WorldWideScience

Sample records for evaluate risk prediction

  1. Risk assessment and remedial policy evaluation using predictive modeling

    International Nuclear Information System (INIS)

    Linkov, L.; Schell, W.R.

    1996-01-01

    As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forest compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment

  2. Systematic Review of Health Economic Impact Evaluations of Risk Prediction Models : Stop Developing, Start Evaluating

    NARCIS (Netherlands)

    van Giessen, Anoukh; Peters, Jaime; Wilcher, Britni; Hyde, Chris; Moons, Carl; de Wit, Ardine; Koffijberg, Erik

    2017-01-01

    Background: Although health economic evaluations (HEEs) are increasingly common for therapeutic interventions, they appear to be rare for the use of risk prediction models (PMs). Objectives: To evaluate the current state of HEEs of PMs by performing a comprehensive systematic review. Methods: Four

  3. 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.

  4. Evaluating and predicting overall process risk using event logs

    NARCIS (Netherlands)

    Pika, A.; Van Der Aalst, W.M.P.; Wynn, M.T.; Fidge, C.J.; Ter Hofstede, A.H.M.

    2016-01-01

    Companies standardise and automate their business processes in order to improve process efficiency and minimise operational risks. However, it is difficult to eliminate all process risks during the process design stage due to the fact that processes often run in complex and changeable environments

  5. 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

  6. Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models.

    Science.gov (United States)

    Campbell, William; Ganna, Andrea; Ingelsson, Erik; Janssens, A Cecile J W

    2016-01-01

    We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS.

    Science.gov (United States)

    Wang, L E; Shaw, Pamela A; Mathelier, Hansie M; Kimmel, Stephen E; French, Benjamin

    2016-03-01

    The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

  8. Net Reclassification Indices for Evaluating Risk-Prediction Instruments: A Critical Review

    Science.gov (United States)

    Kerr, Kathleen F.; Wang, Zheyu; Janes, Holly; McClelland, Robyn L.; Psaty, Bruce M.; Pepe, Margaret S.

    2014-01-01

    Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For pre-defined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true-positive and false-positive rates. We advocate use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid pre-defined risk categories. However, it suffers from many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap

  9. Evaluation of fetal anthropometric measures to predict the risk for shoulder dystocia.

    Science.gov (United States)

    Burkhardt, T; Schmidt, M; Kurmanavicius, J; Zimmermann, R; Schäffer, L

    2014-01-01

    To evaluate the quality of anthropometric measures to improve the prediction of shoulder dystocia by combining different sonographic biometric parameters. This was a retrospective cohort study of 12,794 vaginal deliveries with complete sonographic biometry data obtained within 7 days before delivery. Receiver-operating characteristics (ROC) curves of various combinations of the biometric parameters, namely, biparietal diameter (BPD), occipitofrontal diameter (OFD), head circumference, abdominal diameter (AD), abdominal circumference (AC) and femur length were analyzed. The influences of independent risk factors were calculated and their combination used in a predictive model. The incidence of shoulder dystocia was 1.14%. Different combinations of sonographic parameters showed comparable ROC curves without advantage for a particular combination. The difference between AD and BPD (AD - BPD) (area under the curve (AUC) = 0.704) revealed a significant increase in risk (odds ratio (OR) 7.6 (95% CI 4.2-13.9), sensitivity 8.2%, specificity 98.8%) at a suggested cut-off ≥ 2.6 cm. However, the positive predictive value (PPV) was low (7.5%). The AC as a single parameter (AUC = 0.732) with a cut-off ≥ 35 cm performed worse (OR 4.6 (95% CI 3.3-6.5), PPV 2.6%). BPD/OFD (a surrogate for fetal cranial shape) was not significantly different between those with and those without shoulder dystocia. The combination of estimated fetal weight, maternal diabetes, gender and AD - BPD provided a reasonable estimate of the individual risk. Sonographic fetal anthropometric measures appear not to be a useful tool to screen for the risk of shoulder dystocia due to a low PPV. However, AD - BPD appears to be a relevant risk factor. While risk stratification including different known risk factors may aid in counseling, shoulder dystocia cannot effectively be predicted. Copyright © 2013 ISUOG. Published by John Wiley & Sons Ltd.

  10. 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

  11. Developing and evaluating polygenic risk prediction models for stratified disease prevention.

    Science.gov (United States)

    Chatterjee, Nilanjan; Shi, Jianxin; García-Closas, Montserrat

    2016-07-01

    Knowledge of genetics and its implications for human health is rapidly evolving in accordance with recent events, such as discoveries of large numbers of disease susceptibility loci from genome-wide association studies, the US Supreme Court ruling of the non-patentability of human genes, and the development of a regulatory framework for commercial genetic tests. In anticipation of the increasing relevance of genetic testing for the assessment of disease risks, this Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors. Potential applications of models for primary and secondary disease prevention are illustrated through several case studies, and future challenges and opportunities are discussed.

  12. 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

    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 ...... risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management....

  13. Risk stratification and prediction of cancer of focal thyroid fluorodeoxyglucose uptake during cancer evaluation

    International Nuclear Information System (INIS)

    Kim, Bo-Hyun; Na, Min-A.; Kim, In-Joo; Kim, Seong-Jang; Kim, Yong-Ki

    2010-01-01

    Focal thyroid incidentaloma by F-18 2-deoxy-2-F18-fluoro-D-glucose (FDG) positron emission tomography (PET) has been reported 1-4% of cancer patients and normal healthy population, with a risk of cancer ranging 14-50%. The aim of this study was to investigate the prevalence of thyroid incidentaloma in F-18 FDG PET/CT and risk of cancer, usefulness of visual and SUV max and SUV mean differentiating malignant nodules and to define the predictable variables. A total 159 patients with focal thyroid FDG incidentaloma during cancer evaluation with non-thyroid cancer were enrolled. After F-18 PET/CT, we analyzed the image visually and obtained semiquantitative indices. The incidence of focal FDG thyroid incidentaloma is 1.36% and cancer risk is 23.3%. The incidence of focal thyroid FDG uptake was significantly higher in women (2.88 vs. 0.31%; X 2 =136.4, p max (malignant: median 4.53, range 2.1-12.0; benign: median 3.08, range 1.6-35, p=0.0093). However, SUV mean have no statistical differences (malignant: median 2.17, range 1.77-3.19; benign: median 2.05, range 1.15-5.77, p=0.0541). In ROC analyses, the optimal visual grades were >grade 3, and the optimal semiquantitative indices were 4.46 for SUV max , 2.03 for SUV mean . The visual grade was superior to other variables for the differentiation malignant from benign thyroid incidentalomas. The size and visual grade was the potent predictor by logistic regression analysis. Focal thyroid FDG incidentalomas in non-thyroid cancer patients during evaluation have a high risk of malignancy. The size and visual grade are potential predictors for malignant thyroid incidentaloma. (author)

  14. 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...... the identification of models for cases with noisy in-sewer observations. For the prediction of the overflow risk, no improvement was demonstrated through the application of stochastic forecasts instead of point predictions, although this result is thought to be caused by the notably simplified setup used...

  15. The c-index is not proper for the evaluation of $t$-year predicted risks.

    Science.gov (United States)

    Blanche, Paul; Kattan, Michael W; Gerds, Thomas A

    2018-02-16

    We show that the widely used concordance index for time to event outcome is not proper when interest is in predicting a $t$-year risk of an event, for example 10-year mortality. In the situation with a fixed prediction horizon, the concordance index can be higher for a misspecified model than for a correctly specified model. Impropriety happens because the concordance index assesses the order of the event times and not the order of the event status at the prediction horizon. The time-dependent area under the receiver operating characteristic curve does not have this problem and is proper in this context.

  16. [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

  17. A New Pre-employment Functional Capacity Evaluation Predicts Longer-Term Risk of Musculoskeletal Injury in Healthy Workers

    OpenAIRE

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

    2013-01-01

    Study Design. Prospective cohort study. Objective. To determine if a job-specific pre-employment functional assessment (PEFA) predicts musculoskeletal injury risk in healthy mineworkers. Summary of Background Data. 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 the...

  18. 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...

  19. Evaluation of the white finger risk prediction model in ISO 5349 suggests need for prospective studies.

    Science.gov (United States)

    Gemne, G; Lundström, R

    1996-05-01

    The risk prediction model for white fingers in Annex A of ISO 5349 is not likely to offer protection from all tools and all work processes. It is also probable that some work place changes it has initiated are either redundant or lack the intended effect. The main reasons for these shortcomings are the following. The often demonstrated disagreement between predicted and observed white fingers occurrence may be related to the fact that the model is based on latency data. This leads to an overestimation, to an unknown extent, of true group risks. A possible healthy worker effect, resulting in underestimation, has not been considered, and uncertainty because of recall bias is connected with using latency as effect variable in a slowly developing disorder like white fingers. The diagnostic criteria for white fingers have varied over the years, causing a possible inclusion of circulatory disturbances other than those induced by vibration. Among insufficiently clarified matters unrelated to vibration are variations in individual susceptibility and other host factors that modify vibration effects, uncertainty concerning daily or total effective exposure, and the fact that variation in work methods and processes as well as ergonomic factors other than vibration tend to make different groups incomparable form the viewpoint of risk of injury. Lack of sufficient data on vibration measurements and employment durations add to the uncertainty, as do variations in tool conditions (grinder wheels, etc) and inherent difficulties in measurement. Finally, the ISO 5349 frequency-weighting curve only relates to acute sensory effects rather than chronic effects on vascular functions like white fingers, and directional difference in sensitivity has not been incorporated in the curve. Data on exposure-response relationships are needed from prospective studies that monitor the dose of exposure to special vibration types and all relevant environmental agents, employ diagnostics with good

  20. 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

  1. A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the Productivity Susceptibility Analysis (PSA)

    Science.gov (United States)

    2018-01-01

    Qualitative risk assessment frameworks, such as the Productivity Susceptibility Analysis (PSA), have been developed to rapidly evaluate the risks of fishing to marine populations and prioritize management and research among species. Despite being applied to over 1,000 fish populations, and an ongoing debate about the most appropriate method to convert biological and fishery characteristics into an overall measure of risk, the assumptions and predictive capacity of these approaches have not been evaluated. Several interpretations of the PSA were mapped to a conventional age-structured fisheries dynamics model to evaluate the performance of the approach under a range of assumptions regarding exploitation rates and measures of biological risk. The results demonstrate that the underlying assumptions of these qualitative risk-based approaches are inappropriate, and the expected performance is poor for a wide range of conditions. The information required to score a fishery using a PSA-type approach is comparable to that required to populate an operating model and evaluating the population dynamics within a simulation framework. In addition to providing a more credible characterization of complex system dynamics, the operating model approach is transparent, reproducible and can evaluate alternative management strategies over a range of plausible hypotheses for the system. PMID:29856869

  2. Evaluation of waist-to-height ratio to predict 5 year cardiometabolic risk in sub-Saharan African adults.

    Science.gov (United States)

    Ware, L J; Rennie, K L; Kruger, H S; Kruger, I M; Greeff, M; Fourie, C M T; Huisman, H W; Scheepers, J D W; Uys, A S; Kruger, R; Van Rooyen, J M; Schutte, R; Schutte, A E

    2014-08-01

    Simple, low-cost central obesity measures may help identify individuals with increased cardiometabolic disease risk, although it is unclear which measures perform best in African adults. We aimed to: 1) cross-sectionally compare the accuracy of existing waist-to-height ratio (WHtR) and waist circumference (WC) thresholds to identify individuals with hypertension, pre-diabetes, or dyslipidaemia; 2) identify optimal WC and WHtR thresholds to detect CVD risk in this African population; and 3) assess which measure best predicts 5-year CVD risk. Black South Africans (577 men, 942 women, aged >30years) were recruited by random household selection from four North West Province communities. Demographic and anthropometric measures were taken. Recommended diagnostic thresholds (WC > 80 cm for women, >94 cm for men; WHtR > 0.5) were evaluated to predict blood pressure, fasting blood glucose, lipids, and glycated haemoglobin measured at baseline and 5 year follow up. Women were significantly more overweight than men at baseline (mean body mass index (BMI) women 27.3 ± 7.4 kg/m(2), men 20.9 ± 4.3 kg/m(2)); median WC women 81.9 cm (interquartile range 61-103), men 74.7 cm (63-87 cm), all P women, both WC and WHtR significantly predicted all cardiometabolic risk factors after 5 years. In men, even after adjusting WC threshold based on ROC analysis, WHtR better predicted overall 5-year risk. Neither measure predicted hypertension in men. The WHtR threshold of >0.5 appears to be more consistently supported and may provide a better predictor of future cardiometabolic risk in sub-Saharan Africa. Copyright © 2014 Elsevier B.V. All rights reserved.

  3. Predicting Young Adults Binge Drinking in Nightlife Scenes: An Evaluation of the D-ARIANNA Risk Estimation Model.

    Science.gov (United States)

    Crocamo, Cristina; Bartoli, Francesco; Montomoli, Cristina; Carrà, Giuseppe

    2018-05-25

    Binge drinking (BD) among young people has significant public health implications. Thus, there is the need to target users most at risk. We estimated the discriminative accuracy of an innovative model nested in a recently developed e-Health app (Digital-Alcohol RIsk Alertness Notifying Network for Adolescents and young adults [D-ARIANNA]) for BD in young people, examining its performance to predict short-term BD episodes. We consecutively recruited young adults in pubs, discos, or live music events. Participants self-administered the app D-ARIANNA, which incorporates an evidence-based risk estimation model for the dependent variable BD. They were re-evaluated after 2 weeks using a single-item BD behavior as reference. We estimated D-ARIANNA discriminative ability through measures of sensitivity and specificity, and also likelihood ratios. ROC curve analyses were carried out, exploring variability of discriminative ability across subgroups. The analyses included 507 subjects, of whom 18% reported at least 1 BD episode at follow-up. The majority of these had been identified as at high/moderate or high risk (65%) at induction. Higher scores from the D-ARIANNA risk estimation model reflected an increase in the likelihood of BD. Additional risk factors such as high pocket money availability and alcohol expectancies influence the predictive ability of the model. The D-ARIANNA model showed an appreciable, though modest, predictive ability for subsequent BD episodes. Post-hoc model showed slightly better predictive properties. Using up-to-date technology, D-ARIANNA appears an innovative and promising screening tool for BD among young people. Long-term impact remains to be established, and also the role of additional social and environmental factors.

  4. Become the PPUPET Master: Mastering Pressure Ulcer Risk Assessment With the Pediatric Pressure Ulcer Prediction and Evaluation Tool (PPUPET).

    Science.gov (United States)

    Sterken, David J; Mooney, JoAnn; Ropele, Diana; Kett, Alysha; Vander Laan, Karen J

    2015-01-01

    Hospital acquired pressure ulcers (HAPU) are serious, debilitating, and preventable complications in all inpatient populations. Despite evidence of the development of pressure ulcers in the pediatric population, minimal research has been done. Based on observations gathered during quarterly HAPU audits, bedside nursing staff recognized trends in pressure ulcer locations that were not captured using current pressure ulcer risk assessment tools. Together, bedside nurses and nursing leadership created and conducted multiple research studies to investigate the validity and reliability of the Pediatric Pressure Ulcer Prediction and Evaluation Tool (PPUPET). Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.

    Science.gov (United States)

    Yokota, Shinichiroh; Ohe, Kazuhiko

    2016-04-01

    To construct and evaluate an easy-to-use fall risk prediction model based on the daily condition of inpatients from secondary use electronic medical record system data. The present authors scrutinized electronic medical record system data and created a dataset for analysis by including inpatient fall report data and Intensity of Nursing Care Needs data. The authors divided the analysis dataset into training data and testing data, then constructed the fall risk prediction model FiND from the training data, and tested the model using the testing data. The dataset for analysis contained 1,230,604 records from 46,241 patients. The sensitivity of the model constructed from the training data was 71.3% and the specificity was 66.0%. The verification result from the testing dataset was almost equivalent to the theoretical value. Although the model's accuracy did not surpass that of models developed in previous research, the authors believe FiND will be useful in medical institutions all over Japan because it is composed of few variables (only age, sex, and the Intensity of Nursing Care Needs items), and the accuracy for unknown data was clear. © 2016 Japan Academy of Nursing Science.

  6. A prospective, longitudinal study to evaluate the clinical utility of a predictive algorithm that detects risk of opioid use disorder

    Science.gov (United States)

    Brenton, Ashley; Lee, Chee; Lewis, Katrina; Sharma, Maneesh; Kantorovich, Svetlana; Smith, Gregory A; Meshkin, Brian

    2018-01-01

    Purpose The purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use. Specifically, we sought to assess how physicians were using the profile in patient care and how its use affected patient outcomes. Patients and methods A prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 5,397 patients across 100 clinics in the USA. Using a patent-protected, validated algorithm combining specific genetic risk factors with phenotypic traits, patients were categorized into low-, moderate-, and high-risk patients for opioid abuse. Physicians who ordered precision medicine testing were asked to complete patient evaluations and document their actions, decisions, and perceptions regarding the utility of the precision medicine tests. The patient outcomes associated with each treatment action were carefully documented. Results Physicians used the profile to guide treatment decisions for over half of the patients. Of those, guided treatment decisions for 24.5% of the patients were opioid related, including changing the opioid prescribed, starting an opioid, or titrating a patient off the opioid. Treatment guidance was strongly influenced by profile-predicted opioid use disorder (OUD) risk. Most importantly, patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, including better pain management by medication adjustments, with an average pain decrease of 3.4 points on a scale of 1–10. Conclusion Patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, as measured by decreased pain levels resulting from better pain management with prescribed medications. The clinical utility of the profile is twofold. It provides clinically actionable recommendations that can be used to 1) prevent OUD through limiting initial opioid

  7. 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

    2018-04-01

    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 HEMORR 2 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

  8. 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.

  9. 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…

  10. Prospective Evaluation of Nutritional Factors to Predict the Risk of Complications for Patients Undergoing Radical Cystectomy: A Cohort Study.

    Science.gov (United States)

    Allaire, Janie; Léger, Caroline; Ben-Zvi, Tal; Nguilé-Makao, Molière; Fradet, Yves; Lacombe, Louis; Fradet, Vincent

    2017-01-01

    The objective of this study was to identify nutritional preoperative factors associated with complications after radical cystectomy (RC). We prospectively evaluated the Mini-Nutritional Assessment Score, body mass index (BMI), appetite, stool frequency, hydration, food intake, weight loss, albuminemia, and prealbuminemia of 144 patients who underwent RC between January 2011 and April 2014. Postoperative complications were defined as any adverse event reported in the patient's file up to 90 days after surgery. Each complication was classified according to the Clavien-Dindo and Memorial Sloan-Kettering Cancer Center systems. The adjusted relative risk (RR) computed through a Poisson regression model was used to identify nutritional risk factors associated with post-RC complications. A high BMI >27 kg/m 2 was associated with higher risk of low-grade complications (RR:1.47 [95% CI,1.09-2.00]) at 7 days and a four-fold increased risk of cardiac complications at 7 and 90 days (RR:3.77 [1.15-12.32] and RR:3.28 [1.35-7.98]). Decreased appetite was associated with low-grade (RR:1.43 [1.03-1.99] complications within 90 days. Preoperative weight loss >3 kg was associated with high-grade (RR:2.49 [1.23-5.05]) and wound (RR:2.51 [1.23-5.10]) complications within 90 days. This study showed that preoperative nutritional status of patients may predict the occurrence of complications up to 90 days post-RC. Development of preoperative nutritional interventions may reduce the deleterious impact of RC on patients' health.

  11. 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

  12. Evaluating prediction uncertainty

    International Nuclear Information System (INIS)

    McKay, M.D.

    1995-03-01

    The probability distribution of a model prediction is presented as a proper basis for evaluating the uncertainty in a model prediction that arises from uncertainty in input values. Determination of important model inputs and subsets of inputs is made through comparison of the prediction distribution with conditional prediction probability distributions. Replicated Latin hypercube sampling and variance ratios are used in estimation of the distributions and in construction of importance indicators. The assumption of a linear relation between model output and inputs is not necessary for the indicators to be effective. A sequential methodology which includes an independent validation step is applied in two analysis applications to select subsets of input variables which are the dominant causes of uncertainty in the model predictions. Comparison with results from methods which assume linearity shows how those methods may fail. Finally, suggestions for treating structural uncertainty for submodels are presented

  13. 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.

  14. 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

  15. 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. PMID:23169122

  16. Quantifying prognosis with risk predictions.

    Science.gov (United States)

    Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

    2012-01-01

    Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

  17. The Abdominal Aortic Aneurysm Statistically Corrected Operative Risk Evaluation (AAA SCORE) for predicting mortality after open and endovascular interventions.

    Science.gov (United States)

    Ambler, Graeme K; Gohel, Manjit S; Mitchell, David C; Loftus, Ian M; Boyle, Jonathan R

    2015-01-01

    Accurate adjustment of surgical outcome data for risk is vital in an era of surgeon-level reporting. Current risk prediction models for abdominal aortic aneurysm (AAA) repair are suboptimal. We aimed to develop a reliable risk model for in-hospital mortality after intervention for AAA, using rigorous contemporary statistical techniques to handle missing data. Using data collected during a 15-month period in the United Kingdom National Vascular Database, we applied multiple imputation methodology together with stepwise model selection to generate preoperative and perioperative models of in-hospital mortality after AAA repair, using two thirds of the available data. Model performance was then assessed on the remaining third of the data by receiver operating characteristic curve analysis and compared with existing risk prediction models. Model calibration was assessed by Hosmer-Lemeshow analysis. A total of 8088 AAA repair operations were recorded in the National Vascular Database during the study period, of which 5870 (72.6%) were elective procedures. Both preoperative and perioperative models showed excellent discrimination, with areas under the receiver operating characteristic curve of .89 and .92, respectively. This was significantly better than any of the existing models (area under the receiver operating characteristic curve for best comparator model, .84 and .88; P AAA repair. These models were carefully developed with rigorous statistical methodology and significantly outperform existing methods for both elective cases and overall AAA mortality. These models will be invaluable for both preoperative patient counseling and accurate risk adjustment of published outcome data. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

  18. 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

  19. Preliminary evaluation of a predictive blood assay to identify patients at high risk of chemotherapy-induced nausea.

    Science.gov (United States)

    Kutner, Thomas; Kunkel, Emily; Wang, Yue; George, Kyle; Zeger, Erik L; Ali, Zonera A; Prendergast, George C; Gilman, Paul B; Wallon, U Margaretha

    2017-02-01

    The aim of this study was to test a new blood-based assay for its ability to predict delayed chemotherapy-induced nausea. Blood drawn from consented patients prior to receiving their first platinum-based therapy was tested for glutathione recycling capacity and normalized to total red cell numbers. This number was used to predict nausea and then compared to patient reported outcomes using the Rotterdam Symptom Check List and medical records. We show that the pathways involved in the glutathione recycling are stable for at least 48 h and that the test was able to correctly classify the risk of nausea for 89.1 % of the patients. The overall incidence of nausea was 21.9 % while women had an incidence of 29.6 %. This might be the first objective test to predict delayed nausea for cancer patients receiving highly emetogenic chemotherapy. We believe that this assay could better guide clinicians in their efforts to provide optimal patient-oriented care.

  20. 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.

  1. Risk estimation and evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Ferguson, R A.D.

    1982-10-01

    Risk assessment involves subjectivity, which makes objective decision making difficult in the nuclear power debate. The author reviews the process and uncertainties of estimating risks as well as the potential for misinterpretation and misuse. Risk data from a variety of aspects cannot be summed because the significance of different risks is not comparable. A method for including political, social, moral, psychological, and economic factors, environmental impacts, catastrophes, and benefits in the evaluation process could involve a broad base of lay and technical consultants, who would explain and argue their evaluation positions. 15 references. (DCK)

  2. A risk-based evaluation of the impact of key uncertainties on the prediction of severe accident source terms - STU

    International Nuclear Information System (INIS)

    Ang, M.L.; Grindon, E.; Dutton, L.M.C.; Garcia-Sedano, P.; Santamaria, C.S.; Centner, B.; Auglaire, M.; Routamo, T.; Outa, S.; Jokiniemi, J.; Gustavsson, V.; Wennerstrom, H.; Spanier, L.; Gren, M.; Boschiero, M-H; Droulas, J-L; Friederichs, H-G; Sonnenkalb, M.

    2001-01-01

    The purpose of this project is to address the key uncertainties associated with a number of fission product release and transport phenomena in a wider context and to assess their relevance to key severe accident sequences. This project is a wide-based analysis involving eight reactor designs that are representative of the reactors currently operating in the European Union (EU). In total, 20 accident sequences covering a wide range of conditions have been chosen to provide the basis for sensitivity studies. The appraisal is achieved through a systematic risk-based framework developed within this project. Specifically, this is a quantitative interpretation of the sensitivity calculations on the basis of 'significance indicators', applied above defined threshold values. These threshold values represent a good surrogate for 'large release', which is defined in a number of EU countries. In addition, the results are placed in the context of in-containment source term limits, for advanced light water reactor designs, as defined by international guidelines. Overall, despite the phenomenological uncertainties, the predicted source terms (both into the containment, and subsequently, into the environment) do not display a high degree of sensitivity to the individual fission product issues addressed in this project. This is due, mainly, to the substantial capacity for the attenuation of airborne fission products by the designed safety provisions and the natural fission product retention mechanisms within the containment

  3. Screening Risk Evaluation methodology

    International Nuclear Information System (INIS)

    Hopper, K.M.

    1994-01-01

    The Screening Risk Evaluation (SRE) Guidance document is a set of guidelines provided for the uniform implementation of SREs performed on D ampersand D facilities. These guidelines are designed specifically for the completion of the second (semi-quantitative screening) phase of the D ampersand D Risk-Based Process. 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 risk to human health and the environment from ongoing or probable releases within a one year time period. The Worker Exposure Index (WEI) calculates the risk to workers, occupants, and visitors in D ampersand D facilities of contaminant exposure. The Future Release Index (FRI) calculates the risk of future releases of contaminants, after one year, to human health and the environment. The Physical Hazards Index (PHI) calculates the risk-to human health due to factors other than that of contaminants. The index of 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, determined on a project by project basis. The SRE is the first and most important step in the overall D ampersand D project level decision making process

  4. Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) and Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) risk prediction models.

    Science.gov (United States)

    Krikke, M; Hoogeveen, R C; Hoepelman, A I M; Visseren, F L J; Arends, J E

    2016-04-01

    The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories ( 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. A total of 997 HIV-infected patients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions. © 2015 British HIV Association.

  5. Risk Score for Predicting Treatment-Requiring Retinopathy of Prematurity (ROP) in the Telemedicine Approaches to Evaluating Acute-Phase ROP Study.

    Science.gov (United States)

    Ying, Gui-Shuang; VanderVeen, Deborah; Daniel, Ebenezer; Quinn, Graham E; Baumritter, Agnieshka

    2016-10-01

    To develop a risk score for predicting treatment-requiring retinopathy of prematurity (TR-ROP) in the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity (e-ROP) study. Second analyses of an observational cohort study. Infants with birth weight (BW) prematurity (ROP) examination for determining TR-ROP by study-certified ophthalmologists. Nonphysician trained readers evaluated wide-field retinal image sets for characteristics of ROP, pre-plus/plus disease, and retinal hemorrhage. Risk score points for predicting TR-ROP were derived from the regression coefficients of significant predictors in a multivariate logistic regression model. TR-ROP. Eighty-five of 771 infants (11.0%) developed TR-ROP. In a multivariate model, significant predictors for TR-ROP were gestational age (GA) (odds ratio [OR], 5.7; 95% confidence interval [CI], 1.7-18.9 for ≤25 vs. ≥28 weeks), need for respiratory support (OR, 7.0; 95% CI, 1.3-37.1 for high-frequency oscillatory ventilation vs. no respiratory support), slow weight gain (OR, 2.4; 95% CI, 1.2-4.6 for weight gain ≤12 g/day vs. >15 g/day), and image findings at the first image session including number of quadrants with pre-plus (OR, 3.8; 95% CI, 1.5-9.7 for 4 pre-plus quadrants vs. no pre-plus), stage and zone of ROP (OR, 4.7; 95% CI, 2.1-11.8 for stage 1-2 zone I, OR, 5.9; 95% CI, 2.1-16.6 for stage 3 zone I vs. no ROP), and presence of blot hemorrhage (OR, 3.1; 95% CI, 1.4-6.7). Image findings predicted TR-ROP better than GA (area under receiver operating characteristic curve [AUC] = 0.82 vs. 0.75, P = 0.03). The risk of TR-ROP steadily increased with higher risk score and predicted TR-ROP well (AUC = 0.88; 95% CI, 0.85-0.92). Risk score ≥3 points for predicting TR-ROP had a sensitivity of 98.8%, specificity of 40.1%, and positive and negative predictive values of 17.0% and 99.6%, respectively. Image characteristics at 34 PMA weeks or earlier independently predict TR-ROP. If externally validated in

  6. Tail Risk Premia and Return Predictability

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Todorov, Viktor; Xu, Lai

    The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may be attribu......The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may......-varying economic uncertainty and changes in risk aversion, or market fears, respectively....

  7. Modified risk evaluation method

    International Nuclear Information System (INIS)

    Udell, C.J.; Tilden, J.A.; Toyooka, R.T.

    1993-08-01

    The purpose of this paper is to provide a structured and cost-oriented process to determine risks associated with nuclear material and other security interests. Financial loss is a continuing concern for US Department of Energy contractors. In this paper risk is equated with uncertainty of cost impacts to material assets or human resources. The concept provides a method for assessing the effectiveness of an integrated protection system, which includes operations, safety, emergency preparedness, and safeguards and security. The concept is suitable for application to sabotage evaluations. The protection of assets is based on risk associated with cost impacts to assets and the potential for undesirable events. This will allow managers to establish protection priorities in terms of the cost and the potential for the event, given the current level of protection

  8. Korean risk assessment model for breast cancer risk prediction.

    Science.gov (United States)

    Park, Boyoung; Ma, Seung Hyun; Shin, Aesun; Chang, Myung-Chul; Choi, Ji-Yeob; Kim, Sungwan; Han, Wonshik; Noh, Dong-Young; Ahn, Sei-Hyun; Kang, Daehee; Yoo, Keun-Young; Park, Sue K

    2013-01-01

    We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk. Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort. The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (pKorean women, especially urban women.

  9. Risk assessment and risk evaluation

    International Nuclear Information System (INIS)

    Niehaus, F.

    1978-01-01

    With the help of results of investigations and model calculations the risk of nuclear energy in routine operation is shown. In this context it is pointed out that the excellent operation results of reactors all over the world have led to the acceptability of risks from local loads no longer being in question. The attention of radiation protection is therefore focused on the emissions of long-living isotopes which collect in the atmosphere. With LWRs the risk of accidents is so minimal that statistical data is, and never will be available. One has to therefore fall back upon the so-called fault tree analyses. On the subject of risk evalution the author referred to a poll in Austria. From the result of this investigation one might conclude that nuclear energy serves as a crystallization point for a discussion of varying concepts for future development. More attention should be paid to this aspect from both sides, in order to objectify the further expansion of this source of energy. (orig./HP) [de

  10. 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

  11. 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.

  12. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate 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.

  13. 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.

  14. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal 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. 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.

  16. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung 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. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast 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. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic 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. 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.

  20. 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…

  1. 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.

  2. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular 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.

  3. 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.

  4. [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

  5. Evaluating Predictive Models of Software Quality

    Science.gov (United States)

    Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

    2014-06-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  6. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  7. 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.

  8. Evaluation of environmental impact predictions

    International Nuclear Information System (INIS)

    Cunningham, P.A.; Adams, S.M.; Kumar, K.D.

    1977-01-01

    An analysis and evaluation of the ecological monitoring program at the Surry Nuclear Power Plant showed that predictions of potential environmental impact made in the Final Environmental Statement (FES), which were based on generally accepted ecological principles, were not completely substantiated by environmental monitoring data. The Surry Nuclear Power Plant (Units 1 and 2) was chosen for study because of the facility's relatively continuous operating history and the availability of environmental data adequate for analysis. Preoperational and operational fish monitoring data were used to assess the validity of the FES prediction that fish would congregate in the thermal plume during winter months and would avoid the plume during summer months. Analysis of monitoring data showed that fish catch per unit effort (CPE) was generally high in the thermal plume during winter months; however, the highest fish catches occurred in the plume during the summer. Possible explanations for differences between the FES prediction and results observed in analysis of monitoring data are discussed, and general recommendations are outlined for improving impact assessment predictions

  9. Risk evaluation for structures

    International Nuclear Information System (INIS)

    Freudenthal, A.M.; Schueller, G.I.

    1976-01-01

    The basic principles of the risk analysis, which is based on classical statistics is discussed. The significance of the Asymptotic (Extreme Value) distributions as well as the method of basing the level of acceptable risk on economical optimization procedures is pointed out. The application of the risk analysis to special type structures such as fixed offshore platforms, television towers, reactor containments and the reliability of reactor components under creep and fatigue load is elaborated by carrying out numerical examples. (orig./HP) [de

  10. Evaluation of thermal risk assessment

    International Nuclear Information System (INIS)

    Loos, J.J.; Perry, E.S.

    1993-01-01

    Risk assessment was done in 1983 to estimate the ecological hazard of increasing the generating load and thermal output of an electric generating station. Subsequently, long-term monitoring in the vicinity of the station allowed verification of the predictions made in the risk assessment. This presentation will review the efficacy of early risk assessment methods in producing useful predictions from a resource management point of view. In 1984, the Chalk Point Generating facility of the Potomac Electric Power Company increased it's median generating load by 100%. Prior to this operational change, the Academy of Natural Sciences of Philadelphia synthesized site specific data, model predictions, and results from literature to assess the risk of additional waste heat to the Patuxent River subestuary of Chesapeake Bay. Risk was expressed as the number of days per year that various species of fish and the blue crab would be expected to avoid the discharge vicinity. Accuracy of these predictions is assessed by comparing observed fish and crab distributions and their observed frequencies of avoidance to those predicted. It is concluded that the predictions of this early risk assessment were sufficiently accurate to produce a reliable resource management decision

  11. An evaluation of the use of remotely sensed parameters for prediction of incidence and risk associated with Vibrio parahaemolyticus in Gulf Coast oysters (Crassostrea virginica).

    Science.gov (United States)

    Phillips, A M B; Depaola, A; Bowers, J; Ladner, S; Grimes, D J

    2007-04-01

    The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V. parahaemolyticus in Alabama oysters to assess the utility of the former data for predicting V. parahaemolyticus densities in oysters. Remotely sensed sea surface temperature correlated well with previous in situ measurements (R(2) = 0.86) of bottom water temperature, supporting the notion that remotely sensed sea surface temperature data are a sufficiently accurate substitute for direct measurement. Turbidity and chlorophyll levels were not determined in the previous study, but in comparison with the V. parahaemolyticus data, remotely sensed values for these parameters may explain some of the variation in V. parahaemolyticus levels. More accurate determination of these effects and the temporal and spatial variability of these parameters may further improve the accuracy of prediction models. To illustrate the utility of remotely sensed data as a basis for risk management, predictions based on the U.S. Food and Drug Administration V. parahaemolyticus risk assessment model were integrated with remotely sensed sea surface temperature data to display graphically variations in V. parahaemolyticus density in oysters associated with spatial variations in water temperature. We believe images such as these could be posted in near real time, and that the availability of such information in a user-friendly format could be the basis for timely and informed risk management decisions.

  12. 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.

  13. 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

  14. 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.

  15. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  16. 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...

  17. Prostate Cancer Predictive Simulation Modelling, Assessing the Risk Technique (PCP-SMART): Introduction and Initial Clinical Efficacy Evaluation Data Presentation of a Simple Novel Mathematical Simulation Modelling Method, Devised to Predict the Outcome of Prostate Biopsy on an Individual Basis.

    Science.gov (United States)

    Spyropoulos, Evangelos; Kotsiris, Dimitrios; Spyropoulos, Katherine; Panagopoulos, Aggelos; Galanakis, Ioannis; Mavrikos, Stamatios

    2017-02-01

    We developed a mathematical "prostate cancer (PCa) conditions simulating" predictive model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk determinator [PCRD] index) and a PCa risk equation. We used these to estimate the probability of finding PCa on prostate biopsy, on an individual basis. A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy were enrolled in the present study. Given that PCa risk relates to the total prostate-specific antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age; 2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient's tPSA and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating model. Linear regression analysis was used to derive the coefficient of determination (R 2 ), termed the PCRD index. To estimate the PCRD index's predictive validity, we used the χ 2 test, multiple logistic regression analysis with PCa risk equation formation, calculation of test performance characteristics, and area under the receiver operating characteristic curve analysis using SPSS, version 22 (P regression revealed the PCRD index as an independent PCa predictor, and the formulated risk equation was 91% accurate in predicting the probability of finding PCa. On the receiver operating characteristic analysis, the PCRD index (area under the curve, 0.926) significantly (P < .001) outperformed other, established PCa predictors. The PCRD index effectively predicted the prostate biopsy outcome, correctly identifying 9 of 10 men who were eventually diagnosed with PCa and correctly ruling out PCa for 9 of 10 men who did not have PCa. Its predictive power significantly outperformed established PCa predictors, and the formulated risk equation

  18. 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.

  19. 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

  20. 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.

  1. 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.)

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

    International Nuclear Information System (INIS)

    Perandini, Simone; Soardi, G.A.; Signorini, M.; Motton, M.; Montemezzi, S.; Larici, A.R.; Del Ciello, A.; Rizzardi, G.; Solazzo, A.; Mancino, L.; Zeraj, F.; Bernhart, M.

    2017-01-01

    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.)

  3. Cardiovascular risk prediction tools for populations in Asia.

    Science.gov (United States)

    Barzi, F; Patel, A; Gu, D; Sritara, P; Lam, T H; Rodgers, A; Woodward, M

    2007-02-01

    Cardiovascular risk equations are traditionally derived from the Framingham Study. The accuracy of this approach in Asian populations, where resources for risk factor measurement may be limited, is unclear. To compare "low-information" equations (derived using only age, systolic blood pressure, total cholesterol and smoking status) derived from the Framingham Study with those derived from the Asian cohorts, on the accuracy of cardiovascular risk prediction. Separate equations to predict the 8-year risk of a cardiovascular event were derived from Asian and Framingham cohorts. The performance of these equations, and a subsequently "recalibrated" Framingham equation, were evaluated among participants from independent Chinese cohorts. Six cohort studies from Japan, Korea and Singapore (Asian cohorts); six cohort studies from China; the Framingham Study from the US. 172,077 participants from the Asian cohorts; 25,682 participants from Chinese cohorts and 6053 participants from the Framingham Study. In the Chinese cohorts, 542 cardiovascular events occurred during 8 years of follow-up. Both the Asian cohorts and the Framingham equations discriminated cardiovascular risk well in the Chinese cohorts; the area under the receiver-operator characteristic curve was at least 0.75 for men and women. However, the Framingham risk equation systematically overestimated risk in the Chinese cohorts by an average of 276% among men and 102% among women. The corresponding average overestimation using the Asian cohorts equation was 11% and 10%, respectively. Recalibrating the Framingham risk equation using cardiovascular disease incidence from the non-Chinese Asian cohorts led to an overestimation of risk by an average of 4% in women and underestimation of risk by an average of 2% in men. A low-information Framingham cardiovascular risk prediction tool, which, when recalibrated with contemporary data, is likely to estimate future cardiovascular risk with similar accuracy in Asian

  4. Shoulder dystocia: risk factors, predictability, and preventability.

    Science.gov (United States)

    Mehta, Shobha H; Sokol, Robert J

    2014-06-01

    Shoulder dystocia remains an unpredictable obstetric emergency, striking fear in the hearts of obstetricians both novice and experienced. While outcomes that lead to permanent injury are rare, almost all obstetricians with enough years of practice have participated in a birth with a severe shoulder dystocia and are at least aware of cases that have resulted in significant neurologic injury or even neonatal death. This is despite many years of research trying to understand the risk factors associated with it, all in an attempt primarily to characterize when the risk is high enough to avoid vaginal delivery altogether and prevent a shoulder dystocia, whose attendant morbidities are estimated to be at a rate as high as 16-48%. The study of shoulder dystocia remains challenging due to its generally retrospective nature, as well as dependence on proper identification and documentation. As a result, the prediction of shoulder dystocia remains elusive, and the cost of trying to prevent one by performing a cesarean delivery remains high. While ultimately it is the injury that is the key concern, rather than the shoulder dystocia itself, it is in the presence of an identified shoulder dystocia that occurrence of injury is most common. The majority of shoulder dystocia cases occur without major risk factors. Moreover, even the best antenatal predictors have a low positive predictive value. Shoulder dystocia therefore cannot be reliably predicted, and the only preventative measure is cesarean delivery. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. New methods for fall risk prediction.

    Science.gov (United States)

    Ejupi, Andreas; Lord, Stephen R; Delbaere, Kim

    2014-09-01

    Accidental falls are the leading cause of injury-related death and hospitalization in old age, with over one-third of the older adults experiencing at least one fall or more each year. Because of limited healthcare resources, regular objective fall risk assessments are not possible in the community on a large scale. New methods for fall prediction are necessary to identify and monitor those older people at high risk of falling who would benefit from participating in falls prevention programmes. Technological advances have enabled less expensive ways to quantify physical fall risk in clinical practice and in the homes of older people. Recently, several studies have demonstrated that sensor-based fall risk assessments of postural sway, functional mobility, stepping and walking can discriminate between fallers and nonfallers. Recent research has used low-cost, portable and objective measuring instruments to assess fall risk in older people. Future use of these technologies holds promise for assessing fall risk accurately in an unobtrusive manner in clinical and daily life settings.

  6. Nonparametric predictive pairwise comparison with competing risks

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani

    2014-01-01

    In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed

  7. 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.

  8. Updating risk prediction tools: a case study in prostate cancer.

    Science.gov (United States)

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Credit risk evaluation based on social media.

    Science.gov (United States)

    Yang, Yang; Gu, Jing; Zhou, Zongfang

    2016-07-01

    Social media has been playing an increasingly important role in the sharing of individuals' opinions on many financial issues, including credit risk in investment decisions. This paper analyzes whether these opinions, which are transmitted through social media, can accurately predict enterprises' future credit risk. We consider financial statements oriented evaluation results based on logit and probit approaches as the benchmarks. We then conduct textual analysis to retrieve both posts and their corresponding commentaries published on two of the most popular social media platforms for financial investors in China. Professional advice from financial analysts is also investigated in this paper. We surprisingly find that the opinions extracted from both posts and commentaries surpass opinions of analysts in terms of credit risk prediction. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. The role of risk propensity in predicting self-employment.

    Science.gov (United States)

    Nieß, Christiane; Biemann, Torsten

    2014-09-01

    This study aims to untangle the role of risk propensity as a predictor of self-employment entry and self-employment survival. More specifically, it examines whether the potentially positive effect of risk propensity on the decision to become self-employed turns curvilinear when it comes to the survival of the business. Building on a longitudinal sample of 4,973 individuals from the German Socio-Economic Panel, we used event history analyses to evaluate the influence of risk propensity on self-employment over a 7-year time period. Results indicated that whereas high levels of risk propensity positively predicted the decision to become self-employed, the relationship between risk propensity and self-employment survival followed an inverted U-shaped curve. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  11. 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.

  12. Risk prediction, safety analysis and quantitative probability methods - a caveat

    International Nuclear Information System (INIS)

    Critchley, O.H.

    1976-01-01

    Views are expressed on the use of quantitative techniques for the determination of value judgements in nuclear safety assessments, hazard evaluation, and risk prediction. Caution is urged when attempts are made to quantify value judgements in the field of nuclear safety. Criteria are given the meaningful application of reliability methods but doubts are expressed about their application to safety analysis, risk prediction and design guidances for experimental or prototype plant. Doubts are also expressed about some concomitant methods of population dose evaluation. The complexities of new designs of nuclear power plants make the problem of safety assessment more difficult but some possible approaches are suggested as alternatives to the quantitative techniques criticized. (U.K.)

  13. Evaluation of the field relevance of several injury risk functions.

    Science.gov (United States)

    Prasad, Priya; Mertz, Harold J; Dalmotas, Danius J; Augenstein, Jeffrey S; Diggs, Kennerly

    2010-11-01

    An evaluation of the four injury risk curves proposed in the NHTSA NCAP for estimating the risk of AIS>= 3 injuries to the head, neck, chest and AIS>=2 injury to the Knee-Thigh-Hip (KTH) complex has been conducted. The predicted injury risk to the four body regions based on driver dummy responses in over 300 frontal NCAP tests were compared against those to drivers involved in real-world crashes of similar severity as represented in the NASS. The results of the study show that the predicted injury risks to the head and chest were slightly below those in NASS, and the predicted risk for the knee-thigh-hip complex was substantially below that observed in the NASS. The predicted risk for the neck by the Nij curve was greater than the observed risk in NASS by an order of magnitude due to the Nij risk curve predicting a non-zero risk when Nij = 0. An alternative and published Nte risk curve produced a risk estimate consistent with the NASS estimate of neck injury. Similarly, an alternative and published chest injury risk curve produced a risk estimate that was within the bounds of the NASS estimates. No published risk curve for femur compressive load could be found that would give risk estimates consistent with the range of the NASS estimates. Additional work on developing a femur compressive load risk curve is recommended.

  14. 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

  15. [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.

  16. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico; Kryshtafovych, Andriy; Tramontano, Anna

    2009-01-01

    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

  17. Evaluation of disorder predictions in CASP9

    KAUST Repository

    Monastyrskyy, Bohdan; Fidelis, Krzysztof; Moult, John; Tramontano, Anna; Kryshtafovych, Andriy

    2011-01-01

    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

  18. Evaluating predictions of critical oxygen desaturation events

    International Nuclear Information System (INIS)

    ElMoaqet, Hisham; Tilbury, Dawn M; Ramachandran, Satya Krishna

    2014-01-01

    This paper presents a new approach for evaluating predictions of oxygen saturation levels in blood ( SpO 2 ). A performance metric based on a threshold is proposed to evaluate  SpO 2 predictions based on whether or not they are able to capture critical desaturations in the  SpO 2 time series of patients. We use linear auto-regressive models built using historical  SpO 2 data to predict critical desaturation events with the proposed metric. In 20 s prediction intervals, 88%–94% of the critical events were captured with positive predictive values (PPVs) between 90% and 99%. Increasing the prediction horizon to 60 s, 46%–71% of the critical events were detected with PPVs between 81% and 97%. In both prediction horizons, more than 97% of the non-critical events were correctly classified. The overall classification capabilities for the developed predictive models were also investigated. The area under ROC curves for 60 s predictions from the developed models are between 0.86 and 0.98. Furthermore, we investigate the effect of including pulse rate (PR) dynamics in the models and predictions. We show no improvement in the percentage of the predicted critical desaturations if PR dynamics are incorporated into the  SpO 2 predictive models (p-value = 0.814). We also show that including the PR dynamics does not improve the earliest time at which critical  SpO 2 levels are predicted (p-value = 0.986). Our results indicate oxygen in blood is an effective input to the PR rather than vice versa. We demonstrate that the combination of predictive models with frequent pulse oximetry measurements can be used as a warning of critical oxygen desaturations that may have adverse effects on the health of patients. (paper)

  19. 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…

  20. Machine learning derived risk prediction of anorexia nervosa.

    Science.gov (United States)

    Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

    2016-01-20

    Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

  1. Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.

    Directory of Open Access Journals (Sweden)

    Ian C Scott

    Full Text Available The improved characterisation of risk factors for rheumatoid arthritis (RA suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA. Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls; UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls. HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG. Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001; ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.

  2. Indoor Tanning and the MC1R Genotype: Risk Prediction for Basal Cell Carcinoma Risk in Young People

    OpenAIRE

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

    2015-01-01

    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...

  3. Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

    Science.gov (United States)

    Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

    2014-05-01

    Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    2016-01-01

    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.......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...

  5. Hazard evaluation and risk management

    International Nuclear Information System (INIS)

    Fritzsche, A.F.

    1986-01-01

    The eigth chapter deals with the actual handling of hazards. The principal issue concerns man's behaviour towards hazards as an individual formerly and today; the evaluation of expected results of both a positive and a negative kind as determined by the individual's values which may differ and vary greatly from one individual to the next. The evaluation of benefit and hazard as well as the risk management resulting from decision-taking are political processes in the democratic state. Formal decision-taking tools play a major role in this process which concerns such central issues like who will participate; how the decision is arrived at; the participation of citizens; specialist knowledge and participation of the general public. (HSCH) [de

  6. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

    Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

    2017-12-01

    Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

  7. 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?

  8. Development of an attrition risk prediction tool.

    Science.gov (United States)

    Fowler, John; Norrie, Peter

    To review lecturers' and students' perceptions of the factors that may lead to attrition from pre-registration nursing and midwifery programmes and to identify ways to reduce the impact of such factors on the student's experience. Comparable attrition rates for nursing and midwifery students across various universities are difficult to monitor accurately; however, estimates that there is approximately a 25% national attrition rate are not uncommon. The financial and human implications of this are significant and worthy of investigation. A study was carried out in one medium-sized UK school of nursing and midwifery, aimed at identifying perceived factors associated with attrition and retention. Thirty-five lecturers were interviewed individually; 605 students completed a questionnaire, and of these, 10 were individually interviewed. Attrition data kept by the student service department were reviewed. Data were collected over an 18-month period in 2007-2008. Regression analysis of the student data identified eight significant predictors. Four of these were 'positive' factors in that they aided student retention and four were 'negative' in that they were associated with students' thoughts of resigning. Student attrition and retention is multifactorial, and, as such, needs to be managed holistically. One aspect of this management could be an attrition risk prediction tool.

  9. 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.

  10. Development and validation of a prognostic model to predict death in patients with traumatic bleeding, and evaluation of the effect of tranexamic acid on mortality according to baseline risk: a secondary analysis of a randomised controlled trial.

    Science.gov (United States)

    Perel, P; Prieto-Merino, D; Shakur, H; Roberts, I

    2013-06-01

    prognostic models we included predictors for death in hospital within 4 weeks of injury. For the stratified analysis we reported ORs for all causes of death, death due to bleeding, and fatal and non-fatal thrombotic events associated with the use of TXA according to baseline risk. A total of 3076 (15%) patients died in the CRASH-2 trial and 1705 (12%) in the TARN data set. Glasgow Coma Scale score, age and systolic blood pressure were the strongest predictors of mortality. Discrimination and calibration were satisfactory, with C-statistics > 0.80 in both CRASH-2 trial and TARN data sets. A simple chart was constructed to readily provide the probability of death at the point of care, while a web-based calculator is available for a more detailed risk assessment. TXA reduced all-cause mortality and death due to bleeding in each stratum of baseline risk. There was no evidence of heterogeneity in the effect of TXA on all-cause mortality (p-value for interaction = 0.96) or death due to bleeding (p= 0.98). There was a significant reduction in the odds of fatal and non-fatal thrombotic events with TXA (OR = 0.69, 95% confidence interval 0.53 to 0.89; p= 0.005). There was no evidence of heterogeneity in the effect of TXA on the risk of thrombotic events (p= 0.74). This prognostic model can be used to obtain valid predictions of mortality in patients with traumatic bleeding. TXA can be administered safely to a wide spectrum of bleeding trauma patients and should not be restricted to the most severely injured. Future research should evaluate whether or not the use of this prognostic model in clinical practice has an impact on the management and outcomes of trauma patients.

  11. Credit Risk Evaluation : Modeling - Analysis - Management

    OpenAIRE

    Wehrspohn, Uwe

    2002-01-01

    An analysis and further development of the building blocks of modern credit risk management: -Definitions of default -Estimation of default probabilities -Exposures -Recovery Rates -Pricing -Concepts of portfolio dependence -Time horizons for risk calculations -Quantification of portfolio risk -Estimation of risk measures -Portfolio analysis and portfolio improvement -Evaluation and comparison of credit risk models -Analytic portfolio loss distributions The thesis contributes to the evaluatio...

  12. Predictive analytics for supply chain collaboration, risk management ...

    African Journals Online (AJOL)

    kirstam

    management, and (2) supply chain risk management predicted financial .... overhead costs, delivery of ever-increasing customer value, flexibility with superior ... risk exposure, relationship longevity, trust and communication are considered as.

  13. Elderly fall risk prediction using static posturography

    Science.gov (United States)

    2017-01-01

    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 older

  14. Elderly fall risk prediction using static posturography.

    Science.gov (United States)

    Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan; McIlroy, William E

    2017-01-01

    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 older

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. Melanoma risk prediction using a multilocus genetic risk score in the Women's Health Initiative cohort.

    Science.gov (United States)

    Cho, Hyunje G; Ransohoff, Katherine J; Yang, Lingyao; Hedlin, Haley; Assimes, Themistocles; Han, Jiali; Stefanick, Marcia; Tang, Jean Y; Sarin, Kavita Y

    2018-07-01

    Single-nucleotide polymorphisms (SNPs) associated with melanoma have been identified though genome-wide association studies. However, the combined impact of these SNPs on melanoma development remains unclear, particularly in postmenopausal women who carry a lower melanoma risk. We examine the contribution of a combined polygenic risk score on melanoma development in postmenopausal women. Genetic risk scores were calculated using 21 genome-wide association study-significant SNPs. Their combined effect on melanoma development was evaluated in 19,102 postmenopausal white women in the clinical trial and observational study arms of the Women's Health Initiative dataset. Compared to the tertile of weighted genetic risk score with the lowest genetic risk, the women in the tertile with the highest genetic risk were 1.9 times more likely to develop melanoma (95% confidence interval 1.50-2.42). The incremental change in c-index from adding genetic risk scores to age were 0.075 (95% confidence interval 0.041-0.109) for incident melanoma. Limitations include a lack of information on nevi count, Fitzpatrick skin type, family history of melanoma, and potential reporting and selection bias in the Women's Health Initiative cohort. Higher genetic risk is associated with increased melanoma prevalence and incidence in postmenopausal women, but current genetic information may have a limited role in risk prediction when phenotypic information is available. Copyright © 2018 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  20. 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

  1. 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 reca...

  2. Drug response prediction in high-risk multiple myeloma

    DEFF Research Database (Denmark)

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

    2018-01-01

    from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

  3. Risk stratification in upper gastrointestinal bleeding; prediction, prevention and prognosis

    NARCIS (Netherlands)

    de Groot, N.L.

    2013-01-01

    In the first part of this thesis we developed a novel prediction score for predicting upper gastrointestinal (GI) bleeding in both NSAID and low-dose aspirin users. Both for NSAIDs and low-dose aspirin use risk scores were developed by identifying the five most dominant predictors. The risk of upper

  4. 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.

  5. 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.

  6. 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.

  7. Predictability of cardiovascular risks by psychological measures

    Czech Academy of Sciences Publication Activity Database

    Šolcová, Iva; Kebza, V.

    2008-01-01

    Roč. 23, č. 1 (2008), s. 241-241 ISSN 0887-0446 R&D Projects: GA ČR GA406/06/0747 Institutional research plan: CEZ:AV0Z70250504 Keywords : CVD risks * psychological measures * physiological risks Subject RIV: AN - Psychology

  8. 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....

  9. 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 ...

  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. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

    Science.gov (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

    2018-01-12

    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

  12. 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.

  13. Value of multiple risk factors in predicting coronary artery disease

    International Nuclear Information System (INIS)

    Zhu Zhengbin; Zhang Ruiyan; Zhang Qi; Yang Zhenkun; Hu Jian; Zhang Jiansheng; Shen Weifeng

    2008-01-01

    Objective: This study sought to assess the relationship between correlative comprehension risk factors and coronary arterial disease and to build up a simple mathematical model to evaluate the extension of coronary artery lesion in patients with stable angina. Methods: A total of 1024 patients with chest pain who underwent coronary angiography were divided into CAD group(n=625)and control group(n=399) based on at least one significant coronary artery narrowing more than 50% in diameter. Independent risk factors for CAD were evaluated and multivariate logistic regression model and receiver-operating characteristic(ROC) curves were used to estimate the independent influence factor for CAD and built up a simple formula for clinical use. Results: Multivariate regression analysis revealed that UACR > 7.25 μg/mg(OR=3.6; 95% CI 2.6-4.9; P 20 mmol/L(OR=3.2; 95% CI 2.3-4.4; P 2 (OR=2.3; 95% CI 1.4-3.8; P 2.6 mmol/L (OR 2.141; 95% CI 1.586-2.890; P 7.25 μg/mg + 1.158 x hsCRP > 20 mmol/L + 0.891 GFR 2 + 0.831 x LVEF 2.6 mmol/L + 0.676 x smoking history + 0.594 x male + 0.459 x diabetes + 0.425 x hypertension). Area under the curve was 0.811 (P < 0.01), and the optimal probability value for predicting severe stage of CAD was 0.977 (sensitivity 49.0%, specificity 92.7% ). Conclusions: Risk factors including renal insufficiency were the main predictors for CAD. The logistic regression model is the non-invasive method of choice for predicting the extension of coronary artery lesion in patients with stable agiana. (authors)

  14. 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...... 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...... risk factors for PPP were collected preoperatively (pain from the groin hernia, preoperative AAS score, pain from other body regions, and psychometric assessment). Pain scores were collected on days 7 and 30 postoperatively. Sensory functions including pain response to tonic heat stimulation were...

  15. Lipoprotein metabolism indicators improve cardiovascular risk prediction

    NARCIS (Netherlands)

    Schalkwijk, D.B. van; Graaf, A.A. de; Tsivtsivadze, E.; Parnell, L.D.; Werff-van der Vat, B.J.C. van der; Ommen, B. van; Greef, J. van der; Ordovás, J.M.

    2014-01-01

    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

  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.

    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.

  17. Develop mental dyslexia: predicting individual risk

    OpenAIRE

    Thompson, PA; Hulme, C; Nash, HM; Gooch, Deborah; Hayiou-Thomas, E; Snowling, MJ

    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 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited...

  18. 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

    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...... 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...

  19. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults.

    Science.gov (United States)

    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-08-07

    Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  20. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults

    Science.gov (United States)

    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-01-01

    Background Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. Methods and Results We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. PMID:26254303

  1. Evaluation of consequences and risks in Slovenia

    International Nuclear Information System (INIS)

    Susnik, J.

    1996-01-01

    The paper describes the evaluation of nuclear power plant accident consequences and risks using probabilistic safety codes during the last 12 years at the J. Stefan Institute. They cover classic individual and population risk studies due to assumed potential severe accident scenarios, prediction and estimation of Chernobyl accident consequences, the optimization of emergency countermeasures at the Krsko site, where the 632 MWe Westinghouse PWR NPP went into commercial operation on January 1983, and the ranking of population risk within the public debate in connection with the civil initiative to close the NPP Krsko. We report on the initial use of the CRAC2 code in 1984 and later, when it was first applied for the study of population risk in the area of the second planned Slovenian-Croatian NPP for the Prevlaka site. The study was completed a few weeks before the Chernobyl accident in April 1986. Risk evaluation was also included in the analysis of nuclear safety at the NPP Krsko during the war for Slovenia's independence in 1991. We report on the (CRAC2) analyses of the Chernobyl accident: on initial estimation of the maximal potentially expected consequences in Slovenia, on the effect of the radioactive cloud rise on the consequences relatively close to the NPP; on the further research after the detailed information on the radioactivity release and on the air masses movement were published; then the cloud activity which moved towards Slovenia was assessed and the expected consequences along its path were calculated. As the calculated integral individual exposure to the I 131 inhalation and the ground Cs 137 contamination matched with the measurements in Ljubljana and with the UNSCEAR 1988 data, our reliance on the CRAC2 code and on its ancestors is high. We report on the analyses, performed by the CRAC2 code and since 1993 also by the PC COSYMA code, related to the countermeasure effects. The consequences studied were extended to late health effects. We analyzed

  2. Water erosion risk prediction in eucalyptus plantations

    Directory of Open Access Journals (Sweden)

    Mayesse Aparecida da Silva

    2014-04-01

    Full Text Available Eucalyptus plantations are normally found in vulnerable ecosystems such as steep slope, soil with low natural fertility and lands that were degraded by agriculture. The objective of this study was to obtain Universal Soil Loss Equation (USLE factors and use them to estimate water erosion risk in regions with eucalyptus planted. The USLE factors were obtained in field plots under natural rainfall in the Rio Doce Basin, MG, Brazil, and the model applied to assess erosion risk using USLE in a Geographic Information System. The study area showed rainfall-runoff erosivity values from 10,721 to 10,642 MJ mm ha-1 h-1 yr-1. Some soils (Latosols had very low erodibility values (2.0 x 10-4 and 1.0 x 10-4t h MJ-1 mm-1, the topographic factor ranged from 0.03 to 10.57 and crop and management factor values obtained for native forest, eucalyptus and planted pasture were 0.09, 0.12 and 0.22, respectively. Water erosion risk estimates for current land use indicated that the areas where should receive more attention were mainly areas with greater topographic factors and those with Cambisols. Planning of forestry activities in this region should consider implementation of other conservation practices beyond those already used, reducing areas with a greater risk of soil erosion and increasing areas with very low risk.

  3. 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.

  4. Risk assessment methodologies for predicting phosphorus losses

    NARCIS (Netherlands)

    Schoumans, O.F.; Chardon, W.J.

    2003-01-01

    Risk assessment parameters are needed to assess the contribution of phosphorus (P) losses from soil to surface water, and the effectiveness of nutrient and land management strategies for the reduction of P loss. These parameters need to take into account the large temporal and spatial variation in P

  5. 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.

  6. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  7. Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

    OpenAIRE

    Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

    2012-01-01

    Abstract Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A ...

  8. Elderly fall risk prediction using static posturography

    OpenAIRE

    Howcroft, Jennifer; Lemaire, Edward D.; Kofman, Jonathan; McIlroy, William E.

    2017-01-01

    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 w...

  9. 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.

  10. Evaluations and utilizations of risk importances

    International Nuclear Information System (INIS)

    Vesely, W.E.; Davis, T.C.

    1985-08-01

    This report presents approaches for utilizing Probabilistic Risk Analyses (PRA's) to determine risk importances. Risk importances are determined for design features, plant operations, and other factors that can affect risk. PRA's can be used to identify the importances of risk contributors or proposed changes to designs or operations. The objective of this report is to serve as a handbook and guide in evaluating and applying risk importances. The utilization of both qualitative risk importances and quantitative risk importances is described in this report. Qualitative risk importances are based on the logic models in the PRA, while quantitative risk importances are based on the quantitative results of the PRA. Both types of importances are among the most robust and meaningful information a PRA can provide. A wide variety of risk importance evaluations are described including evaluations of the importances of design changes, testing, maintenance, degrading environments, and aging. Specific utilizations are described in inspection and in reliability assurance programs, however the general approaches have widespread applicability. The role of personal computers and decision support programs in applying risk importance evaluations is also described

  11. 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

    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...... 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....... Based on our findings, the risk categories in our populations should be high risk intermediate risk 7 to 10, and low risk > 10. CONCLUSIONS: The RAPT accurately predicted discharge disposition for high- and low-risk patients in our cohort. Based on our data, intermediate-risk patients should...

  12. PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS QUANTIFYING RISK FACTORS

    Directory of Open Access Journals (Sweden)

    І. К. Churpiy

    2012-11-01

    Full Text Available Explored the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. Highlighted 53 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme of defining the risk of clinical course of diffuse peritonitis can quantify the severity of the source of patients and in most cases correctly predict the results of treatment of disease.

  13. 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...

  14. 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

  15. 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.

  16. Predictive value of updating Framingham risk scores with novel risk markers in the U.S. general population.

    Directory of Open Access Journals (Sweden)

    Bart S Ferket

    Full Text Available BACKGROUND: According to population-based cohort studies CT coronary calcium score (CTCS, carotid intima-media thickness (cIMT, high-sensitivity C- reactive protein (CRP, and ankle-brachial index (ABI are promising novel risk markers for improving cardiovascular risk assessment. Their impact in the U.S. general population is however uncertain. Our aim was to estimate the predictive value of four novel cardiovascular risk markers for the U.S. general population. METHODS AND FINDINGS: Risk profiles, CRP and ABI data of 3,736 asymptomatic subjects aged 40 or older from the National Health and Nutrition Examination Survey (NHANES 2003-2004 exam were used along with predicted CTCS and cIMT values. For each subject, we calculated 10-year cardiovascular risks with and without each risk marker. Event rates adjusted for competing risks were obtained by microsimulation. We assessed the impact of updated 10-year risk scores by reclassification and C-statistics. In the study population (mean age 56±11 years, 48% male, 70% (80% were at low (<10%, 19% (14% at intermediate (≥10-<20%, and 11% (6% at high (≥20% 10-year CVD (CHD risk. Net reclassification improvement was highest after updating 10-year CVD risk with CTCS: 0.10 (95%CI 0.02-0.19. The C-statistic for 10-year CVD risk increased from 0.82 by 0.02 (95%CI 0.01-0.03 with CTCS. Reclassification occurred most often in those at intermediate risk: with CTCS, 36% (38% moved to low and 22% (30% to high CVD (CHD risk. Improvements with other novel risk markers were limited. CONCLUSIONS: Only CTCS appeared to have significant incremental predictive value in the U.S. general population, especially in those at intermediate risk. In future research, cost-effectiveness analyses should be considered for evaluating novel cardiovascular risk assessment strategies.

  17. Predictive Value of Updating Framingham Risk Scores with Novel Risk Markers in the U.S. General Population

    Science.gov (United States)

    Hunink, M. G. Myriam; Agarwal, Isha; Kavousi, Maryam; Franco, Oscar H.; Steyerberg, Ewout W.; Max, Wendy; Fleischmann, Kirsten E.

    2014-01-01

    Background According to population-based cohort studies CT coronary calcium score (CTCS), carotid intima-media thickness (cIMT), high-sensitivity C- reactive protein (CRP), and ankle-brachial index (ABI) are promising novel risk markers for improving cardiovascular risk assessment. Their impact in the U.S. general population is however uncertain. Our aim was to estimate the predictive value of four novel cardiovascular risk markers for the U.S. general population. Methods and Findings Risk profiles, CRP and ABI data of 3,736 asymptomatic subjects aged 40 or older from the National Health and Nutrition Examination Survey (NHANES) 2003–2004 exam were used along with predicted CTCS and cIMT values. For each subject, we calculated 10-year cardiovascular risks with and without each risk marker. Event rates adjusted for competing risks were obtained by microsimulation. We assessed the impact of updated 10-year risk scores by reclassification and C-statistics. In the study population (mean age 56±11 years, 48% male), 70% (80%) were at low (risk. Net reclassification improvement was highest after updating 10-year CVD risk with CTCS: 0.10 (95%CI 0.02–0.19). The C-statistic for 10-year CVD risk increased from 0.82 by 0.02 (95%CI 0.01–0.03) with CTCS. Reclassification occurred most often in those at intermediate risk: with CTCS, 36% (38%) moved to low and 22% (30%) to high CVD (CHD) risk. Improvements with other novel risk markers were limited. Conclusions Only CTCS appeared to have significant incremental predictive value in the U.S. general population, especially in those at intermediate risk. In future research, cost-effectiveness analyses should be considered for evaluating novel cardiovascular risk assessment strategies. PMID:24558385

  18. Obesity Risk Prediction among Women of Upper Egypt: The impact ...

    African Journals Online (AJOL)

    Obesity Risk Prediction among Women of Upper Egypt: The impact of FTO ... with increased obesity risk but there is a lack of association with diabetes. ... (as certain foods or gene therapy) will prevent the percentage of women who is affected ...

  19. JCL roundtable: Risk evaluation and mitigation strategy.

    Science.gov (United States)

    Brown, W Virgil; Bramlet, Dean A; Ross, Joyce L; Underberg, James A

    Many factors enter into the decision by the Food and Drug Administration (FDA) to approve a new drug for use by physicians and other health care providers in treating diseases. Initially, the FDA authority was restricted to issues of safety and only later did the documentation of efficacy become part of the review process required for approval. However, all drugs have the potential for causing harm at some dose level to all and at lower doses in certain patients with vulnerability to the particular pharmacology of the agent. As new drugs have been designed to manage disorders that are uncommon, but of significant consequence, they may have adverse effects that are acceptable only because they are so uniquely beneficial to these specific conditions. The risk of these adverse effects may be acceptable since the benefit can outweigh the harm in most patients and the adversity can be predicted and managed. The approval of this category of drugs has grown rapidly since definition of a mechanism of action to manage and modify the risk has been provided by a process known as known as Risk Evaluation and Mitigation Strategy or "REMS." In 2007, the Food and Drug Administration Amendments Act (FDAAA) allowed the FDA to require postmarketing studies and the authority to mandate the implementation of a REMS for drugs with efficacy but documented potential for harm. Two relatively new drugs useful in the management of severe elevations of low-density lipoprotein cholesterol have been approved under a requirement for a REMS. These are lomitapide, an inhibitor of microsomal triglyceride transfer protein and mipomersen, an antisense oligonucleotide which reduces the synthesis of apolipoprotein B. Copyright © 2016 National Lipid Association. Published by Elsevier Inc. All rights reserved.

  20. Evaluation of Seismic Risk of Siberia Territory

    Science.gov (United States)

    Seleznev, V. S.; Soloviev, V. M.; Emanov, A. F.

    The outcomes of modern geophysical researches of the Geophysical Survey SB RAS, directed on study of geodynamic situation in large industrial and civil centers on the territory of Siberia with the purpose of an evaluation of seismic risk of territories and prediction of origin of extreme situations of natural and man-caused character, are pre- sented in the paper. First of all it concerns the testing and updating of a geoinformation system developed by Russian Emergency Ministry designed for calculations regarding the seismic hazard and response to distructive earthquakes. The GIS database contains the catalogues of earthquakes and faults, seismic zonation maps, vectorized city maps, information on industrial and housing fund, data on character of building and popula- tion in inhabited places etc. The geoinformation system allows to solve on a basis of probabilistic approaches the following problems: - estimating the earthquake impact, required forces, facilities and supplies for life-support of injured population; - deter- mining the consequences of failures on chemical and explosion-dangerous objects; - optimization problems on assurance technology of conduct of salvage operations. Using this computer program, the maps of earthquake risk have been constructed for several seismically dangerous regions of Siberia. These maps display the data on the probable amount of injured people and relative economic damage from an earthquake, which can occur in various sites of the territory according to the map of seismic zona- tion. The obtained maps have allowed determining places where the detailed seismo- logical observations should be arranged. Along with it on the territory of Siberia the wide-ranging investigations with use of new methods of evaluation of physical state of industrial and civil establishments (buildings and structures, hydroelectric power stations, bridges, dams, etc.), high-performance detailed electromagnetic researches of ground conditions of city

  1. The Role of Risk Aversion in Predicting Individual Behaviours

    OpenAIRE

    Guiso, Luigi; Paiella, Monica

    2004-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers’ decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways cons...

  2. The Role of Risk Aversion in Predicting Individual Behaviour

    OpenAIRE

    Monica Paiella; Luigi Guiso

    2004-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers' decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways cons...

  3. Risk avoidance in sympatric large carnivores: reactive or predictive?

    Science.gov (United States)

    Broekhuis, Femke; Cozzi, Gabriele; Valeix, Marion; McNutt, John W; Macdonald, David W

    2013-09-01

    1. Risks of predation or interference competition are major factors shaping the distribution of species. An animal's response to risk can either be reactive, to an immediate risk, or predictive, based on preceding risk or past experiences. The manner in which animals respond to risk is key in understanding avoidance, and hence coexistence, between interacting species. 2. We investigated whether cheetahs (Acinonyx jubatus), known to be affected by predation and competition by lions (Panthera leo) and spotted hyaenas (Crocuta crocuta), respond reactively or predictively to the risks posed by these larger carnivores. 3. We used simultaneous spatial data from Global Positioning System (GPS) radiocollars deployed on all known social groups of cheetahs, lions and spotted hyaenas within a 2700 km(2) study area on the periphery of the Okavango Delta in northern Botswana. The response to risk of encountering lions and spotted hyaenas was explored on three levels: short-term or immediate risk, calculated as the distance to the nearest (contemporaneous) lion or spotted hyaena, long-term risk, calculated as the likelihood of encountering lions and spotted hyaenas based on their cumulative distributions over a 6-month period and habitat-associated risk, quantified by the habitat used by each of the three species. 4. We showed that space and habitat use by cheetahs was similar to that of lions and, to a lesser extent, spotted hyaenas. However, cheetahs avoided immediate risks by positioning themselves further from lions and spotted hyaenas than predicted by a random distribution. 5. Our results suggest that cheetah spatial distribution is a hierarchical process, first driven by resource acquisition and thereafter fine-tuned by predator avoidance; thus suggesting a reactive, rather than a predictive, response to risk. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

  4. Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk.

    Science.gov (United States)

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

    2017-08-01

    A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status. We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses' Health Study during the period 1980-2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third. Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories. The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

  5. 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...

  6. 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

    the ROC curve to 0.766 (95% CI 0.750-0.783). Additional items such as male sex, smoking and family history of diabetes (parent, sibling or both) improved the area under the ROC curve and net reclassification. Bootstrapping showed good internal validity. CONCLUSIONS/INTERPRETATION: The predictive value...... 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...

  7. Decision-Making Competence Predicts Domain-Specific Risk Attitudes

    Directory of Open Access Journals (Sweden)

    Joshua eWeller

    2015-05-01

    Full Text Available Decision Making Competence (DMC reflects individual differences in rational responding across several classic behavioral decision-making tasks. Although it has been associated with real-world risk behavior, less is known about the degree to which DMC contributes to specific components of risk attitudes. Utilizing a psychological risk-return framework, we examined the associations between risk attitudes and DMC. Italian community residents (n = 804 completed an online DMC measure, using a subset of the original Adult-DMC battery (A-DMC; Bruine de Bruin, Parker, & Fischhoff, 2007. Participants also completed a self-reported risk attitude measure for three components of risk attitudes (risk-taking, risk perceptions, and expected benefits across six risk domains. Overall, greater performance on the DMC component scales were inversely, albeit modestly, associated with risk-taking tendencies. Structural equation modeling results revealed that DMC was associated with lower perceived expected benefits for all domains. In contrast, its association with perceived risks was more domain-specific. These analyses also revealed stronger indirect effects for the DMC  expected benefits  risk-taking than the DMC  perceived riskrisk-taking path, especially for risk behaviors that may be considered more antisocial in nature. These results suggest that DMC performance differentially impacts specific components of risk attitudes, and may be more strongly related to the evaluation of expected value of the given behavior.

  8. Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Jae Kwon Kim

    2017-01-01

    Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

  9. Risk effectiveness evaluation of surveillance testing

    International Nuclear Information System (INIS)

    Kim, I.S.; Samanta, P.K.; Martorell, S.; Vesely, W.E.

    1991-01-01

    To address the concerns about nuclear power plant surveillance tests, i.e., their adverse safety impact due to negative effects and too burdensome requirements, it is necessary to evaluate the safety significance or risk effectiveness of such tests explicitly considering both negative and positive effects. This paper defines the negative effects of surveillance testing from a risk perspective, and then presents a methodology to quantify the negative risk impact, i.e., the risk penalty or risk increase caused by the test. The method focuses on two important kinds of negative effects, namely, test-caused transients and test-caused equipment degradations. The concepts and quantitative methods for the risk evaluation can be used in the decision-making process to establish the safety significance of the tests and to screen the plant-specific surveillance test requirements. 6 refs., 2 figs., 2 tabs

  10. Evaluation of the Prostate Cancer Prevention Trial Risk Calculator in a High-Risk Screening Population

    Science.gov (United States)

    Kaplan, David J.; Boorjian, Stephen A.; Ruth, Karen; Egleston, Brian L.; Chen, David Y.T.; Viterbo, Rosalia; Uzzo, Robert G.; Buyyounouski, Mark K.; Raysor, Susan; Giri, Veda N.

    2009-01-01

    Introduction Clinical factors in addition to PSA have been evaluated to improve risk assessment for prostate cancer. The Prostate Cancer Prevention Trial (PCPT) risk calculator provides an assessment of prostate cancer risk based on age, PSA, race, prior biopsy, and family history. This study evaluated the risk calculator in a screening cohort of young, racially diverse, high-risk men with a low baseline PSA enrolled in the Prostate Cancer Risk Assessment Program. Patients and Methods Eligibility for PRAP include men ages 35-69 who are African-American, have a family history of prostate cancer, or have a known BRCA1/2 mutation. PCPT risk scores were determined for PRAP participants, and were compared to observed prostate cancer rates. Results 624 participants were evaluated, including 382 (61.2%) African-American men and 375 (60%) men with a family history of prostate cancer. Median age was 49.0 years (range 34.0-69.0), and median PSA was 0.9 (range 0.1-27.2). PCPT risk score correlated with prostate cancer diagnosis, as the median baseline risk score in patients diagnosed with prostate cancer was 31.3%, versus 14.2% in patients not diagnosed with prostate cancer (p<0.0001). The PCPT calculator similarly stratified the risk of diagnosis of Gleason score ≥7 disease, as the median risk score was 36.2% in patients diagnosed with Gleason ≥7 prostate cancer versus 15.2% in all other participants (p<0.0001). Conclusion PCPT risk calculator score was found to stratify prostate cancer risk in a cohort of young, primarily African-American men with a low baseline PSA. These results support further evaluation of this predictive tool for prostate cancer risk assessment in high-risk men. PMID:19709072

  11. Ethical and affective evaluation of environmental risks

    International Nuclear Information System (INIS)

    Bohm, G.; Pfister, H.R.

    1998-01-01

    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)

  12. Multimethod prediction of child abuse risk in an at-risk sample of male intimate partner violence offenders.

    Science.gov (United States)

    Rodriguez, Christina M; Gracia, Enrique; Lila, Marisol

    2016-10-01

    The vast majority of research on child abuse potential has concentrated on women demonstrating varying levels of risk of perpetrating physical child abuse. In contrast, the current study considered factors predictive of physical child abuse potential in a group of 70 male intimate partner violence offenders, a group that would represent a likely high risk group. Elements of Social Information Processing theory were evaluated, including pre-existing schemas of empathy, anger, and attitudes approving of parent-child aggression considered as potential moderators of negative attributions of child behavior. To lend methodological rigor, the study also utilized multiple measures and multiple methods, including analog tasks, to predict child abuse risk. Contrary to expectations, findings did not support the role of anger independently predicting child abuse risk in this sample of men. However, preexisting beliefs approving of parent-child aggression, lower empathy, and more negative child behavior attributions independently predicted abuse potential; in addition, greater anger, poorer empathy, and more favorable attitudes toward parent-child aggression also exacerbated men's negative child attributions to further elevate their child abuse risk. Future work is encouraged to consider how factors commonly considered in women parallel or diverge from those observed to elevate child abuse risk in men of varying levels of risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. 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...

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

    Science.gov (United States)

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

    2015-01-01

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

  15. Evaluation of a constipation risk assessment scale.

    Science.gov (United States)

    Zernike, W; Henderson, A

    1999-06-01

    This project was undertaken in order to evaluate the utility of a constipation risk assessment scale and the accompanying bowel management protocol. The risk assessment scale was primarily introduced to teach and guide staff in managing constipation when caring for patients. The intention of the project was to reduce the incidence of constipation in patients during their admission to hospital.

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

    Science.gov (United States)

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

    2015-04-01

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

  17. Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.

    Science.gov (United States)

    Yu, Ami; Woo, Sang Myung; Joo, Jungnam; Yang, Hye-Ryung; Lee, Woo Jin; Park, Sang-Jae; Nam, Byung-Ho

    2016-01-01

    There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. Gender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic. A total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability. Gender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.

  18. Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks.

    Science.gov (United States)

    Cox, Louis Anthony Tony

    2015-10-01

    Decision biases can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well-documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk-reducing regulations. Low-regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low-regret learning strategies using regulation of air pollutants with uncertain health effects as an example. © 2015 Society for Risk Analysis.

  19. 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...

  20. Evaluating risk management strategies in resource planning

    International Nuclear Information System (INIS)

    Andrews, C.J.

    1995-01-01

    This paper discusses the evaluation of risk management strategies as a part of integrated resource planning. Value- and scope-related uncertainties can be addressed during the process of planning, but uncertainties in the operating environment require technical analysis within planning models. Flexibility and robustness are two key classes of strategies for managing the risk posed by these uncertainties. This paper reviews standard capacity expansion planning models and shows that they are poorly equipped to compare risk management strategies. Those that acknowledge uncertainty are better at evaluating robustness than flexibility, which implies a bias against flexible options. Techniques are available to overcome this bias

  1. 76 FR 69761 - National Earthquake Prediction Evaluation Council (NEPEC)

    Science.gov (United States)

    2011-11-09

    ... DEPARTMENT OF THE INTERIOR U.S. Geological Survey National Earthquake Prediction Evaluation... 96-472, the National Earthquake Prediction Evaluation Council (NEPEC) will hold a 1\\1/2\\-day meeting.... Geological Survey on proposed earthquake predictions, on the completeness and scientific validity of the...

  2. 76 FR 19123 - National Earthquake Prediction Evaluation Council (NEPEC)

    Science.gov (United States)

    2011-04-06

    ... 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 Prediction Evaluation Council... proposed earthquake predictions, on the completeness and scientific validity of the available data related...

  3. Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

    Science.gov (United States)

    Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

    2015-09-01

    National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the 'high risk' patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien-Dindo classification. The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien-Dindo grade 2-3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4-5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the 'high-risk' patient.

  4. Predicting disease risk using bootstrap ranking and classification algorithms.

    Directory of Open Access Journals (Sweden)

    Ohad Manor

    Full Text Available Genome-wide association studies (GWAS are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a "black box" in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF, suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

  5. Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

    Directory of Open Access Journals (Sweden)

    Wan-Hua Lin

    2013-01-01

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

  6. Machine learning application in online lending risk prediction

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random fo...

  7. Predictive risk factors for moderate to severe hyperbilirubinemia

    OpenAIRE

    Gláucia Macedo de Lima; Maria Amélia Sayeg Campos Porto; Arnaldo Prata Barbosa; Antonio José Ledo Alves da Cunha

    2007-01-01

    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, obstetri...

  8. Risk-based Regulatory Evaluation Program methodology

    International Nuclear Information System (INIS)

    DuCharme, A.R.; Sanders, G.A.; Carlson, D.D.; Asselin, S.V.

    1987-01-01

    The objectives of this DOE-supported Regulatory Evaluation Progrwam are to analyze and evaluate the safety importance and economic significance of existing regulatory guidance in order to assist in the improvement of the regulatory process for current generation and future design reactors. A risk-based cost-benefit methodology was developed to evaluate the safety benefit and cost of specific regulations or Standard Review Plan sections. Risk-based methods can be used in lieu of or in combination with deterministic methods in developing regulatory requirements and reaching regulatory decisions

  9. 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.

  10. 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.

  11. D ampersand D screening risk evaluation guidance

    International Nuclear Information System (INIS)

    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 ampersand D) facilities. Although this method has been developed for D ampersand 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 ampersand 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

  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. THE ROLE OF RISK AVERSION IN PREDICTING INDIVIDUAL BEHAVIOR

    OpenAIRE

    Luigi Guiso; Monica Paiella

    2005-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers� decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways co...

  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. A Knowledge-Base for a Personalized Infectious Disease Risk Prediction System.

    Science.gov (United States)

    Vinarti, Retno; Hederman, Lucy

    2018-01-01

    We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.

  16. 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...

  17. 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 birt...

  18. Predicting the risk of mineral deficiencies in grazing animals

    African Journals Online (AJOL)

    lambs to mineral supplements can be used to predict risks of deficiency will be demonstrated. In both cases .... between body size and appetite, the onset of lactation or the feeding of ... possible importance of this in the aetiology of milk fever.

  19. Mountain Risks: From Prediction to Management and Governance

    Directory of Open Access Journals (Sweden)

    David Petley

    2015-05-01

    Full Text Available Reviewed: Mountain Risks: From Prediction to Management and Governance. Edited by Theo Van Asch, Jordi Corominas, Stefan Greiving, Jean-Philippe Malet, and Sterlacchini Simone. Dordrecht, The Netherlands: Springer, 2014. xi + 413 pp. US$ 129.00, € 90.00, € 104.00. Also available as an e-book. ISBN 978-94-007-6768-3.

  20. Predicting disease risks from highly imbalanced data using random forest

    Directory of Open Access Journals (Sweden)

    Chakraborty Sounak

    2011-07-01

    Full Text Available Abstract Background We present a method utilizing Healthcare Cost and Utilization Project (HCUP dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. Methods We employed the National Inpatient Sample (NIS data, which is publicly available through Healthcare Cost and Utilization Project (HCUP, to train random forest classifiers for disease prediction. Since the HCUP data is highly imbalanced, we employed an ensemble learning approach based on repeated random sub-sampling. This technique divides the training data into multiple sub-samples, while ensuring that each sub-sample is fully balanced. We compared the performance of support vector machine (SVM, bagging, boosting and RF to predict the risk of eight chronic diseases. Results We predicted eight disease categories. Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC curve (AUC. In addition, RF has the advantage of computing the importance of each variable in the classification process. Conclusions In combining repeated random sub-sampling with RF, we were able to overcome the class imbalance problem and achieve promising results. Using the national HCUP data set, we predicted eight disease categories with an average AUC of 88.79%.

  1. 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...

  2. 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

  3. The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

    Science.gov (United States)

    Breisinger, Terry P; Skidmore, Elizabeth R; Niyonkuru, Christian; Terhorst, Lauren; Campbell, Grace B

    2014-12-01

    To evaluate relative accuracy of a newly developed Stroke Assessment of Fall Risk (SAFR) for classifying fallers and non-fallers, compared with a health system fall risk screening tool, the Fall Harm Risk Screen. Prospective quality improvement study conducted at an inpatient stroke rehabilitation unit at a large urban university hospital. Patients admitted for inpatient stroke rehabilitation (N = 419) with imaging or clinical evidence of ischemic or hemorrhagic stroke, between 1 August 2009 and 31 July 2010. Not applicable. Sensitivity, specificity, and area under the curve for Receiver Operating Characteristic Curves of both scales' classifications, based on fall risk score completed upon admission to inpatient stroke rehabilitation. A total of 68 (16%) participants fell at least once. The SAFR was significantly more accurate than the Fall Harm Risk Screen (p Fall Harm Risk Screen, area under the curve was 0.56, positive predictive value was 0.19, and negative predictive value was 0.86. Sensitivity and specificity of the SAFR (0.78 and 0.63, respectively) was higher than the Fall Harm Risk Screen (0.57 and 0.48, respectively). An evidence-derived, population-specific fall risk assessment may more accurately predict fallers than a general fall risk screen for stroke rehabilitation patients. While the SAFR improves upon the accuracy of a general assessment tool, additional refinement may be warranted. © The Author(s) 2014.

  4. 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.

  5. Risk predictive modelling for diabetes and cardiovascular disease.

    Science.gov (United States)

    Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

    2014-02-01

    Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

  6. 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.

  7. Chapter 4. Predicting post-fire erosion and sedimentation risk on a landscape scale

    Science.gov (United States)

    MacDonald, L.H.; Sampson, R.; Brady, D.; Juarros, L.; Martin, Deborah

    2000-01-01

    Historic fire suppression efforts have increased the likelihood of large wildfires in much of the western U.S. Post-fire soil erosion and sedimentation risks are important concerns to resource managers. In this paper we develop and apply procedures to predict post-fire erosion and sedimentation risks on a pixel-, catchment-, and landscape-scale in central and western Colorado.Our model for predicting post-fire surface erosion risk is conceptually similar to the Revised Universal Soil Loss Equation (RUSLE). One key addition is the incorporation of a hydrophobicity risk index (HY-RISK) based on vegetation type, predicted fire severity, and soil texture. Post-fire surface erosion risk was assessed for each 90-m pixel by combining HYRISK, slope, soil erodibility, and a factor representing the likely increase in soil wetness due to removal of the vegetation. Sedimentation risk was a simple function of stream gradient. Composite surface erosion and sedimentation risk indices were calculated and compared across the 72 catchments in the study area.When evaluated on a catchment scale, two-thirds of the catchments had relatively little post-fire erosion risk. Steeper catchments with higher fuel loadings typically had the highest post-fire surface erosion risk. These were generally located along the major north-south mountain chains and, to a lesser extent, in west-central Colorado. Sedimentation risks were usually highest in the eastern part of the study area where a higher proportion of streams had lower gradients. While data to validate the predicted erosion and sedimentation risks are lacking, the results appear reasonable and are consistent with our limited field observations. The models and analytic procedures can be readily adapted to other locations and should provide useful tools for planning and management at both the catchment and landscape scale.

  8. 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.

  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. 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.

  11. 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.

  12. Improvement of Risk Prediction After Transcatheter Aortic Valve Replacement by Combining Frailty With Conventional Risk Scores.

    Science.gov (United States)

    Schoenenberger, Andreas W; Moser, André; Bertschi, Dominic; Wenaweser, Peter; Windecker, Stephan; Carrel, Thierry; Stuck, Andreas E; Stortecky, Stefan

    2018-02-26

    This study sought to evaluate whether frailty improves mortality prediction in combination with the conventional scores. European System for Cardiac Operative Risk Evaluation (EuroSCORE) or Society of Thoracic Surgeons (STS) score have not been evaluated in combined models with frailty for mortality prediction after transcatheter aortic valve replacement (TAVR). This prospective cohort comprised 330 consecutive TAVR patients ≥70 years of age. Conventional scores and a frailty index (based on assessment of cognition, mobility, nutrition, and activities of daily living) were evaluated to predict 1-year all-cause mortality using Cox proportional hazards regression (providing hazard ratios [HRs] with confidence intervals [CIs]) and measures of test performance (providing likelihood ratio [LR] chi-square test statistic and C-statistic [CS]). All risk scores were predictive of the outcome (EuroSCORE, HR: 1.90 [95% CI: 1.45 to 2.48], LR chi-square test statistic 19.29, C-statistic 0.67; STS score, HR: 1.51 [95% CI: 1.21 to 1.88], LR chi-square test statistic 11.05, C-statistic 0.64; frailty index, HR: 3.29 [95% CI: 1.98 to 5.47], LR chi-square test statistic 22.28, C-statistic 0.66). A combination of the frailty index with either EuroSCORE (LR chi-square test statistic 38.27, C-statistic 0.72) or STS score (LR chi-square test statistic 28.71, C-statistic 0.68) improved mortality prediction. The frailty index accounted for 58.2% and 77.6% of the predictive information in the combined model with EuroSCORE and STS score, respectively. Net reclassification improvement and integrated discrimination improvement confirmed that the added frailty index improved risk prediction. This is the first study showing that the assessment of frailty significantly enhances prediction of 1-year mortality after TAVR in combined risk models with conventional risk scores and relevantly contributes to this improvement. Copyright © 2018 American College of Cardiology Foundation

  13. Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

    Science.gov (United States)

    Kerr, Kathleen F; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

    2014-08-07

    The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. Copyright © 2014 by the American Society of Nephrology.

  14. 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

  15. 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.

  16. Risk evaluation of accident management strategies

    International Nuclear Information System (INIS)

    Dingman, S.; Camp, A.

    1992-01-01

    The use of Probabilistic Risk Assessment (PRA) methods to evaluate accident management strategies in nuclear power plants discussed in this paper. The PRA framework allows an integrated evaluation to be performed to give the full implications of a particular strategy. The methodology is demonstrated for a particular accident management strategy, intentional depressurization of the reactor coolant system to avoid containment pressurization during the ejection of molten debris at vessel breach

  17. 78 FR 64973 - National Earthquake Prediction Evaluation Council (NEPEC)

    Science.gov (United States)

    2013-10-30

    ... DEPARTMENT OF THE INTERIOR Geological Survey [GX14GG009950000] National Earthquake Prediction...: Pursuant to Public Law 96-472, the National Earthquake Prediction Evaluation Council (NEPEC) will hold a... Council shall advise the Director of the U.S. Geological Survey on proposed earthquake predictions, on the...

  18. A risk prediction model for xerostomia: a retrospective cohort study.

    Science.gov (United States)

    Villa, Alessandro; Nordio, Francesco; Gohel, Anita

    2016-12-01

    We investigated the prevalence of xerostomia in dental patients and built a xerostomia risk prediction model by incorporating a wide range of risk factors. Socio-demographic data, past medical history, self-reported dry mouth and related symptoms were collected retrospectively from January 2010 to September 2013 for all new dental patients. A logistic regression framework was used to build a risk prediction model for xerostomia. External validation was performed using an independent data set to test the prediction power. A total of 12 682 patients were included in this analysis (54.3%, females). Xerostomia was reported by 12.2% of patients. The proportion of people reporting xerostomia was higher among those who were taking more medications (OR = 1.11, 95% CI = 1.08-1.13) or recreational drug users (OR = 1.4, 95% CI = 1.1-1.9). Rheumatic diseases (OR = 2.17, 95% CI = 1.88-2.51), psychiatric diseases (OR = 2.34, 95% CI = 2.05-2.68), eating disorders (OR = 2.28, 95% CI = 1.55-3.36) and radiotherapy (OR = 2.00, 95% CI = 1.43-2.80) were good predictors of xerostomia. For the test model performance, the ROC-AUC was 0.816 and in the external validation sample, the ROC-AUC was 0.799. The xerostomia risk prediction model had high accuracy and discriminated between high- and low-risk individuals. Clinicians could use this model to identify the classes of medications and systemic diseases associated with xerostomia. © 2015 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

  19. 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

  20. 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.

  1. Predicting risk and human reliability: a new approach

    International Nuclear Information System (INIS)

    Duffey, R.; Ha, T.-S.

    2009-01-01

    Learning from experience describes human reliability and skill acquisition, and the resulting theory has been validated by comparison against millions of outcome data from multiple industries and technologies worldwide. The resulting predictions were used to benchmark the classic first generation human reliability methods adopted in probabilistic risk assessments. The learning rate, probabilities and response times are also consistent with the existing psychological models for human learning and error correction. The new approach also implies a finite lower bound probability that is not predicted by empirical statistical distributions that ignore the known and fundamental learning effects. (author)

  2. Predicting Parent-Child Aggression Risk: Cognitive Factors and Their Interaction With Anger.

    Science.gov (United States)

    Rodriguez, Christina M

    2018-02-01

    Several cognitive elements have previously been proposed to elevate risk for physical child abuse. To predict parent-child aggression risk, the current study evaluated the role of approval of parent-child aggression, perceptions of children as poorly behaved, and discipline attributions. Several dimensions of attributions specifically tied to parents' discipline practices were targeted. In addition, anger experienced during discipline episodes was considered a potential moderator of these cognitive processes. Using a largely multiple-indicator approach, a sample of 110 mothers reported on these cognitive and affective aspects that may occur when disciplining their children as well as responding to measures of parent-child aggression risk. Findings suggest that greater approval of parent-child aggression, negative perceptions of their child's behavior, and discipline attributions independently predicted parent-child aggression risk, with anger significantly interacting with mothers' perception of their child as more poorly behaved to exacerbate their parent-child aggression risk. Of the discipline attribution dimensions evaluated, mothers' sense of external locus of control and believing their child deserved their discipline were related to increase parent-child aggression risk. Future work is encouraged to comprehensively evaluate how cognitive and affective components contribute and interact to increase risk for parent-child aggression.

  3. Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China).

    Science.gov (United States)

    Yang, Xueli; Li, Jianxin; Hu, Dongsheng; Chen, Jichun; Li, Ying; Huang, Jianfeng; Liu, Xiaoqing; Liu, Fangchao; Cao, Jie; Shen, Chong; Yu, Ling; Lu, Fanghong; Wu, Xianping; Zhao, Liancheng; Wu, Xigui; Gu, Dongfeng

    2016-11-08

    The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775-0.814) for men and 0.811 (95% confidence interval, 0.787-0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men (P=0.16) and 12.8 for women (P=0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. © 2016 American Heart Association, Inc.

  4. 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

    electrocardiograms from 173 529 primary care patients aged 50-90 years were collected during 2001-11. The Framingham formula was used for heart rate-correction of the QT interval. Data on medication, comorbidity, and outcomes were retrieved from administrative registries. During a median follow-up period of 6......AIMS: Using a large, contemporary primary care population we aimed to provide absolute long-term risks of cardiovascular death (CVD) based on the QTc interval and to test whether the QTc interval is of value in risk prediction of CVD on an individual level. METHODS AND RESULTS: Digital...

  5. 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.

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

    Science.gov (United States)

    Scruth, Elizabeth Ann; Page, Karen; Cheng, Eugene; Campbell, Michelle; Worrall-Carter, Linda

    2012-01-01

    The objective of the study was to provide comprehensive information for the clinical nurse specialist (CNS) on commonly used clinical prediction (risk assessment) tools used to estimate risk of a secondary cardiac or noncardiac event and mortality in patients undergoing primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI). The evolution and widespread adoption of primary PCI represent major advances in the treatment of acute myocardial infarction, specifically STEMI. The American College of Cardiology and the American Heart Association have recommended early risk stratification for patients presenting with acute coronary syndromes using several clinical risk scores to identify patients' mortality and secondary event risk after PCI. Clinical nurse specialists are integral to any performance improvement strategy. Their knowledge and understandings of clinical prediction tools will be essential in carrying out important assessment, identifying and managing risk in patients who have sustained a STEMI, and enhancing discharge education including counseling on medications and lifestyle changes. Over the past 2 decades, risk scores have been developed from clinical trials to facilitate risk assessment. There are several risk scores that can be used to determine in-hospital and short-term survival. This article critiques the most common tools: the Thrombolytic in Myocardial Infarction risk score, the Global Registry of Acute Coronary Events risk score, and the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications risk score. The importance of incorporating risk screening assessment tools (that are important for clinical prediction models) to guide therapeutic management of patients cannot be underestimated. The ability to forecast secondary risk after a STEMI will assist in determining which patients would require the most aggressive level of treatment and monitoring postintervention including

  7. Nonparametric predictive inference for combined competing risks data

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani; Coolen, Frank P.A.

    2014-01-01

    The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies

  8. 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.

  9. Improving default risk prediction using Bayesian model uncertainty techniques.

    Science.gov (United States)

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  10. 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.

  11. Risk evaluation mitigation strategies: the evolution of risk management policy.

    Science.gov (United States)

    Hollingsworth, Kristen; Toscani, Michael

    2013-04-01

    The United States Food and Drug Administration (FDA) has the primary regulatory responsibility to ensure that medications are safe and effective both prior to drug approval and while the medication is being actively marketed by manufacturers. The responsibility for safe medications prior to marketing was signed into law in 1938 under the Federal Food, Drug, and Cosmetic Act; however, a significant risk management evolution has taken place since 1938. Additional federal rules, entitled the Food and Drug Administration Amendments Act, were established in 2007 and extended the government's oversight through the addition of a Risk Evaluation and Mitigation Strategy (REMS) for certain drugs. REMS is a mandated strategy to manage a known or potentially serious risk associated with a medication or biological product. Reasons for this extension of oversight were driven primarily by the FDA's movement to ensure that patients and providers are better informed of drug therapies and their specific benefits and risks prior to initiation. This article provides an historical perspective of the evolution of medication risk management policy and includes a review of REMS programs, an assessment of the positive and negative aspects of REMS, and provides suggestions for planning and measuring outcomes. In particular, this publication presents an overview of the evolution of the REMS program and its implications.

  12. Applied decision analysis and risk evaluation

    International Nuclear Information System (INIS)

    Ferse, W.; Kruber, S.

    1995-01-01

    During 1994 the workgroup 'Applied Decision Analysis and Risk Evaluation; continued the work on the knowledge based decision support system XUMA-GEFA for the evaluation of the hazard potential of contaminated sites. Additionally a new research direction was started which aims at the support of a later stage of the treatment of contaminated sites: The clean-up decision. For the support of decisions arising at this stage, the methods of decision analysis will be used. Computational aids for evaluation and decision support were implemented and a case study at a waste disposal site in Saxony which turns out to be a danger for the surrounding groundwater ressource was initiated. (orig.)

  13. 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

  14. 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

  15. Risk Prediction Models in Psychiatry: Toward a New Frontier for the Prevention of Mental Illnesses.

    Science.gov (United States)

    Bernardini, Francesco; Attademo, Luigi; Cleary, Sean D; Luther, Charles; Shim, Ruth S; Quartesan, Roberto; Compton, Michael T

    2017-05-01

    -scale, longitudinal studies pertaining to depression, bipolar disorder, anxiety disorders, and other psychiatric illnesses; (2) replicating and carrying out external validations of proposed models; (3) further testing potential selective and indicated preventive interventions; and (4) evaluating effectiveness of such interventions in the context of risk stratification using risk prediction models. © Copyright 2017 Physicians Postgraduate Press, Inc.

  16. 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. 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. Copyright © 2017 The Committee on Space Research (COSPAR

  17. Predicting epidemic risk from past temporal contact data.

    Directory of Open Access Journals (Sweden)

    Eugenio Valdano

    2015-03-01

    Full Text Available Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node's loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node's epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.

  18. Risk-adjusted performance evaluation in three academic thoracic surgery units using the Eurolung risk models.

    Science.gov (United States)

    Pompili, Cecilia; Shargall, Yaron; Decaluwe, Herbert; Moons, Johnny; Chari, Madhu; Brunelli, Alessandro

    2018-01-03

    The objective of this study was to evaluate the performance of 3 thoracic surgery centres using the Eurolung risk models for morbidity and mortality. This was a retrospective analysis performed on data collected from 3 academic centres (2014-2016). Seven hundred and twenty-one patients in Centre 1, 857 patients in Centre 2 and 433 patients in Centre 3 who underwent anatomical lung resections were analysed. The Eurolung1 and Eurolung2 models were used to predict risk-adjusted cardiopulmonary morbidity and 30-day mortality rates. Observed and risk-adjusted outcomes were compared within each centre. The observed morbidity of Centre 1 was in line with the predicted morbidity (observed 21.1% vs predicted 22.7%, P = 0.31). Centre 2 performed better than expected (observed morbidity 20.2% vs predicted 26.7%, P models were successfully used as risk-adjusting instruments to internally audit the outcomes of 3 different centres, showing their applicability for future quality improvement initiatives. © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

  19. 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

  20. 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.

  1. Predictive Modelling Risk Calculators and the Non Dialysis Pathway.

    Science.gov (United States)

    Robins, Jennifer; Katz, Ivor

    2013-04-16

    This guideline will review the current prediction models and survival/mortality scores available for decision making in patients with advanced kidney disease who are being considered for a non-dialysis treatment pathway. Risk prediction is gaining increasing attention with emerging literature suggesting improved patient outcomes through individualised risk prediction (1). Predictive models help inform the nephrologist and the renal palliative care specialists in their discussions with patients and families about suitability or otherwise of dialysis. Clinical decision making in the care of end stage kidney disease (ESKD) patients on a non-dialysis treatment pathway is currently governed by several observational trials (3). Despite the paucity of evidence based medicine in this field, it is becoming evident that the survival advantages associated with renal replacement therapy in these often elderly patients with multiple co-morbidities and limited functional status may be negated by loss of quality of life (7) (6), further functional decline (5, 8), increased complications and hospitalisations. This article is protected by copyright. All rights reserved.

  2. Development and evaluation of an automated fall risk assessment system.

    Science.gov (United States)

    Lee, Ju Young; Jin, Yinji; Piao, Jinshi; Lee, Sun-Mi

    2016-04-01

    Fall risk assessment is the first step toward prevention, and a risk assessment tool with high validity should be used. This study aimed to develop and validate an automated fall risk assessment system (Auto-FallRAS) to assess fall risks based on electronic medical records (EMRs) without additional data collected or entered by nurses. This study was conducted in a 1335-bed university hospital in Seoul, South Korea. The Auto-FallRAS was developed using 4211 fall-related clinical data extracted from EMRs. Participants included fall patients and non-fall patients (868 and 3472 for the development study; 752 and 3008 for the validation study; and 58 and 232 for validation after clinical application, respectively). The system was evaluated for predictive validity and concurrent validity. The final 10 predictors were included in the logistic regression model for the risk-scoring algorithm. The results of the Auto-FallRAS were shown as high/moderate/low risk on the EMR screen. The predictive validity analyzed after clinical application of the Auto-FallRAS was as follows: sensitivity = 0.95, NPV = 0.97 and Youden index = 0.44. The validity of the Morse Fall Scale assessed by nurses was as follows: sensitivity = 0.68, NPV = 0.88 and Youden index = 0.28. This study found that the Auto-FallRAS results were better than were the nurses' predictions. The advantage of the Auto-FallRAS is that it automatically analyzes information and shows patients' fall risk assessment results without requiring additional time from nurses. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

  3. Proarrhythmia risk prediction using human induced pluripotent stem cell-derived cardiomyocytes.

    Science.gov (United States)

    Yamazaki, Daiju; Kitaguchi, Takashi; Ishimura, Masakazu; Taniguchi, Tomohiko; Yamanishi, Atsuhiro; Saji, Daisuke; Takahashi, Etsushi; Oguchi, Masao; Moriyama, Yuta; Maeda, Sanae; Miyamoto, Kaori; Morimura, Kaoru; Ohnaka, Hiroki; Tashibu, Hiroyuki; Sekino, Yuko; Miyamoto, Norimasa; Kanda, Yasunari

    2018-04-01

    Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are expected to become a useful tool for proarrhythmia risk prediction in the non-clinical drug development phase. Several features including electrophysiological properties, ion channel expression profile and drug responses were investigated using commercially available hiPSC-CMs, such as iCell-CMs and Cor.4U-CMs. Although drug-induced arrhythmia has been extensively examined by microelectrode array (MEA) assays in iCell-CMs, it has not been fully understood an availability of Cor.4U-CMs for proarrhythmia risk. Here, we evaluated the predictivity of proarrhythmia risk using Cor.4U-CMs. MEA assay revealed linear regression between inter-spike interval and field potential duration (FPD). The hERG inhibitor E-4031 induced reverse-use dependent FPD prolongation. We next evaluated the proarrhythmia risk prediction by a two-dimensional map, which we have previously proposed. We determined the relative torsade de pointes risk score, based on the extent of FPD with Fridericia's correction (FPDcF) change and early afterdepolarization occurrence, and calculated the margins normalized to free effective therapeutic plasma concentrations. The drugs were classified into three risk groups using the two-dimensional map. This risk-categorization system showed high concordance with the torsadogenic information obtained by a public database CredibleMeds. Taken together, these results indicate that Cor.4U-CMs can be used for drug-induced proarrhythmia risk prediction. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

  4. Prediction of health risks from accidents: A comprehensive assessment methodology

    International Nuclear Information System (INIS)

    MacFarlane, D.R.; Yuan, Y.C.

    1992-01-01

    We have developed two computer programs to predict radiation risks to individuals and/or the collective population from exposures to accidental releases of radioactive materials. When used together, these two codes provide a consistent, comprehensive tool to estimate not only the risks to specific individuals but also the distribution of risks in the exposed population and the total number of individuals within a specific level of risk. Prompt and latent fatalities are estimated for the exposed population, and from these, the risk to an average individual can be derived. Uncertainty in weather conditions is considered by estimating both the ''median'' and the ''maximum'' population doses based on the frequency distribution of wind speeds and stabilities for a given site. The importance of including all dispersible particles (particles smaller than about 100 μm) for dose and health risk analyses from nonfiltered releases for receptor locations within about 10 km from a release has been investigated. The dose contribution of the large particles (> 10 μm) has been shown to be substantially greater than those from the small particles for the dose receptors in various release and exposure conditions. These conditions include, particularly, elevated releases, strong wind weather, and exposure pathways associated with ground-deposited material over extended periods of time

  5. Predicting risk behaviors: development and validation of a diagnostic scale.

    Science.gov (United States)

    Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

    1996-01-01

    The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

  6. Risk evaluation: A cost-oriented approach

    International Nuclear Information System (INIS)

    Rogers, B.H.

    1998-01-01

    This method provides a structured and cost-oriented way to determine risks associated with loss and destruction of industrial security interests consisting of material assets and human resources. Loss and destruction are assumed to be adversary perpetrated, high-impact events in which the health and safety of people or high-value property is at risk. This concept provides a process for: (1) assessing effectiveness of all integrated protection system, which includes facility operations, safety, emergency and security systems, and (2) a qualitative prioritization scheme to determine the level of consequence relative to cost and subsequent risk. The method allows managers the flexibility to establish asset protection appropriate to programmatic requirements and priorities and to decide if funding is appropriate. The evaluation objectives are to: (1) provide for a systematic, qualitative tabletop process to estimate the potential for an undesirable event and its impact; and (2) identify ineffective protection and cost-effective solutions

  7. Maintenance evaluation using risk based criteria

    International Nuclear Information System (INIS)

    Torres Valle, A.

    1996-01-01

    The maintenance evaluation is currently performed by using economic and, in some case, technical equipment failure criteria, however this is done to a specific equipment level. In general, when statistics are used the analysis for maintenance optimization are made isolated and whit a post mortem character; The integration provided by mean of Probabilistic Safety assessment (PSA) together with the possibilities of its applications, allow for evaluation of maintenance on the basis of broader scope criteria in regard to those traditionally used. The evaluate maintenance using risk based criteria, is necessary to follow a dynamic and systematic approach, in studying the maintenance strategy, to allow for updating the initial probabilistic models, for including operational changes that often take place during operation of complex facilities. This paper proposes a dynamic evaluation system of maintenance task. The system is illustrated by means of a practical example

  8. A Novel Risk Scoring System Reliably Predicts Readmission Following Pancreatectomy

    Science.gov (United States)

    Valero, Vicente; Grimm, Joshua C.; Kilic, Arman; Lewis, Russell L.; Tosoian, Jeffrey J.; He, Jin; Griffin, James; Cameron, John L.; Weiss, Matthew J.; Vollmer, Charles M.; Wolfgang, Christopher L.

    2015-01-01

    Background Postoperative readmissions have been proposed by Medicare as a quality metric and may impact provider reimbursement. Since readmission following pancreatectomy is common, we sought to identify factors associated with readmission in order to establish a predictive risk scoring system (RSS). Study Design A retrospective analysis of 2,360 pancreatectomies performed at nine, high-volume pancreatic centers between 2005 and 2011 was performed. Forty-five factors strongly associated with readmission were identified. To derive and validate a RSS, the population was randomly divided into two cohorts in a 4:1 fashion. A multivariable logistic regression model was constructed and scores were assigned based on the relative odds ratio of each independent predictor. A composite Readmission After Pancreatectomy (RAP) score was generated and then stratified to create risk groups. Results Overall, 464 (19.7%) patients were readmitted within 90-days. Eight pre- and postoperative factors, including prior myocardial infarction (OR 2.03), ASA Class ≥ 3 (OR 1.34), dementia (OR 6.22), hemorrhage (OR 1.81), delayed gastric emptying (OR 1.78), surgical site infection (OR 3.31), sepsis (OR 3.10) and short length of stay (OR 1.51), were independently predictive of readmission. The 32-point RAP score generated from the derivation cohort was highly predictive of readmission in the validation cohort (AUC 0.72). The low (0-3), intermediate (4-7) and high risk (>7) groups correlated to 11.7%, 17.5% and 45.4% observed readmission rates, respectively (preadmission following pancreatectomy. Identification of patients with increased risk of readmission using the RAP score will allow efficient resource allocation aimed to attenuate readmission rates. It also has potential to serve as a new metric for comparative research and quality assessment. PMID:25797757

  9. 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

  10. Contract Design: Risk Management and Evaluation.

    Science.gov (United States)

    Mühlbacher, Axel C; Amelung, Volker E; Juhnke, Christin

    2018-01-12

    Effective risk adjustment is an aspect that is more and more given weight on the background of competitive health insurance systems and vital healthcare systems. The risk structure of the providers plays a vital role in Pay for Performance. A prerequisite for optimal incentive-based service models is a (partial) dependence of the agent's returns on the provider's gain level. Integrated care systems as well as accountable care organisations (ACOs) in the US and similar concepts in other countries are advocated as an effective method of improving the performance of healthcare systems. These systems outline a payment and care delivery model that intends to tie provider reimbursements to predefined quality metrics. By this the total costs of care shall be reduced. Little is known about the contractual design and the main challenges of delegating "accountability" to these new kinds of organisations and/or contracts. The costs of market utilisation are highly relevant for the conception of healthcare contracts; furthermore information asymmetries and contract-specific investments are an obstacle to the efficient operation of ACOs. A comprehensive literature review on methods of designing contracts in Integrated Care was conducted. The research question in this article focuses on how reimbursement strategies, evaluation of measures and methods of risk adjustment can best be integrated in healthcare contracting. Each integrated care contract includes challenges for both payers and providers without having sufficient empirical data on both sides. These challenges are clinical, administrative or financial nature. Risk adjusted contracts ensure that the reimbursement roughly matches the true costs resulting from the morbidity of a population. If reimbursement of care provider corresponds to the actual expenses for an individual/population the problem of risk selection is greatly reduced. The currently used methods of risk adjustment have widely differing model and forecast

  11. Contract Design: Risk Management and Evaluation

    Directory of Open Access Journals (Sweden)

    Axel C. Mühlbacher

    2018-01-01

    Full Text Available Introduction: Effective risk adjustment is an aspect that is more and more given weight on the background of competitive health insurance systems and vital healthcare systems. The risk structure of the providers plays a vital role in Pay for Performance. A prerequisite for optimal incentive-based service models is a (partial dependence of the agent’s returns on the provider’s gain level. Integrated care systems as well as accountable care organisations (ACOs in the US and similar concepts in other countries are advocated as an effective method of improving the performance of healthcare systems. These systems outline a payment and care delivery model that intends to tie provider reimbursements to predefined quality metrics. By this the total costs of care shall be reduced.  Methods: Little is known about the contractual design and the main challenges of delegating “accountability” to these new kinds of organisations and/or contracts. The costs of market utilisation are highly relevant for the conception of healthcare contracts; furthermore information asymmetries and contract-specific investments are an obstacle to the efficient operation of ACOs. A comprehensive literature review on methods of designing contracts in Integrated Care was conducted. The research question in this article focuses on how reimbursement strategies, evaluation of measures and methods of risk adjustment can best be integrated in healthcare contracting.  Results: Each integrated care contract includes challenges for both payers and providers without having sufficient empirical data on both sides. These challenges are clinical, administrative or financial nature. Risk adjusted contracts ensure that the reimbursement roughly matches the true costs resulting from the morbidity of a population. If reimbursement of care provider corresponds to the actual expenses for an individual/population the problem of risk selection is greatly reduced. The currently used methods

  12. Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

    Science.gov (United States)

    Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

    2017-12-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of

  13. Predictive cytogenetic biomarkers for colorectal neoplasia in medium risk patients.

    Science.gov (United States)

    Ionescu, E M; Nicolaie, T; Ionescu, M A; Becheanu, G; Andrei, F; Diculescu, M; Ciocirlan, M

    2015-01-01

    DNA damage and chromosomal alterations in peripheral lymphocytes parallels DNA mutations in tumor tissues. The aim of our study was to predict the presence of neoplastic colorectal lesions by specific biomarkers in "medium risk" individuals (age 50 to 75, with no personal or family of any colorectal neoplasia). We designed a prospective cohort observational study including patients undergoing diagnostic or opportunistic screening colonoscopy. Specific biomarkers were analyzed for each patient in peripheral lymphocytes - presence of micronuclei (MN), nucleoplasmic bridges (NPB) and the Nuclear Division Index (NDI) by the cytokinesis-blocked micronucleus assay (CBMN). Of 98 patients included, 57 were "medium risk" individuals. MN frequency and NPB presence were not significantly different in patients with neoplastic lesions compared to controls. In "medium risk" individuals, mean NDI was significantly lower for patients with any neoplastic lesions (adenomas and adenocarcinomas, AUROC 0.668, p 00.5), for patients with advanced neoplasia (advanced adenoma and adenocarcinoma, AUROC 0.636 p 0.029) as well as for patients with adenocarcinoma (AUROC 0.650, p 0.048), for each comparison with the rest of the population. For a cut-off of 1.8, in "medium risk" individuals, an NDI inferior to that value may predict any neoplastic lesion with a sensitivity of 97.7%, an advanced neoplastic lesion with a sensitivity of 97% and adenocarcinoma with a sensitivity of 94.4%. NDI score may have a role as a colorectal cancer-screening test in "medium risk" individuals. DNA = deoxyribonucleic acid; CRC = colorectal cancer; EU = European Union; WHO = World Health Organization; FOBT = fecal occult blood test; CBMN = cytokinesis-blocked micronucleus assay; MN = micronuclei; NPB = nucleoplasmic bridges; NDI = Nuclear Division Index; FAP = familial adenomatous polyposis; HNPCC = hereditary non-polypoid colorectal cancer; IBD = inflammatory bowel diseases; ROC = receiver operating

  14. [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

  15. Threat and error management for anesthesiologists: a predictive risk taxonomy

    Science.gov (United States)

    Ruskin, Keith J.; Stiegler, Marjorie P.; Park, Kellie; Guffey, Patrick; Kurup, Viji; Chidester, Thomas

    2015-01-01

    Purpose of review Patient care in the operating room is a dynamic interaction that requires cooperation among team members and reliance upon sophisticated technology. Most human factors research in medicine has been focused on analyzing errors and implementing system-wide changes to prevent them from recurring. We describe a set of techniques that has been used successfully by the aviation industry to analyze errors and adverse events and explain how these techniques can be applied to patient care. Recent findings Threat and error management (TEM) describes adverse events in terms of risks or challenges that are present in an operational environment (threats) and the actions of specific personnel that potentiate or exacerbate those threats (errors). TEM is a technique widely used in aviation, and can be adapted for the use in a medical setting to predict high-risk situations and prevent errors in the perioperative period. A threat taxonomy is a novel way of classifying and predicting the hazards that can occur in the operating room. TEM can be used to identify error-producing situations, analyze adverse events, and design training scenarios. Summary TEM offers a multifaceted strategy for identifying hazards, reducing errors, and training physicians. A threat taxonomy may improve analysis of critical events with subsequent development of specific interventions, and may also serve as a framework for training programs in risk mitigation. PMID:24113268

  16. EVA: continuous automatic evaluation of protein structure prediction servers.

    Science.gov (United States)

    Eyrich, V A; Martí-Renom, M A; Przybylski, D; Madhusudhan, M S; Fiser, A; Pazos, F; Valencia, A; Sali, A; Rost, B

    2001-12-01

    Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. http://cubic.bioc.columbia.edu/eva. eva@cubic.bioc.columbia.edu

  17. Combined prediction model for supply risk in nuclear power equipment manufacturing industry based on support vector machine and decision tree

    International Nuclear Information System (INIS)

    Shi Chunsheng; Meng Dapeng

    2011-01-01

    The prediction index for supply risk is developed based on the factor identifying of nuclear equipment manufacturing industry. The supply risk prediction model is established with the method of support vector machine and decision tree, based on the investigation on 3 important nuclear power equipment manufacturing enterprises and 60 suppliers. Final case study demonstrates that the combination model is better than the single prediction model, and demonstrates the feasibility and reliability of this model, which provides a method to evaluate the suppliers and measure the supply risk. (authors)

  18. Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.

    Science.gov (United States)

    Parrinello, C M; Matsushita, K; Woodward, M; Wagenknecht, L E; Coresh, J; Selvin, E

    2016-09-01

    To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers. We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [β-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%). These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications. © 2016 John Wiley & Sons Ltd.

  19. Evaluation of residue-residue contact predictions in CASP9

    KAUST Repository

    Monastyrskyy, Bohdan; Fidelis, Krzysztof; Tramontano, Anna; Kryshtafovych, Andriy

    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

  20. 77 FR 53225 - National Earthquake Prediction Evaluation Council (NEPEC)

    Science.gov (United States)

    2012-08-31

    ... DEPARTMENT OF THE INTERIOR Geological Survey [USGS-GX12GG00995NP00] National Earthquake Prediction... meeting. SUMMARY: Pursuant to Public Law 96-472, the National Earthquake Prediction Evaluation Council... National Earthquake Information Center (NEIC), 1711 Illinois Avenue, Golden, Colorado 80401. The Council is...

  1. 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…

  2. 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.

  3. Predictive Risk Factors in the Treatment of Gestational Diabetes Mellitus

    Directory of Open Access Journals (Sweden)

    Lebriz Hale Aktun

    2015-01-01

    Full Text Available Objective This study aims to investigate predictive risk factors in the treatment of gestational diabetes mellitus (GDM. Patients and Methods A total of 256 pregnant women who underwent 75 g oral glucose tolerance test (OGTT during 24–28 weeks of pregnancy were included according to the World Health Organization criteria. Demographic characteristics of the patients, including age, parity, family history of diabetes, body weight before pregnancy, and body weight at the diagnosis of GDM, were recorded. Fasting insulin and hemoglobin A1c (HbA1c values at the time of diagnosis were evaluated. The patients were divided into two groups: those requiring insulin treatment (insulin group, n = 89 and those receiving diet therapy (diet group, n = 167 during pregnancy according to the American Diabetes Association recommendations. Results A total of 34.76% of the pregnant women with GDM required insulin treatment. The mean age of these patients was significantly higher compared to the diet group (34.9 ± 0.6 years vs. 31.9 ± 0.6 years; P = 0.004. Body mass index before pregnancy was also significantly higher in the insulin group than that in the diet group (32 ± 0.9 kg/m 2 vs. 29 ± 0.7 kg/m 2 ; P = 0.004. Fasting blood glucose (FBG during OGTT was 105.6 ± 2.1 mg/dL and 96.7 ± 1.1 mg/dL in the insulin group and diet group, respectively ( P < 0.001. There was no significant difference in fasting plasma glucose during OGTT between the groups ( P = 0.069, while plasma glucose at two hours was 161.1 ± 6.8 mg/dL in the insulin group and 145.1 ± 3.7 mg/dL in the diet group ( P = 0.027. At the time of diagnosis, HbA1c values were significantly higher in the insulin group compared to the diet group (5.3 ± 0.1 vs. 4.9 ± 0.1; P = 0.001. There was no significant difference in FBG and homeostasis model assessment-insulin resistance values between the groups ( P = 0.908, P = 0.073. Conclusion Our study results suggest that age, family history of diabetes, body

  4. The "polyenviromic risk score": Aggregating environmental risk factors predicts conversion to psychosis in familial high-risk subjects.

    Science.gov (United States)

    Padmanabhan, Jaya L; Shah, Jai L; Tandon, Neeraj; Keshavan, Matcheri S

    2017-03-01

    Young relatives of individuals with schizophrenia (i.e. youth at familial high-risk, FHR) are at increased risk of developing psychotic disorders, and show higher rates of psychiatric symptoms, cognitive and neurobiological abnormalities than non-relatives. It is not known whether overall exposure to environmental risk factors increases risk of conversion to psychosis in FHR subjects. Subjects consisted of a pilot longitudinal sample of 83 young FHR subjects. As a proof of principle, we examined whether an aggregate score of exposure to environmental risk factors, which we term a 'polyenviromic risk score' (PERS), could predict conversion to psychosis. The PERS combines known environmental risk factors including cannabis use, urbanicity, season of birth, paternal age, obstetric and perinatal complications, and various types of childhood adversity, each weighted by its odds ratio for association with psychosis in the literature. A higher PERS was significantly associated with conversion to psychosis in young, familial high-risk subjects (OR=1.97, p=0.009). A model combining the PERS and clinical predictors had a sensitivity of 27% and specificity of 96%. An aggregate index of environmental risk may help predict conversion to psychosis in FHR subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. 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.

  6. Technology Evaluation for Environmental Risk Mitigation Compendium

    Science.gov (United States)

    Meinhold, A.; Greene, B.; Dussich, J.; Sorkin, A.; Olsen, W.

    2017-01-01

    The Technology Evaluation for Environmental Risk Mitigation (TEERM) Principal Center and its predecessor organization the Acquisition Pollution Prevention Program (AP2) supported the National Aeronautics and Space Administration (NASA) in identifying technology solutions to risks and costs to NASA programs driven by environmental regulations and requirements. TEERM researched the commercial and government marketplace to locate viable and available technologies that met NASAs needs. TEERM focused on addressing environmentally-driven risks of direct concern to NASA programs and facilities, including hazardous materials in NASA operations and materials that became obsolescent because of environmental regulations. TEERM projects aimed to reduce cost; ensure the health and safety of people, assets, and the environment; promote efficiency; and minimize duplication. Major TEERM and AP2 projects focused on waste minimization and hazardous waste treatment, recycling, corrosion prevention and control, solvent and ozone depleting substances substitution, and aqueous based cleaners. In 2017, NASA made the decision to terminate the TEERM Principal Center. This Compendium Report documents TEERM and AP2 project successes. The Compendium Report traces the evolution of TEERM based on evolving risks and requirements for NASA and its relationship to the Space Shuttle Program, the United States Department of Defense, the European Space Agency, and other public and private stakeholders. This Compendium Report also documents project details from Project Summaries and Joint Test Plans and describes project stakeholders and collaborative effort results.

  7. Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks.

    Science.gov (United States)

    Wu, Cai; Li, Liang

    2018-05-15

    This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies. Copyright © 2018 John Wiley & Sons, Ltd.

  8. Predicting the Risk of Breakthrough Urinary Tract Infections: Primary Vesicoureteral Reflux.

    Science.gov (United States)

    Hidas, Guy; Billimek, John; Nam, Alexander; Soltani, Tandis; Kelly, Maryellen S; Selby, Blake; Dorgalli, Crystal; Wehbi, Elias; McAleer, Irene; McLorie, Gordon; Greenfield, Sheldon; Kaplan, Sherrie H; Khoury, Antoine E

    2015-11-01

    We constructed a risk prediction instrument stratifying patients with primary vesicoureteral reflux into groups according to their 2-year probability of breakthrough urinary tract infection. Demographic and clinical information was retrospectively collected in children diagnosed with primary vesicoureteral reflux and followed for 2 years. Bivariate and binary logistic regression analyses were performed to identify factors associated with breakthrough urinary tract infection. The final regression model was used to compute an estimation of the 2-year probability of breakthrough urinary tract infection for each subject. Accuracy of the binary classifier for breakthrough urinary tract infection was evaluated using receiver operator curve analysis. Three distinct risk groups were identified. The model was then validated in a prospective cohort. A total of 252 bivariate analyses showed that high grade (IV or V) vesicoureteral reflux (OR 9.4, 95% CI 3.8-23.5, p urinary tract infection (OR 5.3, 95% CI 1.1-24.7, p = 0.034) and female gender (OR 2.6, 95% CI 0.097-7.11, p urinary tract infection. Subgroup analysis revealed bladder and bowel dysfunction was a significant risk factor more pronounced in low grade (I to III) vesicoureteral reflux (OR 2.8, p = 0.018). The estimation model was applied for prospective validation, which demonstrated predicted vs actual 2-year breakthrough urinary tract infection rates of 19% vs 21%. Stratifying the patients into 3 risk groups based on parameters in the risk model showed 2-year risk for breakthrough urinary tract infection was 8.6%, 26.0% and 62.5% in the low, intermediate and high risk groups, respectively. This proposed risk stratification and probability model allows prediction of 2-year risk of patient breakthrough urinary tract infection to better inform parents of possible outcomes and treatment strategies. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights

  9. Potential ecological risk assessment and predicting zinc accumulation in soils

    OpenAIRE

    Baran, Agnieszka; Wieczorek, Jerzy; Mazurek, Ryszard; Urbański, Krzysztof; Klimkowicz-Pawlas, Agnieszka

    2017-01-01

    The aims of this study were to investigate zinc content in the studied soils; evaluate the efficiency of geostatistics in presenting spatial variability of zinc in the soils; assess bioavailable forms of zinc in the soils and to assess soil–zinc binding ability; and to estimate the potential ecological risk of zinc in soils. The study was conducted in southern Poland, in the Malopolska Province. This area is characterized by a great diversity of geological structures and types of land use and...

  10. 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.

  11. Application of structural reliability and risk assessment to life prediction and life extension decision making

    International Nuclear Information System (INIS)

    Meyer, T.A.; Balkey, K.R.; Bishop, B.A.

    1987-01-01

    There can be numerous uncertainties involved in performing component life assessments. In addition, sufficient data may be unavailable to make a useful life prediction. Structural Reliability and Risk Assessment (SRRA) is primarily an analytical methodology or tool that quantifies the impact of uncertainties on the structural life of plant components and can address the lack of data in component life prediction. As a prelude to discussing the technical aspects of SRRA, a brief review of general component life prediction methods is first made so as to better develop an understanding of the role of SRRA in such evaluations. SRRA is then presented as it is applied in component life evaluations with example applications being discussed for both nuclear and non-nuclear components

  12. 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

  13. Evaluation and comparison of predictive individual-level general surrogates.

    Science.gov (United States)

    Gabriel, Erin E; Sachs, Michael C; Halloran, M Elizabeth

    2018-07-01

    An intermediate response measure that accurately predicts efficacy in a new setting at the individual level could be used both for prediction and personalized medical decisions. In this article, we define a predictive individual-level general surrogate (PIGS), which is an individual-level intermediate response that can be used to accurately predict individual efficacy in a new setting. While methods for evaluating trial-level general surrogates, which are predictors of trial-level efficacy, have been developed previously, few, if any, methods have been developed to evaluate individual-level general surrogates, and no methods have formalized the use of cross-validation to quantify the expected prediction error. Our proposed method uses existing methods of individual-level surrogate evaluation within a given clinical trial setting in combination with cross-validation over a set of clinical trials to evaluate surrogate quality and to estimate the absolute prediction error that is expected in a new trial setting when using a PIGS. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate individual-level general surrogates over a set of multi-national trials of a pentavalent rotavirus vaccine.

  14. Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

    Science.gov (United States)

    Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

    2012-03-14

    Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified

  15. Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

    Directory of Open Access Journals (Sweden)

    Marschollek Michael

    2012-03-01

    Full Text Available Abstract Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1, and to identify high-risk subgroups from the data (aim#2. Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493. A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack

  16. 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.

  17. Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

    Science.gov (United States)

    Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

    2018-05-18

    Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

  18. Use of the Asthma Control Questionnaire to predict future risk of asthma exacerbation.

    Science.gov (United States)

    Meltzer, Eli O; Busse, William W; Wenzel, Sally E; Belozeroff, Vasily; Weng, Haoling H; Feng, JingYuan; Chon, Yun; Chiou, Chiun-Fang; Globe, Denise; Lin, Shao-Lee

    2011-01-01

    Direct correlation of assessments of a validated composite measure such as the Asthma Control Questionnaire (ACQ) and risk of exacerbation has not been previously demonstrated in a randomized controlled trial. To evaluate the ability of the ACQ score over time to predict risk of a future asthma exacerbation. This analysis included data from a 12-week placebo-controlled trial (N = 292) of AMG 317, an IL-4 receptor α antagonist, in patients with moderate to severe atopic asthma. At baseline, patients had an ACQ score ≥1.5. Exacerbations were defined as requirement for systemic corticosteroids. A Cox proportional hazards model was used, with ACQ score as the time-dependent covariate. The analysis was repeated for individual components of the ACQ. Each 1-point increase in ACQ was associated with a 50% increased risk of exacerbation (hazard ratio, 1.50; 95% CI, 1.03-2.20) for the following 2-week period. Evaluation of individual ACQ components also demonstrated a similar trend, though each to a lesser degree than the full composite ACQ. Although based on a retrospective analysis, with small number of exacerbations, these findings support the utility of the composite ACQ score measurement to predict risk of future exacerbation in clinical trials and clinical practice. The composite ACQ score measurement was found to be a better predictor of future risk than individual ACQ components. Copyright © 2010 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

  19. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

    NARCIS (Netherlands)

    Petersen, Japke F.; Stuiver, Martijn M.; Timmermans, Adriana J.; Chen, Amy; Zhang, Hongzhen; O'Neill, James P.; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T.; Koch, Wayne; van den Brekel, Michiel W. M.

    2017-01-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442

  20. 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.

  1. Clinical audit in gynecological cancer surgery: development of a risk scoring system to predict adverse events.

    Science.gov (United States)

    Kondalsamy-Chennakesavan, Srinivas; Bouman, Chantal; De Jong, Suzanne; Sanday, Karen; Nicklin, Jim; Land, Russell; Obermair, Andreas

    2009-12-01

    Advanced gynecological surgery undertaken in a specialized gynecologic oncology unit may be associated with significant perioperative morbidity. Validated risk prediction models are available for general surgical specialties but currently not for gynecological cancer surgery. The objective of this study was to evaluate risk factors for adverse events (AEs) of patients treated for suspected or proven gynecological cancer and to develop a clinical risk score (RS) to predict such AEs. AEs were prospectively recorded and matched with demographical, clinical and histopathological data on 369 patients who had an abdominal or laparoscopic procedure for proven or suspected gynecological cancer at a tertiary gynecological cancer center. Stepwise multiple logistic regression was used to determine the best predictors of AEs. For the risk score (RS), the coefficients from the model were scaled using a factor of 2 and rounded to the nearest integer to derive the risk points. Sum of all the risk points form the RS. Ninety-five patients (25.8%) had at least one AE. Twenty-nine (7.9%) and 77 (20.9%) patients experienced intra- and postoperative AEs respectively with 11 patients (3.0%) experiencing both. The independent predictors for any AE were complexity of the surgical procedure, elevated SGOT (serum glutamic oxaloacetic transaminase, > or /=35 U/L), higher ASA scores and overweight. The risk score can vary from 0 to 14. The risk for developing any AE is described by the formula 100 / (1 + e((3.697 - (RS /2)))). RS allows for quantification of the risk for AEs. Risk factors are generally not modifiable with the possible exception of obesity.

  2. Predicting risk of substantial weight gain in German adults-a multi-center cohort approach.

    Science.gov (United States)

    Bachlechner, Ursula; Boeing, Heiner; Haftenberger, Marjolein; Schienkiewitz, Anja; Scheidt-Nave, Christa; Vogt, Susanne; Thorand, Barbara; Peters, Annette; Schipf, Sabine; Ittermann, Till; Völzke, Henry; Nöthlings, Ute; Neamat-Allah, Jasmine; Greiser, Karin-Halina; Kaaks, Rudolf; Steffen, Annika

    2017-08-01

    A risk-targeted prevention strategy may efficiently utilize limited resources available for prevention of overweight and obesity. Likewise, more efficient intervention trials could be designed if selection of subjects was based on risk. The aim of the study was to develop a risk score predicting substantial weight gain among German adults. We developed the risk score using information on 15 socio-demographic, dietary and lifestyle factors from 32 204 participants of five population-based German cohort studies. Substantial weight gain was defined as gaining ≥10% of weight between baseline and follow-up (>6 years apart). The cases were censored according to the theoretical point in time when the threshold of 10% baseline-based weight gain was crossed assuming linearity of weight gain. Beta coefficients derived from proportional hazards regression were used as weights to compute the risk score as a linear combination of the predictors. Cross-validation was used to evaluate the score's discriminatory accuracy. The cross-validated c index (95% CI) was 0.71 (0.67-0.75). A cutoff value of ≥475 score points yielded a sensitivity of 71% and a specificity of 63%. The corresponding positive and negative predictive values were 10.4% and 97.6%, respectively. The proposed risk score may support healthcare providers in decision making and referral and facilitate an efficient selection of subjects into intervention trials. © The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association.

  3. Predicting risk of substantial weight gain in German adults—a multi-center cohort approach

    Science.gov (United States)

    Bachlechner, Ursula; Boeing, Heiner; Haftenberger, Marjolein; Schienkiewitz, Anja; Scheidt-Nave, Christa; Vogt, Susanne; Thorand, Barbara; Peters, Annette; Schipf, Sabine; Ittermann, Till; Völzke, Henry; Nöthlings, Ute; Neamat-Allah, Jasmine; Greiser, Karin-Halina; Kaaks, Rudolf

    2017-01-01

    Abstract Background A risk-targeted prevention strategy may efficiently utilize limited resources available for prevention of overweight and obesity. Likewise, more efficient intervention trials could be designed if selection of subjects was based on risk. The aim of the study was to develop a risk score predicting substantial weight gain among German adults. Methods We developed the risk score using information on 15 socio-demographic, dietary and lifestyle factors from 32 204 participants of five population-based German cohort studies. Substantial weight gain was defined as gaining ≥10% of weight between baseline and follow-up (>6 years apart). The cases were censored according to the theoretical point in time when the threshold of 10% baseline-based weight gain was crossed assuming linearity of weight gain. Beta coefficients derived from proportional hazards regression were used as weights to compute the risk score as a linear combination of the predictors. Cross-validation was used to evaluate the score’s discriminatory accuracy. Results The cross-validated c index (95% CI) was 0.71 (0.67–0.75). A cutoff value of ≥475 score points yielded a sensitivity of 71% and a specificity of 63%. The corresponding positive and negative predictive values were 10.4% and 97.6%, respectively. Conclusions The proposed risk score may support healthcare providers in decision making and referral and facilitate an efficient selection of subjects into intervention trials. PMID:28013243

  4. High EDSS can predict risk for upper urinary tract damage in patients with multiple sclerosis.

    Science.gov (United States)

    Ineichen, Benjamin V; Schneider, Marc P; Hlavica, Martin; Hagenbuch, Niels; Linnebank, Michael; Kessler, Thomas M

    2018-04-01

    Neurogenic lower urinary tract dysfunction (NLUTD) is very common in patients with multiple sclerosis (MS), and it might jeopardize renal function and thereby increase mortality. Although there are well-known urodynamic risk factors for upper urinary tract damage, no clinical prediction parameters are available. We aimed to assess clinical parameters potentially predicting urodynamic risk factors for upper urinary tract damage. A consecutive series of 141 patients with MS referred from neurologists for primary neuro-urological work-up including urodynamics were prospectively evaluated. Clinical parameters taken into account were age, sex, duration, and clinical course of MS and Expanded Disability Status Scale (EDSS). Multivariate modeling revealed EDSS as a clinical parameter significantly associated with urodynamic risk factors for upper urinary tract damage (odds ratio = 1.34, 95% confidence interval (CI) = 1.06-1.71, p = 0.02). Using receiver operator characteristic (ROC) curves, an EDSS of 5.0 as cutoff showed a sensitivity of 86%-87% and a specificity of 52% for at least one urodynamic risk factor for upper urinary tract damage. High EDSS is significantly associated with urodynamic risk factors for upper urinary tract damage and allows a risk-dependent stratification in daily neurological clinical practice to identify MS patients requiring further neuro-urological assessment and treatment.

  5. Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain.

    Science.gov (United States)

    Niv, Yael; Edlund, Jeffrey A; Dayan, Peter; O'Doherty, John P

    2012-01-11

    Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric-psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms.

  6. 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...

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

    OpenAIRE

    Wood, Erin E.; Kennison, Shelia M.

    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...

  8. The HEART score is useful to predict cardiovascular risks and reduces unnecessary cardiac imaging in low-risk patients with acute chest pain.

    Science.gov (United States)

    Dai, Siping; Huang, Bo; Zou, Yunliang; Guo, Jianbin; Liu, Ziyong; Pi, Dangyu; Qiu, Yunhong; Xiao, Chun

    2018-06-01

    The present study was to investigate whether the HEART score can be used to evaluate cardiovascular risks and reduce unnecessary cardiac imaging in China.Acute coronary syndrome patients with the thrombosis in myocardial infarction risk score risk HEART score group and 2 patients (1.5%) in the high risk HEART score group had cardiovascular events. The sensitivity of HEART score to predict cardiovascular events was 100% and the specificity was 46.7%. The potential unnecessary cardiac testing was 46.3%. Cox proportional hazards regression analysis showed that per one category increase of the HEART score was associated with nearly 1.3-fold risk of cardiovascular events.In the low-risk acute chest pain patients, the HEART score is useful to physicians in evaluating the risk of cardiovascular events within the first 30 days. In addition, the HEART score is also useful in reducing the unnecessary cardiac imaging.

  9. From risk analysis to risk control in land transport of dangerous materials. Contribution of quantitative evaluation

    International Nuclear Information System (INIS)

    Hubert, Ph.; Pages, P.

    1985-03-01

    The different approaches of risks and risk management system are described: statistics, potential risk, prevention, information and intervention. Quantitative evaluation is developed: data collection, purposes and methods. Two examples of application are given on risks associated to road transport of propane and of uranium hexafluoride. In conclusion level of risk and practical use of studies on risks are examined. 41 refs [fr

  10. Evaluation of burst pressure prediction models for line pipes

    International Nuclear Information System (INIS)

    Zhu, Xian-Kui; Leis, Brian N.

    2012-01-01

    Accurate prediction of burst pressure plays a central role in engineering design and integrity assessment of oil and gas pipelines. Theoretical and empirical solutions for such prediction are evaluated in this paper relative to a burst pressure database comprising more than 100 tests covering a variety of pipeline steel grades and pipe sizes. Solutions considered include three based on plasticity theory for the end-capped, thin-walled, defect-free line pipe subjected to internal pressure in terms of the Tresca, von Mises, and ZL (or Zhu-Leis) criteria, one based on a cylindrical instability stress (CIS) concept, and a large group of analytical and empirical models previously evaluated by Law and Bowie (International Journal of Pressure Vessels and Piping, 84, 2007: 487–492). It is found that these models can be categorized into either a Tresca-family or a von Mises-family of solutions, except for those due to Margetson and Zhu-Leis models. The viability of predictions is measured via statistical analyses in terms of a mean error and its standard deviation. Consistent with an independent parallel evaluation using another large database, the Zhu-Leis solution is found best for predicting burst pressure, including consideration of strain hardening effects, while the Tresca strength solutions including Barlow, Maximum shear stress, Turner, and the ASME boiler code provide reasonably good predictions for the class of line-pipe steels with intermediate strain hardening response. - Highlights: ► This paper evaluates different burst pressure prediction models for line pipes. ► The existing models are categorized into two major groups of Tresca and von Mises solutions. ► Prediction quality of each model is assessed statistically using a large full-scale burst test database. ► The Zhu-Leis solution is identified as the best predictive model.

  11. Evaluation of burst pressure prediction models for line pipes

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Xian-Kui, E-mail: zhux@battelle.org [Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201 (United States); Leis, Brian N. [Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201 (United States)

    2012-01-15

    Accurate prediction of burst pressure plays a central role in engineering design and integrity assessment of oil and gas pipelines. Theoretical and empirical solutions for such prediction are evaluated in this paper relative to a burst pressure database comprising more than 100 tests covering a variety of pipeline steel grades and pipe sizes. Solutions considered include three based on plasticity theory for the end-capped, thin-walled, defect-free line pipe subjected to internal pressure in terms of the Tresca, von Mises, and ZL (or Zhu-Leis) criteria, one based on a cylindrical instability stress (CIS) concept, and a large group of analytical and empirical models previously evaluated by Law and Bowie (International Journal of Pressure Vessels and Piping, 84, 2007: 487-492). It is found that these models can be categorized into either a Tresca-family or a von Mises-family of solutions, except for those due to Margetson and Zhu-Leis models. The viability of predictions is measured via statistical analyses in terms of a mean error and its standard deviation. Consistent with an independent parallel evaluation using another large database, the Zhu-Leis solution is found best for predicting burst pressure, including consideration of strain hardening effects, while the Tresca strength solutions including Barlow, Maximum shear stress, Turner, and the ASME boiler code provide reasonably good predictions for the class of line-pipe steels with intermediate strain hardening response. - Highlights: Black-Right-Pointing-Pointer This paper evaluates different burst pressure prediction models for line pipes. Black-Right-Pointing-Pointer The existing models are categorized into two major groups of Tresca and von Mises solutions. Black-Right-Pointing-Pointer Prediction quality of each model is assessed statistically using a large full-scale burst test database. Black-Right-Pointing-Pointer The Zhu-Leis solution is identified as the best predictive model.

  12. The utility of absolute risk prediction using FRAX® and Garvan Fracture Risk Calculator in daily practice.

    Science.gov (United States)

    van Geel, Tineke A C M; Eisman, John A; Geusens, Piet P; van den Bergh, Joop P W; Center, Jacqueline R; Dinant, Geert-Jan

    2014-02-01

    There are two commonly used fracture risk prediction tools FRAX(®) and Garvan Fracture Risk Calculator (GARVAN-FRC). The objective of this study was to investigate the utility of these tools in daily practice. A prospective population-based 5-year follow-up study was conducted in ten general practice centres in the Netherlands. For the analyses, the FRAX(®) and GARVAN-FRC 10-year absolute risks (FRAX(®) does not have 5-year risk prediction) for all fractures were used. Among 506 postmenopausal women aged ≥60 years (mean age: 67.8±5.8 years), 48 (9.5%) sustained a fracture during follow-up. Both tools, using BMD values, distinguish between women who did and did not fracture (10.2% vs. 6.8%, respectively for FRAX(®) and 32.4% vs. 39.1%, respectively for GARVAN-FRC, pbetter for women who sustained a fracture (higher sensitivity) and FRAX(®) for women who did not sustain a fracture (higher specificity). Similar results were obtained using age related cut off points. The discriminant value of both models is at least as good as models used in other medical conditions; hence they can be used to communicate the fracture risk to patients. However, given differences in the estimated risks between FRAX(®) and GARVAN-FRC, the significance of the absolute risk must be related to country-specific recommended intervention thresholds to inform the patient. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  13. Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

    Science.gov (United States)

    Wang, Ming; Long, Qi

    2016-09-01

    Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society.

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

    Science.gov (United States)

    Major, Piotr; Wysocki, Michał; Pędziwiatr, Michał; Małczak, Piotr; Pisarska, Magdalena; Migaczewski, Marcin; Winiarski, Marek; Budzyński, Andrzej

    2016-01-01

    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. To assess the possibility to use the OS-MRS to predict the risk of perioperative complications related to LSG and LRYGB. 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. 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). It appears that OS-MRS is not useful in predicting risk of perioperative morbidity after bariatric procedures.

  15. 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.

  16. Accounting for predictive uncertainty in a risk analysis focusing on radiological contamination of groundwater

    International Nuclear Information System (INIS)

    Andricevic, R.; Jacobson, R.L.; Daniels, J.I.

    1994-12-01

    This study focuses on the probabilistic travel time approach for predicting transport of radionuclides by groundwater velocity considering parameter uncertainty. The principal entity in the presented model is a travel time probability density function (pdf) conditioned on the set of parameters used to describe different transport processes like advection, dispersion, sorption, and decay. The model is applied to predict the arrival time of radionuclides in groundwater from the Nevada Test Site (NTS) at possible locations of potential human receptors nearby. Because of the lack of sorption the Tritium is found to provide the largest risk. Inclusion of sorption processes indicate that the parameter uncertainty and especially negative correlation between the mean velocity and the sorption strength is instrumental in evaluating the radionuclides arrival time at the prespecified accessible environment. Our analysis of potential health risks takes into consideration uncertainties in physiological attributes, as well as in committed effective dose and the estimate of physical detriment per unit Committed Effective Dose

  17. Predictive value of late decelerations for fetal acidemia in unselective low-risk pregnancies.

    Science.gov (United States)

    Sameshima, Hiroshi; Ikenoue, Tsuyomu

    2005-01-01

    We evaluated the clinical significance of late decelerations (LD) of intrapartum fetal heart rate (FHR) monitoring to detect low pH (LD (occasional, 50%; recurrent, > or = 50%) and severity (reduced baseline FHR accelerations and variability) of LD, and low pH (test, and one-way analysis of variance with the Bonferroni/Dunn test. In the 5522 low-risk pregnancies, 301 showed occasional LD and 99 showed recurrent LD. Blood gases and pH values deteriorated as the incidence of LD increased and as baseline accelerations or variability was decreased. Positive predictive value for low pH (LD, and > 50% in recurrent LD with no baseline FHR accelerations and reduced variability. In low-risk pregnancies, information on LD combined with acceleration and baseline variability enables us to predict the potential incidence of fetal acidemia.

  18. Uncertainty estimation and risk prediction in air quality

    International Nuclear Information System (INIS)

    Garaud, Damien

    2011-01-01

    This work is about uncertainty estimation and risk prediction in air quality. Firstly, we build a multi-model ensemble of air quality simulations which can take into account all uncertainty sources related to air quality modeling. Ensembles of photochemical simulations at continental and regional scales are automatically generated. Then, these ensemble are calibrated with a combinatorial optimization method. It selects a sub-ensemble which is representative of uncertainty or shows good resolution and reliability for probabilistic forecasting. This work shows that it is possible to estimate and forecast uncertainty fields related to ozone and nitrogen dioxide concentrations or to improve the reliability of threshold exceedance predictions. The approach is compared with Monte Carlo simulations, calibrated or not. The Monte Carlo approach appears to be less representative of the uncertainties than the multi-model approach. Finally, we quantify the observational error, the representativeness error and the modeling errors. The work is applied to the impact of thermal power plants, in order to quantify the uncertainty on the impact estimates. (author) [fr

  19. Cardiovascular risk prediction: the old has given way to the new but at what risk-benefit ratio?

    Directory of Open Access Journals (Sweden)

    Yeboah J

    2014-10-01

    Full Text Available Joseph Yeboah Heart and Vascular Center of Excellence, Wake Forest University School of Medicine, Winston-Salem, NC, USA Abstract: The ultimate goal of cardiovascular risk prediction is to identify individuals in the population to whom the application or administration of current proven lifestyle modifications and medicinal therapies will result in reduction in cardiovascular disease events and minimal adverse effects (net benefit to society. The use of cardiovascular risk prediction tools dates back to 1976 when the Framingham coronary heart disease risk score was published. Since then a lot of novel risk markers have been identified and other cardiovascular risk prediction tools have been developed to either improve or replace the Framingham Risk Score (FRS. In 2013, the new atherosclerotic cardiovascular disease risk estimator was published by the American College of Cardiology and the American Heart Association to replace the FRS for cardiovascular risk prediction. It is too soon to know the performance of the new atherosclerotic cardiovascular disease risk estimator. The risk-benefit ratio for preventive therapy (lifestyle modifications, statin +/− aspirin based on cardiovascular disease risk assessed using the FRS is unknown but it was assumed to be a net benefit. Should we also assume the risk-benefit ratio for the new atherosclerotic cardiovascular disease risk estimator is also a net benefit? Keywords: risk prediction, prevention, cardiovascular disease

  20. Designs for Risk Evaluation and Management

    Energy Technology Data Exchange (ETDEWEB)

    2015-12-01

    The Designs for Risk Evaluation and Management (DREAM) tool was developed as part of the effort to quantify the risk of geologic storage of carbon dioxide (CO2) under the U.S. Department of Energy's National Risk Assessment Partnership (NRAP). DREAM is an optimization tool created to identify optimal monitoring schemes that minimize the time to first detection of CO2 leakage from a subsurface storage formation. DREAM acts as a post-processer on user-provided output from subsurface leakage simulations. While DREAM was developed for CO2 leakage scenarios, it is applicable to any subsurface leakage simulation of the same output format. The DREAM tool is comprised of three main components: (1) a Java wizard used to configure and execute the simulations, (2) a visualization tool to view the domain space and optimization results, and (3) a plotting tool used to analyze the results. A secondary Java application is provided to aid users in converting common American Standard Code for Information Interchange (ASCII) output data to the standard DREAM hierarchical data format (HDF5). DREAM employs a simulated annealing approach that searches the solution space by iteratively mutating potential monitoring schemes built of various configurations of monitoring locations and leak detection parameters. This approach has proven to be orders of magnitude faster than an exhaustive search of the entire solution space. The user's manual illustrates the program graphical user interface (GUI), describes the tool inputs, and includes an example application.

  1. Predicting infection risk of airborne foot-and-mouth disease.

    Science.gov (United States)

    Schley, David; Burgin, Laura; Gloster, John

    2009-05-06

    Foot-and-mouth disease is a highly contagious disease of cloven-hoofed animals, the control and eradication of which is of significant worldwide socio-economic importance. The virus may spread by direct contact between animals or via fomites as well as through airborne transmission, with the latter being the most difficult to control. Here, we consider the risk of infection to flocks or herds from airborne virus emitted from a known infected premises. We show that airborne infection can be predicted quickly and with a good degree of accuracy, provided that the source of virus emission has been determined and reliable geo-referenced herd data are available. A simple model provides a reliable tool for estimating risk from known sources and for prioritizing surveillance and detection efforts. The issue of data information management systems was highlighted as a lesson to be learned from the official inquiry into the UK 2007 foot-and-mouth outbreak: results here suggest that the efficacy of disease control measures could be markedly improved through an accurate livestock database incorporating flock/herd size and location, which would enable tactical as well as strategic modelling.

  2. Life history and spatial traits predict extinction risk due to climate change

    Science.gov (United States)

    Pearson, Richard G.; Stanton, Jessica C.; Shoemaker, Kevin T.; Aiello-Lammens, Matthew E.; Ersts, Peter J.; Horning, Ned; Fordham, Damien A.; Raxworthy, Christopher J.; Ryu, Hae Yeong; McNees, Jason; Akçakaya, H. Reşit

    2014-03-01

    There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate. Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change based on the expectation that established assessments such as the IUCN Red List need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened, no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally different from other threats in terms of assessing extinction risks.

  3. 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.

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

    Science.gov (United States)

    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 revealed little use of quantitative animal or human kinetic data in risk evaluations of food chemicals, compared with pesticides and medicines. Risk evaluations of medicines provided sufficient in vivo kinetic data from different species to evaluate the predictive value of animal kinetic data for humans. These data showed a relatively poor correlation between the in vivo bioavailability in rats and dogs versus that in humans. In contrast, in vitro (human) kinetic data have been demonstrated to provide adequate predictions of the fate of compounds in humans, using appropriate in vitro-in vivo scalers and by integration of in vitro kinetic data with in silico kinetic modelling. Even though in vitro kinetic data were found to be occasionally included within risk evaluations of food chemicals, particularly results from Caco-2 absorption experiments and in vitro data on gut-microbial conversions, only minor use of in vitro methods for metabolism and quantitative in vitro-in vivo extrapolation methods was identified. Yet, such quantitative predictions are essential in the development of alternatives to animal testing as well as to increase human relevance of toxicological risk evaluations. Future research should aim at further improving and validating quantitative alternative methods for kinetics, thereby increasing regulatory acceptance of non-animal kinetic data.

  5. Development of a risk prediction model among professional hockey players with visible signs of concussion.

    Science.gov (United States)

    Bruce, Jared M; Echemendia, Ruben J; Meeuwisse, Willem; Hutchison, Michael G; Aubry, Mark; Comper, Paul

    2017-04-04

    Little research examines how to best identify concussed athletes. The purpose of the present study was to develop a preliminary risk decision model that uses visible signs (VS) and mechanisms of injury (MOI) to predict the likelihood of subsequent concussion diagnosis. Coders viewed and documented VS and associated MOI for all NHL games over the course of the 2013-2014 and 2014-2015 regular seasons. After coding was completed, player concussions were identified from the NHL injury surveillance system and it was determined whether players exhibiting VS were subsequently diagnosed with concussions by club medical staff as a result of the coded event. Among athletes exhibiting VS, suspected loss of consciousness, motor incoordination or balance problems, being in a fight, having an initial hit from another player's shoulder and having a secondary hit on the ice were all associated with increased risk of subsequent concussion diagnosis. In contrast, having an initial hit with a stick was associated with decreased risk of subsequent concussion diagnosis. A risk prediction model using a combination of the above VS and MOI was superior to approaches that relied on individual VS and associated MOI (sensitivity=81%, specificity=72%, positive predictive value=26%). Combined use of VS and MOI significantly improves a clinician's ability to identify players who need to be evaluated for possible concussion. A preliminary concussion prediction log has been developed from these data. Pending prospective validation, the use of these methods may improve early concussion detection and evaluation. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  6. Risk score to predict gastrointestinal bleeding after acute ischemic stroke.

    Science.gov (United States)

    Ji, Ruijun; Shen, Haipeng; Pan, Yuesong; Wang, Penglian; Liu, Gaifen; Wang, Yilong; Li, Hao; Singhal, Aneesh B; Wang, Yongjun

    2014-07-25

    Gastrointestinal bleeding (GIB) is a common and often serious complication after stroke. Although several risk factors for post-stroke GIB have been identified, no reliable or validated scoring system is currently available to predict GIB after acute stroke in routine clinical practice or clinical trials. In the present study, we aimed to develop and validate a risk model (acute ischemic stroke associated gastrointestinal bleeding score, the AIS-GIB score) to predict in-hospital GIB after acute ischemic stroke. The AIS-GIB score was developed from data in the China National Stroke Registry (CNSR). Eligible patients in the CNSR were randomly divided into derivation (60%) and internal validation (40%) cohorts. External validation was performed using data from the prospective Chinese Intracranial Atherosclerosis Study (CICAS). Independent predictors of in-hospital GIB were obtained using multivariable logistic regression in the derivation cohort, and β-coefficients were used to generate point scoring system for the AIS-GIB. The area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively. A total of 8,820, 5,882, and 2,938 patients were enrolled in the derivation, internal validation and external validation cohorts. The overall in-hospital GIB after AIS was 2.6%, 2.3%, and 1.5% in the derivation, internal, and external validation cohort, respectively. An 18-point AIS-GIB score was developed from the set of independent predictors of GIB including age, gender, history of hypertension, hepatic cirrhosis, peptic ulcer or previous GIB, pre-stroke dependence, admission National Institutes of Health stroke scale score, Glasgow Coma Scale score and stroke subtype (Oxfordshire). The AIS-GIB score showed good discrimination in the derivation (0.79; 95% CI, 0.764-0.825), internal (0.78; 95% CI, 0.74-0.82) and external (0.76; 95% CI, 0.71-0.82) validation cohorts

  7. Predictive value of clinical risk indicators in child development: final results of a study based on psychoanalytic theory

    Directory of Open Access Journals (Sweden)

    Maria Cristina Machado Kupfer

    2010-03-01

    Full Text Available We present the final results of a study using the IRDI (Clinical Risk Indicators in Child Development. Based on a psychoanalytic approach, 31 risk signs for child development were constructed and applied to 726 children between the ages of 0 and 18 months. One sub-sample was evaluated at the age of three. The results showed a predictive capacity of IRDIs to indicate developmental problems; 15 indicators for the IRDI were also highlighted that predict psychic risk for the constitution of the subject.

  8. 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.

  9. 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

  10. The Impact of EuroSCORE II Risk Factors on Prediction of Long-Term Mortality.

    Science.gov (United States)

    Barili, Fabio; Pacini, Davide; D'Ovidio, Mariangela; Dang, Nicholas C; Alamanni, Francesco; Di Bartolomeo, Roberto; Grossi, Claudio; Davoli, Marina; Fusco, Danilo; Parolari, Alessandro

    2016-10-01

    The European System for Cardiac Operation Risk Evaluation (EuroSCORE) II has not been tested yet for predicting long-term mortality. This study was undertaken to evaluate the relationship between EuroSCORE II and long-term mortality and to develop a new algorithm based on EuroSCORE II factors to predict long-term survival after cardiac surgery. Complete data on 10,033 patients who underwent major cardiac surgery during a 7-year period were retrieved from three prospective institutional databases and linked with the Italian Tax Register Information System. Mortality at follow-up was analyzed with time-to-event analysis. The Kaplan-Meier estimates of survival at 1 and 5 were, respectively, 95.0% ± 0.2% and 84.7% ± 0.4%. Both discrimination and calibration of EuroSCORE II decreased in the prediction of 1-year and 5-year mortality. Nonetheless, EuroSCORE II was confirmed to be an independent predictor of long-term mortality with a nonlinear trend. Several EuroSCORE II variables were independent risk factors for long-term mortality in a regression model, most of all very low ejection fraction (less than 20%), salvage operation, and dialysis. In the final model, isolated mitral valve surgery and isolated coronary artery bypass graft surgery were associated with improved long-term survival. The EuroSCORE II cannot be considered a direct estimator of long-term risk of death, as its performance fades for mortality at follow-up longer than 30 days. Nonetheless, it is nonlinearly associated with long-term mortality, and most of its variables are risk factors for long-term mortality. Hence, they can be used in a different algorithm to stratify the risk of long-term mortality after surgery. Copyright © 2016 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  11. Potential ecological risk assessment and prediction of soil heavy-metal pollution around coal gangue dump

    Science.gov (United States)

    Jiang, X.; Lu, W. X.; Zhao, H. Q.; Yang, Q. C.; Yang, Z. P.

    2014-06-01

    The aim of the present study is to evaluate the potential ecological risk and trend of soil heavy-metal pollution around a coal gangue dump in Jilin Province (Northeast China). The concentrations of Cd, Pb, Cu, Cr and Zn were monitored by inductively coupled plasma mass spectrometry (ICP-MS). The potential ecological risk index method developed by Hakanson (1980) was employed to assess the potential risk of heavy-metal pollution. The potential ecological risk in the order of ER(Cd) > ER(Pb) > ER(Cu) > ER(Cr) > ER(Zn) have been obtained, which showed that Cd was the most important factor leading to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, then the fixed number of years exceeding the standard prediction model was established, which was used to predict the pollution trend of Cd under the accelerated accumulation mode and the uniform mode. Pearson correlation analysis and correspondence analysis are employed to identify the sources of heavy metals and the relationship between sampling points and variables. These findings provided some useful insights for making appropriate management strategies to prevent or decrease heavy-metal pollution around a coal gangue dump in the Yangcaogou coal mine and other similar areas elsewhere.

  12. Risk prediction models for mortality in patients with ventilator-associated pneumonia

    DEFF Research Database (Denmark)

    Larsson, Johan E; Itenov, Theis Skovsgaard; Bestle, Morten Heiberg

    2017-01-01

    the receiver operator characteristic curve (AUC). RESULTS: We identified 19 articles studying 7 different models' ability to predict mortality in VAP patients. The models were Acute Physiology and Chronic Health Evaluation (APACHE) II (9 studies, n = 1398); Clinical Pulmonary Infection Score (4 studies, n...... = 303); "Immunodeficiency, Blood pressure, Multilobular infiltrates on chest radiograph, Platelets and hospitalization 10 days before onset of VAP" (3 studies, n = 406); "VAP Predisposition, Insult Response and Organ dysfunction" (2 studies, n = 589); Sequential Organ Failure Assessment (7 studies, n......: The PubMed and EMBASE were searched in February 2016. We included studies in English that evaluated models' ability to predict the risk of mortality in patients with VAP. The reported mortality with the longest follow-up was used in the meta-analysis. Prognostic accuracy was measured with the area under...

  13. 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.

  14. 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

  15. Evaluation of abutment scour prediction equations with field data

    Science.gov (United States)

    Benedict, S.T.; Deshpande, N.; Aziz, N.M.

    2007-01-01

    The U.S. Geological Survey, in cooperation with FHWA, compared predicted abutment scour depths, computed with selected predictive equations, with field observations collected at 144 bridges in South Carolina and at eight bridges from the National Bridge Scour Database. Predictive equations published in the 4th edition of Evaluating Scour at Bridges (Hydraulic Engineering Circular 18) were used in this comparison, including the original Froehlich, the modified Froehlich, the Sturm, the Maryland, and the HIRE equations. The comparisons showed that most equations tended to provide conservative estimates of scour that at times were excessive (as large as 158 ft). Equations also produced underpredictions of scour, but with less frequency. Although the equations provide an important resource for evaluating abutment scour at bridges, the results of this investigation show the importance of using engineering judgment in conjunction with these equations.

  16. Evaluation of residue-residue contact prediction in CASP10

    KAUST Repository

    Monastyrskyy, Bohdan; D'Andrea, Daniel; Fidelis, Krzysztof; Tramontano, Anna; Kryshtafovych, Andriy

    2013-01-01

    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

  17. 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.

  18. Risk approximation in decision making: approximative numeric abilities predict advantageous decisions under objective risk.

    Science.gov (United States)

    Mueller, Silke M; Schiebener, Johannes; Delazer, Margarete; Brand, Matthias

    2018-01-22

    Many decision situations in everyday life involve mathematical considerations. In decisions under objective risk, i.e., when explicit numeric information is available, executive functions and abilities to handle exact numbers and ratios are predictors of objectively advantageous choices. Although still debated, exact numeric abilities, e.g., normative calculation skills, are assumed to be related to approximate number processing skills. The current study investigates the effects of approximative numeric abilities on decision making under objective risk. Participants (N = 153) performed a paradigm measuring number-comparison, quantity-estimation, risk-estimation, and decision-making skills on the basis of rapid dot comparisons. Additionally, a risky decision-making task with exact numeric information was administered, as well as tasks measuring executive functions and exact numeric abilities, e.g., mental calculation and ratio processing skills, were conducted. Approximative numeric abilities significantly predicted advantageous decision making, even beyond the effects of executive functions and exact numeric skills. Especially being able to make accurate risk estimations seemed to contribute to superior choices. We recommend approximation skills and approximate number processing to be subject of future investigations on decision making under risk.

  19. 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%.

  20. Long-Term Survival Prediction for Coronary Artery Bypass Grafting: Validation of the ASCERT Model Compared With The Society of Thoracic Surgeons Predicted Risk of Mortality.

    Science.gov (United States)

    Lancaster, Timothy S; Schill, Matthew R; Greenberg, Jason W; Ruaengsri, Chawannuch; Schuessler, Richard B; Lawton, Jennifer S; Maniar, Hersh S; Pasque, Michael K; Moon, Marc R; Damiano, Ralph J; Melby, Spencer J

    2018-05-01

    The recently developed American College of Cardiology Foundation-Society of Thoracic Surgeons (STS) Collaboration on the Comparative Effectiveness of Revascularization Strategy (ASCERT) Long-Term Survival Probability Calculator is a valuable addition to existing short-term risk-prediction tools for cardiac surgical procedures but has yet to be externally validated. Institutional data of 654 patients aged 65 years or older undergoing isolated coronary artery bypass grafting between 2005 and 2010 were reviewed. Predicted survival probabilities were calculated using the ASCERT model. Survival data were collected using the Social Security Death Index and institutional medical records. Model calibration and discrimination were assessed for the overall sample and for risk-stratified subgroups based on (1) ASCERT 7-year survival probability and (2) the predicted risk of mortality (PROM) from the STS Short-Term Risk Calculator. Logistic regression analysis was performed to evaluate additional perioperative variables contributing to death. Overall survival was 92.1% (569 of 597) at 1 year and 50.5% (164 of 325) at 7 years. Calibration assessment found no significant differences between predicted and actual survival curves for the overall sample or for the risk-stratified subgroups, whether stratified by predicted 7-year survival or by PROM. Discriminative performance was comparable between the ASCERT and PROM models for 7-year survival prediction (p validated for prediction of long-term survival after coronary artery bypass grafting in all risk groups. The widely used STS PROM performed comparably as a predictor of long-term survival. Both tools provide important information for preoperative decision making and patient counseling about potential outcomes after coronary artery bypass grafting. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. Predictive Accuracy of a Cardiovascular Disease Risk Prediction Model in Rural South India – A Community Based Retrospective Cohort Study

    Directory of Open Access Journals (Sweden)

    Farah N Fathima

    2015-03-01

    Full Text Available Background: Identification of individuals at risk of developing cardiovascular diseases by risk stratification is the first step in primary prevention. Aims & Objectives: To assess the five year risk of developing a cardiovascular event from retrospective data and to assess the predictive accuracy of the non laboratory based National Health and Nutrition Examination Survey (NHANES risk prediction model among individuals in a rural South Indian population. Materials & Methods: A community based retrospective cohort study was conducted in three villages where risk stratification was done for all eligible adults aged between 35-74 years at the time of initial assessment using the NHANES risk prediction charts. Household visits were made after a period of five years by trained doctors to determine cardiovascular outcomes. Results: 521 people fulfilled the eligibility criteria of whom 486 (93.3% could be traced after five years. 56.8% were in low risk, 36.6% were in moderate risk and 6.6% were in high risk categories. 29 persons (5.97% had had cardiovascular events over the last five years of which 24 events (82.7% were nonfatal and five (17.25% were fatal. The mean age of the people who developed cardiovascular events was 57.24 ± 9.09 years. The odds ratios for the three levels of risk showed a linear trend with the odds ratios for the moderate risk and high risk category being 1.35 and 1.94 respectively with the low risk category as baseline. Conclusion: The non laboratory based NHANES charts did not accurately predict the occurrence of cardiovascular events in any of the risk categories.

  6. Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: A Longitudinal Study.

    Science.gov (United States)

    Gracitelli, Carolina P B; Tatham, Andrew J; Boer, Erwin R; Abe, Ricardo Y; Diniz-Filho, Alberto; Rosen, Peter N; Medeiros, Felipe A

    2015-01-01

    To evaluate the ability of longitudinal Useful Field of View (UFOV) and simulated driving measurements to predict future occurrence of motor vehicle collision (MVC) in drivers with glaucoma. Prospective observational cohort study. 117 drivers with glaucoma followed for an average of 2.1 ± 0.5 years. All subjects had standard automated perimetry (SAP), UFOV, driving simulator, and cognitive assessment obtained at baseline and every 6 months during follow-up. The driving simulator evaluated reaction times to high and low contrast peripheral divided attention stimuli presented while negotiating a winding country road, with central driving task performance assessed as "curve coherence". Drivers with MVC during follow-up were identified from Department of Motor Vehicle records. Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors. Mean age at baseline was 64.5 ± 12.6 years. 11 of 117 (9.4%) drivers had a MVC during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of MVC, with a hazard ratio (HR) of 2.19 per 1 SD slower reaction time (95% CI, 1.30 to 3.69; P = 0.003). UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022). Global SAP visual field indices in the better or worse eye were not predictive of MVC. The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18). Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of MVC in glaucoma patients. These findings may help improve the understanding of factors associated with driving impairment related to glaucoma.

  7. Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: A Longitudinal Study.

    Directory of Open Access Journals (Sweden)

    Carolina P B Gracitelli

    Full Text Available To evaluate the ability of longitudinal Useful Field of View (UFOV and simulated driving measurements to predict future occurrence of motor vehicle collision (MVC in drivers with glaucoma.Prospective observational cohort study.117 drivers with glaucoma followed for an average of 2.1 ± 0.5 years.All subjects had standard automated perimetry (SAP, UFOV, driving simulator, and cognitive assessment obtained at baseline and every 6 months during follow-up. The driving simulator evaluated reaction times to high and low contrast peripheral divided attention stimuli presented while negotiating a winding country road, with central driving task performance assessed as "curve coherence". Drivers with MVC during follow-up were identified from Department of Motor Vehicle records.Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors.Mean age at baseline was 64.5 ± 12.6 years. 11 of 117 (9.4% drivers had a MVC during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of MVC, with a hazard ratio (HR of 2.19 per 1 SD slower reaction time (95% CI, 1.30 to 3.69; P = 0.003. UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022. Global SAP visual field indices in the better or worse eye were not predictive of MVC. The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18.Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of MVC in glaucoma patients. These findings may help improve the understanding of factors associated with driving impairment related to glaucoma.

  8. Predictive validity of the Hendrich fall risk model II in an acute geriatric unit.

    Science.gov (United States)

    Ivziku, Dhurata; Matarese, Maria; Pedone, Claudio

    2011-04-01

    Falls are the most common adverse events reported in acute care hospitals, and older patients are the most likely to fall. The risk of falling cannot be completely eliminated, but it can be reduced through the implementation of a fall prevention program. A major evidence-based intervention to prevent falls has been the use of fall-risk assessment tools. Many tools have been increasingly developed in recent years, but most instruments have not been investigated regarding reliability, validity and clinical usefulness. This study intends to evaluate the predictive validity and inter-rater reliability of Hendrich fall risk model II (HFRM II) in order to identify older patients at risk of falling in geriatric units and recommend its use in clinical practice. A prospective descriptive design was used. The study was carried out in a geriatric acute care unit of an Italian University hospital. All over 65 years old patients consecutively admitted to a geriatric acute care unit of an Italian University hospital over 8-month period were enrolled. The patients enrolled were screened for the falls risk by nurses with the HFRM II within 24h of admission. The falls occurring during the patient's hospital stay were registered. Inter-rater reliability, area under the ROC curve, sensitivity, specificity, positive and negative predictive values and time for the administration were evaluated. 179 elderly patients were included. The inter-rater reliability was 0.87 (95% CI 0.71-1.00). The administration time was about 1min. The most frequently reported risk factors were depression, incontinence, vertigo. Sensitivity and specificity were respectively 86% and 43%. The optimal cut-off score for screening at risk patients was 5 with an area under the ROC curve of 0.72. The risk factors more strongly associated with falls were confusion and depression. As falls of older patients are a common problem in acute care settings it is necessary that the nurses use specific validate and reliable

  9. 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

  10. Development and validation of a nomogram predicting recurrence risk in women with symptomatic urinary tract infection.

    Science.gov (United States)

    Cai, Tommaso; Mazzoli, Sandra; Migno, Serena; Malossini, Gianni; Lanzafame, Paolo; Mereu, Liliana; Tateo, Saverio; Wagenlehner, Florian M E; Pickard, Robert S; Bartoletti, Riccardo

    2014-09-01

    To develop and externally validate a novel nomogram predicting recurrence risk probability at 12 months in women after an episode of urinary tract infection. The study included 768 women from Santa Maria Annunziata Hospital, Florence, Italy, affected by urinary tract infections from January 2005 to December 2009. Another 373 women with the same criteria enrolled at Santa Chiara Hospital, Trento, Italy, from January 2010 to June 2012 were used to externally validate and calibrate the nomogram. Univariate and multivariate Cox regression models tested the relationship between urinary tract infection recurrence risk, and patient clinical and laboratory characteristics. The nomogram was evaluated by calculating concordance probabilities, as well as testing calibration of predicted urinary tract infection recurrence with observed urinary tract infections. Nomogram variables included: number of partners, bowel function, type of pathogens isolated (Gram-positive/negative), hormonal status, number of previous urinary tract infection recurrences and previous treatment of asymptomatic bacteriuria. Of the original development data, 261 out of 768 women presented at least one episode of recurrence of urinary tract infection (33.9%). The nomogram had a concordance index of 0.85. The nomogram predictions were well calibrated. This model showed high discrimination accuracy and favorable calibration characteristics. In the validation group (373 women), the overall c-index was 0.83 (P = 0.003, 95% confidence interval 0.51-0.99), whereas the area under the receiver operating characteristic curve was 0.85 (95% confidence interval 0.79-0.91). The present nomogram accurately predicts the recurrence risk of urinary tract infection at 12 months, and can assist in identifying women at high risk of symptomatic recurrence that can be suitable candidates for a prophylactic strategy. © 2014 The Japanese Urological Association.

  11. 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; 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

    2014-01-01

    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. 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-reactive animal model cross-validation. We also tested this small panel of genes in two other independent test cohorts from the United States, one with alcohol dependence (P=0.00012) and one with alcohol abuse (a less severe form of alcoholism; P=0.0094). SNCA by itself was able to separate alcoholics from controls in the alcohol-dependent cohort (P=0.000013) and the alcohol abuse

  12. Performance of Surgical Risk Scores to Predict Mortality after Transcatheter Aortic Valve Implantation

    Directory of Open Access Journals (Sweden)

    Leonardo Sinnott Silva

    2015-01-01

    Full Text Available Abstract Background: Predicting mortality in patients undergoing transcatheter aortic valve implantation (TAVI remains a challenge. Objectives: To evaluate the performance of 5 risk scores for cardiac surgery in predicting the 30-day mortality among patients of the Brazilian Registry of TAVI. Methods: The Brazilian Multicenter Registry prospectively enrolled 418 patients undergoing TAVI in 18 centers between 2008 and 2013. The 30-day mortality risk was calculated using the following surgical scores: the logistic EuroSCORE I (ESI, EuroSCORE II (ESII, Society of Thoracic Surgeons (STS score, Ambler score (AS and Guaragna score (GS. The performance of the risk scores was evaluated in terms of their calibration (Hosmer–Lemeshow test and discrimination [area under the receiver–operating characteristic curve (AUC]. Results: The mean age was 81.5 ± 7.7 years. The CoreValve (Medtronic was used in 86.1% of the cohort, and the transfemoral approach was used in 96.2%. The observed 30-day mortality was 9.1%. The 30-day mortality predicted by the scores was as follows: ESI, 20.2 ± 13.8%; ESII, 6.5 ± 13.8%; STS score, 14.7 ± 4.4%; AS, 7.0 ± 3.8%; GS, 17.3 ± 10.8%. Using AUC, none of the tested scores could accurately predict the 30-day mortality. AUC for the scores was as follows: 0.58 [95% confidence interval (CI: 0.49 to 0.68, p = 0.09] for ESI; 0.54 (95% CI: 0.44 to 0.64, p = 0.42 for ESII; 0.57 (95% CI: 0.47 to 0.67, p = 0.16 for AS; 0.48 (95% IC: 0.38 to 0.57, p = 0.68 for STS score; and 0.52 (95% CI: 0.42 to 0.62, p = 0.64 for GS. The Hosmer–Lemeshow test indicated acceptable calibration for all scores (p > 0.05. Conclusions: In this real world Brazilian registry, the surgical risk scores were inaccurate in predicting mortality after TAVI. Risk models specifically developed for TAVI are required.

  13. 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.

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  15. Evaluating the reliability of predictions made using environmental transfer models

    International Nuclear Information System (INIS)

    1989-01-01

    The development and application of mathematical models for predicting the consequences of releases of radionuclides into the environment from normal operations in the nuclear fuel cycle and in hypothetical accident conditions has increased dramatically in the last two decades. This Safety Practice publication has been prepared to provide guidance on the available methods for evaluating the reliability of environmental transfer model predictions. It provides a practical introduction of the subject and a particular emphasis has been given to worked examples in the text. It is intended to supplement existing IAEA publications on environmental assessment methodology. 60 refs, 17 figs, 12 tabs

  16. 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.

  17. Potential ecological risk assessment and predicting zinc accumulation in soils.

    Science.gov (United States)

    Baran, Agnieszka; Wieczorek, Jerzy; Mazurek, Ryszard; Urbański, Krzysztof; Klimkowicz-Pawlas, Agnieszka

    2018-02-01

    The aims of this study were to investigate zinc content in the studied soils; evaluate the efficiency of geostatistics in presenting spatial variability of zinc in the soils; assess bioavailable forms of zinc in the soils and to assess soil-zinc binding ability; and to estimate the potential ecological risk of zinc in soils. The study was conducted in southern Poland, in the Malopolska Province. This area is characterized by a great diversity of geological structures and types of land use and intensity of industrial development. The zinc content was affected by soil factors, and the type of land use (arable lands, grasslands, forests, wastelands). A total of 320 soil samples were characterized in terms of physicochemical properties (texture, pH, organic C content, total and available Zn content). Based on the obtained data, assessment of the ecological risk of zinc was conducted using two methods: potential ecological risk index and hazard quotient. Total Zn content in the soils ranged from 8.27 to 7221 mg kg -1 d.m. Based on the surface semivariograms, the highest variability of zinc in the soils was observed from northwest to southeast. The point sources of Zn contamination were located in the northwestern part of the area, near the mining-metallurgical activity involving processing of zinc and lead ores. These findings were confirmed by the arrangement of semivariogram surfaces and bivariate Moran's correlation coefficients. The content of bioavailable forms of zinc was between 0.05 and 46.19 mg kg -1 d.m. (0.01 mol dm -3 CaCl 2 ), and between 0.03 and 71.54 mg kg -1 d.m. (1 mol dm -3 NH 4 NO 3 ). Forest soils had the highest zinc solubility, followed by arable land, grassland and wasteland. PCA showed that organic C was the key factor to control bioavailability of zinc in the soils. The extreme, very high and medium zinc accumulation was found in 69% of studied soils. There is no ecological risk of zinc to living organisms in the study area, and in 90

  18. Diagnosis and Tests: Evaluating a Fall or Risk of Falling

    Science.gov (United States)

    ... as a physical therapist, who can evaluate your fall risk. If your healthcare provider concludes that you are ... to check for things that can impact your fall risk, such as electrolyte balance and the possibility of ...

  19. At-Risk Youth Appearance and Job Performance Evaluation

    Science.gov (United States)

    Freeburg, Beth Winfrey; Workman, Jane E.

    2008-01-01

    The goal of this study was to identify the relationship of at-risk youth workplace appearance to other job performance criteria. Employers (n = 30; each employing from 1 to 17 youths) evaluated 178 at-risk high school youths who completed a paid summer employment experience. Appearance evaluations were significantly correlated with evaluations of…

  20. 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

  1. 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.

  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. A decision model to predict the risk of the first fall onset.

    Science.gov (United States)

    Deschamps, Thibault; Le Goff, Camille G; Berrut, Gilles; Cornu, Christophe; Mignardot, Jean-Baptiste

    2016-08-01

    Miscellaneous features from various domains are accepted to be associated with the risk of falling in the elderly. However, only few studies have focused on establishing clinical tools to predict the risk of the first fall onset. A model that would objectively and easily evaluate the risk of a first fall occurrence in the coming year still needs to be built. We developed a model based on machine learning, which might help the medical staff predict the risk of the first fall onset in a one-year time window. Overall, 426 older adults who had never fallen were assessed on 73 variables, comprising medical, social and physical outcomes, at t0. Each fall was recorded at a prospective 1-year follow-up. A decision tree was built on a randomly selected training subset of the cohort (80% of the full-set) and validated on an independent test set. 82 participants experienced a first fall during the follow-up. The machine learning process independently extracted 13 powerful parameters and built a model showing 89% of accuracy for the overall classification with 83%-82% of true positive fallers and 96%-61% of true negative non-fallers (training set vs. independent test set). This study provides a pilot tool that could easily help the gerontologists refine the evaluation of the risk of the first fall onset and prioritize the effective prevention strategies. The study also offers a transparent framework for future, related investigation that would validate the clinical relevance of the established model by independently testing its accuracy on larger cohort. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. 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.

  5. Evaluating emergency risk communications: a dialogue with the experts.

    Science.gov (United States)

    Thomas, Craig W; Vanderford, Marsha L; Crouse Quinn, Sandra

    2008-10-01

    Evaluating emergency risk communications is fraught with challenges since communication can be approached from both a systemic and programmatic level. Therefore, one must consider stakeholders' perspectives, effectiveness issues, standards of evidence and utility, and channels of influence (e.g., mass media and law enforcement). Evaluation issues related to timing, evaluation questions, methods, measures, and accountability are raised in this dialogue with emergency risk communication specialists. Besides the usual evaluation competencies, evaluators in this area need to understand and work collaboratively with stakeholders and be attuned to the dynamic contextual nature of emergency risk communications. Sample resources and measures are provided here to aid in this emerging and exciting field of evaluation.

  6. Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

    Science.gov (United States)

    Clyde, Merlise A.; Palmieri Weber, Rachel; Iversen, Edwin S.; Poole, Elizabeth M.; Doherty, Jennifer A.; Goodman, Marc T.; Ness, Roberta B.; Risch, Harvey A.; Rossing, Mary Anne; Terry, Kathryn L.; Wentzensen, Nicolas; Whittemore, Alice S.; Anton-Culver, Hoda; Bandera, Elisa V.; Berchuck, Andrew; Carney, Michael E.; Cramer, Daniel W.; Cunningham, Julie M.; Cushing-Haugen, Kara L.; Edwards, Robert P.; Fridley, Brooke L.; Goode, Ellen L.; Lurie, Galina; McGuire, Valerie; Modugno, Francesmary; Moysich, Kirsten B.; Olson, Sara H.; Pearce, Celeste Leigh; Pike, Malcolm C.; Rothstein, Joseph H.; Sellers, Thomas A.; Sieh, Weiva; Stram, Daniel; Thompson, Pamela J.; Vierkant, Robert A.; Wicklund, Kristine G.; Wu, Anna H.; Ziogas, Argyrios; Tworoger, Shelley S.; Schildkraut, Joellen M.

    2016-01-01

    Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted. PMID:27698005

  7. 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.

  8. How to interpret a small increase in AUC with an additional risk prediction marker: decision analysis comes through

    NARCIS (Netherlands)

    Baker, Stuart G.; Schuit, Ewoud; Steyerberg, Ewout W.; Pencina, Michael J.; Vickers, Andrew; Vickers, Andew; Moons, Karel G. M.; Mol, Ben W. J.; Lindeman, Karen S.

    2014-01-01

    An important question in the evaluation of an additional risk prediction marker is how to interpret a small increase in the area under the receiver operating characteristic curve (AUC). Many researchers believe that a change in AUC is a poor metric because it increases only slightly with the

  9. A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model.

    Science.gov (United States)

    Harrison, David A; Parry, Gareth J; Carpenter, James R; Short, Alasdair; Rowan, Kathy

    2007-04-01

    To develop a new model to improve risk prediction for admissions to adult critical care units in the UK. Prospective cohort study. The setting was 163 adult, general critical care units in England, Wales, and Northern Ireland, December 1995 to August 2003. Patients were 216,626 critical care admissions. None. The performance of different approaches to modeling physiologic measurements was evaluated, and the best methods were selected to produce a new physiology score. This physiology score was combined with other information relating to the critical care admission-age, diagnostic category, source of admission, and cardiopulmonary resuscitation before admission-to develop a risk prediction model. Modeling interactions between diagnostic category and physiology score enabled the inclusion of groups of admissions that are frequently excluded from risk prediction models. The new model showed good discrimination (mean c index 0.870) and fit (mean Shapiro's R 0.665, mean Brier's score 0.132) in 200 repeated validation samples and performed well when compared with recalibrated versions of existing published risk prediction models in the cohort of patients eligible for all models. The hypothesis of perfect fit was rejected for all models, including the Intensive Care National Audit & Research Centre (ICNARC) model, as is to be expected in such a large cohort. The ICNARC model demonstrated better discrimination and overall fit than existing risk prediction models, even following recalibration of these models. We recommend it be used to replace previously published models for risk adjustment in the UK.

  10. 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

    Background/Objectives: 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. AIMS: 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. Methods: 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. Results: 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. Conclusions: 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. PMID:27336038

  11. Risk evaluation system for facility safeguards and security planning

    International Nuclear Information System (INIS)

    Udell, C.J.; Carlson, R.L.

    1987-01-01

    The Risk Evaluation System (RES) is an integrated approach to determining safeguards and security effectiveness and risk. RES combines the planning and technical analysis into a format that promotes an orderly development of protection strategies, planing assumptions, facility targets, vulnerability and risk determination, enhancement planning, and implementation. In addition, the RES computer database program enhances the capability of the analyst to perform a risk evaluation of the facility. The computer database is menu driven using data input screens and contains an algorithm for determining the probability of adversary defeat and risk. Also, base case and adjusted risk data records can be maintained and accessed easily

  12. Risk evaluation system for facility safeguards and security planning

    International Nuclear Information System (INIS)

    Udell, C.J.; Carlson, R.L.

    1987-01-01

    The Risk Evaluation System (RES) is an integrated approach to determining safeguards and security effectiveness and risk. RES combines the planning and technical analysis into a format that promotes an orderly development of protection strategies, planning assumptions, facility targets, vulnerability and risk determination, enhancement planning, and implementation. In addition, the RES computer database program enhances the capability of the analyst to perform a risk evaluation of the facility. The computer database is menu driven using data input screens and contains an algorithm for determining the probability of adversary defeat and risk. Also, base case and adjusted risk data records can be maintained and accessed easily

  13. The gamma knife: Dose and risk evaluation

    International Nuclear Information System (INIS)

    Jones, E.D.; Alesso, H.P.; Banks, W.W.; Rathbun, P.A.

    1992-01-01

    This paper outlines a risk analysis approach designed to identify and assess most likely failure modes and high-risk, human initiated actions for nuclear medical devices. This approach is being developed under the auspices of the US Nuclear Regulatory Commission, Office of Nuclear Material Safety and Safeguards. The methodology is initiated intended to assess risk associated with the use of the Leksell Gamma Unit (LGU) or gamma knife, a gamma stereotactic radiosurgical device

  14. 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

  15. How to interpret a small increase in AUC with an additional risk prediction marker: decision analysis comes through.

    Science.gov (United States)

    Baker, Stuart G; Schuit, Ewoud; Steyerberg, Ewout W; Pencina, Michael J; Vickers, Andrew; Vickers, Andew; Moons, Karel G M; Mol, Ben W J; Lindeman, Karen S

    2014-09-28

    An important question in the evaluation of an additional risk prediction marker is how to interpret a small increase in the area under the receiver operating characteristic curve (AUC). Many researchers believe that a change in AUC is a poor metric because it increases only slightly with the addition of a marker with a large odds ratio. Because it is not possible on purely statistical grounds to choose between the odds ratio and AUC, we invoke decision analysis, which incorporates costs and benefits. For example, a timely estimate of the risk of later non-elective operative delivery can help a woman in labor decide if she wants an early elective cesarean section to avoid greater complications from possible later non-elective operative delivery. A basic risk prediction model for later non-elective operative delivery involves only antepartum markers. Because adding intrapartum markers to this risk prediction model increases AUC by 0.02, we questioned whether this small improvement is worthwhile. A key decision-analytic quantity is the risk threshold, here the risk of later non-elective operative delivery at which a patient would be indifferent between an early elective cesarean section and usual care. For a range of risk thresholds, we found that an increase in the net benefit of risk prediction requires collecting intrapartum marker data on 68 to 124 women for every correct prediction of later non-elective operative delivery. Because data collection is non-invasive, this test tradeoff of 68 to 124 is clinically acceptable, indicating the value of adding intrapartum markers to the risk prediction model. Copyright © 2014 John Wiley & Sons, Ltd.

  16. Comparative evaluation of risks at regional level

    International Nuclear Information System (INIS)

    Aigueperse, J.; Anguenot, F.; Hardy, S.

    1990-01-01

    In this paper we give the risks for the population of the South-East part of France. The risks are given, in case of chronic intake for energy production (coal and uranium mines, oil refineries, electricity production), for natural and medical irradiation (X-ray radiography-radiotherapy) and for domestic risks (use of chemical products). In case of accidents, the risks are studied in the optic of territorial gestion: transport of dangerous products (natural gas-fuel-chemical products) fires or explosions in chemical manufactories, water pollution by manufactories and agricultural uses [fr

  17. 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....

  18. AN EVALUATION OF RISK MANAGEMENT STRATEGIES FOR DAIRY FARMS

    OpenAIRE

    Bosch, Darrell J.; Johnson, Christian J.

    1992-01-01

    Variability in feed prices and crop yields are important sources of risk to dairy farmers. A simulation model of a representative dairy farm was used to evaluate crop insurance and hedging as risk management strategies. These strategies lowered expected net returns but also reduced risk. The preferred set of strategies at lower levels of risk aversion included hedging and crop insurance, although a base scenario in which no risk management strategies were employed was also efficient. The pref...

  19. Abalone Muscle Texture Evaluation and Prediction Based on TPA Experiment

    Directory of Open Access Journals (Sweden)

    Jiaxu Dong

    2017-01-01

    Full Text Available The effects of different heat treatments on abalones’ texture properties and sensory characteristics were studied. Thermal processing of abalone muscle was analyzed to determine the optimal heat treatment condition based on fuzzy evaluation. The results showed that heat treatment at 85°C for 1 hour had certain desirable effects on the properties of the abalone meat. Specifically, a back propagation (BP neural network was introduced to predict the equations of statistically significant sensory hardness, springiness, and smell using the texture data gained through TPA (texture profile analysis experiments as input and sensory evaluation data as the desired output. The final outcome was that the predictability was proved to be satisfactory, with an average error of 6.93%.

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

    Science.gov (United States)

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

    2017-03-01

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

  1. The Search Performance Evaluation and Prediction in Exploratory Search

    OpenAIRE

    LIU, FEI

    2016-01-01

    The exploratory search for complex search tasks requires an effective search behavior model to evaluate and predict user search performance. Few studies have investigated the relationship between user search behavior and search performance in exploratory search. This research adopts a mixed approach combining search system development, user search experiment, search query log analysis, and multivariate regression analysis to resolve the knowledge gap. Through this study, it is shown that expl...

  2. A Standardized Evaluation System for Decadal Climate Prediction

    Science.gov (United States)

    Kadow, C.; Cubasch, U.

    2012-12-01

    The evaluation of decadal prediction systems is a scientific challenge as well as a technical challenge in the climate research. The major project MiKlip (www.fona-miklip.de) for medium-term climate prediction funded by the Federal Ministry of Education and Research in Germany (BMBF) has the aim to create a model system that can provide reliable decadal forecasts on climate and weather. The model system to be developed will be novel in several aspects, with great challenges for the methodology development. This concerns especially the determination of the initial conditions, the inclusion into the model of processes relevant to decadal predictions, the increase of the spatial resolution through regionalisation, the improvement or adjustment of statistical post-processing, and finally the synthesis and validation of the entire model system. Therefore, a standardized evaluation system will be part of the MiKlip system to validate it - developed by the project 'Integrated data and evaluation system for decadal scale prediction' (INTEGRATION). The presentation gives an overview of the different linkages of such a project, shows the different development stages and gives an outlook for users and possible end users in climate service. The technical interface combines all projects inside of MiKlip and invites them to participate in a common evaluation system. The system design and the validation strategy from a standalone tool in the beginning to a user friendly web based system using GRID technologies to an integrated part of the operational MiKlip system for industry and society will give the opportunity to enhance the MiKlip strategy. First results of different possibilities of such a system will be shown to present the scientific background through Taylor diagrams, ensemble skill scores and e.g. climatological means to show the usability and possibilities of MiKlip and the INTEGRATION project.

  3. 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...

  4. Selenium deficiency risk predicted to increase under future climate change.

    Science.gov (United States)

    Jones, Gerrad D; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P; Seneviratne, Sonia I; Smith, Pete; Winkel, Lenny H E

    2017-03-14

    Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

  5. The term 'risk' and its evaluation bases

    International Nuclear Information System (INIS)

    Brueckner, R.

    1976-01-01

    The term risk, the risk itself and its application for radiation exposure in practised medicine is presented from the following points of view: Life expectation, susceptibility to sickness and permanent inability to work, impaired professional and earning capacity, work accident and sickness. (HP) [de

  6. Driving Strategic Risk Planning With Predictive Modelling For Managerial Accounting

    DEFF Research Database (Denmark)

    Nielsen, Steen; Pontoppidan, Iens Christian

    for managerial accounting and shows how it can be used to determine the impact of different types of risk assessment input parameters on the variability of important outcome measures. The purpose is to: (i) point out the theoretical necessity of a stochastic risk framework; (ii) present a stochastic framework......Currently, risk management in management/managerial accounting is treated as deterministic. Although it is well-known that risk estimates are necessarily uncertain or stochastic, until recently the methodology required to handle stochastic risk-based elements appear to be impractical and too...... mathematical. The ultimate purpose of this paper is to “make the risk concept procedural and analytical” and to argue that accountants should now include stochastic risk management as a standard tool. Drawing on mathematical modelling and statistics, this paper methodically develops risk analysis approach...

  7. Evaluating and comparing algorithms for respiratory motion prediction

    International Nuclear Information System (INIS)

    Ernst, F; Dürichen, R; Schlaefer, A; Schweikard, A

    2013-01-01

    In robotic radiosurgery, it is necessary to compensate for systematic latencies arising from target tracking and mechanical constraints. This compensation is usually achieved by means of an algorithm which computes the future target position. In most scientific works on respiratory motion prediction, only one or two algorithms are evaluated on a limited amount of very short motion traces. The purpose of this work is to gain more insight into the real world capabilities of respiratory motion prediction methods by evaluating many algorithms on an unprecedented amount of data. We have evaluated six algorithms, the normalized least mean squares (nLMS), recursive least squares (RLS), multi-step linear methods (MULIN), wavelet-based multiscale autoregression (wLMS), extended Kalman filtering, and ε-support vector regression (SVRpred) methods, on an extensive database of 304 respiratory motion traces. The traces were collected during treatment with the CyberKnife (Accuray, Inc., Sunnyvale, CA, USA) and feature an average length of 71 min. Evaluation was done using a graphical prediction toolkit, which is available to the general public, as is the data we used. The experiments show that the nLMS algorithm—which is one of the algorithms currently used in the CyberKnife—is outperformed by all other methods. This is especially true in the case of the wLMS, the SVRpred, and the MULIN algorithms, which perform much better. The nLMS algorithm produces a relative root mean square (RMS) error of 75% or less (i.e., a reduction in error of 25% or more when compared to not doing prediction) in only 38% of the test cases, whereas the MULIN and SVRpred methods reach this level in more than 77%, the wLMS algorithm in more than 84% of the test cases. Our work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation. Additionally, we have seen that the structure of a patient

  8. Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting

    Science.gov (United States)

    Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Tedtaotao, Maria; Smith, Gregory A.

    2017-01-01

    Background: Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm (“profile”) incorporating phenotypic and, more uniquely, genotypic risk factors. Methods and Results: In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. Conclusion: The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes. PMID:28890908

  9. 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

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

    Science.gov (United States)

    Mehra, Lucky K; Cowger, Christina; Gross, Kevin; Ojiambo, Peter S

    2016-01-01

    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 assessment of

  11. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea.

    Directory of Open Access Journals (Sweden)

    Aesun Shin

    Full Text Available PURPOSE: Incidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection. METHODS: Gender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30-80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics. RESULTS: Age, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women. CONCLUSIONS: Colorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

  12. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea.

    Science.gov (United States)

    Shin, Aesun; Joo, Jungnam; Yang, Hye-Ryung; Bak, Jeongin; Park, Yunjin; Kim, Jeongseon; Oh, Jae Hwan; Nam, Byung-Ho

    2014-01-01

    Incidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection. Gender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30-80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics. Age, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women. Colorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

  13. Predicting Free Flow Speed and Crash Risk of Bicycle Traffic Flow Using Artificial Neural Network Models

    Directory of Open Access Journals (Sweden)

    Cheng Xu

    2015-01-01

    Full Text Available Free flow speed is a fundamental measure of traffic performance and has been found to affect the severity of crash risk. However, the previous studies lack analysis and modelling of impact factors on bicycles’ free flow speed. The main focus of this study is to develop multilayer back propagation artificial neural network (BPANN models for the prediction of free flow speed and crash risk on the separated bicycle path. Four different models with considering different combinations of input variables (e.g., path width, traffic condition, bicycle type, and cyclists’ characteristics were developed. 459 field data samples were collected from eleven bicycle paths in Hangzhou, China, and 70% of total samples were used for training, 15% for validation, and 15% for testing. The results show that considering the input variables of bicycle types and characteristics of cyclists will effectively improve the accuracy of the prediction models. Meanwhile, the parameters of bicycle types have more significant effect on predicting free flow speed of bicycle compared to those of cyclists’ characteristics. The findings could contribute for evaluation, planning, and management of bicycle safety.

  14. Understanding risk evaluation and mitigation strategies in organ transplantation.

    Science.gov (United States)

    Gabardi, Steven

    2011-07-01

    The United States Food and Drug Administration (FDA) Amendments Act of 2007 mandated that Risk Evaluation and Mitigation Strategies (REMS) be required of manufacturers. These REMS are strategies implemented to manage known or potential risks associated with drugs and to ensure ongoing pharmacovigilance throughout the life of a pharmaceutical product, including once the product becomes available as generic. The elements of an individual REMS program consist of three levels: medication guide or patient package insert, communication plan, and elements to assure safe use (ETASU). A medication guide or patient package insert is used to help prevent serious adverse events, aid in patient decision making, and enhance drug adherence. Communication plans are used to educate health care providers and to encourage their compliance with REMS. The ETASU is a restrictive process that is implemented when it is deemed necessary to ensure that patients have safe access to products with known serious risks that would otherwise be unavailable. To review the components of REMS and specifically assess their impact on health care providers practicing within the organ transplantation arena, a literature search of the MEDLINE database (January 2007-December 2010) was performed, and published materials from the FDA and its Web site were also reviewed. In transplantation, REMS programs exist for both everolimus (medication guide and communication plan) and sirolimus (medication guide). The FDA has stated that all mycophenolic acid derivatives will be subject to a proposed REMS that has not yet been approved; however, both branded mycophenolic acid agents already have approved medication guides. The REMS are a permanent fixture in the development and marketing of pharmaceutical agents, and their further implementation in solid organ transplantation is inevitable. Transplantation providers should take a proactive role in patient education and implementation of REMS within the therapeutic area

  15. Conventional myelography - evaluation of risk and benefit

    International Nuclear Information System (INIS)

    Hentschel, F.

    1989-01-01

    While the benefit and methodic risk of conventional myelography (KMG) are known, a radiation risk of 0.04 to 0.9 annual radiation-induced cancers can be estimated for all inhabitants of the GDR, dependent on the investigated region and the technique used. An optimized technique can reduce the radiation burden to 50 or 25%. With comparable values of benefit and radiation risk spinal CT and KMG are not contradictory but complementary investigations. Alternative methods (MRT, US) must not be discussed from the standpoint of radiation burden, but according to their availability and their methodic limitations. (author)

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

    Science.gov (United States)

    Walsh, Colin G; Sharman, Kavya; Hripcsak, George

    2017-12-01

    Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration

  17. 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

  18. 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% (pAVC scores (pAVC 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.

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

    Science.gov (United States)

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

    2014-01-01

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

  20. 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

  1. Predictive accuracy of the PanCan lung cancer risk prediction model - external validation based on CT from the Danish Lung Cancer Screening Trial

    International Nuclear Information System (INIS)

    Winkler Wille, Mathilde M.; Dirksen, Asger; Riel, Sarah J. van; Jacobs, Colin; Scholten, Ernst T.; Ginneken, Bram van; Saghir, Zaigham; Pedersen, Jesper Holst; Hohwue Thomsen, Laura; Skovgaard, Lene T.

    2015-01-01

    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. 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. 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; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. (orig.)

  2. Predictive accuracy of the PanCan lung cancer risk prediction model - external validation based on CT from the Danish Lung Cancer Screening Trial

    Energy Technology Data Exchange (ETDEWEB)

    Winkler Wille, Mathilde M.; Dirksen, Asger [Gentofte Hospital, Department of Respiratory Medicine, Hellerup (Denmark); Riel, Sarah J. van; Jacobs, Colin; Scholten, Ernst T.; Ginneken, Bram van [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen (Netherlands); Saghir, Zaigham [Herlev Hospital, Department of Respiratory Medicine, Herlev (Denmark); Pedersen, Jesper Holst [Copenhagen University Hospital, Department of Thoracic Surgery, Rigshospitalet, Koebenhavn Oe (Denmark); Hohwue Thomsen, Laura [Hvidovre Hospital, Department of Respiratory Medicine, Hvidovre (Denmark); Skovgaard, Lene T. [University of Copenhagen, Department of Biostatistics, Koebenhavn Oe (Denmark)

    2015-10-15

    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. 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. 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; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. (orig.)

  3. 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.

  4. The usefulness of myocardial SPECT for the preoperative cardiac risk evaluation in noncardiac surgery

    International Nuclear Information System (INIS)

    Lim, Seok Tae; Lee, Dong Soo; Kang, Won Jon; Chung, June Key; Lee, Myung Chul

    1999-01-01

    We investigated whether myocardial SPECT had additional usefulness to clinical, functional or surgical indices for the preoperative evaluation of cardiac risks in noncardiac surgery. 118 patients ( M: F=66: 52, 62.7±10.5 years) were studied retrospectively. Eighteen underwent vascular surgeries and 100 nonvascular surgeries. Rest Tl-201/ stress Tc-99m-MIBI SPECT was performed before operation and cardiac events (hard event: cardiac death and myocardial infarction; soft event: ischemic ECG change, congestive heat failure and unstable angina) were surveyed through perioperative periods (14.6±5.6 days). Clinical risk indices, functional capacity, surgery procedures and SPECT findings were tested for their predictive values of perioperative cardiac events. Peri-operative cardiac events occurred in 25 patients (3 hard events and 22 soft events). Clinical risk indices, surgical procedure risks and SPECT findings but functional capacity were predictive of cardiac events. Reversible perfusion decrease was a better predictor than persistent decrease. Multivariate analysis sorted out surgical procedure risk (p=0.0018) and SPECT findings (p=0.0001) as significant risk factors. SPECT could re-stratify perioperative cardiac risks in patients ranked with surgical procedures. We conclude that myocardial SPECT provides additional predictive value to surgical type risks as well as clinical indexes or functional capacity for the prediction of preoperative cardiac events in noncardiac surgery

  5. 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.

  6. The potential of large studies for building genetic risk prediction models

    Science.gov (United States)

    NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

  7. 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…

  8. Fall Risk Index predicts functional decline regardless of fall experiences among community-dwelling elderly.

    Science.gov (United States)

    Ishimoto, Yasuko; Wada, Taizo; Kasahara, Yoriko; Kimura, Yumi; Fukutomi, Eriko; Chen, Wenling; Hirosaki, Mayumi; Nakatsuka, Masahiro; Fujisawa, Michiko; Sakamoto, Ryota; Ishine, Masayuki; Okumiya, Kiyohito; Otsuka, Kuniaki; Matsubayashi, Kozo

    2012-10-01

    The 21-item Fall Risk Index (FRI-21) has been used to detect elderly persons at risk for falls. The aim of this longitudinal study was to evaluate the FRI-21 as a predictor of decline in basic activities of daily living (BADL) among Japanese community-dwelling elderly persons independent of fall risk. The study population consisted of 518 elderly participants aged 65 years and older who were BADL independent at baseline in Tosa, Japan. We examined risk factors for BADL decline from 2008 to 2009 by multiple logistic regression analysis on the FRI-21 and other functional status measures in all participants. We carried out the same analysis in selected participants who had no experience of falls to remove the effect of falls. A total of 45 of 518 participants showed decline in BADL within 1 year. Multivariate logistic regression analysis showed that age (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.05-1.20), FRI-21 ≥ 10 (OR 3.81, 95% CI 1.49-9.27), intellectual activity dependence (OR 3.25, 95% CI 1.42-7.44) and history of osteoarthropathy (OR 3.17, 95% CI 1.40-7.21) were significant independent risk factors for BADL decline within 1 year. FRI-21 ≥ 10 and intellectual activity dependence (≤ 3) remained significant predictors, even in selected non-fallers. FRI-21 ≥ 10 and intellectual activity dependence were significant predictive factors of BADL decline, regardless of fall experience, after adjustment for confounding variables. The FRI-21 is a brief, useful tool not only for predicting falls, but also future decline in functional ability in community-dwelling elderly persons. © 2012 Japan Geriatrics Society.

  9. Predictive risk modelling under different data access scenarios: who is identified as high risk and for how long?

    Science.gov (United States)

    Johnson, Tracy L; Kaldor, Jill; Sutherland, Kim; Humphries, Jacob; Jorm, Louisa R; Levesque, Jean-Frederic

    2018-01-01

    Objective This observational study critically explored the performance of different predictive risk models simulating three data access scenarios, comparing: (1) sociodemographic and clinical profiles; (2) consistency in high-risk designation across models; and (3) persistence of high-risk status over time. Methods Cross-sectional health survey data (2006–2009) for more than 260 000 Australian adults 45+ years were linked to longitudinal individual hospital, primary care, pharmacy and mortality data. Three risk models predicting acute emergency hospitalisations were explored, simulating conditions where data are accessed through primary care practice management systems, or through hospital-based electronic records, or through a hypothetical ‘full’ model using a wider array of linked data. High-risk patients were identified using different risk score thresholds. Models were reapplied monthly for 24 months to assess persistence in high-risk categorisation. Results The three models displayed similar statistical performance. Three-quarters of patients in the high-risk quintile from the ‘full’ model were also identified using the primary care or hospital-based models, with the remaining patients differing according to age, frailty, multimorbidity, self-rated health, polypharmacy, prior hospitalisations and imminent mortality. The use of higher risk prediction thresholds resulted in lower levels of agreement in high-risk designation across models and greater morbidity and mortality in identified patient populations. Persistence of high-risk status varied across approaches according to updated information on utilisation history, with up to 25% of patients reassessed as lower risk within 1 year. Conclusion/implications Small differences in risk predictors or risk thresholds resulted in comparatively large differences in who was classified as high risk and for how long. Pragmatic predictive risk modelling design decisions based on data availability or projected

  10. Commentary on Holmes et al. (2007): resolving the debate on when extinction risk is predictable.

    Science.gov (United States)

    Ellner, Stephen P; Holmes, Elizabeth E

    2008-08-01

    We reconcile the findings of Holmes et al. (Ecology Letters, 10, 2007, 1182) that 95% confidence intervals for quasi-extinction risk were narrow for many vertebrates of conservation concern, with previous theory predicting wide confidence intervals. We extend previous theory, concerning the precision of quasi-extinction estimates as a function of population dynamic parameters, prediction intervals and quasi-extinction thresholds, and provide an approximation that specifies the prediction interval and threshold combinations where quasi-extinction estimates are precise (vs. imprecise). This allows PVA practitioners to define the prediction interval and threshold regions of safety (low risk with high confidence), danger (high risk with high confidence), and uncertainty.

  11. Scaling range sizes to threats for robust predictions of risks to biodiversity.

    Science.gov (United States)

    Keith, David A; Akçakaya, H Resit; Murray, Nicholas J

    2018-04-01

    Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red-list assessments for decades, appropriate spatial scales of AOO for predicting risks of species' extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale-sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1-1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer-scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid-measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape-scale threats to species and ecosystems. © 2017 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

  12. Risk adjusted surgical audit in gynaecological oncology: P-POSSUM does not predict outcome.

    Science.gov (United States)

    Das, N; Talaat, A S; Naik, R; Lopes, A D; Godfrey, K A; Hatem, M H; Edmondson, R J

    2006-12-01

    To assess the Physiological and Operative Severity Score for the enumeration of mortality and morbidity (POSSUM) and its validity for use in gynaecological oncology surgery. All patients undergoing gynaecological oncology surgery at the Northern Gynaecological Oncology Centre (NGOC) Gateshead, UK over a period of 12months (2002-2003) were assessed prospectively. Mortality and morbidity predictions using the Portsmouth modification of the POSSUM algorithm (P-POSSUM) were compared to the actual outcomes. Performance of the model was also evaluated using the Hosmer and Lemeshow Chi square statistic (testing the goodness of fit). During this period 468 patients were assessed. The P-POSSUM appeared to over predict mortality rates for our patients. It predicted a 7% mortality rate for our patients compared to an observed rate of 2% (35 predicted deaths in comparison to 10 observed deaths), a difference that was statistically significant (H&L chi(2)=542.9, d.f. 8, prisk of mortality for gynaecological oncology patients undergoing surgery. The P-POSSUM algorithm will require further adjustments prior to adoption for gynaecological cancer surgery as a risk adjusted surgical audit tool.

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

    Science.gov (United States)

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

    2009-09-01

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

  14. Credit Risk Evaluation of Swedish SMEs : A Banking Sector Perspective

    OpenAIRE

    Hörstedt, Maria; Linjamaa, Johanna

    2015-01-01

    As a result from the latest financial crisis, the banking industry has undergone major modifications during the last years in order to limit banks’ risks. A vast majority of existing literature tends to focus upon credit risk evaluation methods and techniques mainly concerning quantitative measures and large companies. Thus, the lack of research regarding credit risk evaluation of SMEs is profound, especially considering Sweden. With the dominant market share of SMEs compared to large corpora...

  15. A predictive tool to estimate the risk of axillary metastases in breast cancer patients with negative axillary ultrasound

    DEFF Research Database (Denmark)

    Meretoja, T J; Heikkilä, P S; Mansfield, A S

    2014-01-01

    of this study was to evaluate the risk factors for axillary metastases in breast cancer patients with negative preoperative axillary ultrasound. METHODS: A total of 1,395 consecutive patients with invasive breast cancer and SNB formed the original patient series. A univariate analysis was conducted to assess...... risk factors for axillary metastases. Binary logistic regression analysis was conducted to form a predictive model based on the risk factors. The predictive model was first validated internally in a patient series of 566 further patients and then externally in a patient series of 2,463 patients from......BACKGROUND: Sentinel node biopsy (SNB) is the "gold standard" in axillary staging in clinically node-negative breast cancer patients. However, axillary treatment is undergoing a paradigm shift and studies are being conducted on whether SNB may be omitted in low-risk patients. The purpose...

  16. Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

    Directory of Open Access Journals (Sweden)

    Benjamin A Goldstein

    2014-08-01

    Full Text Available Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers. Materials & Methods: We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD in the Atherosclerosis Risk in Communities (ARIC cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.Results: The addition of a GRS to a clinical risk score (CRS improves both discrimination and calibration for CHD in ARIC. Results are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor SNPs are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider. Conclusion: The proposed method facilitates the standardized incorporation of a GRS in risk assessment.

  17. Testing the Predictive Validity of the Hendrich II Fall Risk Model.

    Science.gov (United States)

    Jung, Hyesil; Park, Hyeoun-Ae

    2018-03-01

    Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

  18. HOW INTERNAL RISK - BASED AUDIT APPRAISES THE EVALUATION OF RISKS MANAGEMENT

    Directory of Open Access Journals (Sweden)

    N. Dorosh

    2017-09-01

    Full Text Available The article deals with the nature and function of the internal risk-based audit process approach to create patterns of risks and methods of evaluation. Deals with the relationship between the level of maturity of the risk of the company and the method of risk-based internal audit. it was emphasized that internal auditing provides an independent and objective opinion to an organization’s management as to whether its risks are being managed to acceptable levels.

  19. 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

  20. 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.

  1. 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...... events and only marginally by the premium associated with normal price fluctuations....

  2. Risk Prediction Models for Other Cancers or Multiple Sites

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing other multiple cancers 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.

  3. Genetically Predicted Body Mass Index and Breast Cancer Risk

    DEFF Research Database (Denmark)

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

    2016-01-01

    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 enviro...

  4. 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) ...

  5. 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.

  6. 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.

  7. 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.

  8. Performance of genetic risk factors in prediction of trichloroethylene induced hypersensitivity syndrome.

    Science.gov (United States)

    Dai, Yufei; Chen, Ying; Huang, Hanlin; Zhou, Wei; Niu, Yong; Zhang, Mingrong; Bin, Ping; Dong, Haiyan; Jia, Qiang; Huang, Jianxun; Yi, Juan; Liao, Qijun; Li, Haishan; Teng, Yanxia; Zang, Dan; Zhai, Qingfeng; Duan, Huawei; Shen, Juan; He, Jiaxi; Meng, Tao; Sha, Yan; Shen, Meili; Ye, Meng; Jia, Xiaowei; Xiang, Yingping; Huang, Huiping; Wu, Qifeng; Shi, Mingming; Huang, Xianqing; Yang, Huanming; Luo, Longhai; Li, Sai; Li, Lin; Zhao, Jinyang; Li, Laiyu; Wang, Jun; Zheng, Yuxin

    2015-07-20

    Trichloroethylene induced hypersensitivity syndrome is dose-independent and potentially life threatening disease, which has become one of the serious occupational health issues and requires intensive treatment. To discover the genetic risk factors and evaluate the performance of risk prediction model for the disease, we conducted genomewide association study and replication study with total of 174 cases and 1761 trichloroethylene-tolerant controls. Fifty seven SNPs that exceeded the threshold for genome-wide significance (P < 5 × 10(-8)) were screened to relate with the disease, among which two independent SNPs were identified, that is rs2857281 at MICA (odds ratio, 11.92; P meta = 1.33 × 10(-37)) and rs2523557 between HLA-B and MICA (odds ratio, 7.33; P meta = 8.79 × 10(-35)). The genetic risk score with these two SNPs explains at least 20.9% of the disease variance and up to 32.5-fold variation in inter-individual risk. Combining of two SNPs as predictors for the disease would have accuracy of 80.73%, the area under receiver operator characteristic curves (AUC) scores was 0.82 with sensitivity of 74% and specificity of 85%, which was considered to have excellent discrimination for the disease, and could be considered for translational application for screening employees before exposure.

  9. 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.

  10. Evaluating a Health Risk Reduction Program.

    Science.gov (United States)

    Nagelberg, Daniel B.

    1981-01-01

    A health risk reduction program at Bowling Green State University (Ohio) tested the efficacy of peer education against the efficacy of returning (by mail) health questionnaire results. A peer health education program did not appear to be effective in changing student attitudes or lifestyles; however, the research methodology may not have been…

  11. Research items regarding seismic residual risk evaluation

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-08-15

    After learning the Fukushima Dai-ichi NPP severe accidents in 2011, the government investigation committee proposed the effective use of probabilistic safety assessment (PSA), and now it is required to establish new safety rules reflecting the results of probabilistic risk assessment (PRA) and proposed severe accident measures. Since the Seismic Design Guide has been revised on September 19, 2006, JNES has been discussing seismic PRA (Levels 1-3) methods to review licensees' residual risk assessment while preparing seismic PRA models. Meanwhile, new safety standards for light water reactors are to be issued and enforced on July 2013, which require the residual risk of tsunami, in addition to earthquakes, should be lowered as much as possible. The Fukushima accidents raised the problems related to risk assessment, e.g. approaches based on multi-hazard (earthquake and tsunami), multi-unit, multi-site, and equipment's common cause failure. This fiscal year, while performing seismic and/or tsunami PRA to work on these problems, JNES picked up the equipment whose failure greatly contribute to core damage, surveyed accident management measures on those equipment as well as effectiveness to reduce core damage probability. (author)

  12. Preoperative evaluation : risk management and implementation aspects

    NARCIS (Netherlands)

    Klei, W.A. van

    2002-01-01

    In preoperative risk management the anesthesiologist uses diagnostic information to estimate the probability of outcomes and to decide on the anesthetic strategy in a particular patient. The aim of this thesis was explore to what extent simple patient characteristics, particularly obtained from

  13. Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians.

    Science.gov (United States)

    Kanjilal, S; Rao, V S; Mukherjee, M; Natesha, B K; Renuka, K S; Sibi, K; Iyengar, S S; Kakkar, Vijay V

    2008-01-01

    The increasing pressure on health resources has led to the emergence of risk assessment as an essential tool in the management of cardiovascular disease (CVD). Concern exists regarding the validity of their generalization to all populations. Existing risk scoring models do not incorporate emerging 'novel' risk factors. In this context, the aim of the study was to examine the relevance of British, European, and Framingham predictive CVD risk scores to the asymptomatic high risk Indian population. Blood samples drawn from the participants were analyzed for various 'traditional' and 'novel' biomarkers, and their CVD risk factor profiling was also done. The Framingham model defined only 5% of the study cohort to be at high risk, which appears to be an underestimation of CVD risk in this genetically predisposed population. These subjects at high risk had significantly elevated levels of lipid, pro-inflammatory, pro-thrombotic, and serological markers. It is more relevant to develop risk predictive scores for application to the Indian population. This study substantiates the argument that alternative approaches to risk stratification are required in order to make them more adaptable and applicable to different populations with varying risk factor and disease patterns.

  14. Evaluation of an inpatient fall risk screening tool to identify the most critical fall risk factors in inpatients.

    Science.gov (United States)

    Hou, Wen-Hsuan; Kang, Chun-Mei; Ho, Mu-Hsing; Kuo, Jessie Ming-Chuan; Chen, Hsiao-Lien; Chang, Wen-Yin

    2017-03-01

    To evaluate the accuracy of the inpatient fall risk screening tool and to identify the most critical fall risk factors in inpatients. Variations exist in several screening tools applied in acute care hospitals for examining risk factors for falls and identifying high-risk inpatients. Secondary data analysis. A subset of inpatient data for the period from June 2011-June 2014 was extracted from the nursing information system and adverse event reporting system of an 818-bed teaching medical centre in Taipei. Data were analysed using descriptive statistics, receiver operating characteristic curve analysis and logistic regression analysis. During the study period, 205 fallers and 37,232 nonfallers were identified. The results revealed that the inpatient fall risk screening tool (cut-off point of ≥3) had a low sensitivity level (60%), satisfactory specificity (87%), a positive predictive value of 2·0% and a negative predictive value of 99%. The receiver operating characteristic curve analysis revealed an area under the curve of 0·805 (sensitivity, 71·8%; specificity, 78%). To increase the sensitivity values, the Youden index suggests at least 1·5 points to be the most suitable cut-off point for the inpatient fall risk screening tool. Multivariate logistic regression analysis revealed a considerably increased fall risk in patients with impaired balance and impaired elimination. The fall risk factor was also significantly associated with days of hospital stay and with admission to surgical wards. The findings can raise awareness about the two most critical risk factors for falls among future clinical nurses and other healthcare professionals and thus facilitate the development of fall prevention interventions. This study highlights the needs for redefining the cut-off points of the inpatient fall risk screening tool to effectively identify inpatients at a high risk of falls. Furthermore, inpatients with impaired balance and impaired elimination should be closely

  15. 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

  16. 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...... with prudent use of resources while maintaining acceptable system performance. High-risk category units are selected for testing by identification of the presence of specific high-risk factor(s), while disregarding other factors that might also influence the risk. On this basis we argue that the most...... 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...

  17. 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.

  18. From the lab - Predicting Autism in High-Risk Infants | NIH MedlinePlus the Magazine

    Science.gov (United States)

    ... High-Risk Infants Follow us Photo: iStock Predicting Autism in High-Risk Infants AN NIH-SUPPORTED STUDY ... high-risk, 6-month-old infants will develop autism spectrum disorder by age 2. Such a tool ...

  19. 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

  20. 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.

  1. A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics

    Science.gov (United States)

    Yuan, Kai; Liu, Jian; Liu, Kaipei; Tan, Tianyuan

    2015-01-01

    Background Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk. Methods This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors – device, structure, load and special operation – a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method. Conclusion Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic. PMID:25789859

  2. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    International Nuclear Information System (INIS)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong; Wu, Chun-Gen; Fang, Wen; Chen, Li; Guo, Jin-He; Deng, Gang; Zhu, Guang-Yu; Teng, Gao-Jun

    2017-01-01

    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.

  3. 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.

  4. Evaluating investments in renewable energy under policy risks

    International Nuclear Information System (INIS)

    Gatzert, Nadine; Vogl, Nikolai

    2016-01-01

    The considerable amount of required infrastructure and renewable energy investments expected in the forthcoming years also implies an increasingly relevant contribution of private and institutional investors. In this context, especially regulatory and policy risks have been shown to play a major role for investors when evaluating investments in renewable energy and should thus also be taken into account in risk assessment and when deriving risk-return profiles. In this paper, we provide a stochastic model framework to quantify policy risks associated with renewable energy investments (e.g. a retrospective reduction of a feed-in tariff), thereby also taking into account energy price risk, resource risk, and inflation risk. The model is illustrated by means of simulations and scenario analyses, and it makes use of expert estimates and fuzzy set theory for quantifying policy risks. Our numerical results for a portfolio of onshore wind farms in Germany and France show that policy risk can strongly impact risk-return profiles, and that cross-country diversification effects can considerably decrease the overall risk for investors. - Highlights: •Quantification of policy risks associated with renewable energy investments. •Results emphasize that policy risk has a major impact on risk and return. •Study of the cross-country diversification potential. •Cross-country diversification can considerably decrease the risk for an investor.

  5. Prediction and theory evaluation: the case of light bending.

    Science.gov (United States)

    Brush, S G

    1989-12-01

    Is a theory that makes successful predictions of new facts better than one that does not? Does a fact provide better evidence for a theory if it was not known before being deduced from the theory? These questions can be answered by analyzing historical cases. Einstein's successful prediction of gravitational light bending from his general theory of relativity has been presented as an important example of how "real" science works (in contrast to alleged pseudosciences like psychoanalysis). But, while this success gained favorable publicity for the theory, most scientists did not give it any more weight than the deduction of the advance of Mercury's perihelion (a phenomenon known for several decades). The fact that scientists often use the word "prediction" to describe the deduction of such previously known facts suggests that novelty may be of little importance in evaluating theories. It may even detract from the evidential value of a fact, until it is clear that competing theories cannot account for the new fact.

  6. Evaluating decadal predictions of northern hemispheric cyclone frequencies

    Directory of Open Access Journals (Sweden)

    Tim Kruschke

    2014-04-01

    Full Text Available Mid-latitudinal cyclones are a key factor for understanding regional anomalies in primary meteorological parameters such as temperature or precipitation. Extreme cyclones can produce notable impacts on human society and economy, for example, by causing enormous economic losses through wind damage. Based on 41 annually initialised (1961–2001 hindcast ensembles, this study evaluates the ability of a single-model decadal forecast system (MPI-ESM-LR to provide skilful probabilistic three-category forecasts (enhanced, normal or decreased of winter (ONDJFM extra-tropical cyclone frequency over the Northern Hemisphere with lead times from 1 yr up to a decade. It is shown that these predictions exhibit some significant skill, mainly for lead times of 2–5 yr, especially over the North Atlantic and Pacific. Skill for intense cyclones is generally higher than for all detected systems. A comparison of decadal hindcasts from two different initialisation techniques indicates that initialising from reanalysis fields yields slightly better results for the first forecast winter (month 10–15, while initialisation based on an assimilation experiment provides better skill for lead times between 2 and 5 yr. The reasons and mechanisms behind this predictive skill are subject to future work. Preliminary analyses suggest a strong relationship of the model's skill over the North Atlantic with the ability to predict upper ocean temperatures modulating lower troposphere baroclinicity for the respective area and time scales.

  7. 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.

  8. 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.

  9. 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

    2017-01-01

    Background Identification of risk factors for lower extremity (LE) injury in sport and military/first-responder occupations is required to inform injury prevention strategies. Objective To determine if poor movement quality is associated with LE injury in sport and military/first-responder occupations. Material and methods Five 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 PRISMA guidelines were followed. Two independent authors assessed the quality [Downs and Black (DB) criteria] and level of evidence (Oxford Centre of Evidence-Based Medicine model). Results 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). Four studies considered interrelationships between risk factors, seven reported diagnostic accuracy and none tested an intervention program 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. Conclusions 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 pre-participation screening and LE injury prevention programs through high quality randomized controlled trials targeting individuals at greater risk of injury based upon screening tests with validated test properties. PMID:27935483

  10. 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

  11. Risk identification and evaluation of customer collaboration in product development

    Directory of Open Access Journals (Sweden)

    Xuefeng Zhang

    2015-05-01

    Full Text Available Purpose: The purpose of this paper is to identify risk factors that caused by customer collaboration in new product development systematically, and propose an approach to judge which risk factors are critical and catch substantial attention. Design/methodology/approach: This study identifies risk factors according to the results of case studies of enterprises in china. On this basis, an improved rough number approach is put forward to evaluate the importance of risk factors. Findings: Firstly, classify risk factors into three aspects. Then, present a risk factor set, which include thirty-seven risk factors. At last, determine which risk factors are critical by using an improved rough number approach. Originality/value: Considering there are few researches studying comprehensive risk factors of customer collaboration and assessing them, this paper explores a risk factor set of customer collaboration in product development stage, and proposes a novel approach, which can help to solve the problem of subjective, vague and lack of prior information of evaluation, to evaluate risk factors.

  12. 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

  13. Microclimate risk evaluation in agroindustrial work environments

    International Nuclear Information System (INIS)

    Monarca, D.; Porceddu, P.; Cecchini, M.; Babucci, V.

    2005-01-01

    The concept of workers' safety includes not only the prevention from accidents, as a result of improvements in the devices utilised, but also their welfare and comfort microclimate, i.e., the complex of parameters that affect the thermal exchange between workers and the surrounding environment is one of the main factors that affect the working environment. The paper describes the main risk assessment methodologies and the main actions to be taken for improving the working environment and workers' personal comfort [it

  14. 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.)

  15. Evaluating the benefits of risk prevention initiatives

    Science.gov (United States)

    Di Baldassarre, G.

    2012-04-01

    The likelihood and adverse impacts of water-related disasters, such as floods and landslides, are increasing in many countries because of changes in climate and land-use. This presentation illustrates some preliminary results of a comprehensive demonstration of the benefits of risk prevention measures, carried out within the European FP7 KULTURisk project. The study is performed by using a variety of case studies characterised by diverse socio-economic contexts, different types of water-related hazards (floods, debris flows and landslides, storm surges) and space-time scales. In particular, the benefits of state-of-the-art prevention initiatives, such as early warning systems, non-structural options (e.g. mapping and planning), risk transfer strategies (e.g. insurance policy), and structural measures, are showed. Lastly, the importance of homogenising criteria to create hazard inventories and build memory, efficient risk communication and warning methods as well as active dialogue with and between public and private stakeholders, is highlighted.

  16. Evaluation of Deep Learning Models for Predicting CO2 Flux

    Science.gov (United States)

    Halem, M.; Nguyen, P.; Frankel, D.

    2017-12-01

    Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

  17. 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.

  18. 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

  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. 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.

  1. Evaluation of test intervals strategies with a risk monitor

    International Nuclear Information System (INIS)

    Soerman, J.

    2005-01-01

    The Swedish nuclear power utility Oskarshamn Power Group (OKG), is investigating how the use of a risk monitor can facilitate and improve risk-informed decision-making at their nuclear power plants. The intent is to evaluate if risk-informed decision-making can be accepted. A pilot project was initiated and carried out in 2004. The project included investigating if a risk monitor can be used for optimising test intervals for diesel- and gas turbine generators with regard to risk level. The Oskarhamn 2 (O2), PSA Level 1 model was converted into a risk monitor using RiskSpectrum RiskWatcher (RSRW) software. The converted PSA model included the complete PSA model for the power operation mode. RSRW then performs a complete requantification for every analysis. Time dependent reliability data are taken into account, i.e. a shorter test interval will increases the components availability (possibility to e.g. start on demand). The converted O2 model was then used to investigate whether it would be possible to balance longer test intervals for diesel generators, gas turbine generators and high pressure injection system with shorter test intervals for the low pressure injection system, while maintaining a low risk level at the plant. The results show that a new mixture of test intervals can be implemented with only marginally changes in the risk calculated with the risk monitor model. The results indicate that the total number of test activities for the systems included in the pilot study could be reduced by 20% with a maintained level of risk. A risk monitor taking into account the impact from test intervals in availability calculations for components is well suited for evaluation of test interval strategies. It also enables the analyst to evaluate the risk level over a period of time including the impact the actual status of the plant may have on the risk level. (author)

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  3. Project finance risk evaluation of the Electric power industry of Serbia

    International Nuclear Information System (INIS)

    Makajic Nikolic, Dragana; Jednak, Sandra; Benkovic, Sladana; Poznanic, Vladimir

    2011-01-01

    From the aspect of the development of a country, the energy sector represents a domain of strategic interest. Generation and use of energy resources most often belongs to the public sector, and are most often under the influence of the government in most countries. This paper analyzes the risks that are characteristic to the business of the public enterprise, Electric Power Industry of Serbia (EPS). EPS has started its restructuring and is adjusting to changes and challenges imposed by the launched reforms in the energy sector. However, due to certain limitations, it is still not possible to implement its complete restructuring and modernization. The paper aims to point at the risks a potential strategic partner faces. The risks have been identified as commercial, financial and political, classification immanent for project finance, and their evaluation was done using Failure Mode and Effects Analysis (FMEA). Risk analysis was performed based on current conditions for two potential scenarios that predict different types of changes in the analyzed period. The results of the analysis show that the potential strategic partner should pay special attention to price risks, estimation, investments, project activity neglect, quasi-risks and debt collection. - Highlights: → Paper analyze all risks characteristic for business running of the public enterprise EPS. → Potential strategic partner faces with the commercial, financial and political risks. → Risk analysis was done using FMEA. → Results are indicating high risk of investing in EPS. → The highest risks are commercial risks, especially price risks.

  4. Risk evaluation of remedial alternatives for the Hanford Site

    International Nuclear Information System (INIS)

    Clark, S.W.; Lane, N.K.; Swenson, L.

    1994-01-01

    Risk assessment is one of the many tools used to evaluate and select remedial alternatives and evaluate the risk associated with selected remedial alternatives during and after implementation. The risk evaluation of remedial alternatives (RERA) is performed to ensure selected alternatives are protective of human health and the environment. Final remedy selection is promulgated in a record of decision (ROD) and risks of the selected alternatives are documented. Included in the ROD documentation are the risk-related analyses for long-term effectiveness, short-term effectiveness, and overall protection of human health and the environment including how a remedy will eliminate, reduce or control risks and whether exposure will be reduced to acceptable levels. A major goal of RERA in the process leading to a ROD is to provide decision-makers with specific risk information that may be needed to choose among alternatives. For the Hanford Site, there are many considerations that must be addressed from a risk perspective. These include the large size of the Hanford Site, the presence of both chemical and radionuclide contamination, one likelihood of many analogues sites, public and worker health and safety, and stakeholder concern with ecological impacts from site contamination and remedial actions. A RERA methodology has been promulgated to (1) identify the points in the process leading to a ROD where risk assessment input is either required or desirable and (2) provide guidance on how to evaluate risks associated with remedial alternatives under consideration. The methodology and evaluations parallel EPA guidance requiring consideration of short-term impacts and the overall protectiveness of remedial actions for evaluating potential human health and ecological risks during selection of remedial alternatives, implementation of remedial measures, and following completion of remedial action

  5. Evaluation of the efficacy of six nutritional screening tools to predict malnutrition in the elderly.

    Science.gov (United States)

    Poulia, Kalliopi-Anna; Yannakoulia, Mary; Karageorgou, Dimitra; Gamaletsou, Maria; Panagiotakos, Demosthenes B; Sipsas, Nikolaos V; Zampelas, Antonis

    2012-06-01

    Malnutrition in the elderly is a multifactorial problem, more prevalent in hospitals and care homes. The absence of a gold standard in evaluating nutritional risk led us to evaluate the efficacy of six nutritional screening tools used in the elderly. Two hundred forty eight elderly patients (129 men, 119 female women, aged 75.2 ± 8.5 years) were examined. Nutritional screening was performed on admission using the following tools: Nutritional Risk Index (NRI), Geriatric Nutritional Risk Index (GNRI), Subjective Global Assessment (SGA), Mini Nutritional Assessment - Screening Form (MNA-SF), Malnutrition Universal Screening Tool (MUST) and Nutritional Risk Screening 2002 (NRS 2002). A combined index for malnutrition was also calculated. Nutritional risk and/or malnutrition varied greatly, ranging from 47.2 to 97.6%, depending on the nutritional screening tool used. MUST was the most valid screening tool (validity coefficient = 0.766, CI 95%: 0.690-0.841), while SGA was in better agreement with the combined index (κ = 0.707, p = 0.000). NRS 2002 although was the highest in sensitivity (99.4%), it was the lowest in specificity (6.1%) and positive predictive value (68.2%). MUST seem to be the most valid in the evaluation of the risk for malnutrition in the elderly upon admission to the hospital. NRS 2002 was found to overestimate nutritional risk in the elderly. Copyright © 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

  6. Update on breast cancer risk prediction and prevention.

    Science.gov (United States)

    Sestak, Ivana; Cuzick, Jack

    2015-02-01

    Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk. The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings. There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.

  7. 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

    1996-12-31

    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)

  8. 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)

  9. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Science.gov (United States)

    King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

    2011-01-01

    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.

  10. 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.

  11. Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

    Science.gov (United States)

    Luo, Wei; Tran, Truyen; Berk, Michael; Venkatesh, Svetha

    2016-01-01

    Background Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. Objective The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. Methods We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). Results The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. Conclusions This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk. PMID:27400764

  12. 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...

  13. Chemical risk evaluation data sources summary

    International Nuclear Information System (INIS)

    Carrillo, M.

    2013-01-01

    The power point presentation is about danger identification, threshold, data concentration PreR, data concentration PosT, timely and probabilistic estimation, deterministic and precise estimates, consumers loyal, Probabilistic models, Random sampling (Monte Carlo simulation), modeling for the evaluation of acute and chronic dietary, dietary exposure assessments acute

  14. A Predictive Model for Estimation Risk of Proliferative Lupus Nephritis

    Directory of Open Access Journals (Sweden)

    Dong-Ni Chen

    2018-01-01

    Conclusion: This study developed and validated a model including demographic and clinical indices to evaluate the probability of presenting proliferative LN to guide therapeutic decisions and outcomes.

  15. A re-evaluation of PETROTOX for predicting acute and chronic toxicity of petroleum substances.

    Science.gov (United States)

    Redman, Aaron D; Parkerton, Thomas F; Leon Paumen, Miriam; Butler, Josh D; Letinski, Daniel J; den Haan, Klass

    2017-08-01

    The PETROTOX model was developed to perform aquatic hazard assessment of petroleum substances based on substance composition. The model relies on the hydrocarbon block method, which is widely used for conducting petroleum substance risk assessments providing further justification for evaluating model performance. Previous work described this model and provided a preliminary calibration and validation using acute toxicity data for limited petroleum substance. The objective of the present study was to re-evaluate PETROTOX using expanded data covering both acute and chronic toxicity endpoints on invertebrates, algae, and fish for a wider range of petroleum substances. The results indicated that recalibration of 2 model parameters was required, namely, the algal critical target lipid body burden and the log octanol-water partition coefficient (K OW ) limit, used to account for reduced bioavailability of hydrophobic constituents. Acute predictions from the updated model were compared with observed toxicity data and found to generally be within a factor of 3 for algae and invertebrates but overestimated fish toxicity. Chronic predictions were generally within a factor of 5 of empirical data. Furthermore, PETROTOX predicted acute and chronic hazard classifications that were consistent or conservative in 93 and 84% of comparisons, respectively. The PETROTOX model is considered suitable for the purpose of characterizing petroleum substance hazard in substance classification and risk assessments. Environ Toxicol Chem 2017;36:2245-2252. © 2017 SETAC. © 2017 SETAC.

  16. In-treatment stroke volume predicts cardiovascular risk in hypertension

    DEFF Research Database (Denmark)

    Lønnebakken, Mai T; Gerdts, Eva; Boman, Kurt

    2011-01-01

    , the prespecified primary study endpoint, was assessed in Cox regression analysis using data from baseline and annual follow-up visits in 855 patients during 4.8 years of randomized losartan-based or atenolol-based treatment in the Losartan Intervention For Endpoint reduction in hypertension (LIFE) echocardiography...... with higher risk of cardiovascular events {hazard ratio 1.69 per 1 SD (6 ml/m2.04) lower stroke volume [95% confidence interval (CI) 1.35–2.11], P secondary model also independent of stress-corrected midwall shortening......, hence, adds information on cardiovascular risk in treated hypertensive patients beyond assessment of left ventricular structure alone....

  17. Risk-informed prediction of feeder end of life

    International Nuclear Information System (INIS)

    Jyrkama, M.; Pandey, M.

    2011-01-01

    The operating life of feeder piping is negatively impacted by flow accelerated corrosion (FAC). In this study, an assessment of a large set of inspection data reveals that FAC in feeders is a relatively stationary process, with variability only at the local scale. Given the added uncertainty from inspection coverage, a new method for estimating the thinning rate and feeder EOL is developed using a probabilistic approach. The results of the study illustrate the benefits of the methodology in supporting risk-informed decision making at the station by quantifying the present and incremental risk in the feeder system over time. (author)

  18. Risk-informed prediction of feeder end of life

    Energy Technology Data Exchange (ETDEWEB)

    Jyrkama, M.; Pandey, M. [Univ. of Waterloo, Ontario (Canada)

    2011-07-01

    The operating life of feeder piping is negatively impacted by flow accelerated corrosion (FAC). In this study, an assessment of a large set of inspection data reveals that FAC in feeders is a relatively stationary process, with variability only at the local scale. Given the added uncertainty from inspection coverage, a new method for estimating the thinning rate and feeder EOL is developed using a probabilistic approach. The results of the study illustrate the benefits of the methodology in supporting risk-informed decision making at the station by quantifying the present and incremental risk in the feeder system over time. (author)

  19. Evaluation of risk effective STIs with specific application to diesels

    International Nuclear Information System (INIS)

    Vesely, W.E.; Samanta, P.K.; Ginzburg, T.

    1987-01-01

    From a risk standpoint, the objective of surveillance tests is to control the risk arising from failures which can occur while the component is on standby. At the same time, risks caused by the test from test-caused failures and test-caused degradations need also to be controlled. Risk-acceptable test intervals balance these risks in an attempt to achieve an acceptable low, overall risk. Risk and reliability approaches are presented which allow risk-acceptable test intervals to be determined for any component. To provide focus for the approaches, diesels are specifically evaluated, however, the approaches can be applied not only to diesels, but to any component with suitable data. Incorporation of the approaches in personal computer (PC) software is discussed, which can provide tools for the regulator or plant personnel for determining acceptable diesel test intervals for any plant specific or generic application. The FRANTIC III computer code was run to validate the approaches and to evaluate specific issues associated with determining risk effective test intervals for diesels. Using the approaches presented, diesel accident unavailability can be more effectively monitored and be controlled on a plant-specific or generic basis. Test intervals can be made more risk effective than they are now, producing more acceptable accident unavailabilities. The methods presented are one step toward performance-based technical specifications, which more directly control risks

  20. Polygenic risk predicts obesity in both white and black young adults.

    Directory of Open Access Journals (Sweden)

    Benjamin W Domingue

    Full Text Available To test transethnic replication of a genetic risk score for obesity in white and black young adults using a national sample with longitudinal data.A prospective longitudinal study using the National Longitudinal Study of Adolescent Health Sibling Pairs (n = 1,303. Obesity phenotypes were measured from anthropometric assessments when study members were aged 18-26 and again when they were 24-32. Genetic risk scores were computed based on published genome-wide association study discoveries for obesity. Analyses tested genetic associations with body-mass index (BMI, waist-height ratio, obesity, and change in BMI over time.White and black young adults with higher genetic risk scores had higher BMI and waist-height ratio and were more likely to be obese compared to lower genetic risk age-peers. Sibling analyses revealed that the genetic risk score was predictive of BMI net of risk factors shared by siblings. In white young adults only, higher genetic risk predicted increased risk of becoming obese during the study period. In black young adults, genetic risk scores constructed using loci identified in European and African American samples had similar predictive power.Cumulative information across the human genome can be used to characterize individual level risk for obesity. Measured genetic risk accounts for only a small amount of total variation in BMI among white and black young adults. Future research is needed to identify modifiable environmental exposures that amplify or mitigate genetic risk for elevated BMI.

  1. Polygenic risk predicts obesity in both white and black young adults.

    Science.gov (United States)

    Domingue, Benjamin W; Belsky, Daniel W; Harris, Kathleen Mullan; Smolen, Andrew; McQueen, Matthew B; Boardman, Jason D

    2014-01-01

    To test transethnic replication of a genetic risk score for obesity in white and black young adults using a national sample with longitudinal data. A prospective longitudinal study using the National Longitudinal Study of Adolescent Health Sibling Pairs (n = 1,303). Obesity phenotypes were measured from anthropometric assessments when study members were aged 18-26 and again when they were 24-32. Genetic risk scores were computed based on published genome-wide association study discoveries for obesity. Analyses tested genetic associations with body-mass index (BMI), waist-height ratio, obesity, and change in BMI over time. White and black young adults with higher genetic risk scores had higher BMI and waist-height ratio and were more likely to be obese compared to lower genetic risk age-peers. Sibling analyses revealed that the genetic risk score was predictive of BMI net of risk factors shared by siblings. In white young adults only, higher genetic risk predicted increased risk of becoming obese during the study period. In black young adults, genetic risk scores constructed using loci identified in European and African American samples had similar predictive power. Cumulative information across the human genome can be used to characterize individual level risk for obesity. Measured genetic risk accounts for only a small amount of total variation in BMI among white and black young adults. Future research is needed to identify modifiable environmental exposures that amplify or mitigate genetic risk for elevated BMI.

  2. Evaluating shielding effectiveness for reducing space radiation cancer risks

    International Nuclear Information System (INIS)

    Cucinotta, Francis A.; Kim, Myung-Hee Y.; Ren, Lei

    2006-01-01

    We discuss calculations of probability distribution functions (PDF) representing uncertainties in projecting fatal cancer risk from galactic cosmic rays (GCR) and solar particle events (SPE). The PDFs are used in significance tests for evaluating the effectiveness of potential radiation shielding approaches. Uncertainties in risk coefficients determined from epidemiology data, dose and dose-rate reduction factors, quality factors, and physics models of radiation environments are considered in models of cancer risk PDFs. Competing mortality risks and functional correlations in radiation quality factor uncertainties are included in the calculations. We show that the cancer risk uncertainty, defined as the ratio of the upper value of 95% confidence interval (CI) to the point estimate is about 4-fold for lunar and Mars mission risk projections. For short-stay lunar missions ( 180d) or Mars missions, GCR risks may exceed radiation risk limits that are based on acceptable levels of risk. For example, the upper 95% CI exceeding 10% fatal risk for males and females on a Mars mission. For reducing GCR cancer risks, shielding materials are marginally effective because of the penetrating nature of GCR and secondary radiation produced in tissue by relativistic particles. At the present time, polyethylene or carbon composite shielding cannot be shown to significantly reduce risk compared to aluminum shielding based on a significance test that accounts for radiobiology uncertainties in GCR risk projection

  3. The Development of a Plant Risk Evaluation (PRE) Tool for Assessing the Invasive Potential of Ornamental Plants

    OpenAIRE

    Conser, Christiana; Seebacher, Lizbeth; Fujino, David W.; Reichard, Sarah; DiTomaso, Joseph M.

    2015-01-01

    Weed Risk Assessment (WRA) methods for evaluating invasiveness in plants have evolved rapidly in the last two decades. Many WRA tools exist, but none were specifically designed to screen ornamental plants prior to being released into the environment. To be accepted as a tool to evaluate ornamental plants for the nursery industry, it is critical that a WRA tool accurately predicts non-invasiveness without falsely categorizing them as invasive. We developed a new Plant Risk Evaluation (PRE) too...

  4. Validation of risk stratification schemes for predicting stroke and thromboembolism in patients with atrial fibrillation: nationwide cohort study.

    Science.gov (United States)

    Olesen, Jonas Bjerring; Lip, Gregory Y H; Hansen, Morten Lock; Hansen, Peter Riis; Tolstrup, Janne Schurmann; Lindhardsen, Jesper; Selmer, Christian; Ahlehoff, Ole; Olsen, Anne-Marie Schjerning; Gislason, Gunnar Hilmar; Torp-Pedersen, Christian

    2011-01-31

    To evaluate the individual risk factors composing the CHADS(2) (Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes, previous Stroke) score and the CHA(2)DS(2)-VASc (CHA(2)DS(2)-Vascular disease, Age 65-74 years, Sex category) score and to calculate the capability of the schemes to predict thromboembolism. Registry based cohort study. Nationwide data on patients admitted to hospital with atrial fibrillation. Population All patients with atrial fibrillation not treated with vitamin K antagonists in Denmark in the period 1997-2006. Stroke and thromboembolism. Of 121,280 patients with non-valvular atrial fibrillation, 73,538 (60.6%) fulfilled the study inclusion criteria. In patients at "low risk" (score = 0), the rate of thromboembolism per 100 person years was 1.67 (95% confidence interval 1.47 to 1.89) with CHADS(2) and 0.78 (0.58 to 1.04) with CHA(2)DS(2)-VASc at one year's follow-up. In patients at "intermediate risk" (score = 1), this rate was 4.75 (4.45 to 5.07) with CHADS(2) and 2.01 (1.70 to 2.36) with CHA(2)DS(2)-VASc. The rate of thromboembolism depended on the individual risk factors composing the scores, and both schemes underestimated the risk associated with previous thromboembolic events. When patients were categorised into low, intermediate, and high risk groups, C statistics at 10 years' follow-up were 0.812 (0.796 to 0.827) with CHADS(2) and 0.888 (0.875 to 0.900) with CHA(2)DS(2)-VASc. The risk associated with a specific risk stratification score depended on the risk factors composing the score. CHA(2)DS(2)-VASc performed better than CHADS(2) in predicting patients at high risk, and those categorised as low risk by CHA(2)DS(2)-VASc were truly at low risk for thromboembolism.

  5. 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

  6. 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

  7. Genetic liability for schizophrenia predicts risk of immune disorders

    NARCIS (Netherlands)