rate and unemployment rate predicted higher levels of local crime risk perception, while the immigrant rate did not. Perceived risk of crime at the societal level did not show significant variation at the county level. Education, being a man, trusting people, and adhesion to post-materialistic values predicted lower levels of societal crime risk perception, while number of sons/daughters and exposure to television news increased it. The limitations and possible development of this study are discussed.

  11. A utility/cost analysis of breast cancer risk prediction algorithms

    Science.gov (United States)

    Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

    2016-03-01

    Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

  12. D & D screening risk evaluation guidance

    Energy Technology Data Exchange (ETDEWEB)

    Robers, S.K.; Golden, K.M.; Wollert, D.A.

    1995-09-01

    The Screening Risk Evaluation (SRE) guidance document is a set of guidelines provided for the uniform implementation of SREs performed on decontamination and decommissioning (D&D) facilities. Although this method has been developed for D&D facilities, it can be used for transition (EM-60) facilities as well. The SRE guidance produces screening risk scores reflecting levels of risk through the use of risk ranking indices. Five types of possible risk are calculated from the SRE: current releases, worker exposures, future releases, physical hazards, and criticality. The Current Release Index (CRI) calculates the current risk to human health and the environment, exterior to the building, from ongoing or probable releases within a one-year time period. The Worker Exposure Index (WEI) calculates the current risk to workers, occupants and visitors inside contaminated D&D facilities due to contaminant exposure. The Future Release Index (FRI) calculates the hypothetical risk of future releases of contaminants, after one year, to human health and the environment. The Physical Hazards Index (PHI) calculates the risks to human health due to factors other than that of contaminants. Criticality is approached as a modifying factor to the entire SRE, due to the fact that criticality issues are strictly regulated under DOE. Screening risk results will be tabulated in matrix form, and Total Risk will be calculated (weighted equation) to produce a score on which to base early action recommendations. Other recommendations from the screening risk scores will be made based either on individual index scores or from reweighted Total Risk calculations. All recommendations based on the SRE will be made based on a combination of screening risk scores, decision drivers, and other considerations, as determined on a project-by-project basis.

  13. Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population.

    Science.gov (United States)

    Selvarajah, Sharmini; Kaur, Gurpreet; Haniff, Jamaiyah; Cheong, Kee Chee; Hiong, Tee Guat; van der Graaf, Yolanda; Bots, Michiel L

    2014-09-01

    Cardiovascular risk-prediction models are used in clinical practice to identify and treat high-risk populations, and to communicate risk effectively. We assessed the validity and utility of four cardiovascular risk-prediction models in an Asian population of a middle-income country. Data from a national population-based survey of 14,863 participants aged 40 to 65 years, with a follow-up duration of 73,277 person-years was used. The Framingham Risk Score (FRS), SCORE (Systematic COronary Risk Evaluation)-high and -low cardiovascular-risk regions and the World Health Organization/International Society of Hypertension (WHO/ISH) models were assessed. The outcome of interest was 5-year cardiovascular mortality. Discrimination was assessed for all models and calibration for the SCORE models. Cardiovascular risk factors were highly prevalent; smoking 20%, obesity 32%, hypertension 55%, diabetes mellitus 18% and hypercholesterolemia 34%. The FRS and SCORE models showed good agreement in risk stratification. The FRS, SCORE-high and -low models showed good discrimination for cardiovascular mortality, areas under the ROC curve (AUC) were 0.768, 0.774 and 0.775 respectively. The WHO/ISH model showed poor discrimination, AUC=0.613. Calibration of the SCORE-high model was graphically and statistically acceptable for men (χ(2) goodness-of-fit, p=0.097). The SCORE-low model was statistically acceptable for men (χ(2) goodness-of-fit, p=0.067). Both SCORE-models underestimated risk in women (p<0.001). The FRS and SCORE-high models, but not the WHO/ISH model can be used to identify high cardiovascular risk in the Malaysian population. The SCORE-high model predicts risk accurately in men but underestimated it in women. Copyright © 2014. Published by Elsevier Ireland Ltd.

  14. Issues in Value-at-Risk Modeling and Evaluation

    NARCIS (Netherlands)

    J. Daníelsson (Jón); C.G. de Vries (Casper); B.N. Jorgensen (Bjørn); P.F. Christoffersen (Peter); F.X. Diebold (Francis); T. Schuermann (Til); J.A. Lopez (Jose); B. Hirtle (Beverly)

    1998-01-01

    textabstractDiscusses the issues in value-at-risk modeling and evaluation. Value of value at risk; Horizon problems and extreme events in financial risk management; Methods of evaluating value-at-risk estimates.

  15. Nutritional Risk Screening Predicts Tumor Response in Lung Cancer Patients.

    Science.gov (United States)

    Illa, Petr; Tomiskova, Marcela; Skrickova, Jana

    2015-01-01

    Malnutrition in cancer patients may be associated with poor tolerance of chemotherapy and lower response rate after oncological treatment. Nutritional Risk Screening 2002 (NRS) adapted for oncological patients was used to assess the risk of undernutrition in a group of 188 patients with lung cancer. The risk was evaluated on a 6-point scale according to common signs of nutritional status (weight loss, body mass index, and dietary intake), tumor, and its treatment risk factors. A score of 3 or more (called "nutritional risk") means significant risk of malnutrition and poor outcome. Acceptable NRS score was found in 50.6%, and in 45.3% a score of 3-5 suggested the risk of malnutrition (nutritional risk). Unexpectedly, the toxicity of anticancer treatment was not significantly different between the subgroups (acceptable score vs nutritional risk). The rate of treatment response evaluated by imaging techniques was significantly higher in patients with an acceptable score compared to nutritional risk. Overall survival rate was significantly higher in cytostatically treated patients with lung cancer with an acceptable score. Nutritional risk screening is a significant predictor of tumor response in patients with lung cancer. Early detection of malnutrition is important to determine the prognosis of cancer patients as well as to plan effective supportive care.

  16. Evaluating Micrometeoroid and Orbital Debris Risk Assessments Using Anomaly Data

    Science.gov (United States)

    Squire, Michael

    2017-01-01

    The accuracy of micrometeoroid and orbital debris (MMOD) risk assessments can be difficult to evaluate. A team from the National Aeronautics and Space Administration (NASA) Engineering and Safety Center (NESC) has completed a study that compared MMOD-related failures on operational satellites to predictions of how many of those failures should occur using NASA's TM"s MMOD risk assessment methodology and tools. The study team used the Poisson probability to quantify the degree of inconsistency between the predicted and reported numbers of failures. Many elements go into a risk assessment, and each of those elements represent a possible source of uncertainty or bias that will influence the end result. There are also challenges in obtaining accurate and useful data on MMOD-related failures.

  17. Evaluating invasion risk for freshwater fishes in South Africa

    Directory of Open Access Journals (Sweden)

    Sean M. Marr

    2017-03-01

    Full Text Available Background: South Africa, as a signatory of the Convention on Biological Diversity, has an obligation to identify, prioritise and manage invasive species and their introduction pathways. However, this requires knowledge of the introduction pathways, factors influencing establishment success, invasive potential, current distributions and ecological impacts. Objectives: To evaluate the Fish Invasiveness Screening Kit (FISK to predict the invasion risk posed by fish species proposed for introduction into South Africa. Method: FISK assessments were compiled for species whose invasion status in South Africa was known. A Receiver operating characteristic (ROC analysis was conducted to calibrate the FISK for South Africa. The calibrated FISK was used to evaluate the risk that three species recently proposed for importation for aquaculture could become invasive in South Africa. Results: A FISK score of 14 was identified as the threshold to delineate between species that could become invasive in South Africa and those that are unlikely to become invasive. Of the three species evaluated, Silurus glanis had a high risk of becoming invasive in South Africa, Lates calcarifer was likely to be invasive and Oncorhynchus tshawytscha was unlikely to be invasive in South Africa. Conclusion: FISK was demonstrated to be a useful risk assessment tool to evaluate the invasion risk posed by species proposed for use in aquaculture. For the large number of fish imported for the pet trade, a rapid screening assessment to flag potentially high risk species was recommended prior to a full FISK assessment for flagged species.

  18. Why hydrological predictions should be evaluated using information theory

    Directory of Open Access Journals (Sweden)

    S. V. Weijs

    2010-12-01

    Full Text Available Probabilistic predictions are becoming increasingly popular in hydrology. Equally important are methods to test such predictions, given the topical debate on uncertainty analysis in hydrology. Also in the special case of hydrological forecasting, there is still discussion about which scores to use for their evaluation. In this paper, we propose to use information theory as the central framework to evaluate predictions. From this perspective, we hope to shed some light on what verification scores measure and should measure. We start from the ''divergence score'', a relative entropy measure that was recently found to be an appropriate measure for forecast quality. An interpretation of a decomposition of this measure provides insight in additive relations between climatological uncertainty, correct information, wrong information and remaining uncertainty. When the score is applied to deterministic forecasts, it follows that these increase uncertainty to infinity. In practice, however, deterministic forecasts tend to be judged far more mildly and are widely used. We resolve this paradoxical result by proposing that deterministic forecasts either are implicitly probabilistic or are implicitly evaluated with an underlying decision problem or utility in mind. We further propose that calibration of models representing a hydrological system should be the based on information-theoretical scores, because this allows extracting all information from the observations and avoids learning from information that is not there. Calibration based on maximizing utility for society trains an implicit decision model rather than the forecasting system itself. This inevitably results in a loss or distortion of information in the data and more risk of overfitting, possibly leading to less valuable and informative forecasts. We also show this in an example. The final conclusion is that models should preferably be explicitly probabilistic and calibrated to maximize the

  19. Why hydrological predictions should be evaluated using information theory

    Science.gov (United States)

    Weijs, S. V.; Schoups, G.; van de Giesen, N.

    2010-12-01

    Probabilistic predictions are becoming increasingly popular in hydrology. Equally important are methods to test such predictions, given the topical debate on uncertainty analysis in hydrology. Also in the special case of hydrological forecasting, there is still discussion about which scores to use for their evaluation. In this paper, we propose to use information theory as the central framework to evaluate predictions. From this perspective, we hope to shed some light on what verification scores measure and should measure. We start from the ''divergence score'', a relative entropy measure that was recently found to be an appropriate measure for forecast quality. An interpretation of a decomposition of this measure provides insight in additive relations between climatological uncertainty, correct information, wrong information and remaining uncertainty. When the score is applied to deterministic forecasts, it follows that these increase uncertainty to infinity. In practice, however, deterministic forecasts tend to be judged far more mildly and are widely used. We resolve this paradoxical result by proposing that deterministic forecasts either are implicitly probabilistic or are implicitly evaluated with an underlying decision problem or utility in mind. We further propose that calibration of models representing a hydrological system should be the based on information-theoretical scores, because this allows extracting all information from the observations and avoids learning from information that is not there. Calibration based on maximizing utility for society trains an implicit decision model rather than the forecasting system itself. This inevitably results in a loss or distortion of information in the data and more risk of overfitting, possibly leading to less valuable and informative forecasts. We also show this in an example. The final conclusion is that models should preferably be explicitly probabilistic and calibrated to maximize the information they provide.

  20. Traffic Predictive Control: Case Study and Evaluation

    Science.gov (United States)

    2017-06-26

    This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...

  1. Validation of the ABCD²-I score to predict stroke risk after transient ischemic attack.

    Science.gov (United States)

    Meng, Xia; Wang, Yilong; Liu, Liping; Pu, Yuehua; Zhao, Xingquan; Wang, Chunxue; Wang, Yongjun

    2011-06-01

    The primary aim of this study was to prospectively validate the predictive value of the ABCD²-I score and to then compare the predictive accuracy of the ABCD² score and ABCD²-I score for 1-year risk of stroke in admitted patients with transient ischemic attack (TIA) as defined by the World Health Organization (WHO) time-based criteria. Data were collected from patients with transient ischemic attack within 7 days of symptom onset, and all patients underwent diffusion-weighted imaging (DWI). The predictive values of stratified 1-year rates of recurrent stroke were compared using the age, blood pressure, clinical signs, symptom duration, and ABCD² score with defined cutoff values (0-3, low-risk, 4-5, medium-risk, 6-7, high-risk) and ABCD²-I score cutoff values (0-3, low-risk, 4-6, medium-risk, 7-10, high-risk). In addition, to evaluate the performance of the two scores, we calculated the area under the curve by receiver-operating characteristic. Four hundred and ten patients with completed DWI and 12-month follow-up with initial TIA were enrolled in this study. Of these, 111 (27.07%) patients had annual stroke risk. The risk of stroke increased with increasing ABCD² score and ABCD²-I score. The ABCD²-I score had the higher predictive value with areas under the curve of 0.77 than the ABCD² score with areas under the curve of 0.59. The ABCD²-I score is a useful tool for stratifying the 1-year risk of stroke in TIA patients, and it improves the discriminatory power of the ABCD² score for the prediction of stroke risk.

  2. Dynamic Bayesian modeling for risk prediction in credit operations

    DEFF Research Database (Denmark)

    Borchani, Hanen; Martinez, Ana Maria; Masegosa, Andres

    2015-01-01

    Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context...

  3. Drug response prediction in high-risk multiple myeloma

    DEFF Research Database (Denmark)

    Vangsted, A J; Helm-Petersen, S; Cowland, J B

    2018-01-01

    A Drug Response Prediction (DRP) score was developed based on gene expression profiling (GEP) from cell lines and tumor samples. Twenty percent of high-risk patients by GEP70 treated in Total Therapy 2 and 3A have a progression-free survival (PFS) of more than 10years. We used available GEP data ...

  4. Improving Personalized Clinical Risk Prediction Based on Causality-Based Association Rules.

    Science.gov (United States)

    Cheng, Chih-Wen; Wang, May D

    2015-09-01

    Developing clinical risk prediction models is one of the main tasks of healthcare data mining. Advanced data collection techniques in current Big Data era have created an emerging and urgent need for scalable, computer-based data mining methods. These methods can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. In our previous study, we developed a tool, called icuARM- II, that can generate personalized clinical risk prediction evidence using a temporal rule mining framework. However, the generation of final risk prediction possibility with icuARM-II still relied on human interpretation, which was subjective and, most of time, biased. In this study, we propose a new mechanism to improve icuARM-II's rule selection by including the concept of causal analysis. The generated risk prediction is quantitatively assessed using calibration statistics. To evaluate the performance of the new rule selection mechanism, we conducted a case study to predict short-term intensive care unit mortality based on personalized lab testing abnormalities. Our results demonstrated a better-calibrated ICU risk prediction using the new causality-base rule selection solution by comparing with conventional confidence-only rule selection methods.

  5. Child and environmental risk factors predicting readiness for learning in children at high risk of dyslexia.

    Science.gov (United States)

    Dilnot, Julia; Hamilton, Lorna; Maughan, Barbara; Snowling, Margaret J

    2017-02-01

    We investigate the role of distal, proximal, and child risk factors as predictors of reading readiness and attention and behavior in children at risk of dyslexia. The parents of a longitudinal sample of 251 preschool children, including children at family risk of dyslexia and children with preschool language difficulties, provided measures of socioeconomic status, home literacy environment, family stresses, and child health via interviews and questionnaires. Assessments of children's reading-related skills, behavior, and attention were used to define their readiness for learning at school entry. Children at family risk of dyslexia and children with preschool language difficulties experienced more environmental adversities and health risks than controls. The risks associated with family risk of dyslexia and with language status were additive. Both home literacy environment and child health predicted reading readiness while home literacy environment and family stresses predicted attention and behavior. Family risk of dyslexia did not predict readiness to learn once other risks were controlled and so seems likely to be best conceptualized as representing gene-environment correlations. Pooling across risks defined a cumulative risk index, which was a significant predictor of reading readiness and, together with nonverbal ability, accounted for 31% of the variance between children.

  6. Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

    Directory of Open Access Journals (Sweden)

    Sungkyoung Choi

    2016-12-01

    Full Text Available The success of genome-wide association studies (GWASs has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR, least absolute shrinkage and selection operator (LASSO, and Elastic-Net (EN. We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

  7. Indoor tanning and the MC1R genotype: risk prediction for basal cell carcinoma risk in young people.

    Science.gov (United States)

    Molinaro, Annette M; Ferrucci, Leah M; Cartmel, Brenda; Loftfield, Erikka; Leffell, David J; Bale, Allen E; Mayne, Susan T

    2015-06-01

    Basal cell carcinoma (BCC) incidence is increasing, particularly in young people, and can be associated with significant morbidity and treatment costs. To identify young individuals at risk of BCC, we assessed existing melanoma or overall skin cancer risk prediction models and built a novel risk prediction model, with a focus on indoor tanning and the melanocortin 1 receptor gene, MC1R. We evaluated logistic regression models among 759 non-Hispanic whites from a case-control study of patients seen between 2006 and 2010 in New Haven, Connecticut. In our data, the adjusted area under the receiver operating characteristic curve (AUC) for a model by Han et al. (Int J Cancer. 2006;119(8):1976-1984) with 7 MC1R variants was 0.72 (95% confidence interval (CI): 0.66, 0.78), while that by Smith et al. (J Clin Oncol. 2012;30(15 suppl):8574) with MC1R and indoor tanning had an AUC of 0.69 (95% CI: 0.63, 0.75). Our base model had greater predictive ability than existing models and was significantly improved when we added ever-indoor tanning, burns from indoor tanning, and MC1R (AUC = 0.77, 95% CI: 0.74, 0.81). Our early-onset BCC risk prediction model incorporating MC1R and indoor tanning extends the work of other skin cancer risk prediction models, emphasizes the value of both genotype and indoor tanning in skin cancer risk prediction in young people, and should be validated with an independent cohort. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  8. Calibration plots for risk prediction models in the presence of competing risks.

    Science.gov (United States)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-08-15

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo-values that are combined with a nearest neighborhood smoother and a cross-validation approach to deal with all three problems. Copyright © 2014 John Wiley & Sons, Ltd.

  9. Risk Prediction in Aortic Valve Replacement: Incremental Value of the Preoperative Echocardiogram.

    Science.gov (United States)

    Tan, Timothy C; Flynn, Aidan W; Chen-Tournoux, Annabel; Rudski, Lawrence G; Mehrotra, Praveen; Nunes, Maria C; Rincon, Luis M; Shahian, David M; Picard, Michael H; Afilalo, Jonathan

    2015-10-26

    Risk prediction is a critical step in patient selection for aortic valve replacement (AVR), yet existing risk scores incorporate very few echocardiographic parameters. We sought to evaluate the incremental predictive value of a complete echocardiogram to identify high-risk surgical candidates before AVR. A cohort of patients with severe aortic stenosis undergoing surgical AVR with or without coronary bypass was assembled at 2 tertiary centers. Preoperative echocardiograms were reviewed by independent observers to quantify chamber size/function and valve function. Patient databases were queried to extract clinical data. The cohort consisted of 432 patients with a mean age of 73.5 years and 38.7% females. Multivariable logistic regression revealed 3 echocardiographic predictors of in-hospital mortality or major morbidity: E/e' ratio reflective of elevated left ventricular (LV) filling pressure; myocardial performance index reflective of right ventricular (RV) dysfunction; and small LV end-diastolic cavity size. Addition of these echocardiographic parameters to the STS risk score led to an integrated discrimination improvement of 4.1% (Pvalue to the STS risk score and should be integrated in prediction when evaluating the risk of AVR. In addition, findings of small hypertrophied LV cavities and/or low mean aortic gradients confer a higher risk of 2-year mortality. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  10. PRiMeUM: A Model for Predicting Risk of Metastasis in Uveal Melanoma.

    Science.gov (United States)

    Vaquero-Garcia, Jorge; Lalonde, Emilie; Ewens, Kathryn G; Ebrahimzadeh, Jessica; Richard-Yutz, Jennifer; Shields, Carol L; Barrera, Alejandro; Green, Christopher J; Barash, Yoseph; Ganguly, Arupa

    2017-08-01

    To create an interactive web-based tool for the Prediction of Risk of Metastasis in Uveal Melanoma (PRiMeUM) that can provide a personalized risk estimate of developing metastases within 48 months of primary uveal melanoma (UM) treatment. The model utilizes routinely collected clinical and tumor characteristics on 1227 UM, with the option of including chromosome information when available. Using a cohort of 1227 UM cases, Cox proportional hazard modeling was used to assess significant predictors of metastasis including clinical and chromosomal characteristics. A multivariate model to predict risk of metastasis was evaluated using machine learning methods including logistic regression, decision trees, survival random forest, and survival-based regression models. Based on cross-validation results, a logistic regression classifier was developed to compute an individualized risk of metastasis based on clinical and chromosomal information. The PRiMeUM model provides prognostic information for personalized risk of metastasis in UM. The accuracy of the risk prediction ranged between 80% (using chromosomal features only), 83% using clinical features only (age, sex, tumor location, and size), and 85% (clinical and chromosomal information). Kaplan-Meier analysis showed these risk scores to be highly predictive of metastasis (P metastasis based on their individual and tumor characteristics. It will aid physicians with decisions concerning frequency of systemic surveillance and can be used as a criterion for entering clinical trials for adjuvant therapies.

  11. The Reliability and Predictive Validity of the Stalking Risk Profile.

    Science.gov (United States)

    McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

    2018-03-01

    This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

  12. Risk prediction models for melanoma: a systematic review.

    Science.gov (United States)

    Usher-Smith, Juliet A; Emery, Jon; Kassianos, Angelos P; Walter, Fiona M

    2014-08-01

    Melanoma incidence is increasing rapidly worldwide among white-skinned populations. Earlier diagnosis is the principal factor that can improve prognosis. Defining high-risk populations using risk prediction models may help targeted screening and early detection approaches. In this systematic review, we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict risk of developing cutaneous melanoma. A total of 4,141 articles were identified from the literature search and six through citation searching. Twenty-five risk models were included. Between them, the models considered 144 possible risk factors, including 18 measures of number of nevi and 26 of sun/UV exposure. Those most frequently included in final risk models were number of nevi, presence of freckles, history of sunburn, hair color, and skin color. Despite the different factors included and different cutoff values for sensitivity and specificity, almost all models yielded sensitivities and specificities that fit along a summary ROC with area under the ROC (AUROC) of 0.755, suggesting that most models had similar discrimination. Only two models have been validated in separate populations and both also showed good discrimination with AUROC values of 0.79 (0.70-0.86) and 0.70 (0.64-0.77). Further research should focus on validating existing models rather than developing new ones. ©2014 American Association for Cancer Research.

  13. Predicting impacts of climate change on Fasciola hepatica risk.

    Directory of Open Access Journals (Sweden)

    Naomi J Fox

    2011-01-01

    Full Text Available Fasciola hepatica (liver fluke is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  14. Predicting impacts of climate change on Fasciola hepatica risk.

    Science.gov (United States)

    Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

    2011-01-10

    Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  15. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool.

    Science.gov (United States)

    Veeravagu, Anand; Li, Amy; Swinney, Christian; Tian, Lu; Moraff, Adrienne; Azad, Tej D; Cheng, Ivan; Alamin, Todd; Hu, Serena S; Anderson, Robert L; Shuer, Lawrence; Desai, Atman; Park, Jon; Olshen, Richard A; Ratliff, John K

    2017-07-01

    OBJECTIVE The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort. METHODS The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery. RESULTS The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60-0.74] in RAT, 0.669 [95% CI 0.60-0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48-0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently

  16. Predictive risk factors for moderate to severe hyperbilirubinemia

    Directory of Open Access Journals (Sweden)

    Gláucia Macedo de Lima

    2007-12-01

    Full Text Available Objective: to describe predictive factors for severity of neonataljaundice in newborn infants treated at the University Neonatal Clinic,highlighting maternal, obstetric and neonatal factors. Methods: Acohort retrospective study by means of review of medical charts todefine risk factors associated with moderate and severe jaundice.The cohort consisted of newborns diagnosed with indirect neonatalhyperbilirubinemia and submitted to phototherapy. Risk was classifiedas maternal, prenatal, obstetric and neonatal factors; risk estimationwas based on the odds ratio (95% confidence interval; a bi-variantmultivariate regression logistic analysis was applied to variables forp < 0.1. Results: Of 818 babies born during the studied period, 94(11% had jaundice prior to hospital discharge. Phototherapy was usedon 69 (73% patients. Predictive factors for severity were multiparity;prolonged rupture of membranes, dystocia, cephalohematoma, a lowApgar score, prematurity and small-for-date babies. Following birth,breastfeeding, sepsis, Rh incompatibility, and jaundice presentingbefore the third day of life were associated with an increased risk ofhyperbilirubinemia and the need for therapy. Conclusion: Other thanthose characteristics that are singly associated with phototherapy,we concluded that multiparity, presumed neonatal asphyxia, low birthweight and infection are the main predictive factors leading to moderateand severe jaundice in newborn infants in our neonatal unit.

  17. Risk prediction of cardiovascular death based on the QTc interval

    DEFF Research Database (Denmark)

    Nielsen, Jonas B; Graff, Claus; Rasmussen, Peter V

    2014-01-01

    interval resulted in the worst prognosis for men whereas in women, a very short QTc interval was equivalent in risk to a borderline prolonged QTc interval. The effect of the QTc interval on the absolute risk of CVD was most pronounced in the elderly and in those with cardiovascular disease whereas.......1 years, 6647 persons died from cardiovascular causes. Long-term risks of CVD were estimated for subgroups defined by age, gender, cardiovascular disease, and QTc interval categories. In general, we observed an increased risk of CVD for both very short and long QTc intervals. Prolongation of the QTc...... the effect was negligible for middle-aged women without cardiovascular disease. The most important improvement in prediction accuracy was noted for women aged 70-90 years. In this subgroup, a total of 9.5% were reclassified (7.2% more accurately vs. 2.3% more inaccurately) within clinically relevant 5-year...

  18. Lung cancer in never smokers Epidemiology and risk prediction models

    Science.gov (United States)

    McCarthy, William J.; Meza, Rafael; Jeon, Jihyoun; Moolgavkar, Suresh

    2012-01-01

    In this chapter we review the epidemiology of lung cancer incidence and mortality among never smokers/ nonsmokers and describe the never smoker lung cancer risk models used by CISNET modelers. Our review focuses on those influences likely to have measurable population impact on never smoker risk, such as secondhand smoke, even though the individual-level impact may be small. Occupational exposures may also contribute importantly to the population attributable risk of lung cancer. We examine the following risk factors in this chapter: age, environmental tobacco smoke, cooking fumes, ionizing radiation including radon gas, inherited genetic susceptibility, selected occupational exposures, preexisting lung disease, and oncogenic viruses. We also compare the prevalence of never smokers between the three CISNET smoking scenarios and present the corresponding lung cancer mortality estimates among never smokers as predicted by a typical CISNET model. PMID:22882894

  19. Schizophrenia polygenic risk score predicts mnemonic hippocampal activity.

    Science.gov (United States)

    Chen, Qiang; Ursini, Gianluca; Romer, Adrienne L; Knodt, Annchen R; Mezeivtch, Karleigh; Xiao, Ena; Pergola, Giulio; Blasi, Giuseppe; Straub, Richard E; Callicott, Joseph H; Berman, Karen F; Hariri, Ahmad R; Bertolino, Alessandro; Mattay, Venkata S; Weinberger, Daniel R

    2018-02-03

    The use of polygenic risk scores has become a practical translational approach to investigating the complex genetic architecture of schizophrenia, but the link between polygenic risk scores and pathophysiological components of this disorder has been the subject of limited research. We investigated in healthy volunteers whether schizophrenia polygenic risk score predicts hippocampal activity during simple memory encoding, which has been proposed as a risk-associated intermediate phenotype of schizophrenia. We analysed the relationship between polygenic risk scores and hippocampal activity in a discovery sample of 191 unrelated healthy volunteers from the USA and in two independent replication samples of 76 and 137 healthy unrelated participants from Europe and the USA, respectively. Polygenic risk scores for each individual were calculated as the sum of the imputation probability of reference alleles weighted by the natural log of odds ratio from the recent schizophrenia genome-wide association study. We examined hippocampal activity during simple memory encoding of novel visual stimuli assessed using blood oxygen level-dependent functional MRI. Polygenic risk scores were significantly associated with hippocampal activity in the discovery sample [P = 0.016, family-wise error (FWE) corrected within Anatomical Automatic Labeling (AAL) bilateral hippocampal-parahippocampal mask] and in both replication samples (P = 0.033, FWE corrected within AAL right posterior hippocampal-parahippocampal mask in Bari sample, and P = 0.002 uncorrected in the Duke Neurogenetics Study sample). The relationship between polygenic risk scores and hippocampal activity was consistently negative, i.e. lower hippocampal activity in individuals with higher polygenic risk scores, consistent with previous studies reporting decreased hippocampal-parahippocampal activity during declarative memory tasks in patients with schizophrenia and in their healthy siblings. Polygenic risk scores accounted for

  20. Reduction in predicted coronary heart disease risk after substantial weight reduction after bariatric surgery.

    Science.gov (United States)

    Vogel, Jody A; Franklin, Barry A; Zalesin, Kerstyn C; Trivax, Justin E; Krause, Kevin R; Chengelis, David L; McCullough, Peter A

    2007-01-15

    In recent years, bariatric surgery has become an increasingly used therapeutic option for morbid obesity. The effect of weight loss after bariatric surgery on the predicted risk of coronary heart disease (CHD) has not previously been studied. We evaluated baseline (preoperative) and follow-up (postoperative) body mass index, CHD risk factors, and Framingham risk scores (FRSs) for 109 consecutive patients with morbid obesity who lost weight after laparoscopic Roux-en-Y gastric bypass surgery. Charts were abstracted using a case-report form by a reviewer blinded to the FRS results. The study included 82 women (75%) and 27 men (25%) (mean age 46 +/- 10 years). Mean body mass index values at baseline and follow-up were 49 +/- 8 and 36 +/- 8 kg/m(2), respectively (p <0.0001). During an average follow-up of 17 months, diabetes, hypertension, and dyslipidemia resolved or improved after weight loss. Thus, the risks of CHD as predicted by FRS decreased by 39% in men and 25% in women. The predicted 10-year CHD risks at baseline and follow-up were 6 +/- 5% and 4 +/- 3%, respectively (p < or =0.0001). For those without CHD, men compared favorably with the age-matched general population, with a final 10-year risk of 5 +/- 4% versus an expected risk of 11 +/- 6% (p <0.0001). Likewise, women achieved a level below the age-adjusted expected 10-year risk of the general population, with a final risk of 3 +/- 3% versus 6 +/- 4% (p <0.0001). In conclusion, weight loss results in a significant decrease in FRS 10-year predicted CHD risk. Bariatric surgery decreases CHD risk to rates lower than the age- and gender-adjusted estimates for the general population. These data suggest substantial and sustained weight loss after bariatric surgery may be a powerful intervention to decrease future rates of myocardial infarction and death in the morbidly obese.

  1. Predicting the onset of major depression in primary care: international validation of a risk prediction algorithm from Spain.

    Science.gov (United States)

    Bellón, J Á; de Dios Luna, J; King, M; Moreno-Küstner, B; Nazareth, I; Montón-Franco, C; GildeGómez-Barragán, M J; Sánchez-Celaya, M; Díaz-Barreiros, M Á; Vicens, C; Cervilla, J A; Svab, I; Maaroos, H-I; Xavier, M; Geerlings, M I; Saldivia, S; Gutiérrez, B; Motrico, E; Martínez-Cañavate, M T; Oliván-Blázquez, B; Sánchez-Artiaga, M S; March, S; del Mar Muñoz-García, M; Vázquez-Medrano, A; Moreno-Peral, P; Torres-González, F

    2011-10-01

    The different incidence rates of, and risk factors for, depression in different countries argue for the need to have a specific risk algorithm for each country or a supranational risk algorithm. We aimed to develop and validate a predictD-Spain risk algorithm (PSRA) for the onset of major depression and to compare the performance of the PSRA with the predictD-Europe risk algorithm (PERA) in Spanish primary care. A prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multi-level logistic regression and inverse probability weighting to build the PSRA. In Spain (4574), Chile (2133) and another five European countries (5184), 11 891 non-depressed adult primary care attendees formed our at-risk population. The main outcome was DSM-IV major depression (CIDI). Six variables were patient characteristics or past events (sex, age, sex×age interaction, education, physical child abuse, and lifetime depression) and six were current status [Short Form 12 (SF-12) physical score, SF-12 mental score, dissatisfaction with unpaid work, number of serious problems in very close persons, dissatisfaction with living together at home, and taking medication for stress, anxiety or depression]. The C-index of the PSRA was 0.82 [95% confidence interval (CI) 0.79-0.84]. The Integrated Discrimination Improvement (IDI) was 0.0558 [standard error (s.e.)=0.0071, Zexp=7.88, p<0.0001] mainly due to the increase in sensitivity. Both the IDI and calibration plots showed that the PSRA functioned better than the PERA in Spain. The PSRA included new variables and afforded an improved performance over the PERA for predicting the onset of major depression in Spain. However, the PERA is still the best option in other European countries.

  2. Predictions and social risk perception. The flawed forecasts about the Spanish public pensions crisis

    Directory of Open Access Journals (Sweden)

    Pablo Francescutti

    2017-09-01

    Full Text Available The debate in Spain about the Welfare State sustainability is fuelled by recurrent catastrophic predictions about the future of public pensions. In this paper a set of such predictions made in the mid-1990s is analyzed. In retrospect, their accuracy is evaluated in relation to the historical evolution of the Spanish public finance. In the same way, the real dimension of pensions risks is assessed. Moreover, their methodology, aims and the recommendations made by their authors are discussed. The results shed light on the influence of these predictions upon risk perception of financial collapse of Social Security, on their reflexive effects as well as on their impact on the process of definition of risks.

  3. Cumulative risk hypothesis: Predicting and preventing child maltreatment recidivism.

    Science.gov (United States)

    Solomon, David; Åsberg, Kia; Peer, Samuel; Prince, Gwendolyn

    2016-08-01

    Although Child Protective Services (CPS) and other child welfare agencies aim to prevent further maltreatment in cases of child abuse and neglect, recidivism is common. Having a better understanding of recidivism predictors could aid in preventing additional instances of maltreatment. A previous study identified two CPS interventions that predicted recidivism: psychotherapy for the parent, which was related to a reduced risk of recidivism, and temporary removal of the child from the parent's custody, which was related to an increased recidivism risk. However, counter to expectations, this previous study did not identify any other specific risk factors related to maltreatment recidivism. For the current study, it was hypothesized that (a) cumulative risk (i.e., the total number of risk factors) would significantly predict maltreatment recidivism above and beyond intervention variables in a sample of CPS case files and that (b) therapy for the parent would be related to a reduced likelihood of recidivism. Because it was believed that the relation between temporary removal of a child from the parent's custody and maltreatment recidivism is explained by cumulative risk, the study also hypothesized that that the relation between temporary removal of the child from the parent's custody and recidivism would be mediated by cumulative risk. After performing a hierarchical logistic regression analysis, the first two hypotheses were supported, and an additional predictor, psychotherapy for the child, also was related to reduced chances of recidivism. However, Hypothesis 3 was not supported, as risk did not significantly mediate the relation between temporary removal and recidivism. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Risk variables in evaluation of transport projects

    Science.gov (United States)

    Vařbuchta, Petr; Kovářová, Hana; Hromádka, Vít; Vítková, Eva

    2017-09-01

    Depending on the constantly increasing demands on assessment of investment projects, especially assessment of large-scale projects in transport and important European projects with wide impacts, there is constantly increasing focus on risk management, whether to find mitigations, creating corrective measures or their implementation in assessment, especially in the context of Cost-Benefit analysis. To project assessment is often used implementation of certain risk variables, which can generate negative impacts of project outputs in framework of assess. Especially in case of transportation infrastructure projects is taken much emphasis on the influence of risk variables. However, currently in case of assessment of transportation projects is in Czech Republic used a few risk variables, which occur in the most projects. This leads to certain limitation in framework of impact assessment of risk variables. This papers aims to specify a new risk variables and process of applying them to already executed project assessment. Based on changes generated by new risk variables will be evaluated differences between original and adapted assessment.

  5. Gasbuggy Site Assessment and Risk Evaluation

    Energy Technology Data Exchange (ETDEWEB)

    None

    2011-03-01

    This report describes the geologic and hydrologic conditions and evaluates potential health risks to workers in the natural gas industry in the vicinity of the Gasbuggy, New Mexico, site, where the U.S. Atomic Energy Commission detonated an underground nuclear device in 1967. The 29-kiloton detonation took place 4,240 feet below ground surface and was designed to evaluate the use of a nuclear detonation to enhance natural gas production from the Pictured Cliffs Formation in the San Juan Basin, Rio Arriba County, New Mexico, on land administered by Carson National Forest. A site-specific conceptual model was developed based on current understanding of the hydrologic and geologic environment. This conceptual model was used for establishing plausible contaminant exposure scenarios, which were then evaluated for human health risk potential. The most mobile and, therefore, the most probable contaminant that could result in human exposure is tritium. Natural gas production wells were identified as having the greatest potential for bringing detonation-derived contaminants (tritium) to the ground surface in the form of tritiated produced water. Three exposure scenarios addressing potential contamination from gas wells were considered in the risk evaluation: a gas well worker during gas-well-drilling operations, a gas well worker performing routine maintenance, and a residential exposure. The residential exposure scenario was evaluated only for comparison; permanent residences on national forest lands at the Gasbuggy site are prohibited

  6. Using prediction markets to forecast research evaluations.

    Science.gov (United States)

    Munafo, Marcus R; Pfeiffer, Thomas; Altmejd, Adam; Heikensten, Emma; Almenberg, Johan; Bird, Alexander; Chen, Yiling; Wilson, Brad; Johannesson, Magnus; Dreber, Anna

    2015-10-01

    The 2014 Research Excellence Framework (REF2014) was conducted to assess the quality of research carried out at higher education institutions in the UK over a 6 year period. However, the process was criticized for being expensive and bureaucratic, and it was argued that similar information could be obtained more simply from various existing metrics. We were interested in whether a prediction market on the outcome of REF2014 for 33 chemistry departments in the UK would provide information similar to that obtained during the REF2014 process. Prediction markets have become increasingly popular as a means of capturing what is colloquially known as the 'wisdom of crowds', and enable individuals to trade 'bets' on whether a specific outcome will occur or not. These have been shown to be successful at predicting various outcomes in a number of domains (e.g. sport, entertainment and politics), but have rarely been tested against outcomes based on expert judgements such as those that formed the basis of REF2014.

  7. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry.

    Science.gov (United States)

    Wen, Wanqing; Shu, Xiao-Ou; Guo, Xingyi; Cai, Qiuyin; Long, Jirong; Bolla, Manjeet K; Michailidou, Kyriaki; Dennis, Joe; Wang, Qin; Gao, Yu-Tang; Zheng, Ying; Dunning, Alison M; García-Closas, Montserrat; Brennan, Paul; Chen, Shou-Tung; Choi, Ji-Yeob; Hartman, Mikael; Ito, Hidemi; Lophatananon, Artitaya; Matsuo, Keitaro; Miao, Hui; Muir, Kenneth; Sangrajrang, Suleeporn; Shen, Chen-Yang; Teo, Soo H; Tseng, Chiu-Chen; Wu, Anna H; Yip, Cheng Har; Simard, Jacques; Pharoah, Paul D P; Hall, Per; Kang, Daehee; Xiang, Yongbing; Easton, Douglas F; Zheng, Wei

    2016-12-08

    Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry. We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk. We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P women in the middle quintile of the PRS, women in the top 1% group had a 2.70-fold elevated risk of breast cancer (95% CI: 2.15-3.40). The risk prediction model with the PRS had an area under the receiver operating characteristic curve of 0.606. The lifetime risk of breast cancer for Shanghai Chinese women in the lowest and highest 1% of the PRS was 1.35% and 10.06%, respectively. Approximately one-half of GWAS-identified breast cancer risk variants can be directly replicated in East Asian women. Collectively, common genetic variants are important predictors for breast cancer risk. Using common genetic variants for breast cancer could help identify women at high risk of breast cancer.

  8. Predicting At-Risk Patient Profiles from Big Prescription Data

    OpenAIRE

    Genevès, Pierre; Calmant, Thomas; Layaïda, Nabil; Lepelley, Marion; Artemova, Svetlana; Bosson, Jean-Luc

    2017-01-01

    We show how the analysis of very large amounts of drug prescription data make it possible to detect, on the day of hospital admission, patients at risk of developing complications during their hospital stay. We explore, for the first time, to which extent volume and variety of big prescription data help in constructing predictive models for the automatic detection of at-risk profiles.Our methodology is designed to validate our claims that: (1) drug prescription data on the day of admission co...

  9. Assessing the predictive power of the multifactorial models of the bankruptcy risk

    Directory of Open Access Journals (Sweden)

    Nicoleta Bărbuţă-Mişu

    2011-03-01

    Full Text Available The bankruptcy prediction of the enterprises is a great interest issue, which has continued such attention to researchers and specialists for several decades. This paper evaluates the risk of bankruptcy of a sample of 20 enterprises acting in the construction sector in Romania, in 2008. The bankruptcy risk is evaluated using 4 models: 2 models very well-known at the international level - Altman model (1968 with 5 variables and Conan & Holder model (1979 - and 2 models created taking into account the specificity of the Romanian business environment: the A model (2000 and the model of determining the financial performance developed especially for features of the enterprises acting in the construction sector (2008. The aim of this paper is to find a link or match between predictive power of the most used multi-factorial models of bankruptcy risk, taking into account the period in which they were created, the specific characteristics of the economy and industry.

  10. Cardiovascular disease risk score prediction models for women and its applicability to Asians

    Directory of Open Access Journals (Sweden)

    Goh LGH

    2014-03-01

    Full Text Available Louise GH Goh,1 Satvinder S Dhaliwal,1 Timothy A Welborn,2 Peter L Thompson,2–4 Bruce R Maycock,1 Deborah A Kerr,1 Andy H Lee,1 Dean Bertolatti,1 Karin M Clark,1 Rakhshanda Naheed,1 Ranil Coorey,1 Phillip R Della5 1School of Public Health, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia; 2Sir Charles Gairdner Hospital, Nedlands, Perth, WA, Australia; 3School of Population Health, University of Western Australia, Perth, WA, Australia; 4Harry Perkins Institute for Medical Research, Perth, WA, Australia; 5School of Nursing and Midwifery, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia Purpose: Although elevated cardiovascular disease (CVD risk factors are associated with a higher risk of developing heart conditions across all ethnic groups, variations exist between groups in the distribution and association of risk factors, and also risk levels. This study assessed the 10-year predicted risk in a multiethnic cohort of women and compared the differences in risk between Asian and Caucasian women. Methods: Information on demographics, medical conditions and treatment, smoking behavior, dietary behavior, and exercise patterns were collected. Physical measurements were also taken. The 10-year risk was calculated using the Framingham model, SCORE (Systematic COronary Risk Evaluation risk chart for low risk and high risk regions, the general CVD, and simplified general CVD risk score models in 4,354 females aged 20–69 years with no heart disease, diabetes, or stroke at baseline from the third Australian Risk Factor Prevalence Study. Country of birth was used as a surrogate for ethnicity. Nonparametric statistics were used to compare risk levels between ethnic groups. Results: Asian women generally had lower risk of CVD when compared to Caucasian women. The 10-year predicted risk was, however, similar between Asian and Australian women, for some models. These findings were

  11. Predicting adolescent's cyberbullying behavior: A longitudinal risk analysis.

    Science.gov (United States)

    Barlett, Christopher P

    2015-06-01

    The current study used the risk factor approach to test the unique and combined influence of several possible risk factors for cyberbullying attitudes and behavior using a four-wave longitudinal design with an adolescent US sample. Participants (N = 96; average age = 15.50 years) completed measures of cyberbullying attitudes, perceptions of anonymity, cyberbullying behavior, and demographics four times throughout the academic school year. Several logistic regression equations were used to test the contribution of these possible risk factors. Results showed that (a) cyberbullying attitudes and previous cyberbullying behavior were important unique risk factors for later cyberbullying behavior, (b) anonymity and previous cyberbullying behavior were valid risk factors for later cyberbullying attitudes, and (c) the likelihood of engaging in later cyberbullying behavior increased with the addition of risk factors. Overall, results show the unique and combined influence of such risk factors for predicting later cyberbullying behavior. Results are discussed in terms of theory. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  12. Prediction of postpartum blood transfusion – risk factors and recurrence

    DEFF Research Database (Denmark)

    Wikkelsø, Anne J; Hjortøe, Sofie; Gerds, Thomas A

    2014-01-01

    OBJECTIVE: The aim was to find clinically useful risk factors for postpartum transfusion and to assess the joint predictive value in a population of women with a first and second delivery. METHODS: All Danish women with a first and second delivery from January 2001 to September 2009 who gave birth...... in a hospital that reported transfusion of red blood cells to a national database: A total of 96 545 women were included. RESULTS: Retained placental tissue explained more than all other risk factors in vaginal deliveries. Retained placental tissue at first delivery was associated with postpartum transfusion...... transfusion is difficult. Retained placental tissue is the strongest predictor of postpartum blood transfusion in vaginal deliveries. Retained placental tissue is usually diagnosed for the first time when the bleeding starts, which limits the clinical value of prediction. We need tools for an early diagnosis...

  13. Predictions of space radiation fatality risk for exploration missions

    Science.gov (United States)

    Cucinotta, Francis A.; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. population. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits.

  14. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk

    Science.gov (United States)

    2006-06-01

    at the initial biopsy, the strength of the family history, meno- pausal status, and histologic findings of the biop- sy, as compared with expected...breast cancers for 646/758 (85%) of the cases. We assessed the significance of benign histology in predicting risk of future breast cancer, examining...TERMS Benign Breast Disease, Biomarkers, Histology , Breast Cancer 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF

  15. How to make predictions about future infectious disease risks

    Science.gov (United States)

    Woolhouse, Mark

    2011-01-01

    Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

  16. Predicted cancer risks induced by computed tomography examinations during childhood, by a quantitative risk assessment approach.

    Science.gov (United States)

    Journy, Neige; Ancelet, Sophie; Rehel, Jean-Luc; Mezzarobba, Myriam; Aubert, Bernard; Laurier, Dominique; Bernier, Marie-Odile

    2014-03-01

    The potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of potential lifetime risks of cancer incidence that would be induced by CT examinations during childhood in French routine practices in pediatrics. Organ doses were estimated from standard radiological protocols in 15 hospitals. Excess risks of leukemia, brain/central nervous system, breast and thyroid cancers were predicted from dose-response models estimated in the Japanese atomic bomb survivors' dataset and studies of medical exposures. Uncertainty in predictions was quantified using Monte Carlo simulations. This approach predicts that 100,000 skull/brain scans in 5-year-old children would result in eight (90 % uncertainty interval (UI) 1-55) brain/CNS cancers and four (90 % UI 1-14) cases of leukemia and that 100,000 chest scans would lead to 31 (90 % UI 9-101) thyroid cancers, 55 (90 % UI 20-158) breast cancers, and one (90 % UI risks without exposure). Compared to background risks, radiation-induced risks would be low for individuals throughout life, but relative risks would be highest in the first decades of life. Heterogeneity in the radiological protocols across the hospitals implies that 5-10 % of CT examinations would be related to risks 1.4-3.6 times higher than those for the median doses. Overall excess relative risks in exposed populations would be 1-10 % depending on the site of cancer and the duration of follow-up. The results emphasize the potential risks of cancer specifically from standard CT examinations in pediatrics and underline the necessity of optimization of radiological protocols.

  17. Toddler self-regulation skills predict risk for pediatric obesity.

    Science.gov (United States)

    Graziano, P A; Calkins, S D; Keane, S P

    2010-04-01

    To investigate the role of early self-regulation skills, including emotion regulation, sustained attention and inhibitory control/reward sensitivity, in predicting pediatric obesity in early childhood. Participants for this study included 57 children (25 girls) obtained from three different cohorts participating in a larger ongoing longitudinal study. At 2 years of age, participants participated in several laboratory tasks designed to assess their self-regulation skills. Height and weight measures were collected when children were 2 and 5.5 years of age. Self-regulation skills in toddlerhood were predictive of both normal variations in body mass index (BMI) development and pediatric obesity. Specifically, emotion regulation was the primary self-regulation skill involved in predicting normative changes in BMI as no effects were found for sustained attention or inhibitory control/reward sensitivity. However, both emotion regulation and inhibitory control/reward sensitivity predicted more extreme weight problems (that is, pediatric obesity), even after controlling for 2-year BMI. Thus, toddlers with poor emotion regulation skills and lower inhibitory control skills/higher reward sensitivity were more likely to be classified as overweight/at risk at 5.5 years of age. Early self-regulation difficulties across domains (that is, behavioral and emotional) represent significant individual risk factors for the development of pediatric obesity. Mechanisms by which early self-regulation skills may contribute to the development of pediatric obesity are discussed.

  18. Risk prediction models for mortality in patients with cardiovascular disease: The BioBank Japan project

    Directory of Open Access Journals (Sweden)

    Jun Hata

    2017-03-01

    Full Text Available Background: Cardiovascular disease (CVD is a leading cause of death in Japan. The present study aimed to develop new risk prediction models for long-term risks of all-cause and cardiovascular death in patients with chronic phase CVD. Methods: Among the subjects registered in the BioBank Japan database, 15,058 patients aged ≥40 years with chronic ischemic CVD (ischemic stroke or myocardial infarction were divided randomly into a derivation cohort (n = 10,039 and validation cohort (n = 5019. These subjects were followed up for 8.55 years in median. Risk prediction models for all-cause and cardiovascular death were developed using the derivation cohort by Cox proportional hazards regression. Their prediction performances for 5-year risk of mortality were evaluated in the validation cohort. Results: During the follow-up, all-cause and cardiovascular death events were observed in 2962 and 962 patients from the derivation cohort and 1536 and 481 from the validation cohort, respectively. Risk prediction models for all-cause and cardiovascular death were developed from the derivation cohort using ten traditional cardiovascular risk factors, namely, age, sex, CVD subtype, hypertension, diabetes, total cholesterol, body mass index, current smoking, current drinking, and physical activity. These models demonstrated modest discrimination (c-statistics, 0.703 for all-cause death; 0.685 for cardiovascular death and good calibration (Hosmer-Lemeshow χ2-test, P = 0.17 and 0.15, respectively in the validation cohort. Conclusions: We developed and validated risk prediction models of all-cause and cardiovascular death for patients with chronic ischemic CVD. These models would be useful for estimating the long-term risk of mortality in chronic phase CVD.

  19. Predicting the risk of chronic bronchitis in teenage smokers

    Directory of Open Access Journals (Sweden)

    S.I. Ilchenko

    2017-05-01

    Full Text Available Background. The purpose of the study was to create a prognostic model of the risk of chronic respiratory pathology in teenage smokers comfortable to use in practical medicine. Materials and methods. 73 teenage smokers aged 14–18 years (average age is 16.4 ± 0.2 years have been exa­mined. They were divided into two groups: group 1 consisted of 36 teenage smo­kers with chronic bronchitis (average age is 16.8 ± 0.2 years and comparison group comprised 37 apparently healthy teenage smokers (average age is 15.9 ± 0.2 years. We have studied clinical-anamnestic, functiona­­linstrumental data (spirometry, radiography of chest organs, level of nitric oxide in expired breath condensate, respiratory muscles strength and molecular-genetic factors of the risk of developing chronic pathology of respiratory organs in teenage smokers — 103 characteristics overall. The method of consequent (sequential analysis of Wald and Bayes strategy were used to create a prognostic model of the risk of chronic bronchitis. Results. The principle of working with a mathematical model for predicting the risk of chronic respiratory pathology development in teenage smokers is to sum up diagnostic factors that are consistent with the signs found in the patient. When the sum of diagnostic components is +13, the deve­lopment of chronic bronchitis is diagnosed in teenage smo­kers with error probability ≤ 5 % (р < 0.05; when the sum is +20 — the probability of diagnosis is 99 % (р < 0.01. Conclusions. Our algorithm for predicting the risk of develo­ping chronic bronchitis in teenage smokers will help early detection of high-risk patients in the formation of this pathology for personalized preventive measures that will allow practitioners to prevent chronic pathological processes and to improve the quality of life.

  20. Framingham risk prediction equations for incidence of cardiovascular disease using detailed measures for smoking

    Directory of Open Access Journals (Sweden)

    John McNeil

    2010-09-01

    Full Text Available Current prediction models for risk of cardiovascular disease (CVD incidence incorporate smoking as a dichotomous yes/no measure. However, the risk of CVD associated with smoking also varies with the intensity and duration of smoking and there is a strong association between time since quitting and the risk of disease onset. This study aims to develop improved risk prediction equations for CVD incidence incorporating intensity and duration of smoking and time since quitting. The risk of developing a first CVD event was evaluated using a Cox’s model for participants in the Framingham offspring cohort who attended the fourth examination (1988-92 between the ages of 30 and 74 years and were free of CVD (n=3751. The full models based on the smoking variables and other risk factors, and reduced models based on the smoking variables and non-laboratory risk factors demonstrated good discrimination, calibration and global fit. The incorporation of both time since quitting among past smokers and pack-years among current smokers resulted in better predictive performance as compared to a dichotomous current/non-smoker measure and a current/quitter/never smoker measure. Compared to never smokers, the risk of CVD incidence increased with pack-years. Risk among those quitting more than five years prior to the baseline exam and within five years prior to the baseline exam were similar and twice as high as that of never smokers. A CVD risk equation incorporating the effects of pack-years and time since quitting provides an improved tool to quantify risk and guide preventive care.

  1. Does the Risk Assessment and Prediction Tool Predict Discharge Disposition After Joint Replacement?

    DEFF Research Database (Denmark)

    Hansen, Viktor J.; Gromov, Kirill; Lebrun, Lauren M

    2015-01-01

    populations is unknown. A low RAPT score is reported to indicate a high risk of needing any form of inpatient rehabilitation after TJA, including short-term nursing facilities. QUESTIONS/PURPOSES: This study attempts (1) to assess predictive accuracy of the RAPT on US patients undergoing total hip and knee......BACKGROUND: Payers of health services and policymakers place a major focus on cost containment in health care. Studies have shown that early planning of discharge is essential in reducing length of stay and achieving financial benefit; tools that can help predict discharge disposition would...

  2. Readmission to medical intensive care units: risk factors and prediction.

    Science.gov (United States)

    Jo, Yong Suk; Lee, Yeon Joo; Park, Jong Sun; Yoon, Ho Il; Lee, Jae Ho; Lee, Choon-Taek; Cho, Young-Jae

    2015-03-01

    The objectives of this study were to find factors related to medical intensive care unit (ICU) readmission and to develop a prediction index for determining patients who are likely to be readmitted to medical ICUs. We performed a retrospective cohort study of 343 consecutive patients who were admitted to the medical ICU of a single medical center from January 1, 2008 to December 31, 2012. We analyzed a broad range of patients' characteristics on the day of admission, extubation, and discharge from the ICU. Of the 343 patients discharged from the ICU alive, 33 (9.6%) were readmitted to the ICU unexpectedly. Using logistic regression analysis, the verified factors associated with increased risk of ICU readmission were male sex [odds ratio (OR) 3.17, 95% confidence interval (CI) 1.29-8.48], history of diabetes mellitus (OR 3.03, 95% CI 1.29-7.09), application of continuous renal replacement therapy during ICU stay (OR 2.78, 95% CI 0.85-9.09), white blood cell count on the day of extubation (OR 1.13, 95% CI 1.07-1.21), and heart rate just before ICU discharge (OR 1.03, 95% CI 1.01-1.06). We established a prediction index for ICU readmission using the five verified risk factors (area under the curve, 0.76, 95% CI 0.66-0.86). By using specific risk factors associated with increased readmission to the ICU, a numerical index could be established as an estimation tool to predict the risk of ICU readmission.

  3. Prediction of tension-type headache risk in adolescents

    Directory of Open Access Journals (Sweden)

    K. A. Stepanchenko

    2016-08-01

    Full Text Available Tension-type headache is the actual problem of adolescent neurology, which is associated with the prevalence of the disease, the tendency of the disease to the chronic course and a negative impact on performance in education, work capacity and quality of patients’ life. The aim. To develop a method for prediction of tension-type headache occurrence in adolescents. Materials and methods. 2342 adolescent boys and girls at the age of 13-17 years in schools of Kharkiv were examined. We used questionnaire to identify the headache. A group of adolescents with tension-type headache - 1430 people (61.1% was selected. The control group included 246 healthy adolescents. Possible risk factors for tension-type headache formation were divided into 4 groups: genetic, biomedical, psychosocial and social. Mathematical prediction of tension-type headache risk in adolescents was performed using the method of intensive indicators normalization of E.N. Shigan, which was based on probabilistic Bayesian’s method. The result was presented in the form of prognostic coefficients. Results. The most informative risk factors for tension-type headache development were the diseases, from which the teenager suffered after 1 year (sleep disorders, gastrointestinal diseases, autonomic disorders in the family history, traumatic brain injury, physical inactivity, poor adaptation of the patient in the kindergarten and school, stresses. Diagnostic scale has been developed to predict the risk of tension-type headache. It includes 23 prognostic factors with their gradation and meaning of integrated risk indicator, depending on individual factor strength influence. The risk of tension-type headache development ranged from 25,27 to 81,43 values of prognostic coefficient (low probability (25,27-43,99, the average probability (43,99-62,71 and high probability (62,71- 81,43. Conclusion. The study of tension-type headache risk factors, which were obtained by using an assessed and

  4. Calibration plots for risk prediction models in the presence of competing risks

    DEFF Research Database (Denmark)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-01-01

    prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves...... such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk...

  5. [Predicting value of 2014 European guidelines risk prediction model for sudden cardiac death (HCM Risk-SCD) in Chinese patients with hypertrophic cardiomyopathy].

    Science.gov (United States)

    Li, W X; Liu, L W; Wang, J; Zuo, L; Yang, F; Kang, N; Lei, C H

    2017-12-24

    Objective: To evaluate the predicting value of the 2014 European Society of Cardiology (ESC) guidelines risk prediction model for sudden cardiac death (HCM Risk-SCD) in Chinese patients with hypertrophic cardiomyopathy (HCM), and to explore the predictors of adverse cardiovascular events in Chinese HCM patients. Methods: The study population consisted of a consecutive 207 HCM patients admitted in our center from October 2014 to October 2016. All patients were followed up to March 2017. The 5-year SCD probability of each patient was estimated using HCM Risk-SCD model based on electrocardiogram, echocardiography and cardiac magnetic resonance (CMR) examination results. The primary, second, and composite endpoints were recorded. The primary endpoint included SCD and appropriate ICD therapy, identical to the HCM Risk-SCD endpoint. The second endpoint included acute myocardial infarction, hospitalization for heart failure, thrombus embolism and end-stage HCM. The composite endpoint was either the primary or the second endpoint. Patients were divided into the 3 categories according to 5-year SCD probability assessed by HCM Risk-SCD model: low risk grouprisk group ≥4% torisk group≥6%. Results: (1) Prevalence of endpoints: All 207 HCM patients completed the follow-up (350 (230, 547) days). During follow-up, 8 (3.86%) patients reached the primary endpoints (3 cases of SCD, 3 cases of survival after defibrillation, and 2 cases of appropriate ICD discharge); 21 (10.14%) patients reached the second endpoints (1 case of acute myocardial infarction, 16 cases of heart failure hospitalization, 2 cases of thromboembolism, and 2 cases of end-stage HCM). (2) Predicting value of HCM Risk-SCD model: Patients with primary endpoints had higher prevalence of syncope and intermediate-high risk of 5-year SCD, as compared to those without primary endpoints (both Pvalue of HCM Risk-SCD model: The low risk group included 122 patients (59%), the intermediate risk group 42 (20%), and the

  6. Sleep duration predicts cardiometabolic risk in obese adolescents.

    Science.gov (United States)

    Iglayreger, Heidi B; Peterson, Mark D; Liu, Dongmei; Parker, Christine A; Woolford, Susan J; Sallinen Gafka, Bethany J; Hassan, Fauziya; Gordon, Paul M

    2014-05-01

    To examine the independent contributions of objectively measured sleep duration and fragmentation on cardiometabolic risk accumulation in free-living obese adolescents. Characteristics of metabolic syndrome (waist circumference, mean arterial pressure, fasting high-density lipoprotein cholesterol, triglycerides, glucose) were measured in obese adolescents and standardized residuals (z-scores) were summed (inverse high-density lipoprotein cholesterol) to create a continuous cardiometabolic risk score (cMetScore), adjusted for age, sex, and race. Sleep and physical activity were objectively measured in habitual, free-living conditions for 7 days (SenseWear Pro3, BodyMedia, Pittsburgh, Pennsylvania; n = 37; 54% female, ages 11-17 years). Associations between sleep duration and cMetScore were assessed via multiple linear regression. Body mass index, total sleep time, and sleep session length were each correlated with cMetScore (P < .05 all). Total sleep time was inversely and independently associated with cMetScore (r = -0.535, P = .001) and was the best independent predictor of metabolic risk. Sleep duration inversely predicts cardiometabolic risk in obese adolescents, even when we controlled for various measures of physical activity, anthropometry, and adiposity. Further research should investigate the biological mechanism of this relationship and the potential treatment effect of sleep intervention in decreasing cardiometabolic risk in this population. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Observed and predicted silicosis risks in heavy clay workers

    Energy Technology Data Exchange (ETDEWEB)

    Miller, B.G.; Soutar, C.A. [Institute of Occupational Medicine, Edinburgh (United Kingdom)

    2007-12-15

    There is increasing pressure to tighten the regulation of workers' exposures to airborne silica, which can lead to severe and in some cases rapid development of disease. However, estimated risks from respirable silica vary greatly across industries. The aim of the paper is to clarify differences in risks between workers in the heavy clay and coal industries with documented exposures to respirable silica, in order to assist decisions on whether further investigation of possible differences might be justified. We applied a published equation for radiological risks from exposure to respirable silica, from a study of Scottish coalworkers (with unusually high exposures) to exposure estimates from an epidemiological study of heavy clay workers, by the same research team and using similar methods. The equation based on coalworkers' risks predicted in the heavy clay workers 31 cases of abnormalities at grade 2/1 + on the International Labour Organization scale, greatly in excess of the eight cases observed. Statistical variation is an implausible explanation (P < 0.0001). While there were some methodological differences between the studies, the disparity in risks provides some support for the case for further investigation of possible differences.

  8. Evaluation of random forest regression for prediction of breeding ...

    Indian Academy of Sciences (India)

    cation of the random forest (RF), a model-free ensemble learning method, is not widely used for prediction. In this study, the ... [Sarkar R. K., Rao A. R., Meher P. K., Nepolean T. and Mohapatra T. 2015 Evaluation of random forest regression for prediction of breeding value from .... Ten-fold cross validation technique (Stone.

  9. 78 FR 64973 - National Earthquake Prediction Evaluation Council (NEPEC)

    Science.gov (United States)

    2013-10-30

    ... Geological Survey National Earthquake Prediction Evaluation Council (NEPEC) AGENCY: U.S. Geological Survey, Interior. ACTION: Notice of meeting. SUMMARY: Pursuant to Public Law 96-472, the National Earthquake... proposed earthquake predictions, on the completeness and scientific validity of the available data related...

  10. Evaluating the Predictive Validity of Graduate Management Admission Test Scores

    Science.gov (United States)

    Sireci, Stephen G.; Talento-Miller, Eileen

    2006-01-01

    Admissions data and first-year grade point average (GPA) data from 11 graduate management schools were analyzed to evaluate the predictive validity of Graduate Management Admission Test[R] (GMAT[R]) scores and the extent to which predictive validity held across sex and race/ethnicity. The results indicated GMAT verbal and quantitative scores had…

  11. A predictive risk model for medical intractability in epilepsy.

    Science.gov (United States)

    Huang, Lisu; Li, Shi; He, Dake; Bao, Weiqun; Li, Ling

    2014-08-01

    This study aimed to investigate early predictors (6 months after diagnosis) of medical intractability in epilepsy. All children models were performed to determine the risk factors for developing medical intractability. Receiver operating characteristic curve was applied to fit the best compounded predictive model. A total of 649 patients were identified, out of which 119 (18%) met the study definition of intractable epilepsy at 2 years after diagnosis, and the rate of intractable epilepsy in patients with idiopathic syndromes was 12%. Multivariate logistic regression analysis revealed that neurodevelopmental delay, symptomatic etiology, partial seizures, and more than 10 seizures before diagnosis were significant and independent risk factors for intractable epilepsy. The best model to predict medical intractability in epilepsy comprised neurological physical abnormality, age at onset of epilepsy under 1 year, more than 10 seizures before diagnosis, and partial epilepsy, and the area under receiver operating characteristic curve was 0.7797. This model also fitted best in patients with idiopathic syndromes. A predictive model of medically intractable epilepsy composed of only four characteristics is established. This model is comparatively accurate and simple to apply clinically. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Risk and protective factors predicting multiple suicide attempts.

    Science.gov (United States)

    Choi, Kyoung Ho; Wang, Sheng-Min; Yeon, Bora; Suh, Soo-Yeon; Oh, Youngmin; Lee, Hae-Kook; Kweon, Yong-Sil; Lee, Chung Tai; Lee, Kyoung-Uk

    2013-12-30

    This study compared demographical and clinical variables between first and multiple suicide attempters and investigated risk and protective factors predicting multiple attempts. 228 patients visiting emergency department after attempting suicide were divided into two groups: first attempter (n=148, 64.9%) and multiple attempter (n=80, 35.1%). Demographic variables, clinical characteristics, factors related with suicide behavior, and psychiatric resources between two groups were compared. Multivariate logistic regression analysis was conducted to investigate risk and protective factors predicting multiple attempts. The results showed that multiple attempters were younger, not married, more severe in psychopathology (e.g., psychiatric disorder, personality disorder, lower function, and suicide family history) and suicidality (e.g., repetitive/severe/continuous suicide ideation), and lower in psychiatric resources (e.g., interpersonal stress/conflict, conflicting interpersonal relationship, socially isolated, lower personal achievement, and lower ability to control emotion) than first attempters. Suicide ideation severity and conflicting interpersonal relationships predicted multiple suicide attempts, whereas past year's highest global functioning score and age over 45 protected against multiple suicide attempts. This study demonstrated that multiple suicide attempters have more severe clinical profile than first suicide attempters. Moreover, decreasing severity of suicide ideation, improving interpersonal relationships, and enhancing functioning level of suicide attempters might be important in preventing them from re-attempting suicide. © 2013 Published by Elsevier Ireland Ltd.

  13. Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO.

    Science.gov (United States)

    Mazzola, Emanuele; Blackford, Amanda; Parmigiani, Giovanni; Biswas, Swati

    2015-01-01

    BRCAPRO is a widely used model for genetic risk prediction of breast cancer. It is a function within the R package BayesMendel and is used to calculate the probabilities of being a carrier of a deleterious mutation in one or both of the BRCA genes, as well as the probability of being affected with breast and ovarian cancer within a defined time window. Both predictions are based on information contained in the counselee's family history of cancer. During the last decade, BRCAPRO has undergone several rounds of successive refinements: the current version is part of release 2.1 of BayesMendel. In this review, we showcase some of the most notable features of the software resulting from these recent changes. We provide examples highlighting each feature, using artificial pedigrees motivated by complex clinical examples. We illustrate how BRCAPRO is a comprehensive software for genetic risk prediction with many useful features that allow users the flexibility to incorporate varying amounts of available information.

  14. Risk Prediction Models for Colorectal Cancer: A Systematic Review.

    Science.gov (United States)

    Usher-Smith, Juliet A; Walter, Fiona M; Emery, Jon D; Win, Aung K; Griffin, Simon J

    2016-01-01

    Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes. ©2015 American Association for Cancer Research.

  15. Analysis of Predictive Values Based on Individual Risk Factors in Multi-Modality Trials

    Directory of Open Access Journals (Sweden)

    Katharina Lange

    2013-03-01

    Full Text Available The accuracy of diagnostic tests with binary end-points is most frequently measured by sensitivity and specificity. However, from the clinical perspective, the main purpose of a diagnostic agent is to assess the probability of a patient actually being diseased and hence predictive values are more suitable here. As predictive values depend on the pre-test probability of disease, we provide a method to take risk factors influencing the patient’s prior probability of disease into account, when calculating predictive values. Furthermore, approaches to assess confidence intervals and a methodology to compare predictive values by statistical tests are presented. Hereby the methods can be used to analyze predictive values of factorial diagnostic trials, such as multi-modality, multi-reader-trials. We further performed a simulation study assessing length and coverage probability for different types of confidence intervals, and we present the R-Package facROC that can be used to analyze predictive values in factorial diagnostic trials in particular. The methods are applied to a study evaluating CT-angiography as a noninvasive alternative to coronary angiography for diagnosing coronary artery disease. Hereby the patients’ symptoms are considered as risk factors influencing the respective predictive values.

  16. Municipal Treated Wastewater Irrigation: Microbiological Risk Evaluation

    Directory of Open Access Journals (Sweden)

    Antonio Lonigro

    2008-06-01

    Full Text Available Municipal wastewater for irrigation, though treated, can contain substances and pathogens toxic for humans and animals. Pathogens, although not harmful from an agronomical aspect, undoubtedly represent a major concern with regards to sanitary and hygienic profile. In fact, vegetable crops irrigated with treated wastewater exalt the risk of infection since these products can also be eaten raw, as well as transformed or cooked. Practically, the evaluation of the microbiological risk is important to verify if the microbial limits imposed by law for treated municipal wastewater for irrigation, are valid, thus justifying the treatments costs, or if they are too low and, therefore, they don’ t justify them. Different probabilistic models have been studied to assess the microbiological risk; among these, the Beta-Poisson model resulted the most reliable. Thus, the Dipartimento di Scienze delle Produzioni Vegetali of the University of Bari, which has been carrying out researches on irrigation with municipal filtered wastewater for several years, considered interesting to verify if the microbial limits imposed by the italian law n.185/03 are too severe, estimating the biological risk by the probabilistic Beta-Poisson model. Results of field trials on vegetable crops irrigated by municipal filtered wastewater, processed by the Beta-Poisson model, show that the probability to get infection and/or illness is extremely low, and that the actual italian microbial limits are excessively restrictive.

  17. Adolescent expectations of early death predict adult risk behaviors.

    Science.gov (United States)

    Nguyen, Quynh C; Villaveces, Andres; Marshall, Stephen W; Hussey, Jon M; Halpern, Carolyn T; Poole, Charles

    2012-01-01

    Only a handful of public health studies have investigated expectations of early death among adolescents. Associations have been found between these expectations and risk behaviors in adolescence. However, these beliefs may not only predict worse adolescent outcomes, but worse trajectories in health with ties to negative outcomes that endure into young adulthood. The objectives of this study were to investigate perceived chances of living to age 35 (Perceived Survival Expectations, PSE) as a predictor of suicidal ideation, suicide attempt and substance use in young adulthood. We examined the predictive capacity of PSE on future suicidal ideation/attempt after accounting for sociodemographics, depressive symptoms, and history of suicide among family and friends to more fully assess its unique contribution to suicide risk. We investigated the influence of PSE on legal and illegal substance use and varying levels of substance use. We utilized the National Longitudinal Study of Adolescent Health (Add Health) initiated in 1994-95 among 20,745 adolescents in grades 7-12 with follow-up interviews in 1996 (Wave II), 2001-02 (Wave III) and 2008 (Wave IV; ages 24-32). Compared to those who were almost certain of living to age 35, perceiving a 50-50 or less chance of living to age 35 at Waves I or III predicted suicide attempt and ideation as well as regular substance use (i.e., exceeding daily limits for moderate drinking; smoking ≥ a pack/day; and using illicit substances other than marijuana at least weekly) at Wave IV. Associations between PSE and detrimental adult outcomes were particularly strong for those reporting persistently low PSE at both Waves I and III. Low PSE at Wave I or Wave III was also related to a doubling and tripling, respectively, of death rates in young adulthood. Long-term and wide-ranging ties between PSE and detrimental outcomes suggest these expectations may contribute to identifying at-risk youth.

  18. Adolescent expectations of early death predict adult risk behaviors.

    Directory of Open Access Journals (Sweden)

    Quynh C Nguyen

    Full Text Available Only a handful of public health studies have investigated expectations of early death among adolescents. Associations have been found between these expectations and risk behaviors in adolescence. However, these beliefs may not only predict worse adolescent outcomes, but worse trajectories in health with ties to negative outcomes that endure into young adulthood. The objectives of this study were to investigate perceived chances of living to age 35 (Perceived Survival Expectations, PSE as a predictor of suicidal ideation, suicide attempt and substance use in young adulthood. We examined the predictive capacity of PSE on future suicidal ideation/attempt after accounting for sociodemographics, depressive symptoms, and history of suicide among family and friends to more fully assess its unique contribution to suicide risk. We investigated the influence of PSE on legal and illegal substance use and varying levels of substance use. We utilized the National Longitudinal Study of Adolescent Health (Add Health initiated in 1994-95 among 20,745 adolescents in grades 7-12 with follow-up interviews in 1996 (Wave II, 2001-02 (Wave III and 2008 (Wave IV; ages 24-32. Compared to those who were almost certain of living to age 35, perceiving a 50-50 or less chance of living to age 35 at Waves I or III predicted suicide attempt and ideation as well as regular substance use (i.e., exceeding daily limits for moderate drinking; smoking ≥ a pack/day; and using illicit substances other than marijuana at least weekly at Wave IV. Associations between PSE and detrimental adult outcomes were particularly strong for those reporting persistently low PSE at both Waves I and III. Low PSE at Wave I or Wave III was also related to a doubling and tripling, respectively, of death rates in young adulthood. Long-term and wide-ranging ties between PSE and detrimental outcomes suggest these expectations may contribute to identifying at-risk youth.

  19. Prostate cancer risk prediction in a urology clinic in Mexico

    Science.gov (United States)

    Liang, Yuanyuan; Messer, Jamie C; Louden, Christopher; Jimenez-Rios, Miguel A; Thompson, Ian M; Camarena-Reynoso, Hector R

    2012-01-01

    Objectives To evaluate factors affecting the risk of prostate cancer (PCa) and high-grade disease (HGPCa, Gleason score ≥7) in a Mexican referral population, with comparison to the Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator (PCPTRC). Methods and Materials From a retrospective study of 826 patients who underwent prostate biopsy between January 2005 and December 2009 at the Instituto Nacional de Cancerología, Mexico, logistic regression was used to assess the effects of age, prostate-specific antigen (PSA), digital rectal exam (DRE), first-degree family history of PCa, and history of a prior prostate biopsy on PCa and HGPCa separately. Internal discrimination, goodness-of-fit and clinical utility of the resulting models were assessed with comparison to the PCPTRC. Results Rates of both PCa (73.2%) and HGPCa (33.3%) were high among referral patients in this Mexican urology clinic. The PCPTRC generally underestimated the risk of PCa but overestimated the risk of HGPCa. Four factors influencing PCa on biopsy were logPSA, DRE, family history and a prior biopsy history (all purological checkups in Mexico imply that men typically first reach specialized clinics with a high cancer risk. This renders diagnostic tools developed on comparatively healthy populations, such as the PCPTRC, of lesser utility. Continued efforts are needed to develop and externally validate new clinical diagnostic tools specific to high-risk referral populations incorporating new biomarkers and more clinical characteristics. PMID:22306115

  20. Early-onset sepsis: a predictive model based on maternal risk factors.

    Science.gov (United States)

    Puopolo, Karen M; Escobar, Gabriel J

    2013-04-01

    Neonatal early-onset sepsis (EOS) is a very low-incidence, but potentially fatal condition among term and late preterm newborns. EOS algorithms based on risk-factor threshold values result in evaluation and empiric antibiotic treatment of large numbers of uninfected newborns, leading to unnecessary antibiotic exposures and maternal/infant separation. Ideally, risk stratification should be quantitative, employ information conserving strategies, and be readily transferable to modern comprehensive electronic medical records. We performed a case-control study of infants born at or above 34 weeks' gestation with blood culture-proven EOS. We defined the relationship of established predictors to the risk of EOS, then used multivariate analyses and split validation to develop a predictive model using objective data. The model provides an estimation of sepsis risk that can identify the same proportion of EOS cases by evaluating fewer infants, as compared with algorithms based on subjective diagnoses and cut-off values for continuous predictors. An alternative approach to EOS risk assessment based only on objective data could decrease the number of infants evaluated and empirically treated for EOS, compared with currently recommended algorithms. Prospective evaluation is needed to determine the accuracy and safety of using the sepsis risk model to guide clinical decision-making.

  1. An Ensemble Multilabel Classification for Disease Risk Prediction

    Directory of Open Access Journals (Sweden)

    Runzhi Li

    2017-01-01

    Full Text Available It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB and label similarity (LS are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance.

  2. Troponin I and cardiovascular risk prediction in the general population

    DEFF Research Database (Denmark)

    Blankenberg, Stefan; Salomaa, Veikko; Makarova, Nataliya

    2016-01-01

    . The addition of troponin I information to a prognostic model for cardiovascular death constructed of ESC SCORE variables increased the C-index discrimination measure by 0.007 and yielded an NRI of 0.048, whereas the addition to prognostic models for cardiovascular disease and total mortality led to lesser C......-index discrimination and NRI increment. In individuals above 6 ng/L of troponin I, a concentration near the upper quintile in BiomarCaRE (5.9 ng/L) and JUPITER (5.8 ng/L), rosuvastatin therapy resulted in higher absolute risk reduction compared with individuals ... reduction was similar. Conclusion: In individuals free of cardiovascular disease, the addition of troponin I to variables of established risk score improves prediction of cardiovascular death and cardiovascular disease....

  3. Limits of Friendship Networks in Predicting Epidemic Risk

    CERN Document Server

    Coviello, Lorenzo; Rahwan, Iyad

    2015-01-01

    The spread of an infection on a real-world social network is determined by the interplay of two processes - the dynamics of the network, whose structure changes over time according to the encounters between individuals, and the dynamics on the network, whose nodes can infect each other after an encounter. Physical encounter is the most common vehicle for the spread of infectious diseases, but detailed information about said encounters is often unavailable because expensive, unpractical to collect or privacy sensitive. The present work asks whether the friendship ties between the individuals in a social network successfully predict who is at risk. Using a dataset from a popular online review service, we build a time-varying network that is a proxy of physical encounter between users and a static network based on their reported friendship. Through computer simulation, we compare infection processes on the resulting networks and show that friendship provides a poor identification of the individuals at risk if th...

  4. Evaluation of residue-residue contact prediction in CASP10

    KAUST Repository

    Monastyrskyy, Bohdan

    2013-08-31

    We present the results of the assessment of the intramolecular residue-residue contact predictions from 26 prediction groups participating in the 10th round of the CASP experiment. The most recently developed direct coupling analysis methods did not take part in the experiment likely because they require a very deep sequence alignment not available for any of the 114 CASP10 targets. The performance of contact prediction methods was evaluated with the measures used in previous CASPs (i.e., prediction accuracy and the difference between the distribution of the predicted contacts and that of all pairs of residues in the target protein), as well as new measures, such as the Matthews correlation coefficient, the area under the precision-recall curve and the ranks of the first correctly and incorrectly predicted contact. We also evaluated the ability to detect interdomain contacts and tested whether the difficulty of predicting contacts depends upon the protein length and the depth of the family sequence alignment. The analyses were carried out on the target domains for which structural homologs did not exist or were difficult to identify. The evaluation was performed for all types of contacts (short, medium, and long-range), with emphasis placed on long-range contacts, i.e. those involving residues separated by at least 24 residues along the sequence. The assessment suggests that the best CASP10 contact prediction methods perform at approximately the same level, and comparably to those participating in CASP9.

  5. Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations.

    Science.gov (United States)

    Martin, Alicia R; Gignoux, Christopher R; Walters, Raymond K; Wojcik, Genevieve L; Neale, Benjamin M; Gravel, Simon; Daly, Mark J; Bustamante, Carlos D; Kenny, Eimear E

    2017-04-06

    The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

  6. A Highly Predictive Risk Model for Pacemaker Implantation After TAVR.

    Science.gov (United States)

    Maeno, Yoshio; Abramowitz, Yigal; Kawamori, Hiroyuki; Kazuno, Yoshio; Kubo, Shunsuke; Takahashi, Nobuyuki; Mangat, Geeteshwar; Okuyama, Kazuaki; Kashif, Mohammad; Chakravarty, Tarun; Nakamura, Mamoo; Cheng, Wen; Friedman, John; Berman, Daniel; Makkar, Raj R; Jilaihawi, Hasan

    2017-10-01

    This study sought to develop a robust and definitive risk model for new permanent pacemaker implantation (PPMI) after SAPIEN 3 (third generation balloon expandable valve) (Edwards Lifesciences, Irvine, California) transcatheter aortic valve replacement (third generation balloon expandable valve TAVR), including calcification in the aortic-valvular complex (AVC). The association between calcium in the AVC and need for PPMI is poorly delineated after third generation balloon expandable valve TAVR. At Cedars-Sinai Heart Institute in Los Angeles, California, a total of 240 patients with severe aortic stenosis underwent third generation balloon expandable valve TAVR and had contrast computed tomography. AVC was characterized precisely by leaflet sector and region. The total new PPMI rate was 14.6%. On multivariate analysis for predictors of PPMI, pre-procedure third generation balloon expandable valve TAVR, right bundle branch block (RBBB), shorter membranous septum (MS) length, and noncoronary cusp device-landing zone calcium volume (NCC-DLZ CA) were included. Predictive probabilities were generated using this logistic regression model. If 3 pre-procedural risk factors were present, the c-statistic of the model for PPMI was area under the curve of 0.88, sensitivity of 77.1%, and specificity of 87.1%; this risk model had high negative predictive value (95.7%). The addition of the procedural factor of device depth to the model, with the parameter of difference between implantation depth and MS length, combined with RBBB and NCC-DLZ CA increased the c-statistic to 0.92, sensitivity to 94.3%, specificity to 83.8%, and negative predictive value to 98.8% CONCLUSIONS: By using a precise characterization of distribution of calcification in the AVC in a single-center, retrospective study, NCC-DLZ CA was found to be an independent predictor of new PPMI post-third generation balloon expandable valve TAVR. The findings also reinforce the importance of short MS length, pre

  7. Validation of a multifactorial risk factor model used for predicting future caries risk with nevada adolescents

    Directory of Open Access Journals (Sweden)

    Mobley Connie

    2011-05-01

    Full Text Available Abstract Background The objective of this study was to measure the validity and reliability of a multifactorial Risk Factor Model developed for use in predicting future caries risk in Nevada adolescents in a public health setting. Methods This study examined retrospective data from an oral health surveillance initiative that screened over 51,000 students 13-18 years of age, attending public/private schools in Nevada across six academic years (2002/2003-2007/2008. The Risk Factor Model included ten demographic variables: exposure to fluoridation in the municipal water supply, environmental smoke exposure, race, age, locale (metropolitan vs. rural, tobacco use, Body Mass Index, insurance status, sex, and sealant application. Multiple regression was used in a previous study to establish which significantly contributed to caries risk. Follow-up logistic regression ascertained the weight of contribution and odds ratios of the ten variables. Researchers in this study computed sensitivity, specificity, positive predictive value (PVP, negative predictive value (PVN, and prevalence across all six years of screening to assess the validity of the Risk Factor Model. Results Subjects' overall mean caries prevalence across all six years was 66%. Average sensitivity across all six years was 79%; average specificity was 81%; average PVP was 89% and average PVN was 67%. Conclusions Overall, the Risk Factor Model provided a relatively constant, valid measure of caries that could be used in conjunction with a comprehensive risk assessment in population-based screenings by school nurses/nurse practitioners, health educators, and physicians to guide them in assessing potential future caries risk for use in prevention and referral practices.

  8. Whole genome prediction of bladder cancer risk with the Bayesian LASSO.

    Science.gov (United States)

    de Maturana, Evangelina López; Chanok, Stephen J; Picornell, Antoni C; Rothman, Nathaniel; Herranz, Jesús; Calle, M Luz; García-Closas, Montserrat; Marenne, Gaëlle; Brand, Angela; Tardón, Adonina; Carrato, Alfredo; Silverman, Debra T; Kogevinas, Manolis; Gianola, Daniel; Real, Francisco X; Malats, Núria

    2014-07-01

    To build a predictive model for urothelial carcinoma of the bladder (UCB) risk combining both genomic and nongenomic data, 1,127 cases and 1,090 controls from the Spanish Bladder Cancer/EPICURO study were genotyped using the HumanHap 1M SNP array. After quality control filters, genotypes from 475,290 variants were available. Nongenomic information comprised age, gender, region, and smoking status. Three Bayesian threshold models were implemented including: (1) only genomic information, (2) only nongenomic data, and (3) both sources of information. The three models were applied to the whole population, to only nonsmokers, to male smokers, and to extreme phenotypes to potentiate the UCB genetic component. The area under the ROC curve allowed evaluating the predictive ability of each model in a 10-fold cross-validation scenario. Smoking status showed the highest predictive ability of UCB risk (AUCtest = 0.62). On the other hand, the AUC of all genetic variants was poorer (0.53). When the extreme phenotype approach was applied, the predictive ability of the genomic model improved 15%. This study represents a first attempt to build a predictive model for UCB risk combining both genomic and nongenomic data and applying state-of-the-art statistical approaches. However, the lack of genetic relatedness among individuals, the complexity of UCB etiology, as well as a relatively small statistical power, may explain the low predictive ability for UCB risk. The study confirms the difficulty of predicting complex diseases using genetic data, and suggests the limited translational potential of findings from this type of data into public health interventions. © 2014 WILEY PERIODICALS, INC.

  9. Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

    Science.gov (United States)

    Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

    2017-03-15

    Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

  10. Gasbuggy Site Assessment and Risk Evaluation

    Energy Technology Data Exchange (ETDEWEB)

    None

    2011-03-01

    The Gasbuggy site is in northern New Mexico in the San Juan Basin, Rio Arriba County (Figure 1-1). The Gasbuggy experiment was designed to evaluate the use of a nuclear detonation to enhance natural gas production from the Pictured Cliffs Formation, a tight, gas-bearing sandstone formation. The 29-kiloton-yield nuclear device was placed in a 17.5-inch wellbore at 4,240 feet (ft) below ground surface (bgs), approximately 40 ft below the Pictured Cliffs/Lewis shale contact, in an attempt to force the cavity/chimney formed by the detonation up into the Pictured Cliffs Sandstone. The test was conducted below the southwest quarter of Section 36, Township 29 North, Range 4 West, New Mexico Principal Meridian. The device was detonated on December 10, 1967, creating a 335-ft-high chimney above the detonation point and a cavity 160 ft in diameter. The gas produced from GB-ER (the emplacement and reentry well) during the post-detonation production tests was radioactive and diluted, primarily by carbon dioxide. After 2 years, the energy content of the gas had recovered to 80 percent of the value of gas in conventionally developed wells in the area. There is currently no technology capable of remediating deep underground nuclear detonation cavities and chimneys. Consequently, the U.S. Department of Energy (DOE) must continue to manage the Gasbuggy site to ensure that no inadvertent intrusion into the residual contamination occurs. DOE has complete control over the 1/4 section (160 acres) containing the shot cavity, and no drilling is permitted on that property. However, oil and gas leases are on the surrounding land. Therefore, the most likely route of intrusion and potential exposure would be through contaminated natural gas or contaminated water migrating into a producing natural gas well outside the immediate vicinity of ground zero. The purpose of this report is to describe the current site conditions and evaluate the potential health risks posed by the most plausible

  11. [Is the suicidal risk assessment scale RSD of predictive value?].

    Science.gov (United States)

    Ducher, J L; Terra, J L

    2006-10-01

    the efficacy of fluvoxamine in reducing the risk of recurrence of depression over 18 months, appears of particular interest. In this multicentre study, patients of both sexes were included, aged between 18 and 70 years, presenting a major depressive episode with a MADRS equal to a minimum of 25, and having had a minimum of two episodes of major depression within the last five years. The resulting analysis carried out on 103 patients showed a satisfactory concurrent validity between the suicidal risk assessment scale RSD and the items "suicide" of the MADRS (rho=0.79; p=0.0001) and the Hamilton Depression Scale (rho=0.70; p=0.0001), and fairly satisfactory concurrent validity with the depression degree assessed by the MADRS overall score (rho=0.40; p=0.0001). The short-term follow-up under treatment revealed enhanced sensitivity of the RSD versus the MADRS. The improvement in suicidal risk, assessed by the RSD, was faster than the improvement in depression, which is interesting from a clinical point of view. The medium-term follow-up tested the predictive validity of RSD and confirmed a greater level of suicidal risk from a score of 7 on the RSD, with the death by suicide of 2 subjects among the 15 who exhibited a score between 7 and 10 on the RSD on inclusion. On the other hand, no acting out, no attempted suicides, and no suicides were noted in the group of 88 subjects whose RSD was lower or equal to 6 on inclusion (p=0.02 using Fisher's exact test). Thus, the RSD appears of interest, from a clinical point of view, by providing a -diagnostic, or a scientific approach.

  12. Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

    Science.gov (United States)

    Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

    2017-04-01

    Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. © British Journal of General Practice 2017.

  13. Prenatal screening with evaluated high risk scores.

    Science.gov (United States)

    Papiernik, E; Grangé, G

    1999-01-01

    This paper reviews data that support the effectiveness of the French approach of using risk scoring for evaluating the risk of preterm delivery. This approach, which was developed in 1969 and spread to obstetricians and midwives throughout France in the early 1970s, includes systematic information about the recognition of uterine contractions, advice about reduction of physical exercise, and the prescription of work-leave for women with heavy or physically demanding work loads. The effectiveness of this prevention strategy is assessed using three different data sets: an evaluation of a preterm prevention program in the Alsace Region of France, five successive French national sample surveys which collected data on pregnant women, and a study of the effectiveness of a prevention program for twins in the district of Haut de Seine near Paris. The authors show that the rate of preterm birth in France declined substantially, but that the decline was concentrated among singleton spontaneous births. Since the 1970s induced preterm births have increased, and, interventions have not reduced the high rates of preterm birth among twins.

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

    Science.gov (United States)

    Dolan, M; Doyle, M

    2000-10-01

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

  15. Elevated risk for gastric adenocarcinoma can be predicted from histomorphology

    Science.gov (United States)

    Vieth, Michael; Stolte, Mandred

    2006-01-01

    The number of patients with gastric cancer has more than doubled since 1985 in developing countries. Thus, the questions of whether it can be predicted from gastritis morphology, who is at risk and who has a lower risk of developing gastric carcinoma are raised. H pylori-infection leads to erosions, ulcerations, carcinoma, mucosa associated lymphoid tissue (MALT)-lymphoma and extragastric diseases only in some individuals. The frequency of ulcerations among H pylori-infected individuals is estimated to be 13%, gastric cancer about 1% and MALT lymphoma around 0.1%. In the literature a multistep model from chronic active H pylori-infection through multifocal atrophy, intestinal metaplasia, dysplasia (intraepithelial neoplasia) and carcinoma has been described. But this model cannot be applied to all routine cases. Since risk factors such as metaplasia and atrophy are paracancerous rather than precancerous conditions, this raises the question whether there is a better morphological marker. Differences in topography, grade and activity of Helicobacter gastritis in the antrum and corpus might be good markers for identifying those who are at risk of developing gastric cancer. It is known that the so-called corpus dominant H pylori gastritis is found more frequently among individuals with early and advanced gastric cancer and within high risk populations. This is valid both for first-degree relatives of gastric cancer patients and for patients with gastric adenoma and hyperplastic polyps. In conclusion, corpus-dominant H pylori gastritis is significantly more common in patients with advanced and early gastric cancer, first-degree relatives of patients with gastric cancer, patients with gastric adenoma and gastric hyperplastic polyps. Therefore, all these patients are at risk of developing gastric cancer. Next, the question of who is at risk of developing corpus-dominant gastritis is raised. It appears that patients with a low acid output more frequently develop gastric

  16. The evaluation of screening and early detection strategies for type 2 diabetes and impaired glucose tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes

    NARCIS (Netherlands)

    Alssema, M.J.; Vistisen, D.; Heijmans, M.W.; Nijpels, G.; Glumer, C.; Zimmet, P.Z.; Shaw, J.E.; Eliasson, M.; Stehouwer, C.D.A.; Tabak, A.G.; Colagiuri, S.; Borch-Johnsen, K.; Dekker, J.M.

    2011-01-01

    Aims/hypothesis: The Finnish diabetes risk questionnaire is a widely used, simple tool for identification of those at risk for drug-treated type 2 diabetes. We updated the risk questionnaire by using clinically diagnosed and screen-detected type 2 diabetes instead of drug-treated diabetes as an

  17. Long-term Failure Prediction based on an ARP Model of Global Risk Network

    Science.gov (United States)

    Lin, Xin; Moussawi, Alaa; Szymanski, Boleslaw; Korniss, Gyorgy

    Risks that threaten modern societies form an intricately interconnected network. Hence, it is important to understand how risk materializations in distinct domains influence each other. In the paper, we study the global risks network defined by World Economic Forum experts in the form of Stochastic Block Model. We model risks as Alternating Renewal Processes with variable intensities driven by hidden values of exogenous and endogenous failure probabilities. Based on the expert assessments and historical status of each risk, we use Maximum Likelihood Evaluation to find the optimal model parameters and demonstrate that the model considering network effects significantly outperforms the others. In the talk, we discuss how the model can be used to provide quantitative means for measuring interdependencies and materialization of risks in the network. We also present recent results of long-term predictions in the form of predicated distributions of materializations over various time periods. Finally we show how the simulation of ARP's enables us to probe limits of the predictability of the system parameters from historical data and ability to recover hidden variable. Supported in part by DTRA, ARL NS-CTA.

  18. Improving the prediction model used in risk equalization: cost and diagnostic information from multiple prior years

    NARCIS (Netherlands)

    S.H.C.M. van Veen (Suzanne); R.C. van Kleef (Richard); W.P.M.M. van de Ven (Wynand); R.C.J.A. van Vliet (René)

    2015-01-01

    markdownabstract__Abstract__ Currently-used risk-equalization models do not adequately compensate insurers for predictable differences in individuals' health care expenses. Consequently, insurers face incentives for risk rating and risk selection, both of which jeopardize affordability of

  19. Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial

    DEFF Research Database (Denmark)

    Winkler Wille, Mathilde M.; van Riel, Sarah J.; Saghir, Zaigham

    2015-01-01

    Objectives: Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. Methods: From...... the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were...... used to evaluate risk discrimination. Results: AUCs of 0.826–0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer...

  20. Dietary Patterns Are Associated with Predicted Cardiovascular Disease Risk in an Urban Mexican Adult Population.

    Science.gov (United States)

    Denova-Gutiérrez, Edgar; Tucker, Katherine L; Flores, Mario; Barquera, Simón; Salmerón, Jorge

    2016-01-01

    Dietary patterns may predict cardiovascular disease (CVD) risk more accurately than does consumption of specific nutrients or foods. We evaluated the association between Mexican adults' dietary patterns and development of a >10% risk of 10-y CVD (using the Framingham risk score) over 7 y of follow-up. This prospective cohort study included 1196 men and women aged 20-80 y with a 10-y predicted risk questionnaire. Dietary intake was evaluated by using a semiquantitative food-frequency questionnaire. The relations between dietary patterns and predicted CVD were analyzed by using pooled logistic regression models. With the use of factor analysis, we identified 3 major dietary patterns in participants' dietary data. The "prudent" pattern was characterized by high positive loadings for the consumption of fresh fruit, vegetables, and whole grains. The "meat/fish" pattern showed positive loadings for the consumption of red meat, processed meat, eggs, fats, fish, and poultry. Finally, the "refined foods" pattern featured positive loadings for corn tortillas, refined grains, soft drinks, and alcohol. After adjustment for potential confounders, compared with participants in the lowest quintile of the prudent pattern, those in the highest quintile had a lower RR of 10-y CVD (RR: 0.40; 95% CI: 0.20, 0.79; P-trend = 0.006). In contrast, participants in the highest quintile of the refined-foods pattern had a greater risk of elevated 10-y CVD (RR: 2.98; 95% CI: 1.46, 6.10; P-trend = 0.020) than did those in the lowest quintile. Finally, the meat/fish dietary pattern was not significantly associated with 10-y CVD. Our data suggest that the prudent pattern is associated with a reduced risk of 10-y CVD, whereas the refined-foods pattern may increase 10-y CVD in Mexican adults. © 2016 American Society for Nutrition.

  1. Predictive accuracy of risk scales following self-harm: multicentre, prospective cohort study.

    Science.gov (United States)

    Quinlivan, Leah; Cooper, Jayne; Meehan, Declan; Longson, Damien; Potokar, John; Hulme, Tom; Marsden, Jennifer; Brand, Fiona; Lange, Kezia; Riseborough, Elena; Page, Lisa; Metcalfe, Chris; Davies, Linda; O'Connor, Rory; Hawton, Keith; Gunnell, David; Kapur, Nav

    2017-06-01

    BackgroundScales are widely used in psychiatric assessments following self-harm. Robust evidence for their diagnostic use is lacking.AimsTo evaluate the performance of risk scales (Manchester Self-Harm Rule, ReACT Self-Harm Rule, SAD PERSONS scale, Modified SAD PERSONS scale, Barratt Impulsiveness Scale); and patient and clinician estimates of risk in identifying patients who repeat self-harm within 6 months.MethodA multisite prospective cohort study was conducted of adults aged 18 years and over referred to liaison psychiatry services following self-harm. Scale a priori cut-offs were evaluated using diagnostic accuracy statistics. The area under the curve (AUC) was used to determine optimal cut-offs and compare global accuracy.ResultsIn total, 483 episodes of self-harm were included in the study. The episode-based 6-month repetition rate was 30% (n = 145). Sensitivity ranged from 1% (95% CI 0-5) for the SAD PERSONS scale, to 97% (95% CI 93-99) for the Manchester Self-Harm Rule. Positive predictive values ranged from 13% (95% CI 2-47) for the Modified SAD PERSONS Scale to 47% (95% CI 41-53) for the clinician assessment of risk. The AUC ranged from 0.55 (95% CI 0.50-0.61) for the SAD PERSONS scale to 0.74 (95% CI 0.69-0.79) for the clinician global scale. The remaining scales performed significantly worse than clinician and patient estimates of risk (Pself-harm have limited clinical utility and may waste valuable resources. Most scales performed no better than clinician or patient ratings of risk. Some performed considerably worse. Positive predictive values were modest. In line with national guidelines, risk scales should not be used to determine patient management or predict self-harm. © The Royal College of Psychiatrists 2017.

  2. Young Children’s Risk-Taking: Mothers’ Authoritarian Parenting Predicts Risk-Taking by Daughters but Not Sons

    OpenAIRE

    Erin E. Wood; Shelia M. Kennison

    2017-01-01

    We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style). In our st...

  3. Noninvasive Prediction of Erosive Esophagitis Using a Controlled Attenuation Parameter (CAP)-Based Risk Estimation Model.

    Science.gov (United States)

    Chung, Hyunsoo; Chon, Young Eun; Kim, Seung Up; Lee, Sang Kil; Jung, Kyu Sik; Han, Kwang-Hyub; Chon, Chae Yoon

    2016-02-01

    Erosive esophagitis and fatty liver share obesity and visceral fat as common critical pathogenesis. However, the relationship between the amount of hepatic fat and the severity of erosive esophagitis was not well investigated, and there is no risk estimation model for erosive esophagitis. To evaluate the relationship between the amount of hepatic fat and the severity of erosive esophagitis and then develop a risk estimation model for erosive esophagitis. We enrolled 1045 consecutive participants (training cohort, n = 705; validation cohort, n = 340) who underwent esophagogastroduodenoscopy and CAP. The relationship between severity of fatty liver and erosive esophagitis was investigated, and independent predictors for erosive esophagitis that have been investigated through logistic regression analyses were used as components for establishing a risk estimation model. The prevalence of erosive gastritis was 10.7 %, and the severity of erosive esophagitis was positively correlated with the degree of hepatic fatty accumulation (P CAP-based risk estimation model for erosive esophagitis using CAP, Body mass index, and significant alcohol Drinking as constituent variables was established and was dubbed the CBD score (AUROC = 0.819, range 0-11). The high-risk group (CBD score ≥3) showed significantly higher risk of having erosive esophagitis than the low-risk group (CBD score CAP-based risk model for predicting erosive esophagitis.

  4. Evaluating Process Effectiveness to Reduce Risk

    Science.gov (United States)

    Shepherd, Christena C.

    2017-01-01

    security; loss of confidence in government; failure of publicly funded projects; damage to the environment; ethics violations, and the list goes on; with local, national and even international consequences. The Plan-Do-Check-Act process, also known as the "process approach" can be used at any time to establish and standardize a process, and it can also be used to check periodically for "process creep" (i.e., informal, unauthorized changes that have occurred over time), any necessary updates and improvements. While ISO 9001 compliance is not mandated for all government agencies, if interpreted correctly, it can be useful in establishing a framework and implementing effective management systems and processes.4 Another method that can be used to evaluate effectiveness is the scorecard definitions in Mallory's Process Management Standard5 as a basis for evaluating work on the process level on effective, and continuously improved and improving processes. With processes on the lower end of the scale, agencies are vulnerable to a great many risks, with employees and managers making up many of the rules as they go, leading to the above listed negative results. Without clear guidance for nominal operations, off-nominal situations can, and do, increase the likelihood of chaos. In an increasingly technical environment, with inter-agency communication and collaboration becoming the norm, agencies need to come to grips with the fact that processes can become rapidly outdated, and that the technical community should take on an increased role in the maturation of the agency's processes. Industry has long known that effective processes are also efficient, and process improvement methods such as Kaizen, Lean, Six Sigma, 5S, and mistake proofing lead to increased productivity, improved quality, and decreased cost. Again, government agencies have different concerns, but inefficiencies and mistakes can have dire and wide reaching consequences for the public that they serve. While no one goes

  5. Does Preeclampsia Predict the Risk of Late Postpartum Eclampsia?

    Directory of Open Access Journals (Sweden)

    Diana S. Wolfe

    2013-05-01

    Full Text Available Objective - To investigate potential predictive symptoms of late postpartum eclampsia (LPE. Study Design - Retrospective review of patients delivered at a single academic medical center and diagnosed with eclampsia greater than 48 hours postdelivery. Results - Among 19 patients with eclampsia, 5 (26% patients with confirmed eclampsia seized greater than 48 hours after delivery. None of these patients showed evidence of preeclampsia intrapartum or immediately postpartum and none received intrapartum magnesium sulfate. Prior to seizure activity, 4 of 5 (80% patients had increased blood pressure and 2 of 5 (40% had central nervous system symptoms (headache and visual changes. Conclusion - Gestational hypertension (GHTN may be a risk factor for LPE. Consideration of seizure prophylaxis for patients with GHTN may facilitate the prevention of LPE.

  6. Can the Obesity Surgery Mortality Risk Score predict postoperative complications other than mortality?

    Directory of Open Access Journals (Sweden)

    Piotr Major

    2016-12-01

    Full Text Available Introduction : Laparoscopic sleeve gastrectomy (LSG and laparoscopic Roux-en-Y gastric bypass (LRYGB are bariatric procedures with acceptable risk of postoperative morbidities and mortalities, but identification of high-risk patients is an ongoing issue. DeMaria et al. introduced the Obesity Surgery Mortality Risk Score (OS-MRS, which was designed for mortality risk assessment but not perioperative morbidity risk. Aim : To assess the possibility to use the OS-MRS to predict the risk of perioperative complications related to LSG and LRYGB. Material and methods: Retrospective analysis of patients operated on for morbid obesity was performed. Patients were evaluated before and after surgery. We included 408 patients (233 LSG, 175 LRYGB. Perioperative complications were defined as adverse effects in the 30-day period. The Clavien-Dindo scale was used for description of complications. Patients were assigned to five grades and three classes according to the OS-MRS results, then risk of morbidity was analyzed. Results: Complications were observed in 30 (7.35% patients. Similar morbidity was related to both procedures (OR = 1.14, 95% CI: 0.53–2.44, p = 0.744. The reoperation and mortality rates were 1.23% and 0.49% respectively. There were no significant differences in median OS-MRS value between the group without and the group with perioperative complications. There were no significant differences in OS-MRS between groups (p = 0.091. Obesity Surgery Mortality Risk Score was not related to Clavien-Dindo grades (p = 0.800. Conclusions : It appears that OS-MRS is not useful in predicting risk of perioperative morbidity after bariatric procedures.

  7. Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

    Science.gov (United States)

    Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

    2017-07-01

    To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

  8. Phase angle assessment by bioelectrical impedance analysis and its predictive value for malnutrition risk in hospitalized geriatric patients.

    Science.gov (United States)

    Varan, Hacer Dogan; Bolayir, Basak; Kara, Ozgur; Arik, Gunes; Kizilarslanoglu, Muhammet Cemal; Kilic, Mustafa Kemal; Sumer, Fatih; Kuyumcu, Mehmet Emin; Yesil, Yusuf; Yavuz, Burcu Balam; Halil, Meltem; Cankurtaran, Mustafa

    2016-12-01

    Phase angle (PhA) value determined by bioelectrical impedance analysis (BIA) is an indicator of cell membrane damage and body cell mass. Recent studies have shown that low PhA value is associated with increased nutritional risk in various group of patients. However, there have been only a few studies performed globally assessing the relationship between nutritional risk and PhA in hospitalized geriatric patients. The aim of the study is to evaluate the predictive value of the PhA for malnutrition risk in hospitalized geriatric patients. One hundred and twenty-two hospitalized geriatric patients were included in this cross-sectional study. Comprehensive geriatric assessment tests and BIA measurements were performed within the first 48 h after admission. Nutritional risk state of the patients was determined with NRS-2002. Phase angle values of the patients with malnutrition risk were compared with the patients that did not have the same risk. The independent variables for predicting malnutrition risk were determined. SPSS version 15 was utilized for the statistical analyzes. The patients with malnutrition risk had significantly lower phase angle values than the patients without malnutrition risk (p = 0.003). ROC curve analysis suggested that the optimum PhA cut-off point for malnutrition risk was 4.7° with 79.6 % sensitivity, 64.6 % specificity, 73.9 % positive predictive value, and 73.9 % negative predictive value. BMI, prealbumin, PhA, and Mini Mental State Examination Test scores were the independent variables for predicting malnutrition risk. PhA can be a useful, independent indicator for predicting malnutrition risk in hospitalized geriatric patients.

  9. Mortality risk prediction by an insurance company and long-term follow-up of 62,000 men.

    Directory of Open Access Journals (Sweden)

    Eric J G Sijbrands

    Full Text Available BACKGROUND: Insurance companies use medical information to classify the mortality risk of applicants. Adding genetic tests to this assessment is currently being debated. This debate would be more meaningful, if results of present-day risk prediction were known. Therefore, we compared the predicted with the observed mortality of men who applied for life insurance, and determined the prognostic value of the risk assessment. METHODS: Long-term follow-up was available for 62,334 male applicants whose mortality risk was predicted with medical evaluation and they were assigned to five groups with increasing risk from 1 to 5. We calculated all cause standardized mortality ratios relative to the Dutch population and compared groups with Cox's regression. We compared the discriminative ability of risk assessments as indicated by a concordance index (c. RESULTS: In 844,815 person years we observed 3,433 deaths. The standardized mortality relative to the Dutch male population was 0.76 (95 percent confidence interval, 0.73 to 0.78. The standardized mortality ratios ranged from 0.54 in risk group 1 to 2.37 in group 5. A large number of risk factors and diseases were significantly associated with increased mortality. The algorithm of prediction was significantly, but only slightly better than summation of the number of disorders and risk factors (c-index, 0.64 versus 0.60, P<0.001. CONCLUSIONS: Men applying for insurance clearly had better survival relative to the general population. Readily available medical evaluation enabled accurate prediction of the mortality risk of large groups, but the deceased men could not have been identified with the applied prediction method.

  10. Mortality risk prediction by an insurance company and long-term follow-up of 62,000 men.

    Science.gov (United States)

    Sijbrands, Eric J G; Tornij, Erik; Homsma, Sietske J

    2009-01-01

    Insurance companies use medical information to classify the mortality risk of applicants. Adding genetic tests to this assessment is currently being debated. This debate would be more meaningful, if results of present-day risk prediction were known. Therefore, we compared the predicted with the observed mortality of men who applied for life insurance, and determined the prognostic value of the risk assessment. Long-term follow-up was available for 62,334 male applicants whose mortality risk was predicted with medical evaluation and they were assigned to five groups with increasing risk from 1 to 5. We calculated all cause standardized mortality ratios relative to the Dutch population and compared groups with Cox's regression. We compared the discriminative ability of risk assessments as indicated by a concordance index (c). In 844,815 person years we observed 3,433 deaths. The standardized mortality relative to the Dutch male population was 0.76 (95 percent confidence interval, 0.73 to 0.78). The standardized mortality ratios ranged from 0.54 in risk group 1 to 2.37 in group 5. A large number of risk factors and diseases were significantly associated with increased mortality. The algorithm of prediction was significantly, but only slightly better than summation of the number of disorders and risk factors (c-index, 0.64 versus 0.60, Psurvival relative to the general population. Readily available medical evaluation enabled accurate prediction of the mortality risk of large groups, but the deceased men could not have been identified with the applied prediction method.

  11. Predicting Risk of Infection in Patients with Newly Diagnosed Multiple Myeloma: Utility of Immune Profiling

    Directory of Open Access Journals (Sweden)

    Benjamin W. Teh

    2017-10-01

    Full Text Available BackgroundA translational study in patients with myeloma to determine the utility of immune profiling to predict infection risk in patients with hematological malignancy was conducted.MethodsBaseline, end of induction, and maintenance peripheral blood mononuclear cells from 40 patients were evaluated. Immune cell populations and cytokines released from 1 × 106 cells/ml cultured in the presence of a panel of stimuli (cytomegalovirus, influenza, S. pneumoniae, phorbol myristate acetate/ionomycin and in media alone were quantified. Patient characteristics and infective episodes were captured from clinical records. Immunological variables associated with increased risk for infection in the 3-month period following sample collection were identified using univariate analysis (p < 0.05 and refined with multivariable analysis to define a predictive immune profile.Results525 stimulant samples with 19,950 stimulant–cytokine combinations across three periods were studied, including 61 episodes of infection. Mitogen-stimulated release of IL3 and IL5 were significantly associated with increased risk for subsequent infection during maintenance therapy. A lower Th1/Th2 ratio and higher cytokine response ratios for IL5 and IL13 during maintenance therapy were also significantly associated with increased risk for infection. On multivariable analysis, only IL5 in response to mitogen stimulation was predictive of infection. The lack of cytokine response and numerical value of immune cells were not predictive of infection.ConclusionProfiling cytokine release in response to mitogen stimulation can assist with predicting subsequent onset of infection in patients with hematological malignancy during maintenance therapy.

  12. Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers.

    Science.gov (United States)

    Rosner, Bernard; Glynn, Robert J; Tamimi, Rulla M; Chen, Wendy Y; Colditz, Graham A; Willett, Walter C; Hankinson, Susan E

    2013-07-15

    Relationships between some risk factors and breast cancer incidence are known to vary by tumor subtype. However, breast tumors can be classified according to a number of markers, which may be correlated, making it difficult to identify heterogeneity of risk factors with specific tumor markers when using standard competing-risk survival analysis. In this paper, we propose a constrained competing-risk survival model that allows for assessment of heterogeneity of risk factor associations according to specific tumor markers while controlling for other markers. These methods are applied to Nurses' Health Study data from 1980-2006, during which 3,398 incident invasive breast cancers occurred over 1.4 million person-years of follow-up. Results suggested that when estrogen receptor (ER) and progesterone receptor (PR) status are mutually considered, some risk factors thought to be characteristic of "estrogen-positive tumors," such as high body mass index during postmenopause and increased height, are actually significantly associated with PR-positive tumors but not ER-positive tumors, while other risk factors thought to be characteristic of "estrogen-negative tumors," such as late age at first birth, are actually significantly associated with PR-negative rather than ER-negative breast cancer. This approach provides a strategy for evaluating heterogeneity of risk factor associations by tumor marker levels while controlling for additional tumor markers.

  13. Non-animal approaches for toxicokinetics in risk evaluations of food chemicals

    NARCIS (Netherlands)

    Punt, Ans; Peijnenburg, Ad A.C.M.; Hoogenboom, Ron L.A.P.; Bouwmeester, Hans

    2017-01-01

    The objective of the present work was to review the availability and predictive value of non-animal toxicokinetic approaches and to evaluate their current use in European risk evaluations of food contaminants, additives and food contact materials, as well as pesticides and medicines. Results

  14. A genetic risk score combining ten psoriasis risk loci improves disease prediction.

    Directory of Open Access Journals (Sweden)

    Haoyan Chen

    2011-04-01

    Full Text Available Psoriasis is a chronic, immune-mediated skin disease affecting 2-3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS and a weighted (wGRS approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7 versus 12.09 (SD 1.8, p = 4.577×10(-40. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63-14.57, p = 2.010×10(-65. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC. The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10(-8. Additionally, the AUC for HLA-C alone (rs10484554 was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18, highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10(-6 and family history (p = 0.020. Using a liability threshold model, we estimated that the 10 risk loci account for only 11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date.

  15. Genetic risk prediction and neurobiological understanding of alcoholism.

    Science.gov (United States)

    Levey, D F; Le-Niculescu, H; Frank, J; Ayalew, M; Jain, N; Kirlin, B; Learman, R; Winiger, E; Rodd, Z; Shekhar, A; Schork, N; Kiefer, F; Kiefe, F; Wodarz, N; Müller-Myhsok, B; Dahmen, N; Nöthen, M; Sherva, R; Farrer, L; Smith, A H; Kranzler, H R; Rietschel, M; Gelernter, J; Niculescu, A B

    2014-05-20

    We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significant P-value SNPs in the top candidate genes discovered by CFG  (n=135 genes, 713 SNPs) was used to generate a genetic  risk prediction score (GRPS), which showed a trend towards significance (P=0.053) in separating  alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. A panel of all the nominally significant P-value single-nucleotide length polymorphisms (SNPs) in the top candidate genes discovered by CFG (n=135 genes, 713 SNPs) were used to generate a Genetic Risk Prediction Score (GRPS), which showed a trend towards significance (P=0.053) in separating alcohol-dependent individuals from controls in an independent German test cohort. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues. A much smaller panel (n=11 genes, 66 SNPs) of the top CFG-discovered genes for alcoholism, cross-validated and prioritized by this stress-reactive animal model showed better predictive ability in the independent German test cohort (P=0.041). The top CFG scoring gene for alcoholism from the initial discovery step, synuclein alpha (SNCA) remained the top gene after the stress

  16. [Early prediction of the neurological result at 12 months in newborns at neurological risk].

    Science.gov (United States)

    Herbón, F; Garibotti, G; Moguilevsky, J

    2015-08-01

    The aim of this study was to evaluate the Amiel-Tison neurological examination (AT) and cranial ultrasound at term for predicting the neurological result at 12 months in newborns with neurological risk. The study included 89 newborns with high risk of neurological damage, who were discharged from the Neonatal Intensive Care of the Hospital Zonal Bariloche, Argentina. The assessment consisted of a neurological examination and cranial ultrasound at term, and neurological examination and evaluation of development at 12 months. The sensitivity, specificity, positive and negative predictor value was calculated. The relationship between perinatal factors and neurodevelopment at 12 month of age was also calculated using logistic regression models. Seventy children completed the follow-up. At 12 months of age, 14% had an abnormal neurological examination, and 17% abnormal development. The neurological examination and the cranial ultrasound at term had low sensitivity to predict abnormal neurodevelopment. At 12 months, 93% of newborns with normal AT showed normal neurological results, and 86% normal development. Among newborns with normal cranial ultrasound the percentages were 90 and 81%, respectively. Among children with three or more perinatal risk factors, the frequency of abnormalities in the neurological response was 5.4 times higher than among those with fewer risk factors, and abnormal development was 3.5 times more frequent. The neurological examination and cranial ultrasound at term had low sensitivity but high negative predictive value for the neurodevelopment at 12 months. Three or more perinatal risk factors were associated with neurodevelopment abnormalities at 12 months of age. Copyright © 2014 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

  17. The independent effect of cancer on outcomes: a potential limitation of surgical risk prediction.

    Science.gov (United States)

    Leeds, Ira L; Canner, Joseph K; Efron, Jonathan E; Ahuja, Nita; Haut, Elliott R; Wick, Elizabeth C; Johnston, Fabian M

    2017-12-01

    Cancer patients are often thought to have worse surgical outcomes. There is a growing view that risk models do not adequately predict these outcomes. This study aims to compare the use of common risk models for benign versus malignant gastrointestinal disease. The National Surgical Quality Improvement Program (NSQIP) 2005-2015 participant use files were queried for patients undergoing elective surgery for benign and malignant diseases with a primary procedure code for major colon, pancreas, or stomach resection. Multivariable logistic regression was performed to identify independent predictors of mortality and morbidity. We identified 264,401 cases (111,563 malignant). The gastrointestinal cancer population was disproportionately male, older than 65, nonwhite, and less functionally independent. Comorbidities more common in the cancer population included diabetes, hypertension, dyspnea, and chronic obstructive pulmonary disease. Cancer patients had a longer length of stay (+0.9 days), higher mortality rate (1.7% versus 1.1%), and higher complication rate (27.4% versus 23.2%). NSQIP prediction models for complications in cancer versus noncancer patients underperformed for predicting mortality (P < 0.001). Multivariable regression demonstrated that a diagnosis of cancer requiring surgery independently conferred an 18% increased odds of death, a 9% increased odds of a complication, and an 8% increased odds of multiple complications compared to patients with benign disease. NSQIP prediction models less effectively evaluate the risk of death in cancer patients as compared to patients with benign disease. A diagnosis of cancer is independently associated with an increased risk of surgical complications. Incorporating cancer diagnosis into surgical risk models may better inform patient and surgeon expectations. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Evaluation of preformance of Predictive Models for Deoxynivalenol in Wheat

    NARCIS (Netherlands)

    Fels, van der H.J.

    2014-01-01

    The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields

  19. 76 FR 69761 - National Earthquake Prediction Evaluation Council (NEPEC)

    Science.gov (United States)

    2011-11-09

    ....S. Geological Survey National Earthquake Prediction Evaluation Council (NEPEC) AGENCY: U.S. Geological Survey. ACTION: Notice of Meeting. SUMMARY: Pursuant to Public Law 96-472, the National Earthquake... Government. The Council shall advise the Director of the U.S. Geological Survey on proposed earthquake...

  20. Image processing system performance prediction and product quality evaluation

    Science.gov (United States)

    Stein, E. K.; Hammill, H. B. (Principal Investigator)

    1976-01-01

    The author has identified the following significant results. A new technique for image processing system performance prediction and product quality evaluation was developed. It was entirely objective, quantitative, and general, and should prove useful in system design and quality control. The technique and its application to determination of quality control procedures for the Earth Resources Technology Satellite NASA Data Processing Facility are described.

  1. Prediction models and risk assessment for silicosis using a retrospective cohort study among workers exposed to silica in China

    Science.gov (United States)

    Tse, Lap Ah; Dai, Juncheng; Chen, Minghui; Liu, Yuewei; Zhang, Hao; Wong, Tze Wai; Leung, Chi Chiu; Kromhout, Hans; Meijer, Evert; Liu, Su; Wang, Feng; Yu, Ignatius Tak-sun; Shen, Hongbing; Chen, Weihong

    2015-01-01

    This study aims to develop a prognostic risk prediction model for the development of silicosis among workers exposed to silica dust in China. The prediction model was performed by using retrospective cohort of 3,492 workers exposed to silica in an iron ore, with 33 years of follow-up. We developed a risk score system using a linear combination of the predictors weighted by the LASSO penalized Cox regression coefficients. The model’s predictive accuracy was evaluated using time-dependent ROC curves. Six predictors were selected into the final prediction model (age at entry of the cohort, mean concentration of respirable silica, net years of dust exposure, smoking, illiteracy, and no. of jobs). We classified workers into three risk groups according to the quartile (Q1, Q3) of risk score; 203 (23.28%) incident silicosis cases were derived from the high risk group (risk score ≥ 5.91), whilst only 4 (0.46%) cases were from the low risk group (risk score < 3.97). The score system was regarded as accurate given the range of AUCs (83–96%). This study developed a unique score system with a good internal validity, which provides scientific guidance to the clinicians to identify high-risk workers, thus has important cost efficient implications. PMID:26090590

  2. Obesity surgery mortality risk score for the prediction of complications after laparoscopic bariatric surgery.

    Science.gov (United States)

    Lorente, Leyre; Ramón, José Manuel; Vidal, Pablo; Goday, Alberto; Parri, Alejandra; Lanzarini, Enrique; Pera, Manuel; Grande, Luis

    2014-05-01

    Morbimortality after bariatric surgery varies according to patient characteristics and associated comorbidities. The aim of this study was to evaluate the usefulness of the Obesity sugery mortality risk score scale (OS-MRS) to predict the risk of postoperative complications after bariatric surgery. A retrospective study was performed of a prospective series of patients undergoing bariatric surgery in which the OS-MRS scale was applied preoperatively. Postoperative complications were classified as proposed by Dindo-Clavien. We analyzed the relationship between the categories of OS-MRS scale: A) low risk, B) intermediate risk, and C) high risk and the presence of complications. Between May 2008 and June 2012, 198 patients were included (85 [42.9%] after gastric bypass and 113 [57.1%] after sleeve gastrectomy). Using the OS-MRS scale, 124 patients were classified as class A (62.6%), 70 as class B (35.4%) and 4 as class C (2%). The overall morbidity rate was 12.6% (25 patients). A significant association between OS-MRS scale and rate of complications (7.3, 20 and 50%, respectively, P=.004) was demonstrated. The gastric bypass was associated with a higher complication rate than sleeve gastrectomy (P=.007). In multivariate analysis, OS-MRS scale and surgical technique were the only significant predictive factors. The OS-MRS scale is a useful tool to predict the risk of complications and can be used as a guide when choosing the type of bariatric surgery. Copyright © 2013 AEC. Published by Elsevier Espana. All rights reserved.

  3. An RES-Based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting

    Science.gov (United States)

    Faramarzi, F.; Ebrahimi Farsangi, M. A.; Mansouri, H.

    2013-07-01

    Most blasting operations are associated with various forms of energy loss, emerging as environmental side effects of rock blasting, such as flyrock, vibration, airblast, and backbreak. Backbreak is an adverse phenomenon in rock blasting operations, which imposes risk and increases operation expenses because of safety reduction due to the instability of walls, poor fragmentation, and uneven burden in subsequent blasts. In this paper, based on the basic concepts of a rock engineering systems (RES) approach, a new model for the prediction of backbreak and the risk associated with a blast is presented. The newly suggested model involves 16 effective parameters on backbreak due to blasting, while retaining simplicity as well. The data for 30 blasts, carried out at Sungun copper mine, western Iran, were used to predict backbreak and the level of risk corresponding to each blast by the RES-based model. The results obtained were compared with the backbreak measured for each blast, which showed that the level of risk achieved is in consistence with the backbreak measured. The maximum level of risk [vulnerability index (VI) = 60] was associated with blast No. 2, for which the corresponding average backbreak was the highest achieved (9.25 m). Also, for blasts with levels of risk under 40, the minimum average backbreaks (<4 m) were observed. Furthermore, to evaluate the model performance for backbreak prediction, the coefficient of correlation ( R 2) and root mean square error (RMSE) of the model were calculated ( R 2 = 0.8; RMSE = 1.07), indicating the good performance of the model.

  4. Moderating the Influence of Current Intention to Improve Suicide Risk Prediction.

    Science.gov (United States)

    Zaher, Nawal A; Buckingham, Christopher D

    2016-01-01

    When assessors evaluate a person's risk of completing suicide, the person's expressed current intention is one of the most influential factors. However, ifpeople say they have no intention, this may not be true for a number of reasons. This paper explores the reliability of negative intention in data provided by mental-health services using the GRiST decision support system in England. It identifies features within a risk assessment record that can classify a negative statement regarding current intention of suicide as being reliable or unreliable. The algorithm is tested on previously conducted assessments, where outcomes found in later assessments do or do not match the initially stated intention. Test results show significant separation between the two classes. It means suicide predictions could be made more accurate by modifying the assessment process and associated risk judgement in accordance with a better understanding of the person's true intention.

  5. Best Clinical Practice: Current Controversies in Evaluation of Low-Risk Chest Pain-Part 1.

    Science.gov (United States)

    Long, Brit; Koyfman, Alex

    2016-12-01

    Chest pain is a common presentation to the emergency department (ED), though the majority of patients are not diagnosed with acute coronary syndrome (ACS). Many patients are admitted to the hospital due to fear of ACS. Our aim was to investigate controversies in low-risk chest pain evaluation, including risk of missed ACS, stress test, and coronary computed tomography angiography (CCTA). Chest pain accounts for 10 million ED visits in the United States annually. Many patients are at low risk for a major cardiac adverse event (MACE). With negative troponin and nonischemic electrocardiogram (ECG), the risk of MACE and myocardial infarction (MI) is evaluation in low- to intermediate-risk patients within 72 h. These modalities add little to further risk stratification. These evaluations do not appropriately risk stratify patients who are already at low risk, nor do they diagnose acute MI. CCTA is an anatomic evaluation of the coronary vasculature with literature support to decrease ED length of stay, though it is associated with downstream testing. Literature is controversial concerning further risk stratification in already low-risk patients. With nonischemic ECG and negative cardiac biomarker, the risk of ACS approaches chest pain patients is controversial. These tests may allow prognostication but do not predict ACS risk beyond ECG and troponin. CCTA may be useful for intermediate-risk patients, though further studies are required. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. PP112. Prediction of preeclampsia based on clinical risk factors: A prospective high-risk cohort study : 18th World Congress of the International Society for the Study of Hypertension in Pregnancy, 9-12 July 2012, Geneva, Switzerland

    NARCIS (Netherlands)

    Wong, T.Y.; Groen, H.; Faas, M.M.; van Pampus, M.G.

    Introduction Early recognition of preeclampsia (PE) is crucial for better obstetric care. Clinical risk factors are easier to identify than biochemical markers and may be useful in the prediction of PE. Objectives To evaluate which risk factors provide the best prediction for PE in a group at

  7. Evaluation of wave runup predictions from numerical and parametric models

    Science.gov (United States)

    Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

    2014-01-01

    Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

  8. The context of medicines’ use in benefit-risk evaluation

    NARCIS (Netherlands)

    Willemen, M.J.C.|info:eu-repo/dai/nl/304822701

    2011-01-01

    In benefit-risk evaluations there is a trend towards a life cycle approach including continuous benefit-risk evaluation instead of a single benefit-risk assessment at a certain (fixed) point in time. The objective of this thesis is to unravel how the context in which a medicine is used adds to the

  9. Consumer Evaluations of Food Risk Management Quality in Europe

    NARCIS (Netherlands)

    Kleef, van E.; Houghton, J.R.; Krystallis, A.; Pfenning, U.; Rowe, G.; Dijk, van H.; Lans, van der I.A.; Frewer, L.J.

    2007-01-01

    In developing and implementing appropriate food risk management strategies, it is important to understand how consumers evaluate the quality of food risk management practices. The aim of this study is to model the underlying psychological factors influencing consumer evaluations of food risk

  10. Predicting risk of atrial fibrillation after heart valve surgery: evaluation of a Brazilian risk score Predizendo risco de fibrilação atrial após cirurgia cardíaca valvar: avaliação de escore de risco brasileiro

    Directory of Open Access Journals (Sweden)

    Michel Pompeu Barros de Oliveira Sá

    2012-03-01

    Full Text Available OBJECTIVE: The aim of this study is to evaluate the applicability of a Brazilian score for predicting atrial fibrillation (AF in patients undergoing heart valve surgery in the Division of Cardiovascular Surgery of Pronto Socorro Cardiológico de Pernambuco - PROCAPE (Recife, PE, Brazil. METHODS: Retrospective study involving 491 consecutive patients operated between May/2007 and December/2010. The registers contained all the information used to calculate the score. The outcome of interest was AF. We calculated association of model factors with AF (univariate analysis and multivariate logistic regression analysis, and association of risk score classes with AF. RESULTS: The incidence of AF was 31.2%. In multivariate analysis, the four variables of the score were predictors of postoperative AF: age >70 years (OR 6.82; 95%CI 3.34-14.10; P 1500 ml at first 24 hours (OR 1.92; 95%CI 1.28-2.88; P=0.002. We observed that the higher the risk class of the patient (low, medium, high, very high, the greater is the incidence of postoperative AF (4.2%; 18.1%; 30.8%; 49.2%, showing that the model seems to be a good predictor of risk of postoperative AF, in a statistically significant association (POBJETIVO: O objetivo deste estudo é avaliar a aplicabilidade de um escore brasileiro na predição de fibrilação atrial (FA pós-operatória em pacientes submetidos à cirurgia cardíaca valvar na Divisão de Cirurgia Cardiovascular do Pronto Socorro Cardiológico de Pernambuco - PROCAPE (Recife, PE, Brasil. MÉTODOS: Estudo retrospectivo envolvendo 491 pacientes consecutivos operados entre maio/2007 e dezembro/2010. Os registros continham todas as informações utilizadas para calcular a pontuação. O desfecho de interesse foi FA. Calculamos associação de fatores do escore com FA (análise univariada e análise de regressão logística multivariada, e associação de classes de risco do escore com FA. RESULTADOS: A incidência de FA foi de 31,2%. Na an

  11. Malignancy risk prediction for primary jejunum-ileal tumors

    Directory of Open Access Journals (Sweden)

    MARQUES Ruy Garcia

    2000-01-01

    Full Text Available This work is aimed at identifying factors associated with primary jejunum-ileal tumors malignancy, defining a prediction model with sensitivity, specificity and accuracy to distinguish malign from benign neoplasms. These tumors are rare, have highly unspecific presentation and, frequently, are diagnosed late. We reviewed the charts of 42 patients with primary jejunum-ileal tumors treated in the Department of General Surgery of Rio de Janeiro State University Hospital, Rio de Janeiro, RJ, Brazil, from 1969 to 1998. We performed bivariate analyses, based on chi² test, searching associations between tumors malignancy and demographic and clinical variables. Then logistic regression was employed to consider the independent effect of variables previously identified on malignancy risk. The malign tumors included 11 adenocarcinomas, 7 leiomyosarcomas, 5 carcinoids and 4 lymphomas; the benign tumors included 10 leiomyomas, 2 hamartomas, and single cases of adenoma, multiple neurilemoma and choristoma. The bivariate analyses indicated the association between malignancy and palpable abdominal mass (P = 0.003, period from signs and symptoms onset to diagnosis (P = 0.016, anemia (P = 0.020, anorexia (P = 0.003, abdominal pain (P = 0.031, weight loss (P = 0.001, nausea and vomit (P = 0.094, and intestinal obstruction (P = 0.066; no association with patients demographic characteristics were found. In the final logistic regression model, weight loss, anemia and intestinal obstruction were statistically associated with the dependent variable of interest. Based only on three variables -- weight loss, anemia and intestinal obstruction -- the model defined was able to predict primary jejunum-ileal tumors malignancy with sensitivity of 85.2%, specificity of 80.0%, and accuracy of 83.3%.

  12. Risk evaluation methods at individual ship and company level

    NARCIS (Netherlands)

    C. Heij (Christiaan); S. Knapp (Sabine)

    2011-01-01

    textabstractSafety management and risk profiling to identify substandard ships are of importance to the shipping industry. Whereas current methods rely heavily on detention risk and flag state performance, we extend the risk assessment by considering various risk dimensions and by evaluating a wide

  13. Prediction of pressure ulcer development in hospitalized patients: a tool for risk assessment

    Science.gov (United States)

    Schoonhoven, L; Grobbee, D E; Donders, A R T; Algra, A; Grypdonck, M H; Bousema, M T; Schrijvers, A J P; Buskens, E

    2006-01-01

    Objectives To identify independent predictors for development of pressure ulcers in hospitalized patients and to develop a simple prediction rule for pressure ulcer development. Design The Prevention and Pressure Ulcer Risk Score Evaluation (prePURSE) study is a prospective cohort study in which patients are followed up once a week until pressure ulcer occurrence, discharge from hospital, or length of stay over 12 weeks. Data were collected between January 1999 and June 2000. Setting Two large hospitals in the Netherlands. Participants Adult patients admitted to the surgical, internal, neurological and geriatric wards for more than 5 days were eligible. A consecutive sample of 1536 patients was visited, 1431 (93%) of whom agreed to participate. Complete follow up data were available for 1229 (80%) patients. Main outcome measures Occurrence of a pressure ulcer grade 2 or worse during admission to hospital. Results Independent predictors of pressure ulcers were age, weight at admission, abnormal appearance of the skin, friction and shear, and planned surgery in coming week. The area under the curve of the final prediction rule was 0.70 after bootstrapping. At a cut off score of 20, 42% of the patient weeks were identified as at risk for pressure ulcer development, thus correctly identifying 70% of the patient weeks in which a pressure ulcer occurred. Conclusion A simple clinical prediction rule based on five patient characteristics may help to identify patients at increased risk for pressure ulcer development and in need of preventive measures. PMID:16456213

  14. Spatial prediction models for landslide hazards: review, comparison and evaluation

    Directory of Open Access Journals (Sweden)

    A. Brenning

    2005-01-01

    Full Text Available The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting 'present' and 'future' landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data.

  15. Risk Evaluation of Endocrine-Disrupting Chemicals

    Directory of Open Access Journals (Sweden)

    Laura Gioiosa

    2015-10-01

    Full Text Available We review here our studies on early exposure to low doses of the estrogenic endocrine-disrupting chemical bisphenol A (BPA on behavior and metabolism in CD-1 mice. Mice were exposed in utero from gestation day (GD 11 to delivery (prenatal exposure or via maternal milk from birth to postnatal day 7 (postnatal exposure to 10 µg/kg body weight/d of BPA or no BPA (controls. Bisphenol A exposure resulted in long-term disruption of sexually dimorphic behaviors. Females exposed to BPA pre- and postnatally showed increased anxiety and behavioral profiles similar to control males. We also evaluated metabolic effects in prenatally exposed adult male offspring of dams fed (from GD 9 to 18 with BPA at doses ranging from 5 to 50 000 µg/kg/d. The males showed an age-related significant change in a number of metabolic indexes ranging from food intake to glucose regulation at BPA doses below the no observed adverse effect level (5000 µg/kg/d. Consistent with prior findings, low but not high BPA doses produced significant effects for many outcomes. These findings provide further evidence of the potential risks that developmental exposure to low doses of the endocrine disrupter BPA may pose to human health, with fetuses and infants being highly vulnerable.

  16. Skinfold reference curves and their use in predicting metabolic syndrome risk in children.

    Science.gov (United States)

    Andaki, Alynne C R; Quadros, Teresa M B de; Gordia, Alex P; Mota, Jorge; Tinôco, Adelson L A; Mendes, Edmar L

    To draw skinfold (SF) reference curves (subscapular, suprailiac, biceps, triceps) and to determine SF cutoff points for predicting the risk of metabolic syndrome (MetS) in children aged 6-10 years old. This was a cross-sectional study with a random sample of 1480 children aged 6-10 years old, 52.2% females, from public and private schools located in the urban and rural areas of the municipality of Uberaba (MG). Anthropometry, blood pressure, and fasting blood samples were taken at school, following specific protocols. The LMS method was used to draw the reference curves and ROC curve analysis to determine the accuracy and cutoff points for the evaluated skinfolds. The four SF evaluated (subscapular, suprailiac, biceps, and triceps) and their sum (∑4SF) were accurate in predicting MetS for both girls and boys. Additionally, cutoffs have been proposed and percentile curves (p5, p10, p25, p50, p75, p90, and p95) were outlined for the four SF and ∑4SF, for both genders. SF measurements were accurate in predicting metabolic syndrome in children aged 6-10 years old. Age- and gender-specific smoothed percentiles curves of SF provide a reference for the detection of risk for MetS in children. Copyright © 2017. Published by Elsevier Editora Ltda.

  17. Using single leg standing time to predict the fall risk in elderly.

    Science.gov (United States)

    Chang, Chun-Ju; Chang, Yu-Shin; Yang, Sai-Wei

    2013-01-01

    In clinical evaluation, we used to evaluate the fall risk according to elderly falling experience or the balance assessment tool. Because of the tool limitation, sometimes we could not predict accurately. In this study, we first analyzed 15 healthy elderly (without falling experience) and 15 falling elderly (1~3 time falling experience) balance performance in previous research. After 1 year follow up, there was only 1 elderly fall down during this period. It seemed like that falling experience had a ceiling effect on the falling prediction. But we also found out that using single leg standing time could be more accurately to help predicting the fall risk, especially for the falling elderly who could not stand over 10 seconds by single leg, and with a significant correlation between the falling experience and single leg standing time (r = -0.474, p = 0.026). The results also showed that there was significant body sway just before they falling down, and the COP may be an important characteristic in the falling elderly group.

  18. Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

    Directory of Open Access Journals (Sweden)

    Hujun He

    2017-01-01

    Full Text Available The prediction and risk classification of collapse is an important issue in the process of highway construction in mountainous regions. Based on the principles of information entropy and Mahalanobis distance discriminant analysis, we have produced a collapse hazard prediction model. We used the entropy measure method to reduce the influence indexes of the collapse activity and extracted the nine main indexes affecting collapse activity as the discriminant factors of the distance discriminant analysis model (i.e., slope shape, aspect, gradient, and height, along with exposure of the structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate, and degree of rock weathering. We employ postearthquake collapse data in relation to construction of the Yingxiu-Wolong highway, Hanchuan County, China, as training samples for analysis. The results were analyzed using the back substitution estimation method, showing high accuracy and no errors, and were the same as the prediction result of uncertainty measure. Results show that the classification model based on information entropy and distance discriminant analysis achieves the purpose of index optimization and has excellent performance, high prediction accuracy, and a zero false-positive rate. The model can be used as a tool for future evaluation of collapse risk.

  19. Predicting patients at risk for pain associated with electrochemotherapy

    DEFF Research Database (Denmark)

    Quaglino, Pietro; Matthiessen, Louise Wichmann; Curatolo, Pietro

    2015-01-01

    BACKGROUND: Electrochemotherapy describes the use of electric pulses to enhance chemotherapy uptake, and has proven highly efficient in treating cutaneous metastases. Patients referred for electrochemotherapy present with diverse clinical pictures, from multiple small lesions to large, ulcerated...... lesions. Post-electrochemotherapy pain has been observed in some patients. The objectives of this study were to evaluate pain scores before and after electrochemotherapy, and to investigate if patients at risk of post-procedure pain could be identified. METHODS: Seven cancer centres in the International...... patients 39% had metastatic melanoma, 18% squamous cell carcinoma, 16% breast cancer, 13% basal-cell carcinoma, and 14% other malignancies. Median size of the largest nodules was 2.3 cm (range 0.3-40 cm). A majority of patients presented with low pain scores, and this continued through follow-up (74...

  20. Predictive model for estimating risk of crush syndrome: a data mining approach.

    Science.gov (United States)

    Aoki, Noriaki; Demsar, Janez; Zupan, Blaz; Mozina, Martin; Pretto, Ernesto A; Oda, Jun; Tanaka, Hiroshi; Sugimoto, Katsuhiko; Yoshioka, Toshiharu; Fukui, Tsuguya

    2007-04-01

    There is no standard triage method for earthquake victims with crush injuries because of a scarcity of epidemiologic and quantitative data. We conducted a retrospective cohort study to develop predictive models based on clinical data for crush injury in the Kobe earthquake. The medical records of 372 patients with crush injuries from the Kobe earthquake were retrospectively analyzed. Twenty-one risk factors were assessed with logistic regression analysis for three outcomes relating to crush syndrome. Two types of predictive triage models--initial evaluation in the field and secondary assessment at the hospital--were developed using logistic regression analysis. Classification accuracy, Brier score and area under the receiver operating characteristic curve (AUC) were used to evaluate the model. The initial triage model, which includes pulse rate, delayed rescue, and abnormal urine color, has an AUC of 0.73. The secondary model, which includes WBC, tachycardia, abnormal urine color, and hyperkalemia, shows an AUC of 0.76. These triage models may be especially useful to nondisaster experts for distinguishing earthquake victims at high risk of severe crush syndrome from those at lower risk. Application of the model may allow relief workers to better utilize limited medical and transportation resources in the aftermath of a disaster.

  1. Deep learning architectures for multi-label classification of intelligent health risk prediction.

    Science.gov (United States)

    Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

    2017-12-28

    Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging

  2. NanoString-based breast cancer risk prediction for women with sclerosing adenosis.

    Science.gov (United States)

    Winham, Stacey J; Mehner, Christine; Heinzen, Ethan P; Broderick, Brendan T; Stallings-Mann, Melody; Nassar, Aziza; Vierkant, Robert A; Hoskin, Tanya L; Frank, Ryan D; Wang, Chen; Denison, Lori A; Vachon, Celine M; Frost, Marlene H; Hartmann, Lynn C; Aubrey Thompson, E; Sherman, Mark E; Visscher, Daniel W; Degnim, Amy C; Radisky, Derek C

    2017-11-01

    Sclerosing adenosis (SA), found in ¼ of benign breast disease (BBD) biopsies, is a histological feature characterized by lobulocentric proliferation of acini and stromal fibrosis and confers a two-fold increase in breast cancer risk compared to women in the general population. We evaluated a NanoString-based gene expression assay to model breast cancer risk using RNA derived from formalin-fixed, paraffin-embedded (FFPE) biopsies with SA. The study group consisted of 151 women diagnosed with SA between 1967 and 2001 within the Mayo BBD cohort, of which 37 subsequently developed cancer within 10 years (cases) and 114 did not (controls). RNA was isolated from benign breast biopsies, and NanoString-based methods were used to assess expression levels of 61 genes, including 35 identified by previous array-based profiling experiments and 26 from biological insight. Diagonal linear discriminant analysis of these data was used to predict cancer within 10 years. Predictive performance was assessed with receiver operating characteristic area under the curve (ROC-AUC) values estimated from 5-fold cross-validation. Gene expression prediction models achieved cross-validated ROC-AUC estimates ranging from 0.66 to 0.70. Performing univariate associations within each of the five folds consistently identified genes DLK2, EXOC6, KIT, RGS12, and SORBS2 as significant; a model with only these five genes showed cross-validated ROC-AUC of 0.75, which compared favorably to risk prediction using established clinical models (Gail/BCRAT: 0.57; BBD-BC: 0.67). Our results demonstrate that biomarkers of breast cancer risk can be detected in benign breast tissue years prior to cancer development in women with SA. These markers can be assessed using assay methods optimized for RNA derived from FFPE biopsy tissues which are commonly available.

  3. Interpreting incremental value of markers added to risk prediction models.

    Science.gov (United States)

    Pencina, Michael J; D'Agostino, Ralph B; Pencina, Karol M; Janssens, A Cecile J W; Greenland, Philip

    2012-09-15

    The discrimination of a risk prediction model measures that model's ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.

  4. Evaluation of Risk Factors Associated with Endometriosis in Infertile Women

    Directory of Open Access Journals (Sweden)

    Mahnaz Ashrafi

    2016-05-01

    Full Text Available Background: Endometriosis affects women’s physical and mental wellbeing. Symptoms include dyspareunia, dysmenorrhea, pelvic pain, and infertility. The purpose of this study is to assess the correlation between some relevant factors and symptoms and risk of an endometriosis diagnosis in infertile women. Materials and Methods: A retrospective study of 1282 surgical patients in an infertility Institute, Iran between 2011 and 2013 were evaluated by laparoscopy. Of these, there were 341 infertile women with endometriosis (cases and 332 infertile women with a normal pelvis (comparison group. Chi-square and t tests were used to compare these two groups. Logistic regression was done to build a prediction model for an endometriosis diagnosis. Results: Gravidity [odds ratio (OR: 0.8, confidence interval (CI: 0.6-0.9, P=0.01], parity (OR: 0.7, CI: 0.6-0.9, P=0.01, family history of endometriosis (OR: 4.9, CI: 2.1-11.3, P0.05. Fatigue, diarrhea, constipation, dysmenorrhea, dyspareunia, pelvic pain and premenstrual spotting were more significant among late-stage endometriosis patients than in those with early-stage endometriosis and more prevalent among patients with endometriosis than that of the comparison group. In the logistic regression model, gravidity, family history of endometriosis, history of galactorrhea, history of pelvic surgery, dysmenorrhoea, pelvic pain, dysparaunia, premenstrual spotting, fatigue, and diarrhea were significantly associated with endometriosis. However, the number of pregnancies was negatively related to endometriosis. Conclusion: Endometriosis is a considerable public health issue because it affects many women and is associated with the significant morbidity. In this study, we built a prediction model which can be used to predict the risk of endometriosis in infertile women.

  5. Health risk perceptions predict smoking-related outcomes in Greek college students.

    Science.gov (United States)

    Jacobson, John D; Catley, Delwyn; Lee, Hyoung S; Harrar, Solomon W; Harris, Kari Jo

    2014-09-01

    Health risk perception in smoking behavior was prospectively evaluated in a cluster-randomized trial for smoking cessation in Greek college students. Perceived Vulnerability (PV), Precaution Effectiveness, Optimistic Bias, and smoking behavior measures (quit attempts and cessation) were assessed in college-aged Greek student smokers at baseline, end of treatment (3 months), and follow-up (6 months). Using generalized estimating equations, baseline risk perception variables and change in risk perception variables between baseline and end of treatment were examined as predictors of the dichotomous smoking outcome variables. Results revealed that higher baseline PV [OR = 1.42 (1.21, 1.68)] predicted a greater likelihood of a quit attempt (n = 267). An increased likelihood of cessation [OR = 1.41 (1.15, 1.72)] was also predicted by an increase in PV from baseline to end of treatment (n = 243). Overall results suggested that PV was the strongest predictor of smoking behavior change, supporting further examination of health risk perceptions in promoting smoking cessation among Greek college smokers.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-12-15

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

  7. Global risk scores and exercise testing for predicting all-cause mortality in a preventive medicine program.

    Science.gov (United States)

    Aktas, Mehmet K; Ozduran, Volkan; Pothier, Claire E; Lang, Richard; Lauer, Michael S

    2004-09-22

    The usefulness of exercise stress test results and global cardiovascular risk systems for predicting all-cause mortality in asymptomatic individuals seen in clinical settings is unclear. To determine the validity for prediction of all-cause mortality of the Framingham Risk Score and of a recently described European global scoring system Systematic Coronary Risk Evaluation (SCORE) for cardiovascular mortality among asymptomatic individuals evaluated in a clinical setting and to determine the potential prognostic value of exercise stress testing once these baseline risks are known. Prospective cohort study of 3554 asymptomatic adults between the ages of 50 and 75 years who underwent exercise stress testing as part of an executive health program between October 1990 and December 2002; participants were followed up for a mean of 8 years. Global risk based on the Framingham Risk Score and the European SCORE. Prospectively recorded exercise stress test result abnormalities included impaired physical fitness, abnormal heart rate recovery, ventricular ectopy, and ST-segment abnormalities. The primary end point was all-cause mortality. There were 114 deaths. The c-index, which corresponds to receiver operating characteristic curve values, and the Akaike Information Criteria found that the European SCORE was superior to the Framingham Risk Score in estimating global mortality risk. In a multivariable model, independent predictors of death were a higher SCORE (for 1% predicted increase in absolute risk, relative risk [RR], 1.07; 95% confidence interval [CI], 1.04-1.09; Pmortality. Among patients in the highest tertile from the SCORE, an abnormal exercise stress test result, defined as either impaired functional capacity or an abnormal heart rate recovery, identified a mortality risk of more than 1% per year. Exercise stress testing when combined with the European global risk SCORE may be useful for stratifying risk in asymptomatic individuals in a comprehensive executive

  8. Evaluation of nosocomial infection risk using APACHE II scores in the neurological intensive care unit.

    Science.gov (United States)

    Li, Hai-Ying; Li, Shu-Juan; Yang, Nan; Hu, Wen-Li

    2014-08-01

    To evaluate the feasibility and accuracy of using the Acute Physiology, Age and Chronic Health Evaluation II (APACHE II) scoring system for predicting the risk of nosocomial infection in the neurological intensive care unit (NICU), 216 patients transferred to NICU within 24hours of admission were retrospectively evaluated. Based on admission APACHE II scores, they were classified into three groups, with higher APACHE II scores representing higher infectious risk. The device utilization ratios and device-associated infection ratios of NICU patients were analyzed and compared with published reports on patient outcome. Statistical analysis of nosocomial infection ratios showed obvious differences between the high-risk, middle-risk and low-risk groups (pAPACHE II model in predicting the risk of nosocomial infection was 0.81, which proved to be reliable and consistent with the expectation. In addition, we found statistical differences in the duration of hospital stay (patient-days) and device utilization (device-days) between different risk groups (pAPACHE II scoring system was validated in predicting the risk of nosocomial infection, duration of patient-days and device-days, and providing accurate assessment of patients' condition, so that appropriate prevention strategies can be implemented based on admission APACHE II scores. Copyright © 2014. Published by Elsevier Ltd.

  9. Endometrial cancer risk prediction including serum-based biomarkers : results from the EPIC cohort

    NARCIS (Netherlands)

    Fortner, Renée T.; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H. Bas|info:eu-repo/dai/nl/06929528X; Peeters, Petra H M|info:eu-repo/dai/nl/074099655; Weiderpass, Elisabete; Gram, Inger T.; Gavrilyuk, Oxana; Quirós, J. Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay Tee; Allen, Naomi E.; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A.; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

    2017-01-01

    Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested

  10. Prediction and classification of cardiovascular disease risk in older adults with diabetes.

    Science.gov (United States)

    Mukamal, K J; Kizer, J R; Djoussé, L; Ix, J H; Zieman, S; Siscovick, D S; Sibley, C T; Tracy, R P; Arnold, A M

    2013-02-01

    We sought to derive and validate a cardiovascular disease (CVD) prediction algorithm for older adults with diabetes, and evaluate the incremental benefit of adding novel circulating biomarkers and measures of subclinical atherosclerosis. As part of the Cardiovascular Health Study (CHS), a population-based cohort of adults aged ≥65 years, we examined the 10 year risk of myocardial infarction, stroke and cardiovascular death in 782 older adults with diabetes, in whom 265 events occurred. We validated predictive models in 843 adults with diabetes, who were followed for 7 years in a second cohort, the Multi-Ethnic Study of Atherosclerosis (MESA); here 71 events occurred. The best fitting standard model included age, smoking, systolic blood pressure, total and HDL-cholesterol, creatinine and the use of glucose-lowering agents; however, this model had a C statistic of 0.64 and poorly classified risk in men. Novel biomarkers did not improve discrimination or classification. The addition of ankle-brachial index, electrocardiographic left ventricular hypertrophy and internal carotid intima-media thickness modestly improved discrimination (C statistic 0.68; p = 0.002) and classification (net reclassification improvement [NRI] 0.12; p = 0.01), mainly in those remaining free of CVD. Results were qualitatively similar in the MESA, with a change in C statistic from 0.65 to 0.68 and an NRI of 0.09 upon inclusion of subclinical disease measures. Standard clinical risk factors and novel biomarkers poorly discriminate and classify CVD risk in older adults with diabetes. The inclusion of subclinical atherosclerotic measures modestly improves these features, but to develop more robust risk prediction, a better understanding of the pathophysiology and determinants of CVD in this patient group is needed.

  11. A large-scale evaluation of computational protein function prediction.

    Science.gov (United States)

    Radivojac, Predrag; Clark, Wyatt T; Oron, Tal Ronnen; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kaßner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Boehm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas A; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo

    2013-03-01

    Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

  12. Evaluation of residue-residue contact predictions in CASP9

    KAUST Repository

    Monastyrskyy, Bohdan

    2011-01-01

    This work presents the results of the assessment of the intramolecular residue-residue contact predictions submitted to CASP9. The methodology for the assessment does not differ from that used in previous CASPs, with two basic evaluation measures being the precision in recognizing contacts and the difference between the distribution of distances in the subset of predicted contact pairs versus all pairs of residues in the structure. The emphasis is placed on the prediction of long-range contacts (i.e., contacts between residues separated by at least 24 residues along sequence) in target proteins that cannot be easily modeled by homology. Although there is considerable activity in the field, the current analysis reports no discernable progress since CASP8.

  13. Is metabolic syndrome predictive of prevalence, extent, and risk of coronary artery disease beyond its components? results from the multinational coronary ct angiography evaluation for clinical outcome: An international multicenter registry (confirm) : An international multicenter registry (confirm)

    NARCIS (Netherlands)

    A. Ahmadi (Amir); J. Leipsic (Jonathon); G.M. Feuchtner (Gudrun); H. Gransar (Heidi); Kalra, D. (Dan); R. Heo (Ran); S. Achenbach (Stephan); D. Andreini (Daniele); M. Al-Mallah (Mouaz); D.S. Berman (Daniel S.); M.J. Budoff (Matthew); F. Cademartiri (Filippo); T.Q. Callister (Tracy); H.-J. Chang (Hyuk-Jae); K. Chinnaiyan (Kavitha); B.J.W. Chow (Benjamin); R.C. Cury (Ricardo); A. Delago (Augustin); M. Gomez (Millie); M. Hadamitzky (Martin); J. Hausleiter (Jörg); N. Hindoyan (Niree); P.A. Kaufmann (Philipp); Y.-J. Kim (Yong-Jin); F.Y. Lin (Fay); E. Maffei (Erica); G. Pontone (Gianluca); G.L. Raff (Gilbert); L.J. Shaw (Leslee); T.C. Villines (Todd); A.M. Dunning (Allison M.); J.K. Min (James)

    2015-01-01

    textabstractAlthough metabolic syndrome is associated with increased risk of cardiovascular disease and events, its added prognostic value beyond its components remains unknown. This study compared the prevalence, severity of coronary artery disease (CAD), and prognosis of patients with metabolic

  14. Statistical procedures for evaluating daily and monthly hydrologic model predictions

    Science.gov (United States)

    Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.

    2004-01-01

    The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.

  15. Biopsy Based Proteomic Assay Predicts Risk of Biochemical Recurrence after Radical Prostatectomy.

    Science.gov (United States)

    Saad, Fred; Latour, Mathieu; Lattouf, Jean-Baptiste; Widmer, Hugues; Zorn, Kevin C; Mes-Masson, Anne-Marie; Ouellet, Veronique; Saad, Genevieve; Prakash, Amol; Choudhury, Sibgat; Han, Gang; Karakiewicz, Pierre; Richie, Jerome P

    2017-04-01

    Current clinicopathological parameters are insufficient to predict the likelihood of biochemical recurrence in patients with prostate cancer after radical prostatectomy. Such information may help identify patients who would likely benefit from adjuvant radiotherapy rather than active surveillance. A multiplex proteomic assay, previously tested on biopsies and found to be predictive of favorable or unfavorable pathology at radical prostatectomy, was assessed for its predictive value to identify patients at higher risk for biochemical relapse. Proteomic assays from core needle biopsies of 288 men who subsequently underwent radical prostatectomy at CHUM (Centre hospitalier de l'Université de Montréal) were evaluated for the prediction of subsequent biochemical recurrence. Of the 288 men, biochemical relapse was observed in 47 (16.3%) and metastases were found in 5 (1.7%). Median followup was 68.5 months. The proteomic assay clearly separated patients into 3 categories, including those at low, intermediate and high risk for biochemical relapse (p = 0.0007). Assay scores predicted biochemical relapse on univariate analysis (HR 1.724, p = 0.0002 per 20% change in score), significantly better than other preoperative prognostic parameters. Additionally, the assay score had a significantly higher p value when combined with clinical National Comprehensive Cancer Network® stage compared to stage alone (HR 1.579, p = 0.0017 per 20% change in score). A protein based assay score derived from diagnostic needle biopsy has strong predictive ability for biochemical relapse after surgery. These results suggest that this assay score can be used at the diagnostic stage to identify patients in whom prostate cancer is potentially more biologically aggressive and active treatment should be considered. Copyright © 2017 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  16. Evaluation and comparison of mammalian subcellular localization prediction methods

    Directory of Open Access Journals (Sweden)

    Fink J Lynn

    2006-12-01

    Full Text Available Abstract Background Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER, peroxisome, and lysosome. The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE

  17. MODERN RISK MEASURES FOR INDIVIDUAL HIGHER EDUCATION INVESTMENT RISK EVALUATION

    Directory of Open Access Journals (Sweden)

    Vona Mate

    2014-07-01

    Full Text Available One of the reasons why people get degree and participate in organized education is that they want to raise their human capital or signal their inner abilities to future employers by sorting themselves out. In both cases they can expect return to their investment, because they can expect higher life-time earnings than those who do not have degree. In this paper we will refer this activity as higher education investment or education investment. In this paper the investment of the state into educating their citizens will not be considered. The question of this paper will develop the findings of Vona (2014. I suggested to introduce modern risk measures because individual risk-taking became a serious question. It was considered that modern risk measures can help to solve some issues with the relation of investment and risk. However before applying some measures from a different field of science, namely investment finance and financial mathematics, to another, economics of education, there must be a very careful consideration, because there are debate over these measures applicability even on their field of science. Value at Risk is not coherent and Expected Shortfall is only one of a great deal of possible tail loss measures. For this reason it will be discussed in detail how should we should adopt the measures, what kind of data is necessary for calculating this risk measures and what kind of new insight they can bring. With the aid of a numerical example it will be shown that with expected shortfall measure we can reflect some large losses, and potential high value of diversification. We show the value at risk based measure is not coherent and this means it points out something different in this environment. It is can be an indicator of loss in opportunities for high end returns.

  18. Evaluation of the Acidic Wet Deposition Predictions of CMAQ

    Science.gov (United States)

    Dennis, R. L.

    2002-05-01

    Acidic deposition is coming back into importance as part of more encompassing multi-pollutant thinking. Acidic and nutrient deposition is an important component of new multi-pollutant legislation being considered by the Administration. The Community Multiscale Air Quality model, CMAQ, was designed to handle multiple pollutants in a one-atmosphere context. Much of the initial evaluation of CMAQ was directed at the criteria pollutants. CMAQ's predictions of acidic deposition also need to be evaluated, not only because of the importance of deposition but also because deposition sets the lifetime of fine particles in the atmosphere. The controlling deposition is wet deposition, hence, we consider it first. We compare wet deposition for selected months throughout 1990, showing that CMAQ captures the main features of seasonality. We note that the previous problem of overprediction of winter wet deposition associated with the RADM cloud parameterization has been addressed through explicit recognition of icy cloud water. We are still plagued by the difficulty of meteorological models to predict precipitation as input to chemical transport models which produces additional scatter. Interestingly, there is a consistent differential between sulfate and nitrate wet deposition, with nitrate wet deposition being slightly lower. We explore several hypotheses for this behavior, including the hypothesis that this is more an issue of mixing than an issue of cloud chemistry. In general, CMAQ appears to be producing reasonable predictions that demonstrate an improvement in our ability to predict wet deposition, although there is room for improvement.

  19. A framework for evaluating forest landscape model predictions using empirical data and knowledge

    Science.gov (United States)

    Wen J. Wang; Hong S. He; Martin A. Spetich; Stephen R. Shifley; Frank R. Thompson; William D. Dijak; Qia. Wang

    2014-01-01

    Evaluation of forest landscape model (FLM) predictions is indispensable to establish the credibility of predictions. We present a framework that evaluates short- and long-term FLM predictions at site and landscape scales. Site-scale evaluation is conducted through comparing raster cell-level predictions with inventory plot data whereas landscape-scale evaluation is...

  20. Evaluating the effectiveness of risk-reduction strategies for consumer chemical products.

    Science.gov (United States)

    Riley, D M; Fischhoff, B; Small, M J; Fischbeck, P

    2001-04-01

    Communication about risks offers a voluntary approach to reducing exposure to pollutants. Its adequacy depends on its impact on behavior. Estimating those impacts first requires characterizing current activities and their associated risk levels, and then predicting the effectiveness of risk-reduction strategies. Characterizing the risks from chemical consumer products requires knowledge of both the physical and the behavioral processes that influence exposures. This article presents an integrated approach that combines consumer interviews, users' beliefs and behaviors, and quantitative exposure modeling. This model was demonstrated in the context of consumer exposure to a methylene chloride-based paint stripper, showing how it could be used to evaluate current levels of risk and predict the effectiveness of proposed voluntary risk-reduction strategies.

  1. Risk prediction models for acute kidney injury following major noncardiac surgery: systematic review

    National Research Council Canada - National Science Library

    Wilson, Todd; Quan, Samuel; Cheema, Kim; Zarnke, Kelly; Quinn, Rob; de Koning, Lawrence; Dixon, Elijah; Pannu, Neesh; James, Matthew T

    Acute kidney injury (AKI) is a serious complication of major noncardiac surgery. Risk prediction models for AKI following noncardiac surgery may be useful for identifying high-risk patients to target with prevention strategies...

  2. Genetic risk score predicting risk of rheumatoid arthritis phenotypes and age of symptom onset.

    Directory of Open Access Journals (Sweden)

    Lori B Chibnik

    Full Text Available Cumulative genetic profiles can help identify individuals at high-risk for developing RA. We examined the impact of 39 validated genetic risk alleles on the risk of RA phenotypes characterized by serologic and erosive status.We evaluated single nucleotide polymorphisms at 31 validated RA risk loci and 8 Human Leukocyte Antigen alleles among 542 Caucasian RA cases and 551 Caucasian controls from Nurses' Health Study and Nurses' Health Study II. We created a weighted genetic risk score (GRS and evaluated it as 7 ordinal groups using logistic regression (adjusting for age and smoking to assess the relationship between GRS group and odds of developing seronegative (RF- and CCP-, seropositive (RF+ or CCP+, erosive, and seropositive, erosive RA phenotypes. In separate case only analyses, we assessed the relationships between GRS and age of symptom onset. In 542 RA cases, 317 (58% were seropositive, 163 (30% had erosions and 105 (19% were seropositive with erosions. Comparing the highest GRS risk group to the median group, we found an OR of 1.2 (95% CI = 0.8-2.1 for seronegative RA, 3.0 (95% CI = 1.9-4.7 for seropositive RA, 3.2 (95% CI = 1.8-5.6 for erosive RA, and 7.6 (95% CI = 3.6-16.3 for seropositive, erosive RA. No significant relationship was seen between GRS and age of onset.Results suggest that seronegative and seropositive/erosive RA have different genetic architecture and support the importance of considering RA phenotypes in RA genetic studies.

  3. Automated analysis of free speech predicts psychosis onset in high-risk youths.

    Science.gov (United States)

    Bedi, Gillinder; Carrillo, Facundo; Cecchi, Guillermo A; Slezak, Diego Fernández; Sigman, Mariano; Mota, Natália B; Ribeiro, Sidarta; Javitt, Daniel C; Copelli, Mauro; Corcoran, Cheryl M

    2015-01-01

    Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry.

  4. Communicating the results of criterion referenced prediction measures: Risk categories for the Static-99R and Static-2002R sexual offender risk assessment tools.

    Science.gov (United States)

    Hanson, R Karl; Babchishin, Kelly M; Helmus, L Maaike; Thornton, David; Phenix, Amy

    2017-05-01

    This article describes principles for developing risk category labels for criterion referenced prediction measures, and demonstrates their utility by creating new risk categories for the Static-99R and Static-2002R sexual offender risk assessment tools. Currently, risk assessments in corrections and forensic mental health are typically summarized in 1 of 3 words: low, moderate, or high. Although these risk labels have strong influence on decision makers, they are interpreted differently across settings, even among trained professionals. The current article provides a framework for standardizing risk communication by matching (a) the information contained in risk tools to (b) a broadly applicable classification of "riskiness" that is independent of any particular offender risk scale. We found that the new, common STATIC risk categories not only increase concordance of risk classification (from 51% to 72%)-they also allow evaluators to make the same inferences for offenders in the same category regardless of which instrument was used to assign category membership. More generally, we argue that the risk categories should be linked to the decisions at hand, and that risk communication can be improved by grounding these risk categories in evidence-based definitions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. Design and evaluation of a novel formulation prediction system.

    Science.gov (United States)

    Guan, Jin; Xiang, Rongwu; Pan, Yusheng; Pan, Hao; Su, Xiangdong; Zhou, Liying; Cui, Yuni; Pan, Weisan

    2010-12-15

    The purpose of this study is to design and evaluate a novel formulation prediction system for formulation study of monolithic osmotic pump tablets (MOPTs). Ternary-component diagram was originally brought forward to evaluate MOPTs formulations. Optimal formulation regions were delimited in ternary-component diagrams. Water-insoluble drug gliclazide was chosen as a model drug for selecting most suitable suspending agent. With five model drugs, we obtained five ternary-component diagrams. By MATLAB(®) software, a triangular prism model was then established regarding doses as vertical coordinate and the five diagrams as cross-sections, with the formation of an optimal formulation channel in it, followed by the design of formulation prediction software (FPS 1.0). The practicality of the system was finally validated. After entering the drug information, we immediately obtained the optimal formulation region for preparing MOPTs with interpolation algorithm. The dissolution test results of the four randomly selected formulations all met the evaluating conditions. Ternary-component diagram is useful for MOPTs formulation optimization. The predictive ability of the system is tentatively confirmed and the experiment efficiency is greatly improved. Copyright © 2010 Elsevier B.V. All rights reserved.

  6. Value of routine blood tests for prediction of mortality risk in hip fracture patients

    DEFF Research Database (Denmark)

    Mosfeldt, Mathias; Pedersen, Ole Birger Vesterager; Riis, Troels

    2012-01-01

    There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission.......There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission....

  7. Beyond Framingham risk factors and coronary calcification: does aortic valve calcification improve risk prediction? The Heinz Nixdorf Recall Study.

    Science.gov (United States)

    Kälsch, Hagen; Lehmann, Nils; Mahabadi, Amir A; Bauer, Marcus; Kara, Kaffer; Hüppe, Patricia; Moebus, Susanne; Möhlenkamp, Stefan; Dragano, Nico; Schmermund, Axel; Stang, Andreas; Jöckel, Karl-Heinz; Erbel, Raimund

    2014-06-01

    Aortic valve calcification (AVC) is considered a manifestation of atherosclerosis. In this study, we investigated whether AVC adds to cardiovascular risk prediction beyond Framingham risk factors and coronary artery calcification (CAC). A total of 3944 subjects from the population based Heinz Nixdorf Recall Study (59.3±7.7 years; 53% females) were evaluated for coronary events, stroke, and cardiovascular disease (CVD) events (including all plus CV death) over 9.1±1.9 years. CT scans were performed to quantify AVC. Cox proportional hazards regressions and Harrell's C were used to examine AVC as event predictor in addition to risk factors and CAC. During follow-up, 138 (3.5%) subjects experienced coronary events, 101 (2.6%) had a stroke, and 257 (6.5%) experienced CVD events. In subjects with AVC>0 versus AVC=0 the incidence of coronary events was 8.0% versus 3.0% (p<0.001) and the incidence of CVD events was 13.0% versus 5.7% (p<0.001). The frequency of events increased significantly with increasing AVC scores (p<0.001). After adjustment for Framingham risk factors, high AVC scores (3rd tertile) remained independently associated with coronary events (HR 2.21, 95% CI 1.28 to 3.81) and CVD events (HR 1.67, 95% CI 1.08 to 2.58). After further adjustment for CAC score, HRs were attenuated (coronary events 1.55, 95% CI 0.89 to 2.69; CVD events 1.29, 95% CI 0.83 to 2.00). When adding AVC to the model containing traditional risk factors and CAC, Harrell's C indices did not increase for coronary events (from 0.744 to 0.744) or CVD events (from 0.759 to 0.759). AVC is associated with incident coronary and CVD events independent of Framingham risk factors. However, AVC fails to improve cardiovascular event prediction over Framingham risk factors and CAC. 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.

  8. Predicting adolescent risk behaviors based on an ecological framework and assets.

    Science.gov (United States)

    Reininger, Belinda M; Evans, Alexandra E; Griffin, Sarah F; Sanderson, Maureen; Vincent, Murray L; Valois, Robert F; Parra-Medina, Deborah

    2005-01-01

    To examine the relationship between an aggregate risk score (smoking, drinking, and number of sex partners) and measures of youth assets in a sample of 3439 youth aged 14-18 years. Linear regression models for African American and white males and females predicted an aggregate risk score. After adjustments, the youth asset most predictive of risk was self/peer values regarding risk behaviors. Perceived school support was also predictive. Taking an ecological approach to the measurement of adolescent health behaviors contributes to our understanding of these risk behaviors.

  9. Beyond sensation seeking: affect regulation as a framework for predicting risk-taking behaviors in high-risk sport.

    Science.gov (United States)

    Castanier, Carole; Le Scanff, Christine; Woodman, Tim

    2010-10-01

    Sensation seeking has been widely studied when investigating individual differences in the propensity for taking risks. However, risk taking can serve many different goals beyond the simple management of physiological arousal. The present study is an investigation of affect self-regulation as a predictor of risk-taking behaviors in high-risk sport. Risk-taking behaviors, negative affectivity, escape self-awareness strategy, and sensation seeking data were obtained from 265 high-risk sportsmen. Moderated hierarchical regression analysis revealed significant main and interaction effects of negative affectivity and escape self-awareness strategy in predicting risk-taking behaviors: high-risk sportsmen's negative affectivity leads them to adopt risk-taking behaviors only if they also use escape self-awareness strategy. Furthermore, the affective model remained significant when controlling for sensation seeking. The present study contributes to an in-depth understanding of risk taking in high-risk sport.

  10. Development and evaluation of a score to predict difficult epidural placement during labor.

    Science.gov (United States)

    Guglielminotti, Jean; Mentré, France; Bedairia, Ennoufous; Montravers, Philippe; Longrois, Dan

    2013-01-01

    Difficult epidural placement (DEP) during labor may be distressing for the patient and may increase the risk of dural puncture. A score predicting DEP based on the combination of individual risk factors could identify high-risk patients. This study aimed to identify risk factors for DEP and build a prediction score. Three hundred thirty patients were prospectively included. Difficult epidural placement was defined as more than 1 skin puncture with a Tuohy needle. Dura puncture occurrence was recorded. The population was randomly split into a training set and a validation set. In the training set, risk factors were identified with logistic regression and used to build a score defining 3 risk groups. Model and score discrimination was assessed with the C-index and clinical usefulness of the score with decision curves. Difficult epidural placement frequency was 30% (95% confidence interval [95% CI], 25%-35%). Dural puncture was more frequent in DEP patients (4% vs 0%, P = 0.007). Three independent risk factors for DEP were identified: difficult interspinous space palpation (odds ratio [OR], 6.1; 95% CI, 2.8-13.9), spinal deformity (OR, 2.4; 95% CI, 1.1-5.3), and inability to flex the back (OR, 3.0; 95% CI, 1.2-7.8). The C-index of the model was 0.81 (95% CI, 0.74-0.88) in the training set and 0.78 (95% CI, 0.70-0.86) in the validation set. A 5-point score was created to define groups with low risk (score 0), intermediate risk (score 1-2), and high risk (score 3-4), with predicted rates of DEP of 9.7%, 30.3%, and 68.9%, respectively. The C-index of the score was 0.79 (95% CI, 0.72-0.86) in the training set and 0.76 (95% CI, 0.69-0.84) in the validation set. Decision curves support the clinical usefulness of the score. This study confirms risk factors for DEP and proposes a score to predict DEP. The score identifies high-risk patients who may benefit from an intervention to decrease DEP. This hypothesis should be evaluated in an impact study.

  11. Four genes predict high risk of progression from smoldering to symptomatic multiple myeloma (SWOG S0120).

    Science.gov (United States)

    Khan, Rashid; Dhodapkar, Madhav; Rosenthal, Adam; Heuck, Christoph; Papanikolaou, Xenofon; Qu, Pingping; van Rhee, Frits; Zangari, Maurizio; Jethava, Yogesh; Epstein, Joshua; Yaccoby, Shmuel; Hoering, Antje; Crowley, John; Petty, Nathan; Bailey, Clyde; Morgan, Gareth; Barlogie, Bart

    2015-09-01

    Multiple myeloma is preceded by an asymptomatic phase, comprising monoclonal gammopathy of uncertain significance and smoldering myeloma. Compared to the former, smoldering myeloma has a higher and non-uniform rate of progression to clinical myeloma, reflecting a subset of patients with higher risk. We evaluated the gene expression profile of smoldering myeloma plasma cells among 105 patients enrolled in a prospective observational trial at our institution, with a view to identifying a high-risk signature. Baseline clinical, bone marrow, cytogenetic and radiologic data were evaluated for their potential to predict time to therapy for symptomatic myeloma. A gene signature derived from four genes, at an optimal binary cut-point of 9.28, identified 14 patients (13%) with a 2-year therapy risk of 85.7%. Conversely, a low four-gene score (smoldering myeloma with a 5.0% chance of progression at 2 years. The top 40 probe sets showed concordance with indices of chromosome instability. These data demonstrate high discriminatory power of a gene-based assay and suggest a role for dysregulation of mitotic checkpoints in the context of genomic instability as a hallmark of high-risk smoldering myeloma. Copyright© Ferrata Storti Foundation.

  12. Research of the risk factors predicting progression and prognosis of acute respiratory distress syndrome

    Directory of Open Access Journals (Sweden)

    Ran WANG

    2017-06-01

    Full Text Available Objective To explore the early diagnosis and risk factors for judging prognosis of acute respiratory distress syndrome (ARDS, and to provide references for clinical intervention. Methods Using the method for prospective cohort study, clinical data were collected from 64 ARDS and 66 high-risk ARDS patients in Department of Respiratory Diseases of Xinqiao Hospital from January 2013 to March 2016. They included patients' demographic data, Acute Physiology and Chronic Health Evaluation system Ⅱ (APACHE Ⅱ score, oxygenation index, blood routine test, coagulation function and inflammatory markers (procalcitonin, C-reaction protein, tumor necrosis factor and interleukin -6 within 24h and the state of survival or death of the 24th day. Risk factors for predicting progression of the high-risk ARDS patients into ARDS patients and influencing the prognosis of the ARDS patients were analyzed by using logistic regression. Results Univariate logistic regression analysis found that the independent risk factors for progression of ARDS were APACHE Ⅱ score (OR=6.764, P=0.001, hypoproteinemia (OR=10.54, P=0.002, white blood cell count (OR=3.912, P=0.012, fibrinogen (OR=9.953, P=0.064, and D-dimer (OR=4.239, P=0.029. The mortality rate was 43.75% (36/64 in ARDS group, and the oxygenation index (OR=6.573, P=0.014, platelet count (OR=9.376, P=0.003, hypoproteinemia (OR=10.738, P=0.056 were the independent risk factors of death in ARDS patients. Multivariate logistic regression showed that combination of multiple indicators for predicting ARDS improved the specificity, but reduce the sensitivity. APACHE Ⅱ and hypoproteinemia (sensitivity 62.50%, specificity 92.42% and APACHE Ⅱ and D dimmer (sensitivity 62.07%, specificity 93.33% had better specificity and sensitivity. The specificity and sensitivity of combining hypoproteinemia and platelet count to predict the risk of death in these patients were 77.78% and 60.71%. Conclusions In high-risk ARDS patients

  13. Designing and evaluating risk-based surveillance systems

    DEFF Research Database (Denmark)

    Willeberg, Preben; Nielsen, Liza Rosenbaum; Salman, Mo

    2012-01-01

    Risk-based surveillance systems reveal occurrence of disease or infection in a sample of population units, which are selected on the basis of risk factors for the condition under study. The purpose of such systems for supporting practical animal disease policy formulations and management decisions...... applicable risk estimate for use in designing and evaluating a risk-based surveillance system would be a crude (unadjusted) relative risk, odds ratio or apparent prevalence. Risk estimates found in the published literature, however, are often the results of multivariable analyses implicitly adjusting...... the estimates for confounding from other risk factors. We describe some potential unintentional effects when using adjusted risk estimates in evaluating the efficacy and sensitivity of risk-based surveillance systems (SSe). In two examples, we quantify and compare the efficacy and SSe using adjusted and crude...

  14. The ACC/AHA 2013 pooled cohort equations compared to a Korean Risk Prediction Model for atherosclerotic cardiovascular disease.

    Science.gov (United States)

    Jung, Keum Ji; Jang, Yangsoo; Oh, Dong Joo; Oh, Byung-Hee; Lee, Sang Hoon; Park, Seong-Wook; Seung, Ki-Bae; Kim, Hong-Kyu; Yun, Young Duk; Choi, Sung Hee; Sung, Jidong; Lee, Tae-Yong; Kim, Sung Hi; Koh, Sang Baek; Kim, Moon Chan; Chang Kim, Hyeon; Kimm, Heejin; Nam, Chungmo; Park, Sungha; Jee, Sun Ha

    2015-09-01

    To evaluate the performance of the American College of Cardiology/American Heart Association (ACC/AHA) 2013 Pooled Cohort Equations in the Korean Heart Study (KHS) population and to develop a Korean Risk Prediction Model (KRPM) for atherosclerotic cardiovascular disease (ASCVD) events. The KHS cohort included 200,010 Korean adults aged 40-79 years who were free from ASCVD at baseline. Discrimination, calibration, and recalibration of the ACC/AHA Equations in predicting 10-year ASCVD risk in the KHS cohort were evaluated. The KRPM was derived using Cox model coefficients, mean risk factor values, and mean incidences from the KHS cohort. In the discriminatory analysis, the ACC/AHA Equations' White and African-American (AA) models moderately distinguished cases from non-cases, and were similar to the KRPM: For men, the area under the receiver operating characteristic curve (AUROCs) were 0.727 (White model), 0.725 (AA model), and 0.741 (KRPM); for women, the corresponding AUROCs were 0.738, 0.739, and 0.745. Absolute 10-year ASCVD risk for men in the KHS cohort was overestimated by 56.5% (White model) and 74.1% (AA model), while the risk for women was underestimated by 27.9% (White model) and overestimated by 29.1% (AA model). Recalibration of the ACC/AHA Equations did not affect discriminatory ability but improved calibration substantially, especially in men in the White model. Of the three ASCVD risk prediction models, the KRPM showed best calibration. The ACC/AHA Equations should not be directly applied for ASCVD risk prediction in a Korean population. The KRPM showed best predictive ability for ASCVD risk. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  15. Evaluating ortholog prediction algorithms in a yeast model clade.

    Directory of Open Access Journals (Sweden)

    Leonidas Salichos

    Full Text Available BACKGROUND: Accurate identification of orthologs is crucial for evolutionary studies and for functional annotation. Several algorithms have been developed for ortholog delineation, but so far, manually curated genome-scale biological databases of orthologous genes for algorithm evaluation have been lacking. We evaluated four popular ortholog prediction algorithms (MultiParanoid; and OrthoMCL; RBH: Reciprocal Best Hit; RSD: Reciprocal Smallest Distance; the last two extended into clustering algorithms cRBH and cRSD, respectively, so that they can predict orthologs across multiple taxa against a set of 2,723 groups of high-quality curated orthologs from 6 Saccharomycete yeasts in the Yeast Gene Order Browser. RESULTS: Examination of sensitivity [TP/(TP+FN], specificity [TN/(TN+FP], and accuracy [(TP+TN/(TP+TN+FP+FN] across a broad parameter range showed that cRBH was the most accurate and specific algorithm, whereas OrthoMCL was the most sensitive. Evaluation of the algorithms across a varying number of species showed that cRBH had the highest accuracy and lowest false discovery rate [FP/(FP+TP], followed by cRSD. Of the six species in our set, three descended from an ancestor that underwent whole genome duplication. Subsequent differential duplicate loss events in the three descendants resulted in distinct classes of gene loss patterns, including cases where the genes retained in the three descendants are paralogs, constituting 'traps' for ortholog prediction algorithms. We found that the false discovery rate of all algorithms dramatically increased in these traps. CONCLUSIONS: These results suggest that simple algorithms, like cRBH, may be better ortholog predictors than more complex ones (e.g., OrthoMCL and MultiParanoid for evolutionary and functional genomics studies where the objective is the accurate inference of single-copy orthologs (e.g., molecular phylogenetics, but that all algorithms fail to accurately predict orthologs when paralogy

  16. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis.

    Science.gov (United States)

    Tangri, Navdeep; Grams, Morgan E; Levey, Andrew S; Coresh, Josef; Appel, Lawrence J; Astor, Brad C; Chodick, Gabriel; Collins, Allan J; Djurdjev, Ognjenka; Elley, C Raina; Evans, Marie; Garg, Amit X; Hallan, Stein I; Inker, Lesley A; Ito, Sadayoshi; Jee, Sun Ha; Kovesdy, Csaba P; Kronenberg, Florian; Heerspink, Hiddo J Lambers; Marks, Angharad; Nadkarni, Girish N; Navaneethan, Sankar D; Nelson, Robert G; Titze, Stephanie; Sarnak, Mark J; Stengel, Benedicte; Woodward, Mark; Iseki, Kunitoshi

    2016-01-12

    Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed. To evaluate the accuracy of the risk equations across different geographic regions and patient populations through individual participant data meta-analysis. Thirty-one cohorts, including 721,357 participants with CKD stages 3 to 5 in more than 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014. Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease. Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original risk equations, cohort-specific hazard ratios were estimated and combined using random-effects meta-analysis to form new pooled kidney failure risk equations. Original and pooled kidney failure risk equation performance was compared, and the need for regional calibration factors was assessed. Kidney failure (treatment by dialysis or kidney transplant). During a median follow-up of 4 years of 721,357 participants with CKD, 23,829 cases kidney failure were observed. The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts (overall C statistic, 0.90; 95% CI, 0.89-0.92 at 2 years; C statistic at 5 years, 0.88; 95% CI, 0.86-0.90); discrimination in subgroups by age, race, and diabetes status was similar. There was no improvement with the pooled equations. Calibration (the difference between observed and predicted risk) was adequate in North American cohorts, but the original risk

  17. Evaluation and Prediction of Water Resources Based on AHP

    Science.gov (United States)

    Li, Shuai; Sun, Anqi

    2017-01-01

    Nowadays, the shortage of water resources is a threat to us. In order to solve the problem of water resources restricted by varieties of factors, this paper establishes a water resources evaluation index model (WREI), which adopts the fuzzy comprehensive evaluation (FCE) based on analytic hierarchy process (AHP) algorithm. After considering influencing factors of water resources, we ignore secondary factors and then hierarchical approach the main factors according to the class, set up a three-layer structure. The top floor is for WREI. Using analytic hierarchy process (AHP) to determine weight first, and then use fuzzy judgment to judge target, so the comprehensive use of the two algorithms reduce the subjective influence of AHP and overcome the disadvantages of multi-level evaluation. To prove the model, we choose India as a target region. On the basis of water resources evaluation index model, we use Matlab and combine grey prediction with linear prediction to discuss the ability to provide clean water in India and the trend of India’s water resources changing in the next 15 years. The model with theoretical support and practical significance will be of great help to provide reliable data support and reference for us to get plans to improve water quality.

  18. Albuminuria as pre-screening tool for better risk prediction

    NARCIS (Netherlands)

    Ozyilmaz, Akin

    2016-01-01

    Cardiovascular disease (CVD) and chronic kidney disease (CKD) are common health problems and originates predominantly from generalized atherosclerosis. Diabetes, hypertension, and hypercholesterolemia are well known risk factors for atherosclerosis. Screening for and treatment of these risk factors

  19. Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models

    Directory of Open Access Journals (Sweden)

    Lucky eMehra

    2016-03-01

    Full Text Available Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB, caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum. The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early

  20. Interaction of erythrocyte eicosapentaenoic acid and physical activity predicts reduced risk of mild cognitive impairment.

    Science.gov (United States)

    Street, Steven John; Parletta, Natalie; Milte, Catherine; Sullivan, Karen; Hills, Andrew P; Buckley, Jonathan; Howe, Peter

    2015-01-01

    To evaluate relationships between self-reported physical activity, proportions of long-chain omega-3 polyunsaturated fatty acids (LCn3) in erythrocyte content (percentage of total fatty acids) and risk of mild cognitive impairment (MCI) in older adults. A cross-sectional study was conducted. Community-dwelling male and female (n = 84) participants over the age of 65 years with and without MCI were tested for erythrocyte proportions of the LCn3s eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Physical activity was measured using a validated questionnaire. The interaction between erythrocyte EPA, but not DHA, and increased physical activity was associated with increased odds of a non-MCI classification. An interaction between physical activity and erythrocyte EPA content (percentage of fatty acids) significantly predicted MCI status in older adults. Randomised control trials are needed to examine the potential for supplementation with EPA in combination with increased physical activity to mitigate the risk of MCI in ageing adults.

  1. Predicting the risk of physical disability in old age using modifiable mid-life risk factors.

    Science.gov (United States)

    Wong, Evelyn; Stevenson, Christopher; Backholer, Kathryn; Woodward, Mark; Shaw, Jonathan E; Peeters, Anna

    2015-01-01

    We aimed to investigate the relationship between potentially modifiable risk factors in middle age and disability after 13 years using the Framingham Offspring Study (FOS). We further aimed to develop a disability risk algorithm to estimate the risk of future disability for those aged 45-65 years. FOS is a longitudinal study. We used examination 5 (1991-1995; 'baseline') and examination 8 (2005-2008; 'follow-up'). We included participants aged between 45-65 years at 'baseline' with complete predictor and outcome measures (n=2031; mean age 53.9 years). Predictors considered were body mass index, smoking, hypertension, diabetes and dyslipidaemia. We used multinomial logistic regression to identify predictors of disability or death.We assessed external validity using Australian data. By examination 8, 156 participants had disability and 198 had died. Disability was associated with smoking (OR (95% CI) 1.81 (1.18 to 2.78)); obesity (2.95 (1.83 to 4.77)); diabetes 1.96 (1.11 to 3.45) and being female (OR 1.67 (1.13 to 2.45). The model performed moderately well in predicting disability and death in an Australian population. Based on our algorithm, a 45-year-old man/woman with the combined risk factors of obesity, diabetes and smoking has similar likelihood of surviving free of disability to a 65-year-old man/woman without any of the same risk factors. The derived risk algorithm allows, for the first time, quantification of the substantial combined impact on future disability of key modifiable risk factors in mid-life. Here we demonstrated the combined impact of obesity, diabetes and smoking to be similar to 20 years of aging. 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.

  2. Lung cancer risk prediction method based on feature selection and artificial neural network.

    Science.gov (United States)

    Xie, Nan-Nan; Hu, Liang; Li, Tai-Hui

    2014-01-01

    A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

  3. Evaluation of the risk of hip fracture.

    Science.gov (United States)

    Kanis, J A; McCloskey, E V

    1996-03-01

    Hip fracture is the most serious complication of osteoporosis and the incidence is rising worldwide. Bone mineral density measurements can be used not only to diagnose osteoporosis at the hip, but also to give prognostic information on the lifetime risk of hip fracture. A number of additional risk factors enhance the ability of density measurements to assess risk. Candidates include markers of bone resorption, prior fragility fractures, hip axis length, and estimates of postural integrity, each of which improve prognostic value independently of bone mineral assessments. Their use in the stratification of risk will help define intervention thresholds for treatments and improve the design of population screening policies, particularly in elderly women in whom the burden of hip fracture is greatest.

  4. Radiographic evaluation of mandible to predict the gender and age.

    Science.gov (United States)

    Bhardwaj, Deepti; Kumar, Jyothi Shiv; Mohan, Vinay

    2014-10-01

    This study is been conducted using digital panoramic radiographs for predicting age in various age groups and the accuracy of the parameters were accessed as age advances. The selected 300 panoramic images were divided into 3 age group of Group A (25-34 years), Group B (35-44 years), and Group C (45 -54 years). Each group comprised of 100 subjects in which 50 were males & 50 females. The age changes were evaluated using five parameters collectively, which were: Gonial angle, Antegonial angle, Mental foramen, Mandibular canal, Mandibular foramen. These parameters were evaluated on panoramic radiographs for age prediction and changes in their position as age advances. Among all the parameters changes in Mandibular canal and mandibular foramen was found to be highly significant (p value ≤0.05) as age advances. These parameters can be used to predict the age of the individual as there were significant changes in Mandibular canal and Mandibular foramen as age advances. For Further studies large sample size, and recent modalities in radiography like CBCT or CT scan are required.

  5. A Panel of Cancer Testis Antigens and Clinical Risk Factors to Predict Metastasis in Colorectal Cancer

    Science.gov (United States)

    Molania, Ramyar; Mahjoubi, Frouzandeh; Mirzaei, Rezvan; Khatami, Saeed-Reza; Mahjoubi, Bahar

    2014-01-01

    Colorectal cancer (CRC) is the third common carcinoma with a high rate of mortality worldwide and several studies have investigated some molecular and clinicopathological markers for diagnosis and prognosis of its malignant phenotypes. The aim of this study is to evaluate expression frequency of PAGE4, SCP-1, and SPANXA/D cancer testis antigen (CTA) genes as well as some clinical risk markers to predict liver metastasis of colorectal cancer patients. The expression frequency of PAGE4, SCP-1, and SPANXA/D cancer/testis antigen (CTA) genes was obtained using reverse transcription polymerase chain reaction (RT-PCR) assay in 90 colorectal tumor samples including both negative and positive liver metastasis tumors. Statistical analysis was performed to assess the association of three studied genes and clinical risk factors with CRC liver metastasis. The frequency of PAGE4 and SCP-1 genes expression was significantly higher in the primary tumours with liver metastasis when statistically compared with primary tumors with no liver metastasis (P < 0.05). Among all clinical risk factors studied, the lymph node metastasis and the depth of invasion were statistically correlated with liver metastasis of CRC patients. In addition, using multiple logistic regression, we constructed a model based on PAGE4 and lymph node metastasis to predict liver metastasis of CRC. PMID:26317029

  6. Functional development in clinical high risk youth: Prediction of schizophrenia versus other psychotic disorders

    Science.gov (United States)

    Tarbox, Sarah I.; Addington, Jean; Cadenhead, Kristin S.; Cannon, Tyrone D.; Cornblatt, Barbara A.; Perkins, Diana O.; Seidman, Larry J.; Tsuang, Ming T.; Walker, Elaine F.; Heinssen, Robert; McGlashan, Thomas H.; Woods, Scott W.

    2013-01-01

    This study evaluates premorbid social and academic functioning in clinical high-risk individuals as predictors of transition to schizophrenia versus another psychotic disorder. Participants were 54 individuals enrolled in phase one of the North American Prodrome Longitudinal Study who over two and a half years of follow-up met criteria for schizophrenia/schizophreniform disorder (n = 28) or another psychotic disorder (n = 26). Social and academic functioning in childhood, early adolescence, and late adolescence was assessed at baseline using the Cannon-Spoor Premorbid Adjustment Scale. Social maladjustment in late adolescence predicted significantly higher odds of transition to schizophrenia versus another psychotic disorder independent of childhood and early adolescent adjustment (OR = 4.02) and conveyed unique risk over academic maladjustment (OR = 5.64). Premorbid academic maladjustment was not associated with psychotic disorder diagnosis. Results support diagnostic specificity of premorbid social dysfunction to schizophrenia in clinical high-risk youth and underscore an important role for social maladjustment in the developmental pathology of schizophrenia and its prediction. PMID:24200216

  7. A Panel of Cancer Testis Antigens and Clinical Risk Factors to Predict Metastasis in Colorectal Cancer

    Directory of Open Access Journals (Sweden)

    Ramyar Molania

    2014-01-01

    Full Text Available Colorectal cancer (CRC is the third common carcinoma with a high rate of mortality worldwide and several studies have investigated some molecular and clinicopathological markers for diagnosis and prognosis of its malignant phenotypes. The aim of this study is to evaluate expression frequency of PAGE4, SCP-1, and SPANXA/D cancer testis antigen (CTA genes as well as some clinical risk markers to predict liver metastasis of colorectal cancer patients. The expression frequency of PAGE4, SCP-1, and SPANXA/D cancer/testis antigen (CTA genes was obtained using reverse transcription polymerase chain reaction (RT-PCR assay in 90 colorectal tumor samples including both negative and positive liver metastasis tumors. Statistical analysis was performed to assess the association of three studied genes and clinical risk factors with CRC liver metastasis. The frequency of PAGE4 and SCP-1 genes expression was significantly higher in the primary tumours with liver metastasis when statistically compared with primary tumors with no liver metastasis (P<0.05. Among all clinical risk factors studied, the lymph node metastasis and the depth of invasion were statistically correlated with liver metastasis of CRC patients. In addition, using multiple logistic regression, we constructed a model based on PAGE4 and lymph node metastasis to predict liver metastasis of CRC.

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

    Science.gov (United States)

    Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

    2014-01-01

    We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients. PMID:24489669

  9. Evaluating the Sensitivity of Electronic Fetal Monitoring Patterns for the Prediction of Intraventricular Hemorrhage.

    Science.gov (United States)

    Hannaford, Karen E; Stout, Molly J; Smyser, Chris D; Mathur, Amit; Cahill, Alison G

    2016-12-01

    Objective We evaluated electronic fetal (heart rate) monitoring (EFM) patterns among very preterm infants with and without intraventricular hemorrhage (IVH) to evaluate the test characteristics of EFM for the prediction of IVH. Study Design We performed a case-control study of preterm infants born ≤ 30 weeks' gestation over a 6-year period. We evaluated differences in EFM patterns between those (cases) with and without IVH (controls). The relative odds ratio of observing differences in EFM patterns between cases and controls was calculated. Regression models were adjusted based on confounding variables. The sensitivity, specificity, and positive and negative predictive values of EFM characteristics were evaluated for the diagnosis of IVH. Results Total 79 very preterm infants underwent cranial ultrasound, 24 of whom had IVH. Infants with IVH were more likely to be males and delivered at earlier gestational ages. Moderate variability was seen in all infants with normal cranial ultrasounds and 83% of infants with IVH. Minimal variability has a sensitivity of 17% in the prediction of IVH. Conclusion While minimal variability was observed more frequently in fetuses that developed IVH, it is poorly predictive of IVH. EFM patterns are not discriminating in identifying very preterm infants at risk for developing IVH. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

  10. Young Children’s Risk-Taking: Mothers’ Authoritarian Parenting Predicts Risk-Taking by Daughters but Not Sons

    Directory of Open Access Journals (Sweden)

    Erin E. Wood

    2017-01-01

    Full Text Available We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style. In our study, mothers reported their own gender roles and parenting styles as well as their children’s risk-taking and activities related to gender roles. The results were only partially consistent with the two theories, as the amount of risk-taking by sons and daughters did not differ significantly and risk-taking by daughters, but not sons, was positively related to mothers’ use of the authoritarian parenting style and the girls’ engagement in masculine activities. Risk-taking by sons was not predicted by any combination of mother-related variables. Overall, mothers who were higher in femininity used more authoritative and less authoritarian parenting styles. Theoretical implications as well as implications for predicting and reducing children’s risk-taking are discussed.

  11. External validation of the Practical Risk Chart for the prediction of delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage.

    Science.gov (United States)

    Foreman, Paul M; Chua, Michelle H; Harrigan, Mark R; Fisher, Winfield S; Tubbs, R Shane; Shoja, Mohammadali M; Griessenauer, Christoph J

    2017-05-01

    OBJECTIVE Delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH) occurs in approximately 30% of patients. The Practical Risk Chart was developed to predict DCI based on admission characteristics; the authors seek to externally validate and critically appraise this prediction tool. METHODS A prospective cohort of aSAH patients was used to externally validate the previously published Practical Risk Chart. The model consists of 4 variables: clinical condition on admission, amount of cisternal and intraventricular blood on CT, and age. External validity was assessed using logistic regression. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS In a cohort of 125 patients with aSAH, the Practical Risk Chart adequately predicted DCI, with an AUC of 0.66 (95% CI 0.55-0.77). Clinical grade on admission and amount of intracranial blood on CT were the strongest predictors of DCI and clinical vasospasm. The best-fit model used a combination of the Hunt and Hess grade and the modified Fisher scale to yield an AUC of 0.76 (95% CI 0.675-0.85) and 0.70 (95% CI 0.602-0.8) for the prediction of DCI and clinical vasospasm, respectively. CONCLUSIONS The Practical Risk Chart adequately predicts the risk of DCI following aSAH. However, the best-fit model represents a simpler stratification scheme, using only the Hunt and Hess grade and the modified Fisher scale, and produces a comparable AUC.

  12. Missing Value Imputation Improves Mortality Risk Prediction Following Cardiac Surgery: An Investigation of an Australian Patient Cohort.

    Science.gov (United States)

    Karim, Md Nazmul; Reid, Christopher M; Tran, Lavinia; Cochrane, Andrew; Billah, Baki

    2017-03-01

    The aim of this study was to evaluate the impact of missing values on the prediction performance of the model predicting 30-day mortality following cardiac surgery as an example. Information from 83,309 eligible patients, who underwent cardiac surgery, recorded in the Australia and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) database registry between 2001 and 2014, was used. An existing 30-day mortality risk prediction model developed from ANZSCTS database was re-estimated using the complete cases (CC) analysis and using multiple imputation (MI) analysis. Agreement between the risks generated by the CC and MI analysis approaches was assessed by the Bland-Altman method. Performances of the two models were compared. One or more missing predictor variables were present in 15.8% of the patients in the dataset. The Bland-Altman plot demonstrated significant disagreement between the risk scores (prisk of mortality. Compared to CC analysis, MI analysis resulted in an average of 8.5% decrease in standard error, a measure of uncertainty. The MI model provided better prediction of mortality risk (observed: 2.69%; MI: 2.63% versus CC: 2.37%, Pvalues improved the 30-day mortality risk prediction following cardiac surgery. Copyright © 2016 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

  13. The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools

    Science.gov (United States)

    Yang, Min; Wong, Stephen C. P.; Coid, Jeremy

    2010-01-01

    Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their…

  14. BRAIN NATRIURETIC PEPTIDE (BNP): BIOMARKER FOR RISK STRATIFICATION AND FUNCTIONAL RECOVERY PREDICTION IN ISCHEMIC STROKE

    OpenAIRE

    Stanescu, Ioana; DOGARU Gabriela

    2015-01-01

    Functional outcome after cardiovascular and cerebrovascular events is traditionally predicted using demographic and clinical variables like age, gender, blood pressure, cholesterol levels, diabetes status, smoking habits or pre-existing morbidity. Identification of new variables will improve the risk stratification of specific categories of patients. Numerous blood-based biomarkers associated with increased cardiovascular risk have been identified; some of them even predict cardiovascular ...

  15. A Risk Model for Prediction of 1-Year Mortality in Patients Undergoing MitraClip Implantation.

    Science.gov (United States)

    Buccheri, Sergio; Capodanno, Davide; Barbanti, Marco; Popolo Rubbio, Antonio; Di Salvo, Maria Elena; Scandura, Salvatore; Mangiafico, Sarah; Ronsivalle, Giuseppe; Chiarandà, Marta; Capranzano, Piera; Grasso, Carmelo; Tamburino, Corrado

    2017-05-01

    There is a lack of specific tools for risk stratification in patients who undergo MitraClip implantation. We aimed at combining preprocedural variables with prognostic impact into a specific risk model for the prediction of 1-year mortality in patients undergoing MitraClip implantation. A total of 311 consecutive patients who underwent MitraClip implantation were included. A lasso-penalized Cox-proportional hazard regression model was used to identify independent predictors of 1-year all-cause mortality. A nomogram (GRASP [Getting Reduction of mitrAl inSufficiency by Percutaneous clip implantation] nomogram) was obtained from the Cox model. Validation was performed using internal bootstrap resampling. Forty-two deaths occurred at 1-year follow-up. The Kaplan-Meier estimate of 1-year survival was 0.845 (95% confidence interval, 0.802 to 0.895). Four independent predictors of mortality (mean arterial blood pressure, hemoglobin natural log-transformed pro-brain natriuretic peptide levels, New York Heart Association class IV at presentation) were identified. At internal bootstrap resampling validation, the GRASP nomogram had good discrimination (area under receiver operating characteristic curve of 0.78, Somers' Dxy statistic of 0.53) and calibration (le Cessie-van Houwelingen-Copas-Hosmer p value of 0.780). Conversely, the discriminative ability of the EuroSCORE II (the European System for Cardiac Operative Risk Evaluation II) and the STS-PROM (the Society of Thoracic Surgeons Predicted Risk of Mortality score) was fairly modest with area under the curve values of 0.61 and 0.55, respectively. A treatment-specific risk model in patients who undergo MitraClip implantation may be useful for the stratification of mortality at 1 year. Further studies are needed to provide external validation and support the generalizability of the GRASP nomogram. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration.

    Science.gov (United States)

    Singh, Anima; Nadkarni, Girish; Gottesman, Omri; Ellis, Stephen B; Bottinger, Erwin P; Guttag, John V

    2015-02-01

    Predictive models built using temporal data in electronic health records (EHRs) can potentially play a major role in improving management of chronic diseases. However, these data present a multitude of technical challenges, including irregular sampling of data and varying length of available patient history. In this paper, we describe and evaluate three different approaches that use machine learning to build predictive models using temporal EHR data of a patient. The first approach is a commonly used non-temporal approach that aggregates values of the predictors in the patient's medical history. The other two approaches exploit the temporal dynamics of the data. The two temporal approaches vary in how they model temporal information and handle missing data. Using data from the EHR of Mount Sinai Medical Center, we learned and evaluated the models in the context of predicting loss of estimated glomerular filtration rate (eGFR), the most common assessment of kidney function. Our results show that incorporating temporal information in patient's medical history can lead to better prediction of loss of kidney function. They also demonstrate that exactly how this information is incorporated is important. In particular, our results demonstrate that the relative importance of different predictors varies over time, and that using multi-task learning to account for this is an appropriate way to robustly capture the temporal dynamics in EHR data. Using a case study, we also demonstrate how the multi-task learning based model can yield predictive models with better performance for identifying patients at high risk of short-term loss of kidney function. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. EVALUATION OF RISK FACTORS IN ACUTE STROKE

    Directory of Open Access Journals (Sweden)

    Putta

    2015-03-01

    Full Text Available Introduction: Cerebrovascular disease is the third most common cause of death in the developed world after cancer and ischemic heart disease. In India, community surveys have shown a crude prevalence rate of 200 per 100000 population for hemiplegia. Aims and objectives: Identification of risk factors for c erebrovascular disease. Materials and Methods: Inclusion Criteria: Cases of acute stroke admitted in S.V.R.R.G.G.H, Tirupati were taken for the study. Exclusion Criteria: Head injury cases, neoplasm cases producing cerebrovascular disease were excluded. Re sults: Stroke was more common in male, 54% patients were male 46% were female. It was more common in 6 th and 7 th decade. More common risk factors were hypertension followed by smoking, diabetes mellitus. More common pathology was infarction. Conclusion: Com mon risk factors for acute stroke are hypertension, smoking, diabetes mellitus, alcoholism, obesity, cardiac disease. Stroke was confirmed by CT scan of brain.

  18. Environmental risk analysis for nanomaterials: Review and evaluation of frameworks

    DEFF Research Database (Denmark)

    Grieger, Khara Deanne; Linkov, Igor; Hansen, Steffen Foss

    2012-01-01

    In response to the challenges of conducting traditional human health and ecological risk assessment for nanomaterials (NM), a number of alternative frameworks have been proposed for NM risk analysis. This paper evaluates various risk analysis frameworks proposed for NM based on a number of criteria...... to occupational settings with minor environmental considerations, and most have not been thoroughly tested on a wide range of NM. Care should also be taken when selecting the most appropriate risk analysis strategy for a given risk context. Given this, we recommend a multi-faceted approach to assess...... the environmental risks of NM as well as increased applications and testing of the proposed frameworks for different NM....

  19. Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study.

    Science.gov (United States)

    Kavousi, Maryam; Elias-Smale, Suzette; Rutten, Joost H W; Leening, Maarten J G; Vliegenthart, Rozemarijn; Verwoert, Germaine C; Krestin, Gabriel P; Oudkerk, Matthijs; de Maat, Moniek P M; Leebeek, Frank W G; Mattace-Raso, Francesco U S; Lindemans, Jan; Hofman, Albert; Steyerberg, Ewout W; van der Lugt, Aad; van den Meiracker, Anton H; Witteman, Jacqueline C M

    2012-03-20

    Whether newer risk markers for coronary heart disease (CHD) improve CHD risk prediction remains unclear. To assess whether newer risk markers for CHD risk prediction and stratification improve Framingham risk score (FRS) predictions. Prospective population-based study. The Rotterdam Study, Rotterdam, the Netherlands. 5933 asymptomatic, community-dwelling participants (mean age, 69.1 years [SD, 8.5]). Traditional CHD risk factors used in the FRS (age, sex, systolic blood pressure, treatment of hypertension, total and high-density lipoprotein cholesterol levels, smoking, and diabetes) and newer CHD risk factors (N-terminal fragment of prohormone B-type natriuretic peptide levels, von Willebrand factor antigen levels, fibrinogen levels, chronic kidney disease, leukocyte count, C-reactive protein levels, homocysteine levels, uric acid levels, coronary artery calcium [CAC] scores, carotid intima-media thickness, peripheral arterial disease, and pulse wave velocity). Adding CAC scores to the FRS improved the accuracy of risk predictions (c-statistic increase, 0.05 [95% CI, 0.02 to 0.06]; net reclassification index, 19.3% overall [39.3% in those at intermediate risk, by FRS]). Levels of N-terminal fragment of prohormone B-type natriuretic peptide also improved risk predictions but to a lesser extent (c-statistic increase, 0.02 [CI, 0.01 to 0.04]; net reclassification index, 7.6% overall [33.0% in those at intermediate risk, by FRS]). Improvements in predictions with other newer markers were marginal. The findings may not be generalizable to younger or nonwhite populations. Among 12 CHD risk markers, improvements in FRS predictions were most statistically and clinically significant with the addition of CAC scores. Further investigation is needed to assess whether risk refinements using CAC scores lead to a meaningful change in clinical outcome. Whether to use CAC score screening as a more routine test for risk prediction requires full consideration of the financial and

  20. Dynamics of Variance Risk Premia, Investors' Sentiment and Return Predictability

    DEFF Research Database (Denmark)

    Rombouts, Jerome V.K.; Stentoft, Lars; Violante, Francesco

    We develop a joint framework linking the physical variance and its risk neutral expectation implying variance risk premia that are persistent, appropriately reacting to changes in level and variability of the variance and naturally satisfying the sign constraint. Using option market data and real......We develop a joint framework linking the physical variance and its risk neutral expectation implying variance risk premia that are persistent, appropriately reacting to changes in level and variability of the variance and naturally satisfying the sign constraint. Using option market data...

  1. Predicting extinction risk of Brazilian Atlantic forest angiosperms.

    Science.gov (United States)

    Leão, Tarciso C C; Fonseca, Carlos R; Peres, Carlos A; Tabarelli, Marcelo

    2014-10-01

    Understanding how plant life history affects species vulnerability to anthropogenic disturbances and environmental change is a major ecological challenge. We examined how vegetation type, growth form, and geographic range size relate to extinction risk throughout the Brazilian Atlantic Forest domain. We used a database containing species-level information of 6,929 angiosperms within 112 families and a molecular-based working phylogeny. We used decision trees, standard regression, and phylogenetic regression to explore the relationships between species attributes and extinction risk. We found a significant phylogenetic signal in extinction risk. Vegetation type, growth form, and geographic range size were related to species extinction risk, but the effect of growth form was not evident after phylogeny was controlled for. Species restricted to either rocky outcrops or scrub vegetation on sandy coastal plains exhibited the highest extinction risk among vegetation types, a finding that supports the hypothesis that species adapted to resource-limited environments are more vulnerable to extinction. Among growth forms, epiphytes were associated with the highest extinction risk in non-phylogenetic regression models, followed by trees, whereas shrubs and climbers were associated with lower extinction risk. However, the higher extinction risk of epiphytes was not significant after correcting for phylogenetic relatedness. Our findings provide new indicators of extinction risk and insights into the mechanisms governing plant vulnerability to extinction in a highly diverse flora where human disturbances are both frequent and widespread. © 2014 Society for Conservation Biology.

  2. Evaluating the risks of clinical research: direct comparative analysis.

    Science.gov (United States)

    Rid, Annette; Abdoler, Emily; Roberson-Nay, Roxann; Pine, Daniel S; Wendler, David

    2014-09-01

    Many guidelines and regulations allow children and adolescents to be enrolled in research without the prospect of clinical benefit when it poses minimal risk. However, few systematic methods exist to determine when research risks are minimal. This situation has led to significant variation in minimal risk judgments, raising concern that some children are not being adequately protected. To address this concern, we describe a new method for implementing the widely endorsed "risks of daily life" standard for minimal risk. This standard defines research risks as minimal when they do not exceed the risks posed by daily life activities or routine examinations. This study employed a conceptual and normative analysis, and use of an illustrative example. Different risks are composed of the same basic elements: Type, likelihood, and magnitude of harm. Hence, one can compare the risks of research and the risks of daily life by comparing the respective basic elements with each other. We use this insight to develop a systematic method, direct comparative analysis, for implementing the "risks of daily life" standard for minimal risk. The method offers a way of evaluating research procedures that pose the same types of risk as daily life activities, such as the risk of experiencing anxiety, stress, or other psychological harm. We thus illustrate how direct comparative analysis can be applied in practice by using it to evaluate whether the anxiety induced by a respiratory CO2 challenge poses minimal or greater than minimal risks in children and adolescents. Direct comparative analysis is a systematic method for applying the "risks of daily life" standard for minimal risk to research procedures that pose the same types of risk as daily life activities. It thereby offers a method to protect children and adolescents in research, while ensuring that important studies are not blocked because of unwarranted concerns about research risks.

  3. Elastography in predicting preterm delivery in asymptomatic, low-risk women: a prospective observational study.

    Science.gov (United States)

    Wozniak, Slawomir; Czuczwar, Piotr; Szkodziak, Piotr; Milart, Pawel; Wozniakowska, Ewa; Paszkowski, Tomasz

    2014-07-21

    Despite the efforts to decrease the rate of preterm birth, preterm delivery is still the main cause of neonatal morbidity and mortality. Identifying patients threatened with preterm delivery remains one of the main obstetric challenges. The aim of this study was to estimate the potential value of elastographic evaluation of internal cervical os stiffness at 18-22 weeks of pregnancy in low risk, asymptomatic women in the prediction of spontaneous preterm delivery. This prospective observational study included 333 low-risk, asymptomatic women presenting for the routine second trimester ultrasound scan according to the Polish Gynecological Society recommendation between 18-22 weeks of pregnancy. Ultrasound examinations of the cervix were performed transvaginally. The following data were recorded: elastographic color assessment of the internal os and ultrasound cervical length at 18-22 and 30 weeks of pregnancy; maternal age; obstetrical history; presence of cervical funneling at 30 weeks of pregnancy; gestational age at birth. Elastographic assessment of the internal os was performed using a color map: red (soft), yellow (medium soft), blue (medium hard) and purple (hard). If two colors were visible in the region of the internal os, the softer option was noted. Statistical analysis was performed using Statistica software (version 10, Statsoft Poland) using the following tests: chi square test to compare frequency of preterm deliveries in various categories of internal os assessment and Spearman correlation test to determine the correlation between elastographic assessment and cervical shortening. To determine the cut off category of internal os elastography assessment in selecting high preterm delivery risk patients we have calculated the sensivity, specifity, negative predictive value and positive predictive value. The number of preterm deliveries (red and yellow groups, than in the blue and purple groups. The sensivity, specifity, NPV and PPV for both red and yellow

  4. Comparison between frailty index of deficit accumulation and fracture risk assessment tool (FRAX) in prediction of risk of fractures.

    Science.gov (United States)

    Li, Guowei; Thabane, Lehana; Papaioannou, Alexandra; Adachi, Jonathan D

    2015-08-01

    A frailty index (FI) of deficit accumulation could quantify and predict the risk of fractures based on the degree of frailty in the elderly. We aimed to compare the predictive powers between the FI and the fracture risk assessment tool (FRAX) in predicting risk of major osteoporotic fracture (hip, upper arm or shoulder, spine, or wrist) and hip fracture, using the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort. There were 3985 women included in the study, with the mean age of 69.4 years (standard deviation [SD] = 8.89). During the follow-up, there were 149 (3.98%) incident major osteoporotic fractures and 18 (0.48%) hip fractures reported. The FRAX and FI were significantly related to each other. Both FRAX and FI significantly predicted risk of major osteoporotic fracture, with a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 1.02-1.05) and 1.02 (95% CI: 1.01-1.04) for per-0.01 increment for the FRAX and FI respectively. The HRs were 1.37 (95% CI: 1.19-1.58) and 1.26 (95% CI: 1.12-1.42) for an increase of per-0.10 (approximately one SD) in the FRAX and FI respectively. Similar discriminative ability of the models was found: c-index = 0.62 for the FRAX and c-index = 0.61 for the FI. When cut-points were chosen to trichotomize participants into low-risk, medium-risk and high-risk groups, a significant increase in fracture risk was found in the high-risk group (HR = 2.04, 95% CI: 1.36-3.07) but not in the medium-risk group (HR = 1.23, 95% CI: 0.82-1.84) compared with the low-risk women for the FI, while for FRAX the medium-risk (HR = 2.00, 95% CI: 1.09-3.68) and high-risk groups (HR = 2.61, 95% CI: 1.48-4.58) predicted risk of major osteoporotic fracture significantly only when survival time exceeded 18months (550 days). Similar findings were observed for hip fracture and in sensitivity analyses. In conclusion, the FI is comparable with FRAX in the prediction of risk of future fractures, indicating that

  5. Is metabolic syndrome predictive of prevalence, extent, and risk of coronary artery disease beyond its components? Results from the multinational coronary CT angiography evaluation for clinical outcome: an international multicenter registry (CONFIRM).

    Science.gov (United States)

    Ahmadi, Amir; Leipsic, Jonathon; Feuchtner, Gudrun; Gransar, Heidi; Kalra, Dan; Heo, Ran; Achenbach, Stephan; Andreini, Daniele; Al-Mallah, Mouaz; Berman, Daniel S; Budoff, Matthew; Cademartiri, Filippo; Callister, Tracy Q; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin; Cury, Ricardo C; Delago, Augustin; Gomez, Millie J; Hadamitzky, Martin; Hausleiter, Joerg; Hindoyan, Niree; Kaufmann, Philipp A; Kim, Yong-Jin; Lin, Fay; Maffei, Erica; Pontone, Gianluca; Raff, Gilbert L; Shaw, Leslee J; Villines, Todd C; Dunning, Allison; Min, James K

    2015-01-01

    Although metabolic syndrome is associated with increased risk of cardiovascular disease and events, its added prognostic value beyond its components remains unknown. This study compared the prevalence, severity of coronary artery disease (CAD), and prognosis of patients with metabolic syndrome to those with individual metabolic syndrome components. The study cohort consisted of 27125 consecutive individuals who underwent ≥ 64-detector row coronary CT angiography (CCTA) at 12 centers from 2003 to 2009. Metabolic syndrome was defined as per NCEP/ATP III criteria. Metabolic syndrome patients (n = 690) were matched 1:1:1 to those with 1 component (n = 690) and 2 components (n = 690) of metabolic syndrome for age, sex, smoking status, and family history of premature CAD using propensity scoring. Major adverse cardiac events (MACE) were defined by a composite of myocardial infarction (MI), acute coronary syndrome, mortality and late target vessel revascularization. Patients with 1 component of metabolic syndrome manifested lower rates of obstructive 1-, 2-, and 3-vessel/left main disease compared to metabolic syndrome patients (9.4% vs 13.8%, 2.6% vs 4.5%, and 1.0% vs 2.3%, respectively; p 0.05). At 2.5 years, metabolic syndrome patients experienced a higher rate of MACE compared to patients with 1 component (4.4% vs 1.6%; p = 0.002), while no difference observed compared to individuals with 2 components (4.4% vs 3.2% p = 0.25) of metabolic syndrome. In conclusion, Metabolic syndrome patients have significantly greater prevalence, severity, and prognosis of CAD compared to patients with 1 but not 2 components of metabolic syndrome.

  6. Is metabolic syndrome predictive of prevalence, extent, and risk of coronary artery disease beyond its components? Results from the multinational coronary CT angiography evaluation for clinical outcome: an international multicenter registry (CONFIRM.

    Directory of Open Access Journals (Sweden)

    Amir Ahmadi

    Full Text Available Although metabolic syndrome is associated with increased risk of cardiovascular disease and events, its added prognostic value beyond its components remains unknown. This study compared the prevalence, severity of coronary artery disease (CAD, and prognosis of patients with metabolic syndrome to those with individual metabolic syndrome components. The study cohort consisted of 27125 consecutive individuals who underwent ≥ 64-detector row coronary CT angiography (CCTA at 12 centers from 2003 to 2009. Metabolic syndrome was defined as per NCEP/ATP III criteria. Metabolic syndrome patients (n = 690 were matched 1:1:1 to those with 1 component (n = 690 and 2 components (n = 690 of metabolic syndrome for age, sex, smoking status, and family history of premature CAD using propensity scoring. Major adverse cardiac events (MACE were defined by a composite of myocardial infarction (MI, acute coronary syndrome, mortality and late target vessel revascularization. Patients with 1 component of metabolic syndrome manifested lower rates of obstructive 1-, 2-, and 3-vessel/left main disease compared to metabolic syndrome patients (9.4% vs 13.8%, 2.6% vs 4.5%, and 1.0% vs 2.3%, respectively; p 0.05. At 2.5 years, metabolic syndrome patients experienced a higher rate of MACE compared to patients with 1 component (4.4% vs 1.6%; p = 0.002, while no difference observed compared to individuals with 2 components (4.4% vs 3.2% p = 0.25 of metabolic syndrome. In conclusion, Metabolic syndrome patients have significantly greater prevalence, severity, and prognosis of CAD compared to patients with 1 but not 2 components of metabolic syndrome.

  7. A Fuzzy Comprehensive Evaluation Model for Sustainability Risk Evaluation of PPP Projects

    Directory of Open Access Journals (Sweden)

    Libiao Bai

    2017-10-01

    Full Text Available Evaluating the sustainability risk level of public–private partnership (PPP projects can reduce project risk incidents and achieve the sustainable development of the organization. However, the existing studies about PPP projects risk management mainly focus on exploring the impact of financial and revenue risks but ignore the sustainability risks, causing the concept of “sustainability” to be missing while evaluating the risk level of PPP projects. To evaluate the sustainability risk level and achieve the most important objective of providing a reference for the public and private sectors when making decisions on PPP project management, this paper constructs a factor system of sustainability risk of PPP projects based on an extensive literature review and develops a mathematical model based on the methods of fuzzy comprehensive evaluation model (FCEM and failure mode, effects and criticality analysis (FMECA for evaluating the sustainability risk level of PPP projects. In addition, this paper conducts computational experiment based on a questionnaire survey to verify the effectiveness and feasibility of this proposed model. The results suggest that this model is reasonable for evaluating the sustainability risk level of PPP projects. To our knowledge, this paper is the first study to evaluate the sustainability risk of PPP projects, which would not only enrich the theories of project risk management, but also serve as a reference for the public and private sectors for the sustainable planning and development. Keywords: sustainability risk eva

  8. The demographic and socioeconomic factors predictive for populations at high-risk for La Crosse virus infection in West Virginia.

    Directory of Open Access Journals (Sweden)

    Andrew D Haddow

    Full Text Available Although a large body of literature exists for the environmental risk factors for La Crosse virus (LACV transmission, the demographic and socioeconomic risk factors for developing LACV infection have not been investigated. Therefore, this study investigated the demographic and socioeconomic risk factors for LACV infection in West Virginia from 2003 to 2007, using two forward stepwise discriminant analyses. The discriminant analyses were used to evaluate a number of demographic and socioeconomic factors for their ability to predict: 1 those census tracts with at least one reported case of LACV infection versus those census tracts with no reported cases of LACV infection and 2 to evaluate significantly high-risk clusters for LACV infection versus significantly low-risk clusters for LACV infection. In the first model, a high school education diploma or a general education diploma or less and a lower housing densitywere found to be predictive of those census tracts with at least one case of LACV infection. A high school or a general education diploma or less, lower housing density, and housing built in 1969 and earlier were all found to be predictive of those census tracts displaying high-risk clusters versus census tracts displaying low-risk clusters in the second model. The cluster discriminant analysis was found to be more predictive than the census tract discriminant analysis as indicated by the Eigenvalues, canonical correlation, and grouping accuracy. The results of this study indicate that socioeconomically disadvantaged populations are at the highest risk for LACV infection and should be a focus of LACV infection prevention efforts.

  9. Predictive value and modeling analysis of MSCT signs in gastrointestinal stromal tumors (GISTs) to pathological risk degree.

    Science.gov (United States)

    Wang, J-K

    2017-03-01

    By analyzing MSCT (multi-slice computed tomography) signs with different risks in gastrointestinal stromal tumors, this paper aimed to discuss the predictive value and modeling analysis of MSCT signs in GISTs (gastrointestinal stromal tumor) to pathological risk degree. 100 cases of primary GISTs with abdominal and pelvic MSCT scan were involved in this study. All MSCT scan findings and enhanced findings were analyzed and compared among cases with different risk degree of pathology. Then GISTs diagnostic model was established by using support vector machine (SVM) algorithm, and its diagnostic value was evaluated as well. All lesions were solitary, among which there were 46 low-risk cases, 24 medium-risk cases and 30 high-risk cases. For all high-risk, medium-risk and low-risk GISTs, there were statistical differences in tumor growth pattern, size, shape, fat space, with or without calcification, ulcer, enhancement method and peritumoral and intratumoral vessels (pvalue at each period (plain scan, arterial phase, venous phase) (p>0.05). The apparent difference lied in plain scan, arterial phase and venous phase for each risk degree. The diagnostic accuracy of SVM diagnostic model established with 10 imaging features as indexes was 70.0%, and it was especially reliable when diagnosing GISTs of high or low risk. Preoperative analysis of MSCT features is clinically significant for its diagnosis of risk degree and prognosis; GISTs diagnostic model established on the basis of SVM possesses high diagnostic value.

  10. Predictive analytics for supply chain collaboration, risk management ...

    African Journals Online (AJOL)

    kirstam

    Key words: Supply chain collaboration, supply chain risk management, financial performance, small to medium .... chain collaboration (SCC), supply chain risk management (SCRM) and financial performance in SMEs. ..... environment, global competitive forces and an unstable industrial relations climate, which relentlessly ...

  11. Resistance training and predicted risk of coronary heart disease in ...

    African Journals Online (AJOL)

    The purpose of this study was to determine the impact of resistance training, designed to prevent the development of coronary heart disease (CHD) based on the Framingham Risk Assessment (FRA) score. Twenty-five healthy sedentary men with low CHD risk were assigned to participate in a 16-week (three days per week) ...

  12. Patterns of aeroallergen sensitization predicting risk for asthma in preschool children with atopic dermatitis.

    Science.gov (United States)

    Calamelli, Elisabetta; Ricci, Giampaolo; Neri, Iria; Ricci, Lorenza; Rondelli, Roberto; Pession, Andrea; Patrizi, Annalisa

    2015-06-01

    Atopic dermatitis (AD) is a chronic inflammatory skin disorder mostly affecting young children. Although several studies aimed to identify the risk factors for asthma in AD children, many aspects still need to be clarified. The aim of this study was to investigate the possible risk factors for asthma at school age in 99 children with early-onset and IgE-mediated AD. All children performed clinical evaluation and total and specific IgE assay for a panel of inhalant and food allergens at two different times (t1 and t2) during preschool, and asthma diagnosis was assessed at one follow-up visit (t3) at school age. At t3, 39% of children had developed asthma. Of the variables compared, the sensitization to more than one class of inhalant allergens at t2 (mean age = 30 months) was associated with asthma, with grass (OR = 3.24, p = 0.020) and cat sensitization (OR = 2.74, p = 0.043) as independent risk factors. The sensitization pattern of a child with early-onset AD, also within the first 2-3 years of life, can reflect his risk to develop asthma. Therefore, testing these children for the more common allergens during this time frame should be recommended to predict the evolution of atopic diseases.

  13. Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.

    Science.gov (United States)

    Schoop, Rotraut; Beyersmann, Jan; Schumacher, Martin; Binder, Harald

    2011-02-01

    Prognostic models for time-to-event data play a prominent role in therapy assignment, risk stratification and inter-hospital quality assurance. The assessment of their prognostic value is vital not only for responsible resource allocation, but also for their widespread acceptance. The additional presence of competing risks to the event of interest requires proper handling not only on the model building side, but also during assessment. Research into methods for the evaluation of the prognostic potential of models accounting for competing risks is still needed, as most proposed methods measure either their discrimination or calibration, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (Statistics in Medicine 1999, 18, 2529–2545) and Gerds and Schumacher (Biometrical Journal 2006, 48, 1029–1040) to handle models with competing risks, i.e. more than one possible event type, and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows.

  14. Prediction of preeclampsia by placental protein 13 and background risk factors and its prevention by aspirin.

    Science.gov (United States)

    Meiri, Hamutal; Sammar, Marei; Herzog, Ayelet; Grimpel, Yael-Inna; Fihaman, Galina; Cohen, Aliza; Kivity, Vered; Sharabi-Nov, Adi; Gonen, Ron

    2014-09-01

    Evaluation of placental protein 13 (PP13) and risk factors (RFs) as markers for predicting preeclampsia (PE) and use of aspirin for PE prevention. First-trimester pregnancy screening was based on having PP13 level ≤0.4 multiple of the median (MoM) and/or at least one major risk factor (RF) for PE. Management was by routine care or combined with daily treatment with 75 mg aspirin between 14 and 35 weeks of gestation. Of 820 deliveries, 63 women developed PE (7.7%). Median PP13 levels was 0.2MoM in the PE group compared with 0.83MoM among unaffected and 1.0MoM in unaffected not treated with aspirin (Pprevention by aspirin was most effective when the risk was determined by low PP13 alone, less effective for combining low PP13 with RFs, and ineffective when determined by RFs alone. When PE risk is determined by low first trimester PP13 or by combined low PP13 and RFs, prevention with aspirin is warranted.

  15. Risk prediction of small pulmonary nodules based on novel CT image texture markers

    Science.gov (United States)

    Han, Fangfang; Song, Bowen; Ma, He; Qian, Wei; Liang, Zhengrong

    2017-03-01

    Among the detected small nodules sized from 3 to 30mm in CT images, a significant portion is undetermined in terms of malignancy which needs biopsy or other follow-up means, resulting in excessive risk and cost. Therefore, predicting the malignancy of the nodules becomes a clinically desirable task. Based on the previous study of texture features extracted from gray-tone spatial-dependence matrices, this study aims to find more efficient texture features or image texture markers in discriminating the nodule malignancy. Two new image texture markers (median and variance) are proposed to classify the small nodules into different malignant levels, thus the risk prediction could be performed through image analysis. These two new image texture markers can minimize the effect of outliers in the feature series, thus can reduce the noise influence to the feature classification. Total 1,353 nodule samples selected from the Lung Image Database Consortium were used to evaluate the efficiency of the proposed new features. All the classification results are shown in the ROC curves and tabulated by the AUC values. The classification outcomes from (1) the most likely and likely benign nodules vs. the most likely and likely malignant nodules, (2) the most likely vs. likely benign nodules, and (3) the most likely vs. likely malignant nodules, are 0.9125+/-0.0096, 0.9239+/-0.0147, and 0.8888+/-0.0197, respectively, in terms of the largest AUC values. From the experimental outcomes on different malignant levels, the two new image texture markers from nodule volumetric CT image data have shown encouraging performance for the risk prediction.

  16. Polygenic risk score predicts prevalence of cardiovascular disease in patients with familial hypercholesterolemia.

    Science.gov (United States)

    Paquette, Martine; Chong, Michael; Thériault, Sébastien; Dufour, Robert; Paré, Guillaume; Baass, Alexis

    Although familial hypercholesterolemia (FH) is a severe monogenic disease, it has been shown that clinical risk factors and common genetic variants can modify cardiovascular disease (CVD) risk. The aim of the study was to evaluate the polygenic contribution to lipid traits and CVD in FH using genetic risk scores (GRSs). Among the 20,434 subjects attending the lipid clinic, we identified and included 725 individuals who carried an FH causing mutation in this retrospective cohort study. We evaluated the association of GRSs for several traits including coronary artery disease (CAD; GRSCAD) as well as plasma concentrations of low-density lipoprotein cholesterol (LDL-C; GRSLDL-C), high-density lipoprotein cholesterol (GRSHDL-C) and triglycerides (GRSTG). A total of 32% (n = 231) of FH subjects presented a CVD event before their first visit. Patients in the highest GRSLDL-C tertile presented an LDL-C 0.4 mmol/L (15.5 mg/dL) higher than the subjects in the lowest tertile (P = .01). The GRSCAD was strongly associated with CVD events (odds ratio 1.80; 95% confidence interval 1.14-2.85; P = .01) even after adjustment for cardiovascular risk factors. Compared with subjects in the first tertile, those in the third GRSCAD tertile had a significantly higher prevalence of events (40.9% vs 24.7%, P < .0001) and a significantly higher number of events (average 0.97 vs 0.57 [P = .0001] events per individual). These results indicate that even in the context of a severe monogenic disease such as FH, common genetic variants can significantly modify the disease phenotype. The use of the 192-SNPs GRSCAD may refine CVD risk prediction in FH patients and this could lead to a more personalized approach to therapy. Copyright © 2017 National Lipid Association. Published by Elsevier Inc. All rights reserved.

  17. Cardiovascular risk prediction in the general population with use of suPAR, CRP, and Framingham Risk Score

    DEFF Research Database (Denmark)

    Lyngbæk, Stig; Marott, Jacob L; Sehestedt, Thomas

    2013-01-01

    for men (p=0.034) and borderline significant for women (p=0.054), while the integrated discrimination improvement was highly significant (P≤0.001) for both genders. CONCLUSIONS: suPAR provides prognostic information of CVD risk beyond FRS and improves risk prediction substantially when combined with CRP...

  18. Nutritional Risk Index predicts mortality in hospitalized advanced heart failure patients.

    Science.gov (United States)

    Adejumo, Oluwayemisi L; Koelling, Todd M; Hummel, Scott L

    2015-11-01

    Hospitalized advanced heart failure (HF) patients are at high risk for malnutrition and death. The Nutritional Risk Index (NRI) is a simple, well-validated tool for identifying patients at risk for nutrition-related complications. We hypothesized that, in advanced HF patients from the ESCAPE (Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness) trial, the NRI would improve risk discrimination for 6-month all-cause mortality. We analyzed the 160 ESCAPE index admission survivors with complete follow-up and NRI data, calculated as follows: NRI = (1.519 × discharge serum albumin [in g/dl]) + (41.7 × discharge weight [in kg] / ideal body weight [in kg]); as in previous studies, if discharge weight is greater than ideal body weight (IBW), this ratio was set to 1. The previously developed ESCAPE mortality model includes: age; 6-minute walk distance; cardiopulmonary resuscitation/mechanical ventilation; discharge β-blocker prescription and diuretic dose; and discharge serum sodium, blood urea nitrogen and brain natriuretic peptide levels. We used Cox proportional hazards modeling for the outcome of 6-month all-cause mortality. Thirty of 160 patients died within 6 months of hospital discharge. The median NRI was 96 (IQR 91 to 102), reflecting mild-to-moderate nutritional risk. The NRI independently predicted 6-month mortality, with adjusted HR 0.60 (95% CI 0.39 to 0.93, p = 0.02) per 10 units, and increased Harrell's c-index from 0.74 to 0.76 when added to the ESCAPE model. Body mass index and NRI at hospital admission did not predict 6-month mortality. The discharge NRI was most helpful in patients with high (≥ 20%) predicted mortality by the ESCAPE model, where observed 6-month mortality was 38% in patients with NRI 100 (p = 0.04). The NRI is a simple tool that can improve mortality risk stratification at hospital discharge in hospitalized patients with advanced HF. Published by Elsevier Inc.

  19. Estimating risk for earth-satellite attenuation prediction

    Science.gov (United States)

    Crane, Robert K.

    1993-01-01

    Annual cumulative distributions of attenuation measurements and of rain-rate measurements were obtained from several locations in Europe and the United States. They were analyzed to estimate the year-to-year variability to be associated with a prediction of the expected cumulative distribution. Two models were constructed to estimate variability, an ad hoc model that summarized a number of observations when compared to model predictions and a probabilistic model that applied the ideas of order statistics to the prediction problem when the number of independent attenuation or rain-rate events in a sample year could be estimated. Based on these models, the statistical uncertainty in a model prediction may be estimated. The estimation procedure also provides an answer to the question of the number of years of observation needed to provide an estimate of the empirical distribution with a specified statistical uncertainty.

  20. Exclusion of emphysematous lung from dose-volume estimates of risk improves prediction of radiation pneumonitis.

    Science.gov (United States)

    Uchida, Yasuki; Tsugawa, Takuya; Tanaka-Mizuno, Sachiko; Noma, Kazuo; Aoki, Ken; Shigemori, Wataru; Nakagawa, Hiroaki; Kinose, Daisuke; Yamaguchi, Masafumi; Osawa, Makoto; Ogawa, Emiko; Nakano, Yasutaka

    2017-10-02

    The risk factors for radiation pneumonitis (RP) in patients with chronic obstructive pulmonary disease (COPD) are unclear. Mean lung dose (MLD) and percentage of irradiated lung volume are common predictors of RP, but the most accurate dosimetric parameter has not been established. We hypothesized that the total lung volume irradiated without emphysema would influence the onset of RP. We retrospectively evaluated 100 patients who received radiotherapy for lung cancer. RP was graded according to the Common Terminology Criteria for Adverse Events (version 4.03). We quantified low attenuation volume (LAV) using quantitative computed tomography analysis. The association between RP and traditional dosimetric parameters including MLD, volume of the lung receiving a dose of ≥2 Gy, ≥ 5 Gy, ≥ 10 Gy, ≥ 20 Gy, and ≥30 Gy, and counterpart measurements of the lung without LAV, were analyzed by logistic regression. We compared each dosimetric parameter for RP using multiple predictive performance measures including area under the receiver operating characteristic curve (AUC) and integrated discrimination improvement (IDI). Of 100 patients, RP of Grades 1, 2, 3, 4, and 5 was diagnosed in 24, 12, 13, 1, and 1 patients, respectively. Compared with traditional dosimetric parameters, counterpart measurements without LAV improved risk prediction of symptomatic RP. The ratio of the lung without LAV receiving ≥30 Gy to the total lung volume without LAV most accurately predicted symptomatic RP (AUC, 0.894; IDI, 0.064). Irradiated lung volume without LAV predicted RP more accurately than traditional dosimetric parameters.

  1. Evaluation of Head and Brain Injury Risk Functions Using Sub-Injurious Human Volunteer Data.

    Science.gov (United States)

    Sanchez, Erin J; Gabler, Lee F; McGhee, James S; Olszko, Ardyn V; Chancey, V Carol; Crandall, Jeff R; Panzer, Matthew B

    2017-08-15

    Risk assessment models are developed to estimate the probability of brain injury during head impact using mechanical response variables such as head kinematics and brain tissue deformation. Existing injury risk functions have been developed using different datasets based on human volunteer and scaled animal injury responses to impact. However, many of these functions have not been independently evaluated with respect to laboratory-controlled human response data. In this study, the specificity of 14 existing brain injury risk functions was assessed by evaluating their ability to correctly predict non-injurious response using previously conducted sled tests with well-instrumented human research volunteers. Six degrees-of-freedom head kinematics data were obtained for 335 sled tests involving subjects in frontal, lateral, and oblique sled conditions up to 16 Gs peak sled acceleration. A review of the medical reports associated with each individual test indicated no clinical diagnosis of mild or moderate brain injury in any of the cases evaluated. Kinematic-based head and brain injury risk probabilities were calculated directly from the kinematic data, while strain-based risks were determined through finite element model simulation of the 335 tests. Several injury risk functions substantially over predict the likelihood of concussion and diffuse axonal injury; proposed maximum principal strain-based injury risk functions predicted nearly 80 concussions and 14 cases of severe diffuse axonal injury out of the 335 non-injurious cases. This work is an important first step in assessing the efficacy of existing brain risk functions and highlights the need for more predictive injury assessment models.

  2. A comparison of the predictive properties of nine sex offender risk assessment instruments

    NARCIS (Netherlands)

    Smid, W.J.; Kamphuis, J.H.; Wever, E.C.; van Beek, D.J.

    2014-01-01

    Sex offender treatment is most effective when tailored to risk-need-responsivity principles, which dictate that treatment levels should match risk levels as assessed by structured risk assessment instruments. The predictive properties, missing values, and interrater agreement of the scores of 9

  3. Prediction of performance and evaluation of flexible pavement rehabilitation strategies

    Directory of Open Access Journals (Sweden)

    Kang-Won Wayne Lee

    2017-04-01

    Full Text Available Five test sections with different additives and strategies were established to rehabilitate a State-maintained highway more effectively in Rhode Island (RI: control, calcium chloride, asphalt emulsion, Portland cement and geogrid. Resilient moduli of subgrade soils and subbase materials before and after full depth rehabilitation were employed as input parameters to predict the performance of pavement structures using AASHTOWare Pavement ME Design (Pavement ME software in terms of rutting, cracking and roughness. It was attempted to use Level 1 input (which includes traffic full spectrum data, climate data and structural layer properties for Pavement ME. Traffic data was obtained from a Weigh-in-Motion (WIM instrument and Providence station was used for collecting climatic data. Volumetric properties, dynamic modulus and creep compliance were used as input parameters for 19 mm (0.75 in. warm mix asphalt (WMA base and 12.5 mm (0.5 in. WMA surface layer. The results indicated that all test sections observed AC top-down (longitudinal cracking except Portland cement section which passed for all criteria. The order in terms of performance (best to worst for all test sections by Pavement ME was Portland cement, calcium chloride, control, geogrid, and asphalt emulsion. It was also observed that all test sections passed for both bottom up and top down fatigue cracking by increasing thickness of either of the two top asphalt layers. Test sections with five different base/subbase materials were evaluated in last two years through visual condition survey and measurements of deflection and roughness to confirm the prediction, but there was no serious distress and roughness. Thus these experiments allowed selecting the best rehabilitation/reconstruction techniques for the particular and/or similar highway, and a framework was formulated to select an optimal technique and/or strategy for future rehabilitation/reconstruction projects. Finally, guidelines for

  4. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

    Science.gov (United States)

    Luo, Gang

    2016-01-01

    Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

  5. Evaluating effects of normobaric oxygen therapy in acute stroke with MRI-based predictive models

    Directory of Open Access Journals (Sweden)

    Wu Ona

    2012-03-01

    Full Text Available Abstract Background Voxel-based algorithms using acute multiparametric-MRI data have been shown to accurately predict tissue outcome after stroke. We explored the potential of MRI-based predictive algorithms to objectively assess the effects of normobaric oxygen therapy (NBO, an investigational stroke treatment, using data from a pilot study of NBO in acute stroke. Methods The pilot study of NBO enrolled 11 patients randomized to NBO administered for 8 hours, and 8 Control patients who received room-air. Serial MRIs were obtained at admission, during gas therapy, post-therapy, and pre-discharge. Diffusion/perfusion MRI data acquired at admission (pre-therapy was used in generalized linear models to predict the risk of lesion growth at subsequent time points for both treatment scenarios: NBO or Control. Results Lesion volume sizes 'during NBO therapy' predicted by Control-models were significantly larger (P = 0.007 than those predicted by NBO models, suggesting that ischemic lesion growth is attenuated during NBO treatment. No significant difference was found between the predicted lesion volumes at later time-points. NBO-treated patients, despite showing larger lesion volumes on Control-models than NBO-models, tended to have reduced lesion growth. Conclusions This study shows that NBO has therapeutic potential in acute ischemic stroke, and demonstrates the feasibility of using MRI-based algorithms to evaluate novel treatments in early-phase clinical trials.

  6. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong [Southeast University, Department of Radiology, Medical School, Zhongda Hospital (China); Wu, Chun-Gen [Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Department of Diagnostic and Interventional Radiology (China); Fang, Wen; Chen, Li; Guo, Jin-He; Deng, Gang; Zhu, Guang-Yu; Teng, Gao-Jun, E-mail: gjteng@vip.sina.com [Southeast University, Department of Radiology, Medical School, Zhongda Hospital (China)

    2017-02-15

    PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy of this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.

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

    Science.gov (United States)

    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.

  8. Prometheus unbound - challenges of risk evaluation, risk classification, and risk management

    Energy Technology Data Exchange (ETDEWEB)

    Klinke, A.; Renn, O.

    1999-11-01

    For dealing with risks in a rational fashion, it is necessary to characterize risks and use the parameters of characterization as tools for designing appropriate actions. This reports suggests a set of criteria that one can use in evaluating risks. These criteria include: - Damage potential, i.e. the amount of damage that the hazard can cause; - probability of occurrence, i.e. the likelihood that a specific damage will occur; - incertitude, i.e., the remaining uncertainties that are not covered by the assessment of probabilities (subdivided in statistical uncertainties, genuine uncertainty, and ignorance); - ubiquity which defines the geographic dispersion of potential damages (intragenerational justice); - persistency which defines the temporal extension of potential damages (intergenerational justice); - irreversibility which describes the impossible restoration of the situation to the state before the damage occurred (possible restoration are e.g. reforestation and cleaning of water); - delay effects which characterize the time of latency between the initial event and the actual impact of damage. The time of latency could be of physical, chemical or biological nature; and - potential of mobilization which is understood as violation of individual, social or cultural interests and values generating social conflicts and psychological reactions by affected people. (orig.)

  9. Computerized Prediction of Risk for Developing Breast Cancer Based on Bilateral Mammographic Breast Tissue Asymmetry

    Science.gov (United States)

    Wang, Xingwei; Lederman, Dror; Tan, Jun; Wang, Xiao Hui; Zheng, Bin

    2011-01-01

    This study developed and assessed a computerized scheme to detect breast abnormalities and predict the risk of developing cancer based on bilateral mammographic tissue asymmetry. A digital mammography database of 100 randomly selected negative cases and 100 positive cases for having high-risk of developing breast cancer was established. Each case includes four images of craniocaudal (CC) and mediolateral oblique (MLO) views of the left and right breast. To detect bilateral mammographic tissue asymmetry, a pool of 20 computed features was assembled. A genetic algorithm was applied to select optimal features and build an artificial neural network based classifier to predict the likelihood of a test case being positive. The leave-one-case-out validation method was used to evaluate the classifier performance. Several approaches were investigated to improve the classification performance including extracting asymmetrical tissue features from either selected regions of interests or the entire segmented breast area depicted on bilateral images in one view, and the fusion of classification results from two views. The results showed that (1) using the features computed from the entire breast area, the classifier yielded the higher performance than using ROIs, and (2) using a weighted average fusion method, the classifier achieved the highest performance with the area under ROC curve of 0.781±0.023. At 90% specificity, the scheme detected 58.3% of high-risk cases in which cancers developed and verified 6 to 18 months later. The study demonstrated the feasibility of applying a computerized scheme to detect cases with high risk of developing breast cancer based on computer-detected bilateral mammographic tissue asymmetry. PMID:21482168

  10. Serum 25-Hydroxyvitamin D Level Could Predict the Risk for Peritoneal Dialysis-Associated Peritonitis.

    Science.gov (United States)

    Pi, Hai-Chen; Ren, Ye-Ping; Wang, Qin; Xu, Rong; Dong, Jie

    2015-12-01

    ♦ As an immune system regulator, vitamin D is commonly deficient among patients on peritoneal dialysis (PD), which may contribute to their impaired immune function and increased risk for PD-related peritonitis. In this study, we aimed to investigate whether vitamin D deficiency could predict the risk of peritonitis in a prospective cohort of patients on PD. ♦ We collected 346 prevalent and incident PD patients from 2 hospitals. Baseline demographic data and clinical characteristics were recorded. Serum 25-hydroxyvitamin D (25[OH]D) was measured at baseline and prior to peritonitis. The mean doses of oral active vitamin D used during the study period were also recorded. The outcome was the occurrence of peritonitis. ♦ The mean age of patients and duration of PD were 58.95 ± 13.67 years and 28.45 (15.04 - 53.37) months, respectively. Baseline 25(OH)D level was 16.15 (12.13 - 21.16) nmol/L, which was closely associated with diabetic status, longer PD duration, malnutrition, and inflammation. Baseline serum 25(OH)D predicted the occurrence of peritonitis independently of active vitamin D supplementation with a hazard ratio (HR) of 0.94 (95% confidence interval [CI] 0.90 - 0.98) after adjusting for recognized confounders (age, gender, dialysis duration, diabetes, albumin, residual renal function, and history of peritonitis). Compared to the low tertile, middle and high 25(OH)D level tertiles were associated with a decreased risk for peritonitis with HRs of 0.54 (95% CI 0.31 - 0.94) and 0.39 (95% CI 0.20 - 0.75), respectively. ♦ Vitamin D deficiency evaluated by serum 25(OH)D rather than active vitamin D supplementation is closely associated with a higher risk of peritonitis. Copyright © 2015 International Society for Peritoneal Dialysis.

  11. The more from East-Asian, the better: risk prediction of colorectal cancer risk by GWAS-identified SNPs among Japanese.

    Science.gov (United States)

    Abe, Makiko; Ito, Hidemi; Oze, Isao; Nomura, Masatoshi; Ogawa, Yoshihiro; Matsuo, Keitaro

    2017-12-01

    Little is known about the difference of genetic predisposition for CRC between ethnicities; however, many genetic traits common to colorectal cancer have been identified. This study investigated whether more SNPs identified in GWAS in East Asian population could improve the risk prediction of Japanese and explored possible application of genetic risk groups as an instrument of the risk communication. 558 Patients histologically verified colorectal cancer and 1116 first-visit outpatients were included for derivation study, and 547 cases and 547 controls were for replication study. Among each population, we evaluated prediction models for the risk of CRC that combined the genetic risk group based on SNPs from GWASs in European-population and a similarly developed model adding SNPs from GWASs in East Asian-population. We examined whether adding East Asian-specific SNPs would improve the discrimination. Six SNPs (rs6983267, rs4779584, rs4444235, rs9929218, rs10936599, rs16969681) from 23 SNPs by European-based GWAS and five SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279) among ten SNPs by Asian-based GWAS were selected in CRC risk prediction model. Compared with a 6-SNP-based model, an 11-SNP model including Asian GWAS-SNPs showed improved discrimination capacity in Receiver operator characteristic analysis. A model with 11 SNPs resulted in statistically significant improvement in both derivation (P = 0.0039) and replication studies (P = 0.0018) compared with six SNP model. We estimated cumulative risk of CRC by using genetic risk group based on 11 SNPs and found that the cumulative risk at age 80 is approximately 13% in the high-risk group while 6% in the low-risk group. We constructed a more efficient CRC risk prediction model with 11 SNPs including newly identified East Asian-based GWAS SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279). Risk grouping based on 11 SNPs depicted lifetime difference of CRC risk. This might be useful for

  12. How well can adolescents really judge risk? Simple, self reported risk factors out-predict teens' self estimates of personal risk

    Directory of Open Access Journals (Sweden)

    Alexander Persoskie

    2013-01-01

    Full Text Available Recent investigations of adolescents' beliefs about risk have led to surprisingly optimistic conclusions: Teens' self estimates of their likelihood of experiencing various life events not only correlate sensibly with relevant risk factors (Fischhoff et al., 2000, but they also significantly predict later experiencing the events (Bruine de Bruin et al., 2007. Using the same dataset examined in previous investigations, the present study extended these analyses by comparing the predictive value of self estimates of risk to that of traditional risk factors for each outcome. The analyses focused on the prediction of pregnancy, criminal arrest, and school enrollment. Three findings emerged. First, traditional risk factor information tended to out-predict self assessments of risk, even when the risk factors included crude, potentially unreliable measures (e.g., a simple tally of self-reported criminal history and when the risk factors were aggregated in a nonoptimal way (i.e., unit weighting. Second, despite the previously reported correlations between self estimates and outcomes, perceived invulnerability was a problem among the youth: Over half of the teens who became pregnant, half of those who were not enrolled in school, and nearly a third of those who were arrested had, one year earlier, indicated a 0% chance of experiencing these outcomes. Finally, adding self estimates of risk to the other risk factor information produced only small gains in predictive accuracy. These analyses point to the need for greater education about the situations and behaviors that lead to negative outcomes.

  13. Predicting sport and occupational lower extremity injury risk through movement quality screening: a systematic review.

    Science.gov (United States)

    Whittaker, Jackie L; Booysen, Nadine; de la Motte, Sarah; Dennett, Liz; Lewis, Cara L; Wilson, Dave; McKay, Carly; Warner, Martin; Padua, Darin; Emery, Carolyn A; Stokes, Maria

    2017-04-01

    Identification of risk factors for lower extremity (LE) injury in sport and military/first-responder occupations is required to inform injury prevention strategies. To determine if poor movement quality is associated with LE injury in sport and military/first-responder occupations. 5 electronic databases were systematically searched. Studies selected included original data; analytic design; movement quality outcome (qualitative rating of functional compensation, asymmetry, impairment or efficiency of movement control); LE injury sustained with sport or military/first-responder occupation. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were followed. 2 independent authors assessed the quality (Downs and Black (DB) criteria) and level of evidence (Oxford Centre of Evidence-Based Medicine model). Of 4361 potential studies, 17 were included. The majority were low-quality cohort studies (level 4 evidence). Median DB score was 11/33 (range 3-15). Heterogeneity in methodology and injury definition precluded meta-analyses. The Functional Movement Screen was the most common outcome investigated (15/17 studies). 4 studies considered inter-relationships between risk factors, 7 reported diagnostic accuracy and none tested an intervention programme targeting individuals identified as high risk. There is inconsistent evidence that poor movement quality is associated with increased risk of LE injury in sport and military/first-responder occupations. Future research should focus on high-quality cohort studies to identify the most relevant movement quality outcomes for predicting injury risk followed by developing and evaluating preparticipation screening and LE injury prevention programmes through high-quality randomised controlled trials targeting individuals at greater risk of injury based on screening tests with validated test properties. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted

  14. Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs.

    Science.gov (United States)

    McCarthy, John F; Bossarte, Robert M; Katz, Ira R; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M; Nielson, Christopher; Schoenbaum, Michael

    2015-09-01

    The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions.

  15. Wind power prediction risk indices based on numerical weather prediction ensembles

    OpenAIRE

    Holmgren, Erik; Nils, Siebert; Kariniotakis, Georges

    2010-01-01

    International audience; The large-scale integration of wind generation imposes several difficulties in the management of power systems. Wind power forecasting up to a few days ahead contributes to a secure and economic power system operation. Prediction models of today are mainly focused on spot or probabilistic predictions of wind power. However, in many applications, endusers require additional tools for the on-line estimation of the uncertainty of the predictions. One solution to this is p...

  16. A cumulative genetic risk score predicts progression in Parkinson's disease.

    Science.gov (United States)

    Pihlstrøm, Lasse; Morset, Kristina Rebekka; Grimstad, Espen; Vitelli, Valeria; Toft, Mathias

    2016-04-01

    The contribution of genetic variability to clinical heterogeneity in Parkinson's disease is insufficiently understood. We aimed to investigate the effect of cumulative genetic risk on clinical outcomes. In a single-center study of 336 patients we genotyped 19 independent susceptibility variants identified in genome-wide association studies of Parkinson's disease. We tested for association between a cumulative genetic risk score and 3 outcome measures: survival, time until progression to Hoehn and Yahr stage 3, and Unified Parkinson's Disease Rating Scale motor score severity. Genetic risk score was significantly associated with time from diagnosis to Hoehn and Yahr stage 3 in a Cox regression model (P = 0.010). We observed no clear association for the other outcomes. We present results linking cumulative genetic risk to a motor outcome in Parkinson's disease. Our findings provide a valuable starting point for future large-scale efforts to map the genetic determinants of phenotypic variability. © 2016 International Parkinson and Movement Disorder Society.

  17. Cardiovascular risk prediction in chronic kidney disease patients

    Directory of Open Access Journals (Sweden)

    Santiago Cedeño Mora

    2017-05-01

    Conclusion: The cardiovascular risk scores (FRS-CVD and ASCVD [AHA/ACC 2013] can estimate the probability of atherosclerotic cardiovascular events in patients with CKD regardless of renal function, albuminuria and previous cardiovascular events.

  18. Development and validation of a lung cancer risk prediction model for African-Americans.

    Science.gov (United States)

    Etzel, Carol J; Kachroo, Sumesh; Liu, Mei; D'Amelio, Anthony; Dong, Qiong; Cote, Michele L; Wenzlaff, Angela S; Hong, Waun Ki; Greisinger, Anthony J; Schwartz, Ann G; Spitz, Margaret R

    2008-09-01

    Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self-reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67-0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57-0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model

  19. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

    Directory of Open Access Journals (Sweden)

    Yiming Hu

    2017-06-01

    Full Text Available Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

  20. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction

    DEFF Research Database (Denmark)

    Paige, Ellie; Barrett, Jessica; Pennells, Lisa

    2017-01-01

    The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data...... encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary...... improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction....

  1. Predicting breast cancer risk: implications of a "weak" family history.

    Science.gov (United States)

    Anderson, Elaine; Berg, Jonathan; Black, Roger; Bradshaw, Nicola; Campbell, Joyce; Cetnarskyj, Roseanne; Drummond, Sarah; Davidson, Rosemarie; Dunlop, Jacqueline; Fordyce, Alison; Gibbons, Barbara; Goudie, David; Gregory, Helen; Hanning, Kirstie; Holloway, Susan; Longmuir, Mark; McLeish, Lorna; Murday, Vicky; Miedzybrodska, Zosia; Nicholson, Donna; Pearson, Pauline; Porteous, Mary; Reis, Marta; Slater, Sheila; Smith, Karen; Smyth, Elizabeth; Snadden, Lesley; Steel, Michael; Stirling, Diane; Watt, Cathy; Whyte, Catriona; Young, Dorothy

    2008-01-01

    Published guidelines adopted in many countries recommend that women whose family history of breast cancer places them at a risk>or=1.7 times that of the age-matched general population, should be considered for inclusion in special surveillance programmes. However validation of risk assessment models has been called for as a matter of urgency. The databases of the four Scottish Familial Breast Cancer clinics and the Scottish Cancer Registry have been searched to identify breast cancers occurring among 1,125 women aged 40-56, with family histories placing them below the "moderate" level of genetic risk. The observed incidence over 6 years was compared with age-specific data for the Scottish population. Our findings confirm that when there are two affected relatives (one first degree) the relative risk (RR) exceeds 1.7 regardless of their ages at diagnosis. When only one (first degree) relative was affected at any age from 40 to 55, the RR does not reach 1.7 if that relative was a mother but exceeds it if the relative was a sister. The probable explanation is that sisters are more likely than mother/daughter pairs to share homozygosity for a risk allele. Surveillance programmes might therefore accommodate sisters of women affected before age 55. Evidence that "low penetrance" alleles contributing to breast cancer risk may be recessive should be taken into account in strategies for identifying them.

  2. Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests.

    Science.gov (United States)

    Hu, Chen; Steingrimsson, Jon Arni

    2017-10-19

    A crucial component of making individualized treatment decisions is to accurately predict each patient's disease risk. In clinical oncology, disease risks are often measured through time-to-event data, such as overall survival and progression/recurrence-free survival, and are often subject to censoring. Risk prediction models based on recursive partitioning methods are becoming increasingly popular largely due to their ability to handle nonlinear relationships, higher-order interactions, and/or high-dimensional covariates. The most popular recursive partitioning methods are versions of the Classification and Regression Tree (CART) algorithm, which builds a simple interpretable tree structured model. With the aim of increasing prediction accuracy, the random forest algorithm averages multiple CART trees, creating a flexible risk prediction model. Risk prediction models used in clinical oncology commonly use both traditional demographic and tumor pathological factors as well as high-dimensional genetic markers and treatment parameters from multimodality treatments. In this article, we describe the most commonly used extensions of the CART and random forest algorithms to right-censored outcomes. We focus on how they differ from the methods for noncensored outcomes, and how the different splitting rules and methods for cost-complexity pruning impact these algorithms. We demonstrate these algorithms by analyzing a randomized Phase III clinical trial of breast cancer. We also conduct Monte Carlo simulations to compare the prediction accuracy of survival forests with more commonly used regression models under various scenarios. These simulation studies aim to evaluate how sensitive the prediction accuracy is to the underlying model specifications, the choice of tuning parameters, and the degrees of missing covariates.

  3. A risk prediction model for screening bacteremic patients: a cross sectional study.

    Directory of Open Access Journals (Sweden)

    Franz Ratzinger

    Full Text Available Bacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia.In this retrospective hospital-wide cohort study evaluating 15,985 patients with suspected bacteraemia, 51 variables were assessed for their diagnostic potency. A derivation cohort (n = 14.699 was used for feature and model selection as well as for cut-off specification. Models were established using the A2DE classifier, a supervised Bayesian classifier. Two internally validated models were further evaluated by a validation cohort (n = 1,286.The proportion of neutrophile leukocytes in differential blood count was the best individual variable to predict bacteraemia (ROC-AUC: 0.694. Applying the A2DE classifier, two models, model 1 (20 variables and model 2 (10 variables were established with an area under the receiver operating characteristic curve (ROC-AUC of 0.767 and 0.759, respectively. In the validation cohort, ROC-AUCs of 0.800 and 0.786 were achieved. Using predefined cut-off points, 16% and 12% of patients were allocated to the low risk group with a negative predictive value of more than 98.8%.Applying the proposed models, more than ten percent of patients with suspected blood stream infection were identified having minimal risk for bacteraemia. Based on these data the application of this model as an automated decision support tool for physicians is conceivable leading to a potential increase in the cost-effectiveness of blood culture sampling. External prospective validation of the model's generalizability is needed

  4. Health risk evaluation of nitrogen oxides

    Energy Technology Data Exchange (ETDEWEB)

    Berglund, M.; Ewetz, L.; Gustafsson, L.; Moldeus, P.; Pershagen, G.; Victorin, K. [Karolinska Inst., Stockholm (Sweden). Inst. of Environmental Medicine

    1995-12-31

    At the request of the Swedish Environmental Protection Agency a criteria document on nitrogen oxides has been prepared, and is intended to serve as a basis for revised air quality standards in Sweden. The criteria document is based on a thorough literature survey, and the health risk assessment is summarized in this presentation. The present standard for nitrogen dioxide (NO{sub 2}) is 110 {mu}g/m{sup 3} as 1-hour mean (98th percentile); 75 {mu}g/m{sup 3} as 24- hour mean (98th percentile); and 50 {mu}g/m{sup 3} as 6-month mean (arithmetic eman during winter half-year). (author)

  5. Description of a risk predictive model of 30-day postoperative mortality after elective abdominal aortic aneurysm repair.

    Science.gov (United States)

    Eslami, Mohammad H; Rybin, Denis V; Doros, Gheorghe; Farber, Alik

    2017-01-01

    Despite vast improvement in the field of vascular surgery, elective abdominal aortic aneurysm (AAA) repair still leads to perioperative death. Patients with asymptomatic AAAs, therefore, would benefit from an individual risk assessment to help with decisions regarding operative intervention. The purpose of this study was to describe such a 30-day postoperative (POD) risk prediction model using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) data. The NSQIP database (2005-2011) was queried for patients undergoing elective AAA repair using open or endovascular techniques. Clinical variables and known predictors of mortality were included in a full prediction model. These variables included procedure type, patient's age, functional dependence and comorbidities, and surgeon's specialty. Backward elimination with alpha-level of 0.2 was used to construct a parsimonious model. Model discrimination was evaluated in equally sized risk quintiles. The overall mortality rate for 18,917 elective AAA patients was 1.7%. In this model, surgeon's specialty was not predictive of POD. The most significant factors affecting POD included open repair (odds ratio [OR], 2.712; 95% confidence interval [CI], 2.119-3.469; P 70 (OR, 2.243; 95% CI, 1.695-3.033; P model was reasonable (C-statistic = 0.751) and corrected to 0.736 after internal validation. The NSQIP model performed well predicting mortality among risk-group quintiles. The NSQIP risk prediction model is a robust vehicle to predict POD among patient undergoing elective AAA repair. This model can be used for risk stratification of patients undergoing elective AAA repair. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

  6. Risk factors and a predictive model for acute hepatic failure after transcatheter arterial chemoembolization in patients with hepatocellular carcinoma.

    Science.gov (United States)

    Min, Yang Won; Kim, Jeong; Kim, Seonwoo; Sung, Young Kyung; Lee, Jin Hee; Gwak, Geum-Youn; Paik, Yong Han; Choi, Moon Seok; Koh, Kwang Cheol; Paik, Seung Woon; Yoo, Byung Chul; Lee, Joon Hyeok

    2013-02-01

    Acute hepatic failure (AHF) is one of the most serious complications of transcatheter arterial chemoembolization (TACE). The aims of this study were to investigate risk factors of AHF after TACE and to establish a predictive model for AHF. In the evaluation set, a total of 820 patients who underwent TACE as a first treatment for hepatocellular carcinoma were included. The demographic, laboratory, radiological and treatment-related factors were analysed to identify risk factors for AHF after TACE and a predictive model was established using the identified risk factors. In the validation set, a different cohort of 438 patients was included to validate the predictive model. The incidence of post-TACE AHF was 15.1% (124/820). Multivariate analysis revealed that presence of portal vein thrombosis, high aspartate aminotransferase, bilirubin, and log alpha-foetoprotein levels, and low albumin and sodium levels were independent risk factors. A mathematical model was established using these independent risk factors, and the area under the receiver operating characteristic curve of the model was 0.773 (95% confidence interval, 0.726-0.820). The cut-off value of 9 had a sensitivity of 78.2%, a specificity of 72.3%, a positive likelihood ratio of 2.82, a negative likelihood ratio of 0.30, a positive predictive value of 28.9% and a negative predictive value of 95.8%. The risk factors of post-TACE AHF were presence of portal vein thrombosis, high aspartate aminotransferase, bilirubin, and alpha-foetoprotein levels, and low serum albumin and sodium levels. A mathematical model to predict post-TACE AHF was established. © 2012 John Wiley & Sons A/S.

  7. Comparison of three data mining models for predicting diabetes or prediabetes by risk factors

    Directory of Open Access Journals (Sweden)

    Xue-Hui Meng

    2013-02-01

    Full Text Available The purpose of this study was to compare the performance of logistic regression, artificial neural networks (ANNs and decision tree models for predicting diabetes or prediabetes using common risk factors. Participants came from two communities in Guangzhou, China; 735 patients confirmed to have diabetes or prediabetes and 752 normal controls were recruited. A standard questionnaire was administered to obtain information on demographic characteristics, family diabetes history, anthropometric measurements and lifestyle risk factors. Then we developed three predictive models using 12 input variables and one output variable from the questionnaire information; we evaluated the three models in terms of their accuracy, sensitivity and specificity. The logistic regression model achieved a classification accuracy of 76.13% with a sensitivity of 79.59% and a specificity of 72.74%. The ANN model reached a classification accuracy of 73.23% with a sensitivity of 82.18% and a specificity of 64.49%; and the decision tree (C5.0 achieved a classification accuracy of 77.87% with a sensitivity of 80.68% and specificity of 75.13%. The decision tree model (C5.0 had the best classification accuracy, followed by the logistic regression model, and the ANN gave the lowest accuracy.

  8. Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.

    Science.gov (United States)

    Seyednasrollah, Fatemeh; Mäkelä, Johanna; Pitkänen, Niina; Juonala, Markus; Hutri-Kähönen, Nina; Lehtimäki, Terho; Viikari, Jorma; Kelly, Tanika; Li, Changwei; Bazzano, Lydia; Elo, Laura L; Raitakari, Olli T

    2017-06-01

    Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity. © 2017 American Heart Association, Inc.

  9. Landscape-scale disease risk quantification and prediction.

    Science.gov (United States)

    Yuen, Jonathan; Mila, Asimina

    2015-01-01

    The study of plant disease epidemics at a landscape scale can be extended to allow for predictions about disease occurrence at this scale. Examined within the context of the disease triangle, systems developed to incorporate information primarily about the pathogen and conditions conducive to the infection process. Parametric methods can be used to relate environmental conditions to disease, and specifically relate environment to the inoculum production, the resulting infection process, or both. Aspects relating to the presence or absence of the host plant within the landscape, or patterns of the host within the landscape, are much rarer in disease prediction, although analyses incorporating these factors have been conducted. Predictive systems at the landscape scale may concentrate only on the conditions for infection or possible migratory paths of pathogen propagules. Incorporation of all components of the disease triangle may be one way to improve these systems.

  10. A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations.

    Science.gov (United States)

    Qian, David C; Han, Younghun; Byun, Jinyoung; Shin, Hae Ri; Hung, Rayjean J; McLaughlin, John R; Landi, Maria Teresa; Seminara, Daniela; Amos, Christopher I

    2016-08-01

    Although genome-wide association studies (GWAS) have identified many genetic variants that are strongly associated with lung cancer, these variants have low penetrance and serve as poor predictors of lung cancer in individuals. We sought to increase the predictive value of germline variants by considering their cumulative effects in the context of biologic pathways. For individuals in the Environment and Genetics in Lung Cancer Etiology study (1,815 cases/1,971 controls), we computed pathway-level susceptibility effects as the sum of relevant SNP variant alleles weighted by their log-additive effects from a separate lung cancer GWAS meta-analysis (7,766 cases/37,482 controls). Logistic regression models based on age, sex, smoking, genetic variants, and principal components of pathway effects and pathway-smoking interactions were trained and optimized in cross-validation and further tested on an independent dataset (556 cases/830 controls). We assessed prediction performance using area under the receiver operating characteristic curve (AUC). Compared with typical binomial prediction models that have epidemiologic predictors (AUC = 0.607) in addition to top GWAS variants (AUC = 0.617), our pathway-based smoking-interactive multinomial model significantly improved prediction performance in external validation (AUC = 0.656, P approach demonstrated a larger increase in AUC over nongenetic counterpart models relative to previous approaches that incorporate variants. This model is the first of its kind to evaluate lung cancer prediction using subtype-stratified genetic effects organized into pathways and interacted with smoking. We propose pathway-exposure interactions as a potentially powerful new contributor to risk inference. Cancer Epidemiol Biomarkers Prev; 25(8); 1208-15. ©2016 AACR. ©2016 American Association for Cancer Research.

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

    Science.gov (United States)

    Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem

    2017-01-01

    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. 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). 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. 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.

  12. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study.

    Science.gov (United States)

    Hippisley-Cox, Julia; Coupland, Carol; Brindle, Peter

    2017-05-23

    Objectives To develop and validate updated QRISK3 prediction algorithms to estimate the 10 year risk of cardiovascular disease in women and men accounting for potential new risk factors.Design Prospective open cohort study.Setting General practices in England providing data for the QResearch database.Participants 1309 QResearch general practices in England: 981 practices were used to develop the scores and a separate set of 328 practices were used to validate the scores. 7.89 million patients aged 25-84 years were in the derivation cohort and 2.67 million patients in the validation cohort. Patients were free of cardiovascular disease and not prescribed statins at baseline.Methods Cox proportional hazards models in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QRISK2 (age, ethnicity, deprivation, systolic blood pressure, body mass index, total cholesterol: high density lipoprotein cholesterol ratio, smoking, family history of coronary heart disease in a first degree relative aged less than 60 years, type 1 diabetes, type 2 diabetes, treated hypertension, rheumatoid arthritis, atrial fibrillation, chronic kidney disease (stage 4 or 5)) and new risk factors (chronic kidney disease (stage 3, 4, or 5), a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, systemic lupus erythematosus (SLE), atypical antipsychotics, severe mental illness, and HIV/AIDs). We also considered erectile dysfunction diagnosis or treatment in men. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status.Main outcome measures Incident cardiovascular disease recorded on any of the following three linked data sources: general practice, mortality, or hospital admission records.Results 363

  13. Dietary Patterns Are Associated with Predicted Cardiovascular Disease Risk in an Urban Mexican Adult Population

    National Research Council Canada - National Science Library

    Denova-Gutiérrez, Edgar; Tucker, Katherine L; Flores, Mario; Barquera, Simón; Salmerón, Jorge

    2016-01-01

    .... Dietary intake was evaluated by using a semiquantitative food-frequency questionnaire. The relations between dietary patterns and predicted CVD were analyzed by using pooled logistic regression models...

  14. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Directory of Open Access Journals (Sweden)

    Michael King

    Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  15. Changes in dynamic risk and protective factors for violence during inpatient forensic psychiatric treatment: predicting reductions in postdischarge community recidivism.

    Science.gov (United States)

    De Vries Robbé, Michiel; de Vogel, Vivienne; Douglas, Kevin S; Nijman, Henk L I

    2015-02-01

    Empirical studies have rarely investigated the association between improvements on dynamic risk and protective factors for violence during forensic psychiatric treatment and reduced recidivism after discharge. The present study aimed to evaluate the effects of treatment progress in risk and protective factors on violent recidivism. For a sample of 108 discharged forensic psychiatric patients pre- and posttreatment assessments of risk (HCR-20) and protective factors (SAPROF) were compared. Changes were related to violent recidivism at different follow-up times after discharge. Improvements on risk and protective factors during treatment showed good predictive validity for abstention from violence for short- (1 year) as well as long-term (11 years) follow-up. This study demonstrates the sensitivity of the HCR-20 and the SAPROF to change and shows improvements on dynamic risk and protective factors are associated with lower violent recidivism long after treatment.

  16. Risk map for wolf threats to livestock still predictive 5 years after construction.

    Directory of Open Access Journals (Sweden)

    Adrian Treves

    Full Text Available Risk maps are spatial models of environmental hazards such as predation on livestock. We tested the long-term validity of a published risk map built from locations where Wisconsin wolves attacked livestock from 1999-2006. Using data collected after model construction, we verified the predictive accuracy of the risk map exceeded 91% for the period 2007-2011. Predictive power lasting 5 years or more substantiates the claim that risk maps are both valid and verified tools for anticipating spatial hazards. Classification errors coincided with verifier uncertainty about which wolves might be responsible. Perceived threats by wolves to domestic animals were not as well predicted (82% as verified attacks had been and errors in classification coincided with incidents involved domestic animals other than bovids and verifier uncertainty about which wolves were involved. We recommend risk maps be used to target interventions selectively at high-risk sites.

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

    Science.gov (United States)

    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. MANAGING BRAND EQUITY RISK: ADDING EXOGENOUS RISKS TO AN EVALUATION MODEL

    OpenAIRE

    Catalin Mihail Barbu; Sorin Tudor; Dorian Laurentiu Florea

    2014-01-01

    Risk can no longer be ignored when talking about brand management, as risk management can no longer disregard brands for manifold reasons. Building on the risk-based brand equity model, this paper contributes to the development of an evaluation model, by suggesting formulas for 3 exogenous risk sources related to the market and competitive structure: the new brand marketing effort, consumer behavior change, and the extant brands adaptation.

  19. A Method to Assess Localized Impact of Better Floodplain Topography on Flood Risk Prediction

    Directory of Open Access Journals (Sweden)

    Guy J.-P. Schumann

    2016-01-01

    Full Text Available Many studies have highlighted the need for a higher accuracy global digital elevation model (DEM, mainly in river floodplains and deltas and along coastlines. In this paper, we present a method to infer the impact of a better DEM on applications and science using the Lower Zambezi basin as a use case. We propose an analysis based on a targeted observation algorithm to evaluate potential data acquisition subregions in terms of their impact on the prediction of flood risk over the entire study area. Consequently, it becomes trivial to rank these subregions in terms of their contribution to the overall accuracy of flood prediction. The improvement from better topography data may be expressed in terms of economic output and population affected, providing a multifaceted assessment of the value of acquiring better elevation data. Our results highlight the notion that having higher resolution measurements would improve our current large-scale flood inundation prediction capabilities in the Lower Zambezi by at least 30% and significantly reduce the number of people affected as well as the economic loss associated with high magnitude flooding. We believe this procedure to be simple enough to be applied to other regions where high quality topographic and hydrodynamic data are currently unavailable.

  20. Predicting dentists' perceived occupational risk for HIV infection.

    Science.gov (United States)

    Kunzel, C; Sadowsky, D

    1993-06-01

    This study posed two questions: what is the level of perceived occupational risk among American general practice dentists (GPDs)? What factors influence perception of occupational risk for HIV infection among GPDs? In data obtained from a national mail survey of 1351 GPDs (response rate, 88%) 31% of American GPDs expressed disagreement with the statement that HIV+ individuals can be safely treated in their office settings. Of the 16 variables entered into a multiple regression equation, 9 variables had a statistically significant influence on dentists' assessment of occupational risk. In order of their influence they were (1) concern re the economic viability of the practice, (2) ethical obligation to treat patients at risk, (3) certainty of having treated patients with HIV infection, (4) risk attributed to four accidental occupational exposures, (5) concern re treatment of homosexuals, (6) relevant continuing education exposure, (7) personal worry re transmission of HIV infection from patients, (8) implementation of infection control behaviors, (9) number of patients seen per week. Statistically nonsignificant predictors of interest included age, knowledge level re HIV transmission routes, practice location in a high prevalence area, and perceived effectiveness of infection control behaviors. Results argue for intervention programs with less focus on delivery of factual information regarding the transmission of the disease and the effectiveness of infection control techniques, and more emphasis on the themes of practice economic viability, professional ethics, and structured educational encounters involving dentists' knowing exposure to HIV-infected patients.

  1. Prediction of Cardiovascular Disease Risk among Low-Income Urban Dwellers in Metropolitan Kuala Lumpur, Malaysia

    OpenAIRE

    Tin Tin Su; Mohammadreza Amiri; Farizah Mohd Hairi; Nithiah Thangiah; Awang Bulgiba; Hazreen Abdul Majid

    2015-01-01

    We aimed to predict the ten-year cardiovascular disease (CVD) risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS) models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM). Altogether 882 adults (≥30 years old with no CVD history) were randomly selected. The classic FRS mode...

  2. Predicting Business Failure: Identifying High-Risk Contractors.

    Science.gov (United States)

    1983-06-01

    March 21, 1983). Cartwright, Darwin P., "Analysis of Qualitative Materials," in Research Methods in the Behavioral Sciences, Leon Festinger and...information may exist that is useful for the analysis and prediction of corporate failure. Weick (1983) has suggested that research in psychology and

  3. Evolutionary theory as a tool for predicting extinction risk.

    Science.gov (United States)

    Gallagher, Austin J; Hammerschlag, Neil; Cooke, Steven J; Costa, Daniel P; Irschick, Duncan J

    2015-02-01

    Timely and proactive wildlife conservation requires strategies for determining which species are most at the greatest threat of extinction. Here, we suggest that evolutionary theory, particularly the concept of specialization, can be a useful tool to inform such assessments and may greatly aid in our ability to predict the vulnerabilities of species to anthropogenic impacts. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Anorectal function evaluation and predictive factors for faecal incontinence in 600 patients

    NARCIS (Netherlands)

    Lam, T.J.; Kuik, D.J.; Felt-Bersma, R.J.F.

    2012-01-01

    Aim Anorectal function was assessed in patients with and without faecal incontinence (FI) Risk factors predictive for FI were determined. Method Between 2003 and 2009, all consecutive patients referred were assessed by questionnaire, anorectal manometry and anal endosonography. Predictive factors

  5. Antiphospholipid antibodies: evaluation of the thrombotic risk.

    Science.gov (United States)

    Devreese, Katrien M J

    2012-10-01

    The laboratory diagnosis of the antiphospholipid syndrome (APS) via antiphospholipid antibody (aPL) tests, including lupus anticoagulant (LAC), anti-cardiolipin (aCL), or anti-beta2 glycoprotein I (aβ2GPI) antibodies remains a challenge. Coagulation tests for LAC as well as solid phase assays for aCL and aβ2GPI have methodological shortcomings, although for LAC large progress have been made in standardization. All assays are associated with clinical APS-criteria (thrombotic and/or pregnancy complications) but with limited specificity. Besides, clinical studies demonstrating the association between the presence of aPL and thrombosis are not always well designed and result in wide ranges of odds ratio with large variation between studies. The best association between thrombotic complications and aPL is found for LAC. The association between thrombosis and aCL or aβ2GPI is at least inconsistent. The inclusion of more specific assays, such as the domain-I-β2GPI.antibodies is too premature and depends on further investigation in large clinical studies and the commercial availability. The search for new assays should proceed to identify patients with aPL with increased risk for thrombosis, preferable in large prospective studies. Meanwhile, with the current available LAC, aCL and aβ2GPI assays it is strongly recommended to make antibody profiles. Multiple positivity of tests seems clinically more relevant. The strengths and weaknesses of the current laboratory criteria for APS are discussed in view of their role in risk stratification of patients with thrombotic events. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. In vivo indices for predicting acidosis risk of grains in cattle: Comparison with in vitro methods.

    Science.gov (United States)

    Lean, I J; Golder, H M; Black, J L; King, R; Rabiee, A R

    2013-06-01

    Our objective was to evaluate a near-infrared reflectance spectroscopy (NIRS) used in the feed industry to estimate the potential for grains to increase the risk of ruminal acidosis. The existing NIRS calibration was developed from in sacco and in vitro measures in cattle and grain chemical composition measurements. To evaluate the existing model, 20 cultivars of 5 grain types were fed to 40 Holstein heifers using a grain challenge protocol and changes in rumen VFA, ammonia, lactic acids, and pH that are associated with acidosis were measured. A method development study was performed to determine a grain feeding rate sufficient to induce non-life threatening but substantial ruminal changes during grain challenge. Feeding grain at a rate of 1.2% of BW met these criteria, lowering rumen pH (P = 0.01) and increasing valerate (P rumen sample was taken by stomach tube 5, 65, 110, 155, and 200 min after grain consumption. The rumen is not homogenous and samples of rumen fluid obtained by stomach tube will differ from those gained by other methods. Rumen pH was measured immediately; individual VFA, ammonia, and D- and L-lactate concentrations were analyzed later. Rumen pH (P = 0.002) and all concentrations of fermentation products differed among grains (P = 0.001). A previously defined discriminant score calculated at 200 min after challenge was used to rank grains for acidosis risk. A significant correlation between the discriminant score and the NIRS ranking (r = 0.731, P = 0.003) demonstrated the potential for using NIRS calibrations for predicting acidosis risk of grains in cattle. The overall rankings of grains for acidosis risk were wheat > triticale > barley > oats > sorghum.

  7. Maternal smoking predicts the risk of spontaneous abortion

    DEFF Research Database (Denmark)

    Nielsen, Ann; Hannibal, Charlotte Gerd; Lindekilde, Bodil Eriksen

    2006-01-01

    BACKGROUND: Few studies have examined smoking prior to pregnancy and the occurrence of spontaneous abortion, as most studies have addressed the risk of spontaneous abortion in relation to smoking during pregnancy. However, results are not entirely consistent. The aim of the present study...... was to assess the risk of spontaneous abortion considering smoking prior to pregnancy. METHODS: We performed a nested case-control study using prospective data from a population-based cohort comprising 11,088 women aged 20-29 years. From this cohort, women who experienced either a spontaneous abortion (n=343......) or who gave birth (n=1,578) during follow-up were selected. Associations between self-reported smoking at enrollment and subsequent spontaneous abortion were analyzed by means of multiple logistic regression. RESULTS: The risk of spontaneous abortion in relation to pre-pregnancy smoking showed a clear...

  8. Methods to Predict and Lower the Risk of Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Barbara Ercole

    2011-01-01

    Full Text Available Chemoprevention for prostate cancer (PCa continues to generate interest from both physicians and the patient population. The goal of chemoprevention is to stop the malignant transformation of prostate cells into cancer. Multiple studies on different substances ranging from supplements to medical therapy have been undertaken. Thus far, only the studies on 5α-reductase inhibitors (the Prostate Cancer Prevention Trial [PCPT] and Reduction by Dutasteride of Prostate Cancer Events [REDUCE] trial have demonstrated a reduction in the risk of PCa, while results from the Selenium and Vitamin E Cancer Prevention Trial (SELECT concluded no decreased risk for PCa with selenium or vitamin E.

  9. Effectiveness evaluation of contingency sum as a risk management ...

    African Journals Online (AJOL)

    Construction managers in a bid to effectively manage risks prone projects have adopted several methods, one of which is contingency sum. This study aims at evaluating the effectiveness of contingency sum as a risk management tool for construction projects in Niger Delta region of Nigeria. The objectives are to establish ...

  10. An Overview on Evaluating and Predicting Scholarly Article Impact

    Directory of Open Access Journals (Sweden)

    Xiaomei Bai

    2017-06-01

    Full Text Available Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars.

  11. Evaluation of model quality predictions in CASP9

    KAUST Repository

    Kryshtafovych, Andriy

    2011-01-01

    CASP has been assessing the state of the art in the a priori estimation of accuracy of protein structure prediction since 2006. The inclusion of model quality assessment category in CASP contributed to a rapid development of methods in this area. In the last experiment, 46 quality assessment groups tested their approaches to estimate the accuracy of protein models as a whole and/or on a per-residue basis. We assessed the performance of these methods predominantly on the basis of the correlation between the predicted and observed quality of the models on both global and local scales. The ability of the methods to identify the models closest to the best one, to differentiate between good and bad models, and to identify well modeled regions was also analyzed. Our evaluations demonstrate that even though global quality assessment methods seem to approach perfection point (weighted average per-target Pearson\\'s correlation coefficients are as high as 0.97 for the best groups), there is still room for improvement. First, all top-performing methods use consensus approaches to generate quality estimates, and this strategy has its own limitations. Second, the methods that are based on the analysis of individual models lag far behind clustering techniques and need a boost in performance. The methods for estimating per-residue accuracy of models are less accurate than global quality assessment methods, with an average weighted per-model correlation coefficient in the range of 0.63-0.72 for the best 10 groups.

  12. Using ABAQUS Scripting Interface for Materials Evaluation and Life Prediction

    Science.gov (United States)

    Powers, Lynn M.; Arnold, Steven M.; Baranski, Andrzej

    2006-01-01

    An ABAQUS script has been written to aid in the evaluation of the mechanical behavior of viscoplastic materials. The purposes of the script are to: handle complex load histories; control load/displacement with alternate stopping criteria; predict failure and life; and verify constitutive models. Material models from the ABAQUS library may be used or the UMAT routine may specify mechanical behavior. User subroutines implemented include: UMAT for the constitutive model; UEXTERNALDB for file manipulation; DISP for boundary conditions; and URDFIL for results processing. Examples presented include load, strain and displacement control tests on a single element model. The tests are creep with a life limiting strain criterion, strain control with a stress limiting cycle and a complex interrupted cyclic relaxation test. The techniques implemented in this paper enable complex load conditions to be solved efficiently with ABAQUS.

  13. An etiologic prediction model incorporating biomarkers to predict the bladder cancer risk associated with occupational exposure to aromatic amines: a pilot study.

    Science.gov (United States)

    Mastrangelo, Giuseppe; Carta, Angela; Arici, Cecilia; Pavanello, Sofia; Porru, Stefano

    2017-01-01

    No etiological prediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; the area under the curve was used to evaluate discriminatory ability of models. Area under the curve was 0.93 for the full model (including age, smoking and coffee habits, DNA adducts, 12 genotypes) and 0.86 for the short model (including smoking, DNA adducts, 3 genotypes). Using the "best cut-off" of predicted probability of a positive outcome, percentage of cases correctly classified was 92% (full model) against 75% (short model). Cancers classified as "positive outcome" are those to be referred for evaluation by an occupational physician for etiological diagnosis; these patients were 28 (full model) or 60 (short model). Using 3 genotypes instead of 12 can double the number of patients with suspect of aromatic amine related cancer, thus increasing costs of etiologic appraisal. Integrating clinical, laboratory and genetic factors, we developed the first etiologic prediction model for aromatic amine related bladder cancer. Discriminatory ability was excellent, particularly for the full model, allowing individualized predictions. Validation of our model in external populations is essential for practical use in the clinical setting.

  14. Pipeline integrity handbook risk management and evaluation

    CERN Document Server

    Singh, Ramesh

    2013-01-01

    Based on over 40 years of experience in the field, Ramesh Singh goes beyond corrosion control, providing techniques for addressing present and future integrity issues. Pipeline Integrity Handbook provides pipeline engineers with the tools to evaluate and inspect pipelines, safeguard the life cycle of their pipeline asset and ensure that they are optimizing delivery and capability. Presented in easy-to-use, step-by-step order, Pipeline Integrity Handbook is a quick reference for day-to-day use in identifying key pipeline degradation mechanisms and threats to pipeline integrity. The book begins

  15. Traditional risk factors are predictive on segmental localization of coronary artery disease.

    Science.gov (United States)

    Tacoy, Gulten; Balcioglu, Akif Serhat; Akinci, Sinan; Erdem, Güliz; Kocaman, Sinan Altan; Timurkaynak, Timur; Cengel, Atiye

    2008-01-01

    The aim of this study was to investigate the relationship between established risk factors and segmental localization of coronary artery disease. A total of 2760 patients who underwent coronary angiography were enrolled into the study. Coronary angiographic segmental evaluation was performed according to the scheme of American Heart Association. Patients were classified into 2 groups (group 1: normal coronary artery segments, group 2: coronary artery segments with coronary artery disease). Smoking was highly related with left main coronary artery disease (odds ratio = 7.5; P = .005). Diabetes mellitus and male sex increased the risk of atherosclerosis in all coronary vasculature (odds ratio = 2.7-2.2; P < .001-P < .001). Hypertension was correlated with distal coronary artery (odds ratio = 1.4; P < .001) and family history with distal circumflex lesions (odds ratio = 4.5; P = .005) High triglyceride levels were associated with right coronary artery lesions (odds ratio = 1.00; P =.03). The effect of advanced age was small (odds ratio = 1.08; P < .001). Risk factors may be predictive for segmental localization.

  16. ANALYSIS MODEL USING ROBU MIRONIUC IN PREDICTING RISK OF BANKRUPTCY ROMANIAN COMPANIES

    Directory of Open Access Journals (Sweden)

    ŞTEFĂNIŢĂ ŞUŞU

    2014-08-01

    Full Text Available Bankruptcy risk and made the subject of many research studies that aim to identify the time of the bankruptcy, the factors that compete to achieve this state, and the indicators that best expresses this orientation (the bankruptcy. The threats to enterprises require knowledge managers continually economic and financial situations, and vulnerable areas with development potential. Managers need to identify and properly manage the threats that would prevent achieving the targets. In terms of methods known in the literature of assessment and evaluation of bankruptcy risk they are static, functional, strategic and non-scoring. Analysis by scoring methods is usually carried out by banks in the analysis of creditworthiness, when a company asks for a bank loan. Each bank has its own analysis, including a feature-score calculated internally based on indicators defined in its credit manual. To have a national comparability, however, a scoring system should be based on more data in the situation of "public data" or available to all stakeholders. In this article, in order to achieve bankruptcy risk prediction model is used Robu-Mironiuc on the passage benchmarking 2009-2013. The source of information is the profit and loss account and balance sheet of the two companies listed on the Bucharest Stock Exchange (Turism Covasna and Dorna Turism companies. The results of the analysis are interpreted while trying to formulate solutions to the economic and financial viability of the entity.

  17. Nutritional risk screening 2002 and ASA score predict mortality after elective liver resection for malignancy

    Science.gov (United States)

    Ferreira, Nelio

    2017-01-01

    Introduction The aim of the study was to evaluate whether Nutritional risk screening 2002 (NRS 2002) at hospital admission may predict postoperative mortality and complications within 90 days after elective liver resection for malignancy. Material and methods A retrospective cohort study of a prospective database was performed. Two-hundred and three patients with elective liver resection for malignancy between 9 November 2007 and 27 May 2014 were included. Clinical data, NRS 2002, surgical procedures and histology were recorded. The primary endpoint was 90-day mortality. Complications were registered within 90 days postoperatively according to the Clavien-Dindo classification. Results The 90-day mortality was 5.9% and the overall complication rate was 59.1%. Multivariate analysis identified NRS 2002 score ≥ 4 (odds ratio (OR) = 9.24; p = 0.005) and American Society of Anesthesiologists (ASA) score ≥ 3 (OR = 6.20; p = 0.009) as predictors of 90-day mortality. The 90-day mortality was 27.6% (8/29) for patients with both risk factors (NRS 2002 score ≥ 4 and ASA score ≥ 3) vs. 2.3% (4/174) for patients without or with only one risk factor (p < 0.001). Conclusions In the present study NRS 2002 score ≥ 4 and ASA score ≥ 3 were predictors of 90-day mortality after elective liver resection for malignancy. PMID:28261289

  18. PREDICTION OF PESTICIDE RISKS TO HUMAN HEALTH BY DRINKING WATER EXTRACTED FROM UNDERGROUND SOURCES.

    Science.gov (United States)

    Antonenko, A; Vavrinevych, O; Omelchuk, S; Korshun, M

    2015-01-01

    The aim of our work was to develop the method of prediction of the risk of contamination of groundwater with different classes' fungicides in soil and climatic conditions of Ukraine and other European countries, as well as hygienic assessment of their impact on public health. The calculation and comparative evaluation of various indices of pesticides migration into groundwater were conducted. It was established that the most optimal and complete is an LEACN index according to which in soil and climatic conditions of Ukraine the risk of contamination of ground and surface water by all studied fungicides is low, except penconazole and tebuconazole for which there is medium contamination risk. We have developed a method of integrated assessment of the potential hazard of pesticide exposure on the human organism when it enters ground and surface waters, which are intensive used for drinking water supplying. Integral index of this method - IGCHI - is obtained by adding of scores assigned to main indicators characterizing the danger to humans in pesticides gets into the water: index of leaching (LEACН), half life period in water (DT50) and the allowable daily intake (ADI). According developed method all studied fungicides are low hazard for human, except benalaxy-M and tebuconazole which are hazard and highly hazard, respectively. It was established that, benalaxy-M is hazard when it leached into groundwater and surface water, tebuconazole is highly hazard, which is primarily due to their high stability in water (in both cases) and significant potential for leaching (in the latter case).

  19. [Interest of Geriatric Nutritional Risk Index for mortality prediction in hemodialysis patients: preliminary study].

    Science.gov (United States)

    Sirajedine, Khaled; Fardous, Rida; Al Adib, Mohamad; Colomb, Henry; Maurin, Audrey

    2012-07-01

    Geriatric Nutritional Risk Index (GNRI) is a simple and quantitative method (based on three objective measurements: weight, height, albumin) for screening patients at risk for malnutrition. However no data are available regarding its relation with mortality in Caucasian hemodialysis patients. We tested the predictive value of GNRI on mortality in a hemodialysis population followed up prospectively for 18 months. A total of 46 stable prevalent (mean age: 76 ± 11 years, range: 42-95) hemodialysis patients from one center were included in the study. GNRI with other nutritional parameters were evaluated for all patients. Sixteen patients (35%) died during the 18 months of follow-up. Multiple logistic model showed that GNRI and Charlson co-morbidity score were significant predictors of mortality. Age and gender were not significant. Our preliminary study carried out on a series of prevalent hemodialysis patients suggests that GNRI is predictor of mortality. To recommend the use of this index for the screening of hemodialysis patients with malnutrition at risk of mortality, our results should be confirmed by a large cohort study. Copyright © 2012 Association Société de néphrologie. Published by Elsevier SAS. All rights reserved.

  20. Optimization of agricultural field workability predictions for improved risk management

    Science.gov (United States)

    Risks introduced by weather variability are key considerations in agricultural production. The sensitivity of agriculture to weather variability is of special concern in the face of climate change. In particular, the availability of workable days is an important consideration in agricultural practic...

  1. Torsade de Pointes: Risk prediction and role of ventricular activation

    NARCIS (Netherlands)

    Stams, T.R.G.

    2014-01-01

    Torsade de Pointes (TdP) is a ventricular tachycardia that can result in sudden death. Risk factors include the congenital long QT syndrome, but also acquired factors including drugs that prolong the repolarization (QT interval). If concerns exist about the cardiac safety of (novel) drug, an animal

  2. Genetic liability for schizophrenia predicts risk of immune disorders

    NARCIS (Netherlands)

    Stringer, Sven; Kahn, René S.; de Witte, Lot D.; Ophoff, Roel A.; Derks, Eske M.

    2014-01-01

    Schizophrenia patients and their parents have an increased risk of immune disorders compared to population controls and their parents. This may be explained by genetic overlap in the pathogenesis of both types of disorders. The purpose of this study was to investigate the genetic overlap between

  3. Genetic liability for schizophrenia predicts risk of immune disorders

    NARCIS (Netherlands)

    Stringer, Sven; Kahn, René S; de Witte, Lot D; Ophoff, Roel A; Derks, Eske M

    2014-01-01

    BACKGROUND: Schizophrenia patients and their parents have an increased risk of immune disorders compared to population controls and their parents. This may be explained by genetic overlap in the pathogenesis of both types of disorders. The purpose of this study was to investigate the genetic overlap

  4. Reliability of blood pressure measurement and cardiovascular risk prediction

    NARCIS (Netherlands)

    van der Hoeven, N.V.

    2016-01-01

    High blood pressure is one of the leading risk factors for cardiovascular disease, but difficult to reliably assess because there are many factors which can influence blood pressure including stress, exercise or illness. The first part of this thesis focuses on possible ways to improve the

  5. Androidal fat dominates in predicting cardiometabolic risk in postmenopausal women

    Science.gov (United States)

    We hypothesized that soy isoflavones would attenuate the anticipated increase in androidal fat mass in postmenopausal women during the 36-month treatment, and thereby favorably modify the circulating cardiometabolic risk factors: triacylglycerol, LDLC, HDL-C, glucose, insulin, uric acid, C-reactive ...

  6. Obesity Risk Prediction among Women of Upper Egypt: The impact ...

    African Journals Online (AJOL)

    Obesity is one of the main threats to the human health. It is a major risk factor for hyperinsulinemia, hypertension, hyperlipidemia, type II diabetes mellitus, and atherosclerotic cardiovascular disease. FTO gene variants have been associated with obesity and diabetes mellitus in different populations, but its role in the ...

  7. Driving Strategic Risk Planning With Predictive Modelling For Managerial Accounting

    DEFF Research Database (Denmark)

    Nielsen, Steen; Pontoppidan, Iens Christian

    for modelling and computing stochastic input variables; and (iii) illustrate how currently available technology has made this stochastic framework easier. The Global Financial Crisis of the last couple of years has re-accentuated the relevance of a concept of risk, and the need for coherence and interrelations...

  8. Self-Esteem and Future Orientation Predict Adolescents' Risk Engagement

    Science.gov (United States)

    Jackman, Danielle M.; MacPhee, David

    2017-01-01

    This study's purpose was to examine the relations among future orientation, self-esteem, and later adolescent risk behaviors, and to compare two mediational models involving self-esteem versus future orientation as mediators. An ethnically diverse sample of 12- to 14-year-olds (N = 862, 54% female, 53% ethnic minority) was assessed longitudinally.…

  9. Psychological Factors Predicting Risk-Taking Propensity of Poultry ...

    African Journals Online (AJOL)

    ... (Sd=10.70) were randomly selected among members of Poultry Farmers Association of Nigeria (POFAN), Ibadan Branch. Using a correlational design, the following measures were used: Performance Failure Appraisal Inventory (PFAI), Need for achievement scale, Locus of Control Behaviour and Risk-taking propensity.

  10. Imaging of atherosclerosis: study design and cardiovascular risk prediction

    NARCIS (Netherlands)

    Peters, S.A.E.|info:eu-repo/dai/nl/341652326

    2012-01-01

    Cardiovascular disease is still the leading cause of morbidity and mortality worldwide.1 The majority of cardiovascular disease events is caused by atherosclerosis. Atherosclerosis is a slow and progressive disease of the arterial wall that is on the pathway between the effects of exposure to risk

  11. Predicting low-risk prostate cancer from transperineal saturation biopsies

    NARCIS (Netherlands)

    P. van Leeuwen (Pim); Siriwardana, A. (Amila); M.J. Roobol-Bouts (Monique); Ting, F. (Francis); D. Nieboer (Daan); Thompson, J. (James); W.J. Delprado (Warick); Haynes, A.-M. (Anne-Marie); Brenner, P. (Phillip); Stricker, P. (Phillip)

    2016-01-01

    textabstractIntroduction. To assess the performance of five previously described clinicopathological definitions of low-risk prostate cancer (PC). Materials and Methods. Men who underwent radical prostatectomy (RP) for clinical stage ≤T2, PSA <10 ng/mL, Gleason score <8 PC, diagnosed by

  12. Predicting Children's Depressive Symptoms from Community and Individual Risk Factors

    Science.gov (United States)

    Dallaire, Danielle H.; Cole, David A.; Smith, Thomas M.; Ciesla, Jeffrey A.; LaGrange, Beth; Jacquez, Farrah M.; Pineda, Ashley Q.; Truss, Alanna E.; Folmer, Amy S.

    2008-01-01

    Community, demographic, familial, and personal risk factors of childhood depressive symptoms were examined from an ecological theoretical approach using hierarchical linear modeling. Individual-level data were collected from an ethnically diverse (73% African-American) community sample of 197 children and their parents; community-level data were…

  13. Incidence and evaluation of risk factors of microalbuminuria among ...

    African Journals Online (AJOL)

    The lipid atherogenic components minus TAG were found to relate strongly with microalbuminuria in diabetics of < 1yr duration. Elevated BMI strongly predicts the risk of microalbuminuria in the non-diabetics examined. Keywords: microalbuminuria, diabetics, non-diabetics, atherogenic parameters, Lagos Nigerian Journal ...

  14. Do obesity and parental history of myocardial infarction improve cardiovascular risk prediction?

    Science.gov (United States)

    van Dis, Ineke; Geleijnse, Johanna M; Kromhout, Daan; Boer, Jolanda M A; Boshuizen, Hendriek; Verschuren, W M Monique

    2013-10-01

    In clinical practice, individuals at increased risk of cardiovascular diseases (CVD) are identified on the basis of age, sex, smoking, blood pressure, and serum total and high-density lipoprotein cholesterol. We examined whether CVD risk prediction improved when obesity (body mass index ≥30 kg/m(2)) and premature (risk factor model. Risk factors were measured in 1993-97 in 12,818 participants (53% female) aged 35-65 in the Dutch MORGEN project. Cases of fatal and nonfatal CVD during 10 years of follow up were identified through record linkage. Classical risk factor equations, obtained by Cox proportional hazard analysis, were extended with obesity, paternal MI, and maternal MI. We calculated the net reclassification index (NRI), a measure for correct reclassification of subjects, to check improvement in risk prediction using 5 and 10% increments in absolute CVD risk. A CVD event occurred in 280 men and 140 women. Obesity and maternal MI were positively and significantly related to total CVD after adjustment for classical risk factors (both hazard ratios ∼1.5). Adding obesity and parental MI to CVD risk prediction yielded a significant NRI of 4.5% in men and a non-significant NRI of 2.6% in women when 5% risk categories were used. For 10% categories, the NRIs were slightly larger (5.5% and 3.3%, respectively). The improvements in risk prediction were mainly due to obesity. Modest improvements in CVD risk prediction can be obtained when obesity and, to a lesser extent, parental MI are added to the risk function.

  15. Framingham risk score and alternatives for prediction of coronary heart disease in older adults.

    Directory of Open Access Journals (Sweden)

    Nicolas Rodondi

    Full Text Available Guidelines for the prevention of coronary heart disease (CHD recommend use of Framingham-based risk scores that were developed in white middle-aged populations. It remains unclear whether and how CHD risk prediction might be improved among older adults. We aimed to compare the prognostic performance of the Framingham risk score (FRS, directly and after recalibration, with refit functions derived from the present cohort, as well as to assess the utility of adding other routinely available risk parameters to FRS.Among 2193 black and white older adults (mean age, 73.5 years without pre-existing cardiovascular disease from the Health ABC cohort, we examined adjudicated CHD events, defined as incident myocardial infarction, CHD death, and hospitalization for angina or coronary revascularization.During 8-year follow-up, 351 participants experienced CHD events. The FRS poorly discriminated between persons who experienced CHD events vs. not (C-index: 0.577 in women; 0.583 in men and underestimated absolute risk prediction by 51% in women and 8% in men. Recalibration of the FRS improved absolute risk prediction, particulary for women. For both genders, refitting these functions substantially improved absolute risk prediction, with similar discrimination to the FRS. Results did not differ between whites and blacks. The addition of lifestyle variables, waist circumference and creatinine did not improve risk prediction beyond risk factors of the FRS.The FRS underestimates CHD risk in older adults, particularly in women, although traditional risk factors remain the best predictors of CHD. Re-estimated risk functions using these factors improve accurate estimation of absolute risk.

  16. Parkinsonian motor impairment predicts personality domains related to genetic risk and treatment outcomes in schizophrenia.

    Science.gov (United States)

    Molina, Juan L; Calvó, María; Padilla, Eduardo; Balda, Mara; Alemán, Gabriela González; Florenzano, Néstor V; Guerrero, Gonzalo; Kamis, Danielle; Rangeon, Beatriz Molina; Bourdieu, Mercedes; Strejilevich, Sergio A; Conesa, Horacio A; Escobar, Javier I; Zwir, Igor; Cloninger, C Robert; de Erausquin, Gabriel A

    2017-01-01

    Identifying endophenotypes of schizophrenia is of critical importance and has profound implications on clinical practice. Here we propose an innovative approach to clarify the mechanims through which temperament and character deviance relates to risk for schizophrenia and predict long-term treatment outcomes. We recruited 61 antipsychotic naïve subjects with chronic schizophrenia, 99 unaffected relatives, and 68 healthy controls from rural communities in the Central Andes. Diagnosis was ascertained with the Schedules of Clinical Assessment in Neuropsychiatry; parkinsonian motor impairment was measured with the Unified Parkinson's Disease Rating Scale; mesencephalic parenchyma was evaluated with transcranial ultrasound; and personality traits were assessed using the Temperament and Character Inventory. Ten-year outcome data was available for ~40% of the index cases. Patients with schizophrenia had higher harm avoidance and self-transcendence (ST), and lower reward dependence (RD), cooperativeness (CO), and self-directedness (SD). Unaffected relatives had higher ST and lower CO and SD. Parkinsonism reliably predicted RD, CO, and SD after correcting for age and sex. The average duration of untreated psychosis (DUP) was over 5 years. Further, SD was anticorrelated with DUP and antipsychotic dosing at follow-up. Baseline DUP was related to antipsychotic dose-years. Further, 'explosive/borderline', 'methodical/obsessive', and 'disorganized/schizotypal' personality profiles were associated with increased risk of schizophrenia. Parkinsonism predicts core personality features and treatment outcomes in schizophrenia. Our study suggests that RD, CO, and SD are endophenotypes of the disease that may, in part, be mediated by dopaminergic function. Further, SD is an important determinant of treatment course and outcome.

  17. Prospective External Validation of Three Preoperative Risk Scores for Prediction of New Onset Atrial Fibrillation After Cardiac Surgery.

    Science.gov (United States)

    Cameron, Matthew J; Tran, Diem T T; Abboud, Jean; Newton, Ethan K; Rashidian, Houman; Dupuis, Jean-Yves

    2017-05-12

    between the observed and expected rates of POAF. Net benefit analysis showed that AF preventive strategies based on these scores, and targeting patients with moderate or high risk of POAF, improve decisionmaking in comparison to preventive strategies of treating all patients. The 3 prediction scores evaluated in this study have limited ability to predict POAF in cardiac surgical patients. Despite this, they may be useful in preventive strategies targeting patients with moderate or high risk of PAOF in comparison with preventive strategies applied to all patients.

  18. Applications of the gambling score in evaluating earthquake predictions and forecasts

    Science.gov (United States)

    Zhuang, Jiancang; Zechar, Jeremy D.; Jiang, Changsheng; Console, Rodolfo; Murru, Maura; Falcone, Giuseppe

    2010-05-01

    This study presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points bet by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. For discrete predictions, we apply this method to evaluate performance of Shebalin's predictions made by using the Reverse Tracing of Precursors (RTP) algorithm and of the outputs of the predictions from the Annual Consultation Meeting on Earthquake Tendency held by China Earthquake Administration. For the continuous case, we use it to compare the probability forecasts of seismicity in the Abruzzo region before and after the L'aquila earthquake based on the ETAS model and the PPE model.

  19. Impairment of executive function and attention predicts onset of affective disorder in healthy high-risk twins

    DEFF Research Database (Denmark)

    Vinberg, Maj; Miskowiak, Kamilla W; Kessing, Lars Vedel

    2013-01-01

    To investigate whether measures of cognitive function can predict onset of affective disorder in individuals at heritable risk.......To investigate whether measures of cognitive function can predict onset of affective disorder in individuals at heritable risk....

  20. Predictable risk factors and clinical courses for prolonged transient tachypnea of the newborn

    Directory of Open Access Journals (Sweden)

    Ji Young Chang

    2010-03-01

    Full Text Available Purpose : Transient tachypnea of the newborn (TTN is usually benign and improves within 72 hours. However, it can also progress to prolonged tachypnea over 72 hours, profound hypoxemia, respiratory failure, and even death. The aim of this study is to find predictable risk factors and describe the clinical courses and outcomes of prolonged TTN (PTTN. Methods : The medical records of 107 newborns, &gt;35+0 weeks of gestational age with TTN, who were admitted to the NICU at Seoul Asan Medical Center from January 2001 to September 2007 were reviewed. They were divided into 2 groups based on duration of tachypnea. PTTN was defined as tachypnea ?#247;2 hours of age, and simple TTN (STTN as tachypnea &lt;72 hours of age. We randomly selected 126 healthy-term newborns as controls. We evaluated neonatal and maternal demographic findings, and various clinical factors. Results : Fifty-five infants (51% with total TTN were PTTN. PTTN infants had grunting, tachypnea &gt;90/min, FiO2 &gt;0.4, and required ventilator care more frequently than STTN infants. PTTN had lower level of serum total protein and albumin than STTN. The independent predictable risk factors for PTTN were grunting, maximal respiration rate &gt;90/min, and FiO2 &gt;0.4 within 6 hours of life. Conclusion : When a newborn has grunting, respiration rate &gt;90/min, and oxygen requirement &gt;0.4 of FiO2 within 6 hours of life, the infant is at high risk of having persistent tachypnea ?#247;2 hours. We need further study to find the way to reduce PTTN.

  1. A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

    Science.gov (United States)

    Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

    2017-07-01

    An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

  2. The importance of virulence prediction and gene networks in microbial risk assessment

    DEFF Research Database (Denmark)

    Wassenaar, Gertrude Maria; Gamieldien, Junaid; Shatkin, JoAnne

    2007-01-01

    For microbial risk assessment, it is necessary to recognize and predict Virulence of bacterial pathogens, including their ability to contaminate foods. Hazard characterization requires data on strain variability regarding virulence and survival during food processing. Moreover, information on vir...

  3. Blood test could predict risk of heart attack and subsequent death.

    Science.gov (United States)

    2017-01-18

    A high-sensitivity blood test, known as a troponin test, could predict the risk of heart attack and death and patients' response to statins, say researchers from the Universities of Edinburgh and Glasgow.

  4. Importance of environmental and biomass dynamics in predicting chemical exposure in ecological risk assessment

    NARCIS (Netherlands)

    Morselli, M.; Semplice, M.; Leander, de F.; Brink, van den P.J.; Guardo, Di A.

    2015-01-01

    In ecological risk assessment, exposure is generally modelled assuming static conditions, herewith neglecting the potential role of emission, environmental and biomass dynamics in affecting bioavailable concentrations. In order to investigate the influence of such dynamics on predicted bioavailable

  5. Development and evaluation of a score to predict difficult epidural placement during labor.

    OpenAIRE

    Guglielminotti, Jean; Mentré, France; Bedairia, Ennoufous; Montravers, Philippe; Longrois, Dan

    2013-01-01

    International audience; BACKGROUND AND OBJECTIVES: Difficult epidural placement (DEP) during labor may be distressing for the patient and may increase the risk of dural puncture. A score predicting DEP based on the combination of individual risk factors could identify high-risk patients. This study aimed to identify risk factors for DEP and build a prediction score. METHODS: Three hundred thirty patients were prospectively included. Difficult epidural placement was defined as more than 1 skin...

  6. Scoring life insurance applicants' laboratory results, blood pressure and build to predict all-cause mortality risk.

    Science.gov (United States)

    Fulks, Michael; Stout, Robert L; Dolan, Vera F

    2012-01-01

    Evaluate the degree of medium to longer term mortality prediction possible from a scoring system covering all laboratory testing used for life insurance applicants, as well as blood pressure and build measurements. Using the results of testing for life insurance applicants who reported a Social Security number in conjunction with the Social Security Death Master File, the mortality associated with each test result was defined by age and sex. The individual mortality scores for each test were combined for each individual and a composite mortality risk score was developed. This score was then tested against the insurance applicant dataset to evaluate its ability to discriminate risk across age and sex. The composite risk score was highly predictive of all-cause mortality risk in a linear manner from the best to worst quintile of scores in a nearly identical fashion for each sex and decade of age. Laboratory studies, blood pressure and build from life insurance applicants can be used to create scoring that predicts all-cause mortality across age and sex. Such an approach may hold promise for preventative health screening as well.

  7. Dietary restraint and US devaluation predict evaluative learning.

    Science.gov (United States)

    Brunstrom, Jeffrey M; Higgs, Suzanne; Mitchell, Gemma L

    2005-08-07

    Previous research has indicated that flavor-flavor learning is impaired in restrained eaters. In Experiment 1 we sought to extend this finding using a larger sample and a more comprehensive assessment of dietary behavior. Participants (N=90, including 30 current dieters) sampled three novel flavors (CSs), each on 10 separate occasions, in a randomized order. Each flavor was paired with chocolate (US) either 10%, 50%, or 90% of the time. We then assessed liking for the three CSs and asked participants to complete the DEBQ-restraint and TFEQ-disinhibition sub-scales. After these CS-US parings, restrained eaters tended to prefer the 10% paired flavor whereas unrestrained eaters tended to prefer the 90% paired flavor. Differential CS liking was not evident in dieters and it was not predicted by disinhibition. Using a similar methodology, in Experiment 2 (N=76) we assessed evaluative change following picture-sweet pairings. Relative to the other CSs, the restrained eaters reported a greater increase in their liking for the 10% paired CS and the unrestrained eaters reported a greater increase in their liking for the 90% paired CS. We also discovered that evaluative change is related to the level of US devaluation that takes place during conditioning. Evidence that a sweet US can bring about a decrease in liking has not been reported previously. One interpretation is that negative beliefs and attitudes can contaminate the representation of the US during training.

  8. External Validation of Risk Prediction Scores for Invasive Candidiasis in a Medical/Surgical Intensive Care Unit: An Observational Study

    Science.gov (United States)

    Ahmed, Armin; Baronia, Arvind Kumar; Azim, Afzal; Marak, Rungmei S. K.; Yadav, Reema; Sharma, Preeti; Gurjar, Mohan; Poddar, Banani; Singh, Ratender Kumar

    2017-01-01

    Background: The aim of this study was to conduct external validation of risk prediction scores for invasive candidiasis. Methods: We conducted a prospective observational study in a 12-bedded adult medical/surgical Intensive Care Unit (ICU) to evaluate Candida score >3, colonization index (CI) >0.5, corrected CI >0.4 (CCI), and Ostrosky's clinical prediction rule (CPR). Patients' characteristics and risk factors for invasive candidiasis were noted. Patients were divided into two groups; invasive candidiasis and no-invasive candidiasis. Results: Of 198 patients, 17 developed invasive candidiasis. Discriminatory power (area under receiver operator curve [AUROC]) for Candida score, CI, CCI, and CPR were 0.66, 0.67, 0.63, and 0.62, respectively. A large number of patients in the no-invasive candidiasis group (114 out of 181) were exposed to antifungal agents during their stay in ICU. Subgroup analysis was carried out after excluding such patients from no-invasive candidiasis group. AUROC of Candida score, CI, CCI, and CPR were 0.7, 0.7, 0.65, and 0.72, respectively, and positive predictive values (PPVs) were in the range of 25%–47%, along with negative predictive values (NPVs) in the range of 84%–96% in the subgroup analysis. Conclusion: Currently available risk prediction scores have good NPV but poor PPV. They are useful for selecting patients who are not likely to benefit from antifungal therapy. PMID:28904481

  9. Breast Cancer Risk Prediction with Heterogeneous Risk Profiles According to Breast Cancer Tumor Markers

    OpenAIRE

    Rosner, Bernard; Glynn, Robert J.; Tamimi, Rulla M.; Chen, Wendy Y.; Colditz, Graham A.; Willett, Walter C.; Hankinson, Susan E.

    2013-01-01

    Relationships between some risk factors and breast cancer incidence are known to vary by tumor subtype. However, breast tumors can be classified according to a number of markers, which may be correlated, making it difficult to identify heterogeneity of risk factors with specific tumor markers when using standard competing-risk survival analysis. In this paper, we propose a constrained competing-risk survival model that allows for assessment of heterogeneity of risk factor associations accordi...

  10. Attitudes and misconceptions about predictive genetic testing for cancer risk.

    Science.gov (United States)

    Rose, Abigail L; Peters, Nikki; Shea, Judy A; Armstrong, Katrina

    2005-01-01

    To describe awareness, knowledge, and attitudes about genetic testing for cancer risk among the general public. Thirty-eight adults participated in focus groups in West Philadelphia, Pennsylvania. Participants' beliefs about what genetic testing is ranged from 'dianetics' to an accurate description of DNA analysis. Themes included misconceptions about genetic tests, the ability to gain control of one's life through genetic testing, anxiety that might be caused by testing, risk of insurance and employment discrimination, use of genetic information for racial or ethnic discrimination, concerns about medical information confidentiality and lack of informed consent. Although there was some accurate understanding of what genetic testing is and how the results could be used, there also exist significant misconceptions. In many cases, misconceptions may be barriers to uptake of genetic testing. Dispelling these misconceptions is an important step in the translation of advances in human genomics into improvements in health.

  11. Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries

    DEFF Research Database (Denmark)

    Ueda, Peter; Woodward, Mark; Lu, Yuan

    2017-01-01

    BACKGROUND: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores...

  12. Lactic acidosis, risk factors and predictive laboratory markers: a ...

    African Journals Online (AJOL)

    Results: Lactic acidosis occurred in 79 (17 per 1 000 person-years) of 1 762 people living with HIV on ART. Significant factors were being female [adjusted odds ratio (AOR) of 5.4] and increased body weight (adjusted OR of 1.1 per kg). The risk of lactic acidosis increased 6.6, 6.9 and 95 times (adjusted ORs) as weight ...

  13. How well can adolescents really judge risk? Simple, self reported risk factors out-predict teens' self estimates of personal risk

    OpenAIRE

    Alexander Persoskie

    2013-01-01

    Recent investigations of adolescents' beliefs about risk have led to surprisingly optimistic conclusions: Teens' self estimates of their likelihood of experiencing various life events not only correlate sensibly with relevant risk factors (Fischhoff et al., 2000), but they also significantly predict later experiencing the events (Bruine de Bruin et al., 2007). Using the same dataset examined in previous investigations, the present study extended these analyses by comparing the predictive valu...

  14. Maternal mental health predicts risk of developmental problems at 3 years of age: follow up of a community based trial

    Directory of Open Access Journals (Sweden)

    Leew Shirley

    2008-05-01

    Full Text Available Abstract Background Undetected and untreated developmental problems can have a significant economic and social impact on society. Intervention to ameliorate potential developmental problems requires early identification of children at risk of future learning and behaviour difficulties. The objective of this study was to estimate the prevalence of risk for developmental problems among preschool children born to medically low risk women and identify factors that influence outcomes. Methods Mothers who had participated in a prenatal trial were followed up three years post partum to answer a telephone questionnaire. Questions were related to child health and development, child care, medical care, mother's lifestyle, well-being, and parenting style. The main outcome measure was risk for developmental problems using the Parents' Evaluation of Developmental Status (PEDS. Results Of 791 children, 11% were screened by the PEDS to be at high risk for developmental problems at age three. Of these, 43% had previously been referred for assessment. Children most likely to have been referred were those born preterm. Risk factors for delay included: male gender, history of ear infections, a low income environment, and a mother with poor emotional health and a history of abuse. A child with these risk factors was predicted to have a 53% chance of screening at high risk for developmental problems. This predicted probability was reduced to 19% if the child had a mother with good emotional health and no history of abuse. Conclusion Over 10% of children were identified as high risk for developmental problems by the screening, and more than half of those had not received a specialist referral. Risk factors for problems included prenatal and perinatal maternal and child factors. Assessment of maternal health and effective screening of child development may increase detection of children at high risk who would benefit from early intervention. Trial registration Current

  15. Perceived extrinsic mortality risk and reported effort in looking after health: testing a behavioral ecological prediction.

    Science.gov (United States)

    Pepper, Gillian V; Nettle, Daniel

    2014-09-01

    Socioeconomic gradients in health behavior are pervasive and well documented. Yet, there is little consensus on their causes. Behavioral ecological theory predicts that, if people of lower socioeconomic position (SEP) perceive greater personal extrinsic mortality risk than those of higher SEP, they should disinvest in their future health. We surveyed North American adults for reported effort in looking after health, perceived extrinsic and intrinsic mortality risks, and measures of SEP. We examined the relationships between these variables and found that lower subjective SEP predicted lower reported health effort. Lower subjective SEP was also associated with higher perceived extrinsic mortality risk, which in turn predicted lower reported health effort. The effect of subjective SEP on reported health effort was completely mediated by perceived extrinsic mortality risk. Our findings indicate that perceived extrinsic mortality risk may be a key factor underlying SEP gradients in motivation to invest in future health.

  16. Epidemiological geomatics in evaluation of mine risk education in Afghanistan: introducing population weighted raster maps.

    Science.gov (United States)

    Andersson, Neil; Mitchell, Steven

    2006-01-03

    Evaluation of mine risk education in Afghanistan used population weighted raster maps as an evaluation tool to assess mine education performance, coverage and costs. A stratified last-stage random cluster sample produced representative data on mine risk and exposure to education. Clusters were weighted by the population they represented, rather than the land area. A "friction surface" hooked the population weight into interpolation of cluster-specific indicators. The resulting population weighted raster contours offer a model of the population effects of landmine risks and risk education. Five indicator levels ordered the evidence from simple description of the population-weighted indicators (level 0), through risk analysis (levels 1-3) to modelling programme investment and local variations (level 4). Using graphic overlay techniques, it was possible to metamorphose the map, portraying the prediction of what might happen over time, based on the causality models developed in the epidemiological analysis. Based on a lattice of local site-specific predictions, each cluster being a small universe, the "average" prediction was immediately interpretable without losing the spatial complexity.

  17. Epidemiological geomatics in evaluation of mine risk education in Afghanistan: introducing population weighted raster maps

    Directory of Open Access Journals (Sweden)

    Andersson Neil

    2006-01-01

    Full Text Available Abstract Evaluation of mine risk education in Afghanistan used population weighted raster maps as an evaluation tool to assess mine education performance, coverage and costs. A stratified last-stage random cluster sample produced representative data on mine risk and exposure to education. Clusters were weighted by the population they represented, rather than the land area. A "friction surface" hooked the population weight into interpolation of cluster-specific indicators. The resulting population weighted raster contours offer a model of the population effects of landmine risks and risk education. Five indicator levels ordered the evidence from simple description of the population-weighted indicators (level 0, through risk analysis (levels 1–3 to modelling programme investment and local variations (level 4. Using graphic overlay techniques, it was possible to metamorphose the map, portraying the prediction of what might happen over time, based on the causality models developed in the epidemiological analysis. Based on a lattice of local site-specific predictions, each cluster being a small universe, the "average" prediction was immediately interpretable without losing the spatial complexity.

  18. Food and Drug Administration Evaluation and Cigarette Smoking Risk Perceptions

    Science.gov (United States)

    Kaufman, Annette R.; Waters, Erika A.; Parascandola, Mark; Augustson, Erik M.; Bansal-Travers, Maansi; Hyland, Andrew; Cummings, K. Michael

    2011-01-01

    Objectives: To examine the relationship between a belief about Food and Drug Administration (FDA) safety evaluation of cigarettes and smoking risk perceptions. Methods: A nationally representative, random-digit-dialed telephone survey of 1046 adult current cigarette smokers. Results: Smokers reporting that the FDA does not evaluate cigarettes for…

  19. Risk evaluation and monitoring in multiple sclerosis therapeutics.

    Science.gov (United States)

    Clanet, Michel C; Wolinsky, Jerry S; Ashton, Raymond J; Hartung, Hans-Peter; Reingold, Stephen C

    2014-09-01

    Risk for multiple sclerosis (MS) disease-modifying therapies (DMT) must be assessed on an ongoing basis. Early concerns regarding the first-approved DMTs for MS have been mitigated, but recently licensed therapies have been linked to possibly greater risks. The objective of this review is to discuss risk assessment in MS therapeutics based on an international workshop and comprehensive literature search and recommend strategies for risk assessment/monitoring. Assessment and perception of therapeutic risks vary between patients, doctors and regulators. Acceptability of risk depends on the magnitude of risk and the demonstrated clinical benefits of any agent. Safety signals must be distinguishable from chance occurrences in a clinical trial and in long-term use of medications. Post-marketing research is crucial for assessing longer-term safety in large patient cohorts. Reporting of adverse events is becoming more proactive, allowing more rapid identification of risks. Communication about therapeutic risks and their relationship to clinical benefit must involve patients in shared decision making. It is difficult to produce a general risk-assessment algorithm for all MS therapies. Specific algorithms are required for each DMT in every treated-patient population. New and evolving risks must be evaluated and communicated rapidly to allow patients and physicians to be well informed and able to share treatment decisions. © The Author(s) 2013.

  20. Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression

    Directory of Open Access Journals (Sweden)

    Li Jian

    2017-01-01

    Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.

  1. The CAIDE Dementia Risk Score App: The development of an evidence-based mobile application to predict the risk of dementia

    OpenAIRE

    Sindi, Shireen; Calov, Elisabeth; Fokkens, Jasmine; Ngandu, Tiia; Soininen, Hilkka; Tuomilehto, Jaakko; Kivipelto, Miia

    2015-01-01

    Background The CAIDE (Cardiovascular Risk Factors, Aging, and Incidence of Dementia) Dementia Risk Score is a validated tool to predict late-life dementia risk (20?years later), based on midlife vascular risk factors. The goal was to render this prediction tool widely accessible. Methods The CAIDE Risk Score (mobile application) App was developed based on the CAIDE Dementia Risk Score, involving information on age, educational level, hypertension, hypercholesterolemia, obesity, and physical i...

  2. Predicting nosocomial lower respiratory tract infections by a risk index based system

    NARCIS (Netherlands)

    Chen, Yong; Shan, Xue; Zhao, Jingya; Han, Xuelin; Tian, Shuguang; Chen, Fangyan; Su, Xueting; Sun, Yansong; Huang, Liuyu; Grundmann, Hajo; Wang, Hongyuan; Han, Li

    2017-01-01

    Although belonging to one of the most common type of nosocomial infection, there was currently no simple prediction model for lower respiratory tract infections (LRTIs). This study aims to develop a risk index based system for predicting nosocomial LRTIs based on data from a large point-prevalence

  3. Evaluation of the predictive indices for candidemia in an adult intensive care unit

    Directory of Open Access Journals (Sweden)

    Gilberto Gambero Gaspar

    2015-02-01

    Full Text Available INTRODUCTION: To evaluate predictive indices for candidemia in an adult intensive care unit (ICU and to propose a new index. METHODS: A prospective cohort study was conducted between January 2011 and December 2012. This study was performed in an ICU in a tertiary care hospital at a public university and included 114 patients staying in the adult ICU for at least 48 hours. The association of patient variables with candidemia was analyzed. RESULTS: There were 18 (15.8% proven cases of candidemia and 96 (84.2% cases without candidemia. Univariate analysis revealed the following risk factors: parenteral nutrition, severe sepsis, surgical procedure, dialysis, pancreatitis, acute renal failure, and an APACHE II score higher than 20. For the Candida score index, the odds ratio was 8.50 (95% CI, 2.57 to 28.09; the sensitivity, specificity, positive predictive value, and negative predictive value were 0.78, 0.71, 0.33, and 0.94, respectively. With respect to the clinical predictor index, the odds ratio was 9.45 (95%CI, 2.06 to 43.39; the sensitivity, specificity, positive predictive value, and negative predictive value were 0.89, 0.54, 0.27, and 0.96, respectively. The proposed candidemia index cutoff was 8.5; the sensitivity, specificity, positive predictive value, and negative predictive value were 0.77, 0.70, 0.33, and 0.94, respectively. CONCLUSIONS: The Candida score and clinical predictor index excluded candidemia satisfactorily. The effectiveness of the candidemia index was comparable to that of the Candida score.

  4. Socioeconomic status in one's childhood predicts offspring cardiovascular risk.

    Science.gov (United States)

    Schreier, Hannah M C; Chen, Edith

    2010-11-01

    To test whether effects of socioeconomic environments can persist across generations, we examined whether parents' childhood socioeconomic status (SES) could predict blood pressure (BP) trajectories in their youth across a 12-month study period and C-reactive protein (CRP) levels at one year follow-up. BP was assessed in 88 healthy youth (M age = 13 ± 2.4) at three study visits, each 6 months apart. CRP was also assessed in youth at baseline and one year follow-up. Parents reported on current and their own childhood SES (education and crowding). If parents' childhood SES was lower, their children displayed increasing SBP and CRP over the 12-month period, or conversely, the higher parents' childhood SES, the greater the decrease in SBP and CRP in their youth over time. These effects persisted even after controlling for current SES. A number of other factors, including child health behaviors, parent psychosocial characteristics, general family functioning, and parent physiology could not explain these effects. Our study suggests that the SES environment parents grow up in may influence physical health across generations, here, SBP and CRP in their children, and hence that intergenerational histories are important to consider in predicting cardiovascular health in youth. Copyright © 2010 Elsevier Inc. All rights reserved.

  5. Predicting Macrosomia

    National Research Council Canada - National Science Library

    Pates, Jason A; McIntire, Donald D; Casey, Brian M; Leveno, Kenneth J

    2008-01-01

    Objective. The purpose of this study was to evaluate the prediction of fetal macrosomia based on ultrasound estimates of fetal weight and amniotic fluid volume combined with clinical risk factors. Methods...

  6. Hip and fragility fracture prediction by 4-item clinical risk score and mobile heel BMD: a women cohort study

    Directory of Open Access Journals (Sweden)

    Thulesius Hans

    2010-03-01

    Full Text Available Abstract Background One in four Swedish women suffers a hip fracture yielding high morbidity and mortality. We wanted to revalidate a 4-item clinical risk score and evaluate a portable heel bone mineral density (BMD technique regarding hip and fragility fracture risk among elderly women. Methods In a population-based prospective cohort study we used clinical risk factors from a baseline questionnaire and heel BMD to predict a two-year hip and fragility fracture outcome for women, in a fracture preventive program. Calcaneal heel BMD was measured by portable dual X-ray laser absorptiometry (DXL and compared to hip BMD, measured with stationary dual X-ray absorptiometry (DXA technique. Results Seven women suffered hip fracture and 14 women fragility fracture/s (at hip, radius, humerus and pelvis among 285 women; 60% having heel BMD ≤ -2.5 SD. The 4-item FRAMO (Fracture and Mortality Index combined the clinical risk factors age ≥80 years, weight Conclusions In a follow-up study we identified high risk groups for hip and fragility fracture with our plain 4-item risk model. Increased fracture risk was also related to decreasing heel BMD in calcaneal bone, measured with a mobile DXL technique. A combination of high FRAMO Index, prior fragility fracture, and very low BMD restricted the high risk group to 11%, among whom most hip fractures occurred (71%. These practical screening methods co