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

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.

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...

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

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....

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.

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.

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.)

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...

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.

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.

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)

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.

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

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.

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

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.

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....

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.

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.

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

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.

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

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.

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.

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.

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.

Assessment of three risk evaluation systems for patients aged ≥70 in East China: performance of SinoSCORE, EuroSCORE II and the STS risk evaluation system.

Science.gov (United States)

Shan, Lingtong; Ge, Wen; Pu, Yiwei; Cheng, Hong; Cang, Zhengqiang; Zhang, Xing; Li, Qifan; Xu, Anyang; Wang, Qi; Gu, Chang; Zhang, Yangyang

2018-01-01

To assess and compare the predictive ability of three risk evaluation systems (SinoSCORE, EuroSCORE II and the STS risk evaluation system) in patients aged ≥70, and who underwent coronary artery bypass grafting (CABG) in East China. Three risk evaluation systems were applied to 1,946 consecutive patients who underwent isolated CABG from January 2004 to September 2016 in two hospitals. Patients were divided into two subsets according to their age: elderly group (age ≥70) with a younger group (age evaluation system were 0.78(0.64)%, 1.43(1.14)% and 0.78(0.77)%, respectively. SinoSCORE achieved the best discrimination (the area under the receiver operating characteristic curve (AUC) = 0.829), followed by the STS risk evaluation system (AUC = 0.790) and EuroSCORE II (AUC = 0.769) in the entire cohort. In the elderly group, the observed mortality rate was 4.82% while it was 1.38% in the younger group. SinoSCORE (AUC = .829) also achieved the best discrimination in the elderly group, followed by the STS risk evaluation system (AUC = .730) and EuroSCORE II (AUC = 0.640) while all three risk evaluation systems all had good performances in the younger group. SinoSCORE, EuroSCORE II and the STS risk evaluation system all achieved positive calibrations in the entire cohort and subsets. The performance of the three risk evaluation systems was not ideal in the entire cohort. In the elderly group, SinoSCORE appeared to achieve better predictive efficiency than EuroSCORE II and the STS risk evaluation system.

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...

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

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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.

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.

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?

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.

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.

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...

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.

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.

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...

[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

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.

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

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.

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.

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.

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.

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.

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

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

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.

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...

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.

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

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

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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.

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 ...

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.

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

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...

Crash Prediction and Risk Evaluation Based on Traffic Analysis Zones

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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.

[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

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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

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

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.

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

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.

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

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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.

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

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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

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

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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.

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 ...

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

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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

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

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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.

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

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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.

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

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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

Predicting Rib Fracture Risk With Whole-Body Finite Element Models: Development and Preliminary Evaluation of a Probabilistic Analytical Framework

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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

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...

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...

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

Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

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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.

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...

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

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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.

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

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

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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.

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

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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.

Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

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Liu, Yang; Traskin, Mikhail; Lorch, Scott A; George, Edward I; Small, Dylan

2015-03-01

A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.

Epidemiological geomatics in evaluation of mine risk education in Afghanistan: introducing population weighted raster maps

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Andersson Neil

2006-01-01

Full Text Available Abstract Evaluation of mine risk education in Afghanistan used population weighted raster maps as an evaluation tool to assess mine education performance, coverage and costs. A stratified last-stage random cluster sample produced representative data on mine risk and exposure to education. Clusters were weighted by the population they represented, rather than the land area. A "friction surface" hooked the population weight into interpolation of cluster-specific indicators. The resulting population weighted raster contours offer a model of the population effects of landmine risks and risk education. Five indicator levels ordered the evidence from simple description of the population-weighted indicators (level 0, through risk analysis (levels 1–3 to modelling programme investment and local variations (level 4. Using graphic overlay techniques, it was possible to metamorphose the map, portraying the prediction of what might happen over time, based on the causality models developed in the epidemiological analysis. Based on a lattice of local site-specific predictions, each cluster being a small universe, the "average" prediction was immediately interpretable without losing the spatial complexity.

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

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).

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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

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.

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.

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.

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

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...

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.

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...

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.

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

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)

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.

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.

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.

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

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...

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.

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.

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.

Developing a clinical utility framework to evaluate prediction models in radiogenomics

Science.gov (United States)

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

2015-03-01

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

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.

Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data.

Science.gov (United States)

Rahman, M Shafiqur; Sultana, Mahbuba

2017-02-23

When developing risk models for binary data with small or sparse data sets, the standard maximum likelihood estimation (MLE) based logistic regression faces several problems including biased or infinite estimate of the regression coefficient and frequent convergence failure of the likelihood due to separation. The problem of separation occurs commonly even if sample size is large but there is sufficient number of strong predictors. In the presence of separation, even if one develops the model, it produces overfitted model with poor predictive performance. Firth-and logF-type penalized regression methods are popular alternative to MLE, particularly for solving separation-problem. Despite the attractive advantages, their use in risk prediction is very limited. This paper evaluated these methods in risk prediction in comparison with MLE and other commonly used penalized methods such as ridge. The predictive performance of the methods was evaluated through assessing calibration, discrimination and overall predictive performance using an extensive simulation study. Further an illustration of the methods were provided using a real data example with low prevalence of outcome. The MLE showed poor performance in risk prediction in small or sparse data sets. All penalized methods offered some improvements in calibration, discrimination and overall predictive performance. Although the Firth-and logF-type methods showed almost equal amount of improvement, Firth-type penalization produces some bias in the average predicted probability, and the amount of bias is even larger than that produced by MLE. Of the logF(1,1) and logF(2,2) penalization, logF(2,2) provides slight bias in the estimate of regression coefficient of binary predictor and logF(1,1) performed better in all aspects. Similarly, ridge performed well in discrimination and overall predictive performance but it often produces underfitted model and has high rate of convergence failure (even the rate is higher than that

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.

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.

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

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Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

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

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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.

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

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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.

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

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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

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

Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent.

Directory of Open Access Journals (Sweden)

Yan Guo

2016-08-01

Full Text Available Observational epidemiological studies have shown that high body mass index (BMI is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors.We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC (cases  =  46,325, controls  =  42,482. We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively.In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]  =  0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32 × 10-10. The associations were similar for both premenopausal (OR   =   0.44, 95% CI:0.31-0.62, p  =  9.91 × 10-8 and postmenopausal breast cancer (OR  =  0.57, 95% CI: 0.46-0.71, p  =  1.88 × 10-8. This association was replicated in the data from the DRIVE consortium (OR  =  0.72, 95% CI: 0.60-0.84, p   =   1.64 × 10-7. Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs in association with breast cancer risk at p < 0.05; for 16 of them, the

Alternative Testing Methods for Predicting Health Risk from Environmental Exposures

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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.

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

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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.

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

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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.

Development and evaluation of an automated fall risk assessment system.

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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.

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)

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.

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)

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

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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.

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

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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

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

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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.

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

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

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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.

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

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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.

The evaluation-mediation hypothesis: does the specification of potential side effects influence the perceived risk of medication?

International Nuclear Information System (INIS)

Reimer, T.

1998-01-01

Full text of publication follows: starting from the assumptions of support theory, this project analyzed the extent to which the specification of potential side effects influences the perceived risk associated, with a particular medication. Respondents were presented with an instruction leaflet for a medication which indicated (a) the overall probability that a side effect will occur or (b) the probability of occurrence of several specific side effects. Support theory predicts that the cognitive availability of potential side effects and therefore the perceived risk increases as a function of the specificity with which the side effects are presented. In contrast the evaluation-mediation hypothesis predicts that a more detailed presentation of potential side effects enhances the perceived quality of the information leaflet and thereby leads to a reduction of perceived risk. Support for the evaluation-mediation hypothesis was found in a series of studies which included the editing hypothesis and the elaboration likelihood model as additional explanations: the more detailed the information about potential side effects, the lower the estimated risk of suffering a side effect on taking the medication. As predicted, the influence of presentation specificity on perceived risk was mediated almost exclusively by the perceived quality of the information leaflet. A current series of studies seeks to support the evaluation-mediation hypothesis in a completely different domain, the perceived risk of environmental pollution by motor vehicles. (author)

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs.

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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.

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.

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.

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.

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.

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

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

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

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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…

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

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.)

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.)

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.

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

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

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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.

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.

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.

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...

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.

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.

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

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.

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

The potential of large studies for building genetic risk prediction models

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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

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.

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.

Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

Science.gov (United States)

Lin, Xin; Moussawi, Alaa; Korniss, Gyorgy; Bakdash, Jonathan Z; Szymanski, Boleslaw K

2017-07-27

Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model's prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.

Evaluation of LRINEC Scale Feasibility for Predicting Outcomes of Fournier Gangrene.

Science.gov (United States)

Kincius, Marius; Telksnys, Titas; Trumbeckas, Darius; Jievaltas, Mindaugas; Milonas, Daimantas

2016-08-01

Fournier gangrene (FG) is a fulminant necrotizing infection of the perineal, perianal, and periurethral tissues. The Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) scale is used for diagnosis of necrotizing fasciitis. However, data on its relevance and usefulness in FG are lacking. The aim of this study was to evaluate the utility of the LRINEC scale in predicting the outcome of FG. This retrospective case study included 41 patents with FG treated at our institution from 2000 to 2013. The patients were divided into survivors and non-survivors. The mortality rate was 22%. The median age (75 vs. 62.5 y; p = 0.013), rate of co-existing diabetes mellitus (66.7% vs. 3.1%; p < 0.001), and median affected skin surface (4% vs. 1%; p < 0.001) were greater in the non-survivors. Seven of nine patients (77.8%) who did not survive (compared with 37.5% who survived) had a polymicrobial infection (p = 0.032). Of all the causative pathogens isolated, Proteus mirabilis was more common in non-survivors (55.6% vs. 6.3%; p = 0.001). The median calculated LRINEC score for survivors was 5 compared with 10 for the non-survivors (p < 0.001). Regression analysis showed that all the aforementioned variables, except for polymicrobial culture, were significant risk factors for predicting death. The area under the receiver operating characteristic curve for the LRINEC score was the highest, 0.976 (95% confidence interval 0.872-0.999; p < 0.0001), and the cut-off value was ≥9 with 93.7% specificity and 100% susceptibility for the prediction of a lethal outcome. The LRINEC score could be used for prediction of disease severity and outcomes. A threshold of 9 could be a high-value predictor of death during the initial evaluation of patients with FG.

A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

Science.gov (United States)

Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

2012-04-01

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the

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

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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.

Efficacy of adjuvant therapy with 3.7 GBq radioactive iodine in intermediate-risk patients with 'higher risk features' and predictive value of postoperative nonstimulated thyroglobulin.

Science.gov (United States)

Rosario, Pedro W; Mourão, Gabriela F; Calsolari, Maria Regina

2016-11-01

This study evaluated the efficacy of adjuvant therapy with 3.7 GBq radioactive iodine (RAI) in patients with papillary thyroid carcinoma (PTC) of intermediate risk with higher risk features and determined the predictive value of postoperative nonstimulated thyroglobulin (Tg). This was a prospective study including 85 patients with PTC of intermediate risk and higher risk features: tumor greater than 1 cm and aggressive histological subtype or vascular invasion; and/or more than three positive lymph node (LN) or LN greater than 1.5 cm or showing macroscopic extracapsular extension; and/or a combination of tumor greater than 4 cm, microscopic extrathyroidal extension, aggressive histology, and LN metastases (cN1). After thyroidectomy, all patients had nonstimulated Tg of at least 0.3 ng/ml and ultrasonography showed no anomalies. When evaluated 12 months after RAI therapy, an excellent response to initial therapy was achieved in 61 patients (71.7%). Structural disease was detected in five patients (5.9%). During follow-up, 6/80 patients (7.5%) without structural disease 1 year after RAI developed relapse. In the last assessment, 80 patients (94.1%) had nonstimulated Tg less than 1 ng/ml and no evidence of structural disease. There was no case of death because of the tumor. Postoperative nonstimulated Tg was a predictive factor of the main outcome (structural disease 1 year after RAI or recurrence) and the best cut-off was 1.8 ng/ml (sensitivity: 72.7%, specificity: 83.4%, negative predictive value: 95.4%). In patients with PTC of intermediate risk with higher risk features treated with 3.7 GBq RAI, postoperative nonstimulated Tg up to 1.8 ng/ml was a predictor of low risk of structural disease 1 year after therapy or recurrence.

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.

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.

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...

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.

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

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

Development of a flood-induced health risk prediction model for Africa

Science.gov (United States)

Lee, D.; Block, P. J.

2017-12-01

Globally, many floods occur in developing or tropical regions where the impact on public health is substantial, including death and injury, drinking water, endemic disease, and so on. Although these flood impacts on public health have been investigated, integrated management of floods and flood-induced health risks is technically and institutionally limited. Specifically, while the use of climatic and hydrologic forecasts for disaster management has been highlighted, analogous predictions for forecasting the magnitude and impact of health risks are lacking, as is the infrastructure for health early warning systems, particularly in developing countries. In this study, we develop flood-induced health risk prediction model for African regions using season-ahead flood predictions with climate drivers and a variety of physical and socio-economic information, such as local hazard, exposure, resilience, and health vulnerability indicators. Skillful prediction of flood and flood-induced health risks can contribute to practical pre- and post-disaster responses in both local- and global-scales, and may eventually be integrated into multi-hazard early warning systems for informed advanced planning and management. This is especially attractive for areas with limited observations and/or little capacity to develop flood-induced health risk warning systems.

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.

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

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...

Online gaming and risks predict cyberbullying perpetration and victimization in adolescents.

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Chang, Fong-Ching; Chiu, Chiung-Hui; Miao, Nae-Fang; Chen, Ping-Hung; Lee, Ching-Mei; Huang, Tzu-Fu; Pan, Yun-Chieh

2015-02-01

The present study examined factors associated with the emergence and cessation of youth cyberbullying and victimization in Taiwan. A total of 2,315 students from 26 high schools were assessed in the 10th grade, with follow-up performed in the 11th grade. Self-administered questionnaires were collected in 2010 and 2011. Multiple logistic regression was conducted to examine the factors. Multivariate analysis results indicated that higher levels of risk factors (online game use, exposure to violence in media, internet risk behaviors, cyber/school bullying experiences) in the 10th grade coupled with an increase in risk factors from grades 10 to 11 could be used to predict the emergence of cyberbullying perpetration/victimization. In contrast, lower levels of risk factors in the 10th grade and higher levels of protective factors coupled with a decrease in risk factors predicted the cessation of cyberbullying perpetration/victimization. Online game use, exposure to violence in media, Internet risk behaviors, and cyber/school bullying experiences can be used to predict the emergence and cessation of youth cyberbullying perpetration and victimization.

A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma.

Science.gov (United States)

Tian, Meng-Xin; He, Wen-Jun; Liu, Wei-Ren; Yin, Jia-Cheng; Jin, Lei; Tang, Zheng; Jiang, Xi-Fei; Wang, Han; Zhou, Pei-Yun; Tao, Chen-Yang; Ding, Zhen-Bin; Peng, Yuan-Fei; Dai, Zhi; Qiu, Shuang-Jian; Zhou, Jian; Fan, Jia; Shi, Ying-Hong

2018-01-01

Backgrounds: Regarding the difficulty of CHC diagnosis and potential adverse outcomes or misuse of clinical therapies, an increasing number of patients have undergone liver transplantation, transcatheter arterial chemoembolization (TACE) or other treatments. Objective: To construct a convenient and reliable risk prediction model for identifying high-risk individuals with combined hepatocellular-cholangiocarcinoma (CHC). Methods: 3369 patients who underwent surgical resection for liver cancer at Zhongshan Hospital were enrolled in this study. The epidemiological and clinical characteristics of the patients were collected at the time of tumor diagnosis. Variables ( P model discrimination. Calibration was performed using the Hosmer-Lemeshow test and a calibration curve. Internal validation was performed using a bootstrapping approach. Results: Among the entire study population, 250 patients (7.42%) were pathologically defined with CHC. Age, HBcAb, red blood cells (RBC), blood urea nitrogen (BUN), AFP, CEA and portal vein tumor thrombus (PVTT) were included in the final risk prediction model (area under the curve, 0.69; 95% confidence interval, 0.51-0.77). Bootstrapping validation presented negligible optimism. When the risk threshold of the prediction model was set at 20%, 2.73% of the patients diagnosed with liver cancer would be diagnosed definitely, which could identify CHC patients with 12.40% sensitivity, 98.04% specificity, and a positive predictive value of 33.70%. Conclusions: Herein, the study established a risk prediction model which incorporates the clinical risk predictors and CT/MRI-presented PVTT status that could be adopted to facilitate the diagnosis of CHC patients preoperatively.

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

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...

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.

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.

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.

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.

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...

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.

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.

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.

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.

Predictive value of quantitative dipyridamole-thallium scintigraphy in assessing cardiovascular risk after vascular surgery in diabetes mellitus

International Nuclear Information System (INIS)

Lane, S.E.; Lewis, S.M.; Pippin, J.J.; Kosinski, E.J.; Campbell, D.; Nesto, R.W.; Hill, T.

1989-01-01

Cardiac complications represent a major risk to patients undergoing vascular surgery. Diabetic patients may be particularly prone to such complications due to the high incidence of concomitant coronary artery disease, the severity of which may be clinically unrecognized. Attempts to stratify groups by clinical criteria have been useful but lack the predictive value of currently used noninvasive techniques such as dipyridamole-thallium scintigraphy. One hundred one diabetic patients were evaluated with dipyridamole-thallium scintigraphy before undergoing vascular surgery. The incidence of thallium abnormalities was high (80%) and did not correlate with clinical markers of coronary disease. Even in a subgroup of patients with no overt clinical evidence of underlying heart disease, thallium abnormalities were present in 59%. Cardiovascular complications, however, occurred in only 11% of all patients. Statistically significant prediction of risk was not achieved with simple assessment of thallium results as normal or abnormal. Quantification of total number of reversible defects, as well as assessment of ischemia in the distribution of the left anterior descending coronary artery was required for optimum predictive accuracy. The prevalence of dipyridamole-thallium abnormalities in a diabetic population is much higher than that reported in nondiabetic patients and cannot be predicted by usual clinical indicators of heart disease. In addition, cardiovascular risk of vascular surgery can be optimally assessed by quantitative analysis of dipyridamole-thallium scintigraphy and identification of high- and low-risk subgroups

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

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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.

U.S. Civil Air Show Crashes, 1993 to 2013: Burden, Fatal Risk Factors, and Evaluation of a Risk Index for Aviation Crashes.

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Ballard, Sarah-Blythe; Osorio, Victor B

2015-01-01

This study provides new public health data about U.S. civil air shows. Risk factors for fatalities in civil air show crashes were analyzed. The value of the FIA score in predicting fatal outcomes was evaluated. With the use of the FAA's General Aviation and Air Taxi Survey and the National Transportation Safety Board's data, the incidence of civil air show crashes from 1993 to 2013 was calculated. Fatality risk factors for crashes were analyzed by means of regression methods. The FIA index was validated to predict fatal outcomes by using the factors of fire, instrument conditions, and away-from-airport location, and was evaluated through receiver operating characteristic (ROC) curves. The civil air show crash rate was 31 crashes per 1,000 civil air events. Of the 174 civil air show crashes that occurred during the study period, 91 (52%) involved at least one fatality; on average, 1.1 people died per fatal crash. Fatalities were associated with four major risk factors: fire [adjusted odds ratio (AOR) = 7.1, 95% confidence interval (CI) = 2.4 to 20.6, P Civil air show crashes were marked by a high risk of fatal outcomes to pilots in aerobatic performances but rare mass casualties. The FIA score was not a valid measurement of fatal risk in civil air show crashes.

Applying a new mammographic imaging marker to predict breast cancer risk

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Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-02-01

Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

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

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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.

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

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

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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.

Evaluating the Predictive Value of Growth Prediction Models

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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…

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

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Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

2015-07-01

As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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

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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.

Common carotid artery intima-media thickness is as good as carotid intima-media thickness of all carotid artery segments in improving prediction of coronary heart disease risk in the Atherosclerosis Risk in Communities (ARIC) study.

Science.gov (United States)

Nambi, Vijay; Chambless, Lloyd; He, Max; Folsom, Aaron R; Mosley, Tom; Boerwinkle, Eric; Ballantyne, Christie M

2012-01-01

Carotid intima-media thickness (CIMT) and plaque information can improve coronary heart disease (CHD) risk prediction when added to traditional risk factors (TRF). However, obtaining adequate images of all carotid artery segments (A-CIMT) may be difficult. Of A-CIMT, the common carotid artery intima-media thickness (CCA-IMT) is relatively more reliable and easier to measure. We evaluated whether CCA-IMT is comparable to A-CIMT when added to TRF and plaque information in improving CHD risk prediction in the Atherosclerosis Risk in Communities (ARIC) study. Ten-year CHD risk prediction models using TRF alone, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque were developed for the overall cohort, men, and women. The area under the receiver operator characteristic curve (AUC), per cent individuals reclassified, net reclassification index (NRI), and model calibration by the Grønnesby-Borgan test were estimated. There were 1722 incident CHD events in 12 576 individuals over a mean follow-up of 15.2 years. The AUC for TRF only, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque models were 0.741, 0.754, and 0.753, respectively. Although there was some discordance when the CCA-IMT + plaque- and A-CIMT + plaque-based risk estimation was compared, the NRI and clinical NRI (NRI in the intermediate-risk group) when comparing the CIMT models with TRF-only model, per cent reclassified, and test for model calibration were not significantly different. Coronary heart disease risk prediction can be improved by adding A-CIMT + plaque or CCA-IMT + plaque information to TRF. Therefore, evaluating the carotid artery for plaque presence and measuring CCA-IMT, which is easier and more reliable than measuring A-CIMT, provide a good alternative to measuring A-CIMT for CHD risk prediction.

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

A Fuzzy Comprehensive Evaluation Model for Sustainability Risk Evaluation of PPP Projects

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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

Why hydrological predictions should be evaluated using information theory

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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

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

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

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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.

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

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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.

The prediction of the bankruptcy risk

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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.

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

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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.

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

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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

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.

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...

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...

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

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

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.

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

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.

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%.

Evaluation of the Yale New Haven Readmission Risk Score for Pneumonia in a General Hospital Population.

Science.gov (United States)

Schaefer, Gabrielle; El-Kareh, Robert; Quartarolo, Jennifer; Seymann, Gregory

2017-09-01

The Yale New Haven Readmission Risk Score (YNHRRS) for pneumonia is a clinical prediction tool developed to assess risk for 30-day readmission. This tool was validated in a cohort of Medicare patients; generalizability to a broader patient population has not been evaluated. In addition, it lacks indicators of functional status or social support, which have been shown in other studies to be predictors of readmission. The objective of this study was to evaluate the generalizability of the YNHRRS for pneumonia in a general population of hospitalized patients, and assess the impact of incorporating measures of functional status and social support on its predictive value. This retrospective chart review comprised all patients admitted to a 563-bed academic medical center with a primary diagnosis of pneumonia between March 2014 and March 2015. Abstraction of clinical variables allowed calculation of the YNHRRS and additional indicators of functional status and social support. The primary outcome was 30-day readmission rate. We created a logistic regression model to predict readmission using the YNHRRS, functional status, and social support as covariates. Among 270 discharges with pneumonia, the observed readmission rate was 23%. The YNHRRS was a significant predictor of readmission in our multivariate model, with an odds ratio of 2.20 (95% confidence interval, 1.29-3.73) for each 10% increase in calculated risk. Indicators of functional status and social support were not significant predictors of readmission. The YNHRRS can be applied to an unselected population as a tool to predict patients with pneumonia at risk for readmission. Copyright © 2017 Elsevier Inc. All rights reserved.

Reporting and evaluation criteria as means towards a transparent use of ecotoxicity data for environmental risk assessment of pharmaceuticals

International Nuclear Information System (INIS)

Agerstrand, M.; Kuester, A.; Bachmann, J.; Breitholtz, M.; Ebert, I.; Rechenberg, B.; Ruden, C.

2011-01-01

Ecotoxicity data with high reliability and relevance are needed to guarantee the scientific quality of environmental risk assessments of pharmaceuticals. The main advantages of a more structured approach to data evaluation include increased transparency and predictability of the risk assessment process, and the possibility to use non-standard data. In this collaboration, between the research project MistraPharma and the German Federal Environment Agency, a new set of reporting and evaluation criteria is presented and discussed. The new criteria are based on the approaches in the literature and the OECD reporting requirements, and have been further developed to include both reliability and relevance of test data. Intended users are risk assessors and researchers performing ecotoxicological experiments, but the criteria can also be used for education purposes and in the peer-review process for scientific papers. This approach intends to bridge the gap between the regulator and the scientist's needs and way of work. - Highlights: → A structured approach to data evaluation increases the transparency and predictability of the risk assessment process. → A structured approach to data reporting opens up for use of data from the open scientific literature in risk assessments. → Both relevance and reliability aspects are included in the reporting and evaluation criteria. → The criteria can be used by risk assessors, by researchers, for education purposes and in the peer-review process. - The need for reporting and evaluation criteria towards a transparent and reliable use of ecotoxicity data.

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...

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 ...

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.

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.

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.

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

Predicting risk for childhood asthma by pre-pregnancy, perinatal, and postnatal factors.

Science.gov (United States)

Wen, Hui-Ju; Chiang, Tung-Liang; Lin, Shio-Jean; Guo, Yue Leon

2015-05-01

Symptoms of atopic disease start early in human life. Predicting risk for childhood asthma by early-life exposure would contribute to disease prevention. A birth cohort study was conducted to investigate early-life risk factors for childhood asthma and to develop a predictive model for the development of asthma. National representative samples of newborn babies were obtained by multistage stratified systematic sampling from the 2005 Taiwan Birth Registry. Information on potential risk factors and children's health was collected by home interview when babies were 6 months old and 5 yr old, respectively. Backward stepwise regression analysis was used to identify the risk factors of childhood asthma for predictive models that were used to calculate the probability of childhood asthma. A total of 19,192 children completed the study satisfactorily. Physician-diagnosed asthma was reported in 6.6% of 5-yr-old children. Pre-pregnancy factors (parental atopy and socioeconomic status), perinatal factors (place of residence, exposure to indoor mold and painting/renovations during pregnancy), and postnatal factors (maternal postpartum depression and the presence of atopic dermatitis before 6 months of age) were chosen for the predictive models, and the highest predicted probability of asthma in 5-yr-old children was 68.1% in boys and 78.1% in girls; the lowest probability in boys and girls was 4.1% and 3.2%, respectively. This investigation provides a technique for predicting risk of childhood asthma that can be used to developing a preventive strategy against asthma. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A simple risk scoring system for prediction of relapse after inpatient alcohol treatment.

Science.gov (United States)

Pedersen, Mads Uffe; Hesse, Morten

2009-01-01

Predicting relapse after alcoholism treatment can be useful in targeting patients for aftercare services. However, a valid and practical instrument for predicting relapse risk does not exist. Based on a prospective study of alcoholism treatment, we developed the Risk of Alcoholic Relapse Scale (RARS) using items taken from the Addiction Severity Index and some basic demographic information. The RARS was cross-validated using two non-overlapping samples, and tested for its ability to predict relapse across different models of treatment. The RARS predicted relapse to drinking within 6 months after alcoholism treatment in both the original and the validation sample, and in a second validation sample it predicted admission to new treatment 3 years after treatment. The RARS can identify patients at high risk of relapse who need extra aftercare and support after treatment.

Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol.

Science.gov (United States)

Taljaard, Monica; Tuna, Meltem; Bennett, Carol; Perez, Richard; Rosella, Laura; Tu, Jack V; Sanmartin, Claudia; Hennessy, Deirdre; Tanuseputro, Peter; Lebenbaum, Michael; Manuel, Douglas G

2014-10-23

Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible

The New York State risk score for predicting in-hospital/30-day mortality following percutaneous coronary intervention.

Science.gov (United States)

Hannan, Edward L; Farrell, Louise Szypulski; Walford, Gary; Jacobs, Alice K; Berger, Peter B; Holmes, David R; Stamato, Nicholas J; Sharma, Samin; King, Spencer B

2013-06-01

This study sought to develop a percutaneous coronary intervention (PCI) risk score for in-hospital/30-day mortality. Risk scores are simplified linear scores that provide clinicians with quick estimates of patients' short-term mortality rates for informed consent and to determine the appropriate intervention. Earlier PCI risk scores were based on in-hospital mortality. However, for PCI, a substantial percentage of patients die within 30 days of the procedure after discharge. New York's Percutaneous Coronary Interventions Reporting System was used to develop an in-hospital/30-day logistic regression model for patients undergoing PCI in 2010, and this model was converted into a simple linear risk score that estimates mortality rates. The score was validated by applying it to 2009 New York PCI data. Subsequent analyses evaluated the ability of the score to predict complications and length of stay. A total of 54,223 patients were used to develop the risk score. There are 11 risk factors that make up the score, with risk factor scores ranging from 1 to 9, and the highest total score is 34. The score was validated based on patients undergoing PCI in the previous year, and accurately predicted mortality for all patients as well as patients who recently suffered a myocardial infarction (MI). The PCI risk score developed here enables clinicians to estimate in-hospital/30-day mortality very quickly and quite accurately. It accurately predicts mortality for patients undergoing PCI in the previous year and for MI patients, and is also moderately related to perioperative complications and length of stay. Copyright © 2013 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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.

High-risk lesions diagnosed at MRI-guided vacuum-assisted breast biopsy: can underestimation be predicted?

Energy Technology Data Exchange (ETDEWEB)

Crystal, Pavel [Mount Sinai Hospital, University Health Network, Division of Breast Imaging, Toronto, ON (Canada); Mount Sinai Hospital, Toronto, ON (Canada); Sadaf, Arifa; Bukhanov, Karina; Helbich, Thomas H. [Mount Sinai Hospital, University Health Network, Division of Breast Imaging, Toronto, ON (Canada); McCready, David [Princess Margaret Hospital, Department of Surgical Oncology, Toronto, ON (Canada); O' Malley, Frances [Mount Sinai Hospital, Department of Pathology, Laboratory Medicine, Toronto, ON (Canada)

2011-03-15

To evaluate the frequency of diagnosis of high-risk lesions at MRI-guided vacuum-assisted breast biopsy (MRgVABB) and to determine whether underestimation may be predicted. Retrospective review of the medical records of 161 patients who underwent MRgVABB was performed. The underestimation rate was defined as an upgrade of a high-risk lesion at MRgVABB to malignancy at surgery. Clinical data, MRI features of the biopsied lesions, and histological diagnosis of cases with and those without underestimation were compared. Of 161 MRgVABB, histology revealed 31 (19%) high-risk lesions. Of 26 excised high-risk lesions, 13 (50%) were upgraded to malignancy. The underestimation rates of lobular neoplasia, atypical apocrine metaplasia, atypical ductal hyperplasia, and flat epithelial atypia were 50% (4/8), 100% (5/5), 50% (3/6) and 50% (1/2) respectively. There was no underestimation in the cases of benign papilloma without atypia (0/3), and radial scar (0/2). No statistically significant differences (p > 0.1) between the cases with and those without underestimation were seen in patient age, indications for breast MRI, size of lesion on MRI, morphological and kinetic features of biopsied lesions. Imaging and clinical features cannot be used reliably to predict underestimation at MRgVABB. All high-risk lesions diagnosed at MRgVABB require surgical excision. (orig.)

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)

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

Predictive value of NT-proBNP for 30-day mortality in patients with non-ST-elevation acute coronary syndromes: a comparison with the GRACE and TIMI risk scores.

Science.gov (United States)

Schellings, Dirk Aam; Adiyaman, Ahmet; Dambrink, Jan-Henk E; Gosselink, At Marcel; Kedhi, Elvin; Roolvink, Vincent; Ottervanger, Jan Paul; Van't Hof, Arnoud Wj

2016-01-01

The biomarker N-terminal pro-brain natriuretic peptide (NT-proBNP) predicts outcome in patients with non-ST-elevation acute coronary syndromes (NSTE-ACS). Whether NT-proBNP has incremental prognostic value beyond established risk strategies is still questionable. To evaluate the predictive value of NT-proBNP for 30-day mortality over and beyond the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI) risk scores in patients with NSTE-ACS. Patients included in our ACS registry were candidates. NT-proBNP levels on admission were measured and the GRACE and TIMI risk scores were assessed. We compared the predictive value of NT-proBNP to both risk scores and evaluated whether NT-proBNP improves prognostication by using receiver operator curves and measures of discrimination improvement. A total of 1324 patients were included and 50 patients died during follow-up. On logistic regression analysis NT-proBNP and the GRACE risk score (but not the TIMI risk score) both independently predicted mortality at 30 days. The predictive value of NT-proBNP did not differ significantly compared to the GRACE risk score (area under the curve [AUC]) 0.85 vs 0.87 p =0.67) but was considerably higher in comparison to the TIMI risk score (AUC 0.60 p risk score by adding NT-proBNP did not improve prognostication: AUC 0.86 ( p =0.57), integrated discrimination improvement 0.04 ( p =0.003), net reclassification improvement 0.12 ( p =0.21). In patients with NSTE-ACS, NT-proBNP and the GRACE risk score (but not the TIMI risk score) both have good and comparable predictive value for 30-day mortality. However, incremental prognostic value of NT-proBNP beyond the GRACE risk score could not be demonstrated.

An etiologic prediction model incorporating biomarkers to predict the bladder cancer risk associated with occupational exposure to aromatic amines: a pilot study.

Science.gov (United States)

Mastrangelo, Giuseppe; Carta, Angela; Arici, Cecilia; Pavanello, Sofia; Porru, Stefano

2017-01-01

No etiological prediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; the area under the curve was used to evaluate discriminatory ability of models. Area under the curve was 0.93 for the full model (including age, smoking and coffee habits, DNA adducts, 12 genotypes) and 0.86 for the short model (including smoking, DNA adducts, 3 genotypes). Using the "best cut-off" of predicted probability of a positive outcome, percentage of cases correctly classified was 92% (full model) against 75% (short model). Cancers classified as "positive outcome" are those to be referred for evaluation by an occupational physician for etiological diagnosis; these patients were 28 (full model) or 60 (short model). Using 3 genotypes instead of 12 can double the number of patients with suspect of aromatic amine related cancer, thus increasing costs of etiologic appraisal. Integrating clinical, laboratory and genetic factors, we developed the first etiologic prediction model for aromatic amine related bladder cancer. Discriminatory ability was excellent, particularly for the full model, allowing individualized predictions. Validation of our model in external populations is essential for practical use in the clinical setting.

Environmental risk prediction and emergency plan for liquid ammonia leakage fault

International Nuclear Information System (INIS)

He Zhanfei; Lian Guoxi; Zhang Yuntao; Sun Juan; Du Juan

2014-01-01

Taking liquid ammonia storage in a uranium production process as an example, a multi-puff Gassian model was used to predict and analyze the environmental risk under the scenario of the maximum credible accident as well as the most unfavorable weather conditions. According to the results of prediction, the suggestions for safety evacuation and evacuation way were made, thus providing theoretical basis and technical guideline for uranium mine making risk management and contingency plan. (authors)

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.

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.

Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance.

Science.gov (United States)

Smith, Lauren N; Makam, Anil N; Darden, Douglas; Mayo, Helen; Das, Sandeep R; Halm, Ethan A; Nguyen, Oanh Kieu

2018-01-01

Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown. We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models. Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions

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...

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...

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.

Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients.

Directory of Open Access Journals (Sweden)

Mark E Sherman

Full Text Available Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC for 37,939 invasive breast cancers (1996-2007, we estimated 5-year breast cancer risk (<1%; 1-1.66%; ≥1.67% with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions; Breast Cancer Risk Assessment Tool (BCRAT; and BCSC 5-year risk model (BCSC-5. Breast cancer-specific mortality post-diagnosis (range: 1-13 years; median: 5.4-5.6 years was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35-44; 45-54; 55-69; 70-89 years models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years.Of 6,021 deaths, 2,993 (49.7% were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR = 0.82 (95% CI = 0.75-0.90; BCRAT: HR = 0.72 (95% CI = 0.65-0.81 and BCSC-5: HR = 0.84 (95% CI = 0.75-0.94. Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55-69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35-44 years.Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high

Recent development of risk-prediction models for incident hypertension: An updated systematic review.

Directory of Open Access Journals (Sweden)

Dongdong Sun

Full Text Available Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative.Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc.From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI, age, smoking, blood pressure (BP level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%.The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment.

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.

Hope, Core Self-Evaluations, Emotional Well-Being, Health-Risk Behaviors, and Academic Performance in University Freshmen.

Science.gov (United States)

Griggs, Stephanie; Crawford, Sybil L

2017-09-01

The purpose of the current online cross-sectional study was to examine the relationship between hope, core self-evaluations (CSE), emotional well-being, health-risk behaviors, and academic performance in students enrolled in their first year of college. Freshmen (N = 495) attending a large public university in the Northeastern United States completed an online survey between February 1 and 13, 2017. Linear regression, path analysis, and structural equation modeling procedures were performed. CSE mediated the relationship between hope and emotional well-being and academic performance. Contrary to the hypotheses, higher hope predicted more sexual risk-taking behaviors and alcohol use. CSE is an important component of Hope Theory, which is useful for predicting emotional well-being and academic performance, but not as useful for predicting drug use, alcohol use, and sexual risk taking. Hope and CSE interventions are needed to improve academic performance and emotional well-being in university freshmen. [Journal of Psychosocial Nursing and Mental Health Services, 55(9), 33-42.]. Copyright 2017, SLACK Incorporated.

Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

Science.gov (United States)

Wen, Yalu; Shen, Xiaoxi; Lu, Qing

2018-05-31

With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative. Copyright © 2018 John Wiley & Sons, Ltd.

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.

Can Image-Defined Risk Factors Predict Surgical Complications in Localized Neuroblastoma?

Science.gov (United States)

Yoneda, Akihiro; Nishikawa, Masanori; Uehara, Shuichiro; Oue, Takaharu; Usui, Noriaki; Inoue, Masami; Fukuzawa, Masahiro; Okuyama, Hiroomi

2016-02-01

Image-defined risk factors (IDRFs) have been propounded for predicting the surgical risks associated with localized neuroblastoma (NB) since 2009. In 2011, a new guideline (NG) for assessing IDRFs was published. According to the NG, the situation in which "the tumor is only in contact with renal vessels," should be considered to be "IDRF-present." Previously, this situation was diagnosed as "IDRF absent." In this study, we evaluated the IDRFs in localized NB patients to clarify the predictive capability of IDRFs for surgical complications, as well as the usefulness of the NG. Materials and A total of 107 localized patients with NB were included in this study. The enhanced computed tomography and magnetic resonance images from the time of their diagnoses were evaluated by a single radiologist. We also analyzed the association of clinical factors, including the IDRFs (before and after applying the NG), with surgical complications. Of the 107 patients, 33 and 74 patients were diagnosed as IDRF-present (OP group), and IDRF-absent (ON group) before the NG, respectively. According to the NG, there were 76 and 31 patients who were classified as IDRF-present (NP group) and IDRF absent (NN group), respectively. Thus, 43 (40%) patients in the ON group were reassigned to the NP group after the NG. Surgical complications were observed in 17 of 82 patients who underwent surgical resection. Of the patients who underwent secondary operations, surgical complication rates were 55% in the OP group and 44% in the NP group. According to a univariate analysis, non-INSS 1, IDRFs before and after the NG and secondary operations were significantly associated with surgical complications. In a multivariate analysis, non-INSS 1 status and IDRFs after the NG were significantly associated with surgical complications. Georg Thieme Verlag KG Stuttgart · New York.

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...

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)

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.

A Risk Prediction Model for In-hospital Mortality in Patients with Suspected Myocarditis.

Science.gov (United States)

Xu, Duo; Zhao, Ruo-Chi; Gao, Wen-Hui; Cui, Han-Bin

2017-04-05

Myocarditis is an inflammatory disease of the myocardium that may lead to cardiac death in some patients. However, little is known about the predictors of in-hospital mortality in patients with suspected myocarditis. Thus, the aim of this study was to identify the independent risk factors for in-hospital mortality in patients with suspected myocarditis by establishing a risk prediction model. A retrospective study was performed to analyze the clinical medical records of 403 consecutive patients with suspected myocarditis who were admitted to Ningbo First Hospital between January 2003 and December 2013. A total of 238 males (59%) and 165 females (41%) were enrolled in this study. We divided the above patients into two subgroups (survival and nonsurvival), according to their clinical in-hospital outcomes. To maximize the effectiveness of the prediction model, we first identified the potential risk factors for in-hospital mortality among patients with suspected myocarditis, based on data pertaining to previously established risk factors and basic patient characteristics. We subsequently established a regression model for predicting in-hospital mortality using univariate and multivariate logistic regression analyses. Finally, we identified the independent risk factors for in-hospital mortality using our risk prediction model. The following prediction model for in-hospital mortality in patients with suspected myocarditis, including creatinine clearance rate (Ccr), age, ventricular tachycardia (VT), New York Heart Association (NYHA) classification, gender and cardiac troponin T (cTnT), was established in the study: P = ea/(1 + ea) (where e is the exponential function, P is the probability of in-hospital death, and a = -7.34 + 2.99 × [Ccr model demonstrated that a Ccr prediction model for in-hospital mortality in patients with suspected myocarditis. In addition, sufficient life support during the early stage of the disease might improve the prognoses of patients with

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.

Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression

Directory of Open Access Journals (Sweden)

Li Jian

2017-01-01

Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.

Risk score for predicting long-term mortality after coronary artery bypass graft surgery.

Science.gov (United States)

Wu, Chuntao; Camacho, Fabian T; Wechsler, Andrew S; Lahey, Stephen; Culliford, Alfred T; Jordan, Desmond; Gold, Jeffrey P; Higgins, Robert S D; Smith, Craig R; Hannan, Edward L

2012-05-22

No simplified bedside risk scores have been created to predict long-term mortality after coronary artery bypass graft surgery. The New York State Cardiac Surgery Reporting System was used to identify 8597 patients who underwent isolated coronary artery bypass graft surgery in July through December 2000. The National Death Index was used to ascertain patients' vital statuses through December 31, 2007. A Cox proportional hazards model was fit to predict death after CABG surgery using preprocedural risk factors. Then, points were assigned to significant predictors of death on the basis of the values of their regression coefficients. For each possible point total, the predicted risks of death at years 1, 3, 5, and 7 were calculated. It was found that the 7-year mortality rate was 24.2 in the study population. Significant predictors of death included age, body mass index, ejection fraction, unstable hemodynamic state or shock, left main coronary artery disease, cerebrovascular disease, peripheral arterial disease, congestive heart failure, malignant ventricular arrhythmia, chronic obstructive pulmonary disease, diabetes mellitus, renal failure, and history of open heart surgery. The points assigned to these risk factors ranged from 1 to 7; possible point totals for each patient ranged from 0 to 28. The observed and predicted risks of death at years 1, 3, 5, and 7 across patient groups stratified by point totals were highly correlated. The simplified risk score accurately predicted the risk of mortality after coronary artery bypass graft surgery and can be used for informed consent and as an aid in determining treatment choice.

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

In-hospital risk prediction for post-stroke depression: development and validation of the Post-stroke Depression Prediction Scale.

Science.gov (United States)

de Man-van Ginkel, Janneke M; Hafsteinsdóttir, Thóra B; Lindeman, Eline; Ettema, Roelof G A; Grobbee, Diederick E; Schuurmans, Marieke J

2013-09-01

The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early identification of stroke patients at increased risk for post-stroke depression. The study included 410 consecutive stroke patients who were able to communicate adequately. Predictors were collected within the first week after stroke. Between 6 to 8 weeks after stroke, major depressive disorder was diagnosed using the Composite International Diagnostic Interview. Multivariable logistic regression models were fitted. A bootstrap-backward selection process resulted in a reduced model. Performance of the model was expressed by discrimination, calibration, and accuracy. The model included a medical history of depression or other psychiatric disorders, hypertension, angina pectoris, and the Barthel Index item dressing. The model had acceptable discrimination, based on an area under the receiver operating characteristic curve of 0.78 (0.72-0.85), and calibration (P value of the U-statistic, 0.96). Transforming the model to an easy-to-use risk-assessment table, the lowest risk category (sum score, depression, which increased to 82% in the highest category (sum score, >21). The clinical prediction model enables clinicians to estimate the degree of the depression risk for an individual patient within the first week after stroke.

Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure.

Science.gov (United States)

Ghosh, Shameek; Feng, Mengling; Nguyen, Hung; Li, Jinyan

2016-09-01

Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

Science.gov (United States)

Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David

2015-12-01

We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk

Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

Science.gov (United States)

Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

2015-01-01

Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

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

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.

Mortality Risk Prediction in Scleroderma-Related Interstitial Lung Disease: The SADL Model.

Science.gov (United States)

Morisset, Julie; Vittinghoff, Eric; Elicker, Brett M; Hu, Xiaowen; Le, Stephanie; Ryu, Jay H; Jones, Kirk D; Haemel, Anna; Golden, Jeffrey A; Boin, Francesco; Ley, Brett; Wolters, Paul J; King, Talmadge E; Collard, Harold R; Lee, Joyce S

2017-11-01

Interstitial lung disease (ILD) is an important cause of morbidity and mortality in patients with scleroderma (Scl). Risk prediction and prognostication in patients with Scl-ILD are challenging because of heterogeneity in the disease course. We aimed to develop a clinical mortality risk prediction model for Scl-ILD. Patients with Scl-ILD were identified from two ongoing longitudinal cohorts: 135 patients at the University of California, San Francisco (derivation cohort) and 90 patients at the Mayo Clinic (validation cohort). Using these two separate cohorts, a mortality risk prediction model was developed and validated by testing every potential candidate Cox model, each including three or four variables of a possible 19 clinical predictors, for time to death. Model discrimination was assessed using the C-index. Three variables were included in the final risk prediction model (SADL): ever smoking history, age, and diffusing capacity of the lung for carbon monoxide (% predicted). This continuous model had similar performance in the derivation (C-index, 0.88) and validation (C-index, 0.84) cohorts. We created a point scoring system using the combined cohort (C-index, 0.82) and used it to identify a classification with low, moderate, and high mortality risk at 3 years. The SADL model uses simple, readily accessible clinical variables to predict all-cause mortality in Scl-ILD. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Simulation for Prediction of Entry Article Demise (SPEAD): An Analysis Tool for Spacecraft Safety Analysis and Ascent/Reentry Risk Assessment

Science.gov (United States)

Ling, Lisa

2014-01-01

For the purpose of performing safety analysis and risk assessment for a potential off-nominal atmospheric reentry resulting in vehicle breakup, a synthesis of trajectory propagation coupled with thermal analysis and the evaluation of node failure is required to predict the sequence of events, the timeline, and the progressive demise of spacecraft components. To provide this capability, the Simulation for Prediction of Entry Article Demise (SPEAD) analysis tool was developed. The software and methodology have been validated against actual flights, telemetry data, and validated software, and safety/risk analyses were performed for various programs using SPEAD. This report discusses the capabilities, modeling, validation, and application of the SPEAD analysis tool.

An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons

DEFF Research Database (Denmark)

Friis-Møller, Nina; Ryom, Lene; Smith, Colette

2016-01-01

,663 HIV-positive persons from 20 countries in Europe and Australia, who were free of CVD at entry into the Data-collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study. Cox regression models (full and reduced) were developed that predict the risk of a global CVD endpoint. The predictive performance...... significantly predicted risk more accurately than the recalibrated Framingham model (Harrell's c-statistic of 0.791, 0.783 and 0.766 for the D:A:D full, D:A:D reduced, and Framingham models respectively; p models also more accurately predicted five-year CVD-risk for key prognostic subgroups...... to quantify risk and to guide preventive care....

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.

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.

Validation of a risk prediction model for Barrett's esophagus in an Australian population.

Science.gov (United States)

Ireland, Colin J; Gordon, Andrea L; Thompson, Sarah K; Watson, David I; Whiteman, David C; Reed, Richard L; Esterman, Adrian

2018-01-01

Esophageal adenocarcinoma is a disease that has a high mortality rate, the only known precursor being Barrett's esophagus (BE). While screening for BE is not cost-effective at the population level, targeted screening might be beneficial. We have developed a risk prediction model to identify people with BE, and here we present the external validation of this model. A cohort study was undertaken to validate a risk prediction model for BE. Individuals with endoscopy and histopathology proven BE completed a questionnaire containing variables previously identified as risk factors for this condition. Their responses were combined with data from a population sample for analysis. Risk scores were derived for each participant. Overall performance of the risk prediction model in terms of calibration and discrimination was assessed. Scores from 95 individuals with BE and 636 individuals from the general population were analyzed. The Brier score was 0.118, suggesting reasonable overall performance. The area under the receiver operating characteristic was 0.83 (95% CI 0.78-0.87). The Hosmer-Lemeshow statistic was p =0.14. Minimizing false positives and false negatives, the model achieved a sensitivity of 74% and a specificity of 73%. This study has validated a risk prediction model for BE that has a higher sensitivity than previous models.

Incremental value of a genetic risk score for the prediction of new vascular events in patients with clinically manifest vascular disease.

Science.gov (United States)

Weijmans, Maaike; de Bakker, Paul I W; van der Graaf, Yolanda; Asselbergs, Folkert W; Algra, Ale; Jan de Borst, Gert; Spiering, Wilko; Visseren, Frank L J

2015-04-01

Several genetic markers are related to incidence of cardiovascular events. We evaluated whether a genetic risk score (GRS) based on 30 single-nucleotide-polymorphisms associated with coronary artery disease (CAD) can improve prediction of 10-year risk of new cardiovascular events in patients with clinical manifest vascular disease. In 5742 patients with symptomatic vascular disease enrolled in the SMART study, we developed Cox regression models based on the SMART Risk Score (SRS) and based on the SRS plus the GRS in all patients, in patients with a history of acute arterial thrombotic events and in patients with a history of more stable atherosclerosis and without CAD. The discriminatory ability was expressed by the c-statistic. Model calibration was evaluated by calibration plots. The incremental value of adding the GRS was assessed by net reclassification index (NRI) and decision curve analysis. During a median follow-up of 6.5 years (IQR4.0-9.5), the composite outcome of myocardial infarction, stroke, or vascular death occurred in 933 patients. Hazard ratios of GRS ranging from 0.86 to 1.35 were observed. The discriminatory capacity of the SRS for prediction of 10-year risk of cardiovascular events was fairly good (c-statistic 0.70, 95%CI 0.68-0.72), similar to the model based on the SRS plus the GRS. Calibration of the models based on SRS and SRS plus GRS was adequate. No increase in c-statistics, categorical NRIs and decision curves was observed when adding the GRS. The continuous NRI improved only in patients with stable atherosclerosis (0.14, 95%CI 0.03-0.25), increasing further excluding patients with a history of CAD (0.21, 95%CI 0.06-0.36). In patients with symptomatic vascular disease, a GRS did not improve risk prediction of 10-year risk of cardiovascular events beyond clinical characteristics. The GRS might improve risk prediction of first vascular events in the subgroup of patients with a history of stable atherosclerosis. Copyright © 2015 Elsevier

Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy

Energy Technology Data Exchange (ETDEWEB)

Eley, John G., E-mail: jeley@som.umaryland.edu [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); University of Texas Graduate School of Biomedical Sciences, Houston, Texas (United States); Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland (United States); Friedrich, Thomas [GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt (Germany); Homann, Kenneth L.; Howell, Rebecca M. [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); University of Texas Graduate School of Biomedical Sciences, Houston, Texas (United States); Scholz, Michael; Durante, Marco [GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt (Germany); Newhauser, Wayne D. [Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, Baton Rouge, Louisiana (United States); Mary Bird Perkins Cancer Center, Baton Rouge, Louisiana (United States)

2016-05-01

Purpose: This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients. Methods and Materials: We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breast by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model. Results: For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, , to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180). Conclusions: Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy.

Screening for Malnutrition in Community Dwelling Older Japanese: Preliminary Development and Evaluation of the Japanese Nutritional Risk Screening Tool (NRST).

Science.gov (United States)

Htun, N C; Ishikawa-Takata, K; Kuroda, A; Tanaka, T; Kikutani, T; Obuchi, S P; Hirano, H; Iijima, K

2016-02-01

Early and effective screening for age-related malnutrition is an essential part of providing optimal nutritional care to older populations. This study was performed to evaluate the adaptation of the original SCREEN II questionnaire (Seniors in the Community: Risk Evaluation for Eating and Nutrition, version II) for use in Japan by examining its measurement properties and ability to predict nutritional risk and sarcopenia in community-dwelling older Japanese people. The ultimate objective of this preliminary validation study is to develop a license granted full Japanese version of the SCREEN II. The measurement properties and predictive validity of the NRST were examined in this cross-sectional study of 1921 community-dwelling older Japanese people. Assessments included medical history, and anthropometric and serum albumin measurements. Questions on dietary habits that corresponded to the original SCREEN II were applied to Nutritional Risk Screening Tool (NRST) scoring system. Nutritional risk was assessed by the Geriatric Nutrition Risk Index (GNRI) and the short form of the Mini-Nutritional Assessment (MNA-SF). Sarcopenia was diagnosed according to the criteria of the European Working Group on Sarcopenia in Older People. The nutritional risk prevalences determined by the GNRI and MNA-SF were 5.6% and 34.7%, respectively. The prevalence of sarcopenia was 13.3%. Mean NRST scores were significantly lower in the nutritionally at-risk than in the well-nourished groups. Concurrent validity analysis showed significant correlations between NRST scores and both nutritional risk parameters (GNRI or MNA-SF) and sarcopenia. The areas under the receiver operating characteristic curves (AUC) of NRST for the prediction of nutritional risk were 0.635 and 0.584 as assessed by GNRI and MNA-SF, respectively. AUCs for the prediction of sarcopenia were 0.602 (NRST), 0.655 (age-integrated NRST), and 0.676 (age and BMI-integrated NRST). These results indicate that the NRST is a

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

Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data.

Science.gov (United States)

Walters, K; Hardoon, S; Petersen, I; Iliffe, S; Omar, R Z; Nazareth, I; Rait, G

2016-01-21

Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. Dementia incidence was 1.88 (95% CI, 1.83-1.93) and 16.53 (95% CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95% CI, 1.95-2.11), C index 0.84 (95% CI, 0.81-0.87), and calibration slope 0.98 (95% CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model. Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This

Preclinical Torsades-de-Pointes screens: advantages and limitations of surrogate and direct approaches in evaluating proarrhythmic risk.

Science.gov (United States)

Gintant, Gary A

2008-08-01

The successful development of novel drugs requires the ability to detect (and avoid) compounds that may provoke Torsades-de-Pointes (TdeP) arrhythmia while endorsing those compounds with minimal torsadogenic risk. As TdeP is a rare arrhythmia not readily observed during clinical or post-marketing studies, numerous preclinical models are employed to assess delayed or altered ventricular repolarization (surrogate markers linked to enhanced proarrhythmic risk). This review evaluates the advantages and limitations of selected preclinical models (ranging from the simplest cellular hERG current assay to the more complex in vitro perfused ventricular wedge and Langendorff heart preparations and in vivo chronic atrio-ventricular (AV)-node block model). Specific attention is paid to the utility of concentration-response relationships and "risk signatures" derived from these studies, with the intention of moving beyond predicting clinical QT prolongation and towards prediction of TdeP risk. While the more complex proarrhythmia models may be suited to addressing questionable or conflicting proarrhythmic signals obtained with simpler preclinical assays, further benchmarking of proarrhythmia models is required for their use in the robust evaluation of safety margins. In the future, these models may be able to reduce unwarranted attrition of evolving compounds while becoming pivotal in the balanced integrated risk assessment of advancing compounds.

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

Applications of the gambling score in evaluating earthquake predictions and forecasts

Science.gov (United States)

Zhuang, Jiancang; Zechar, Jeremy D.; Jiang, Changsheng; Console, Rodolfo; Murru, Maura; Falcone, Giuseppe

2010-05-01

This study presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points bet by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. For discrete predictions, we apply this method to evaluate performance of Shebalin's predictions made by using the Reverse Tracing of Precursors (RTP) algorithm and of the outputs of the predictions from the Annual Consultation Meeting on Earthquake Tendency held by China Earthquake Administration. For the continuous case, we use it to compare the probability forecasts of seismicity in the Abruzzo region before and after the L'aquila earthquake based on the ETAS model and the PPE model.

External Validation of Risk Prediction Scores for Invasive Candidiasis in a Medical/Surgical Intensive Care Unit: An Observational Study

Science.gov (United States)

Ahmed, Armin; Baronia, Arvind Kumar; Azim, Afzal; Marak, Rungmei S. K.; Yadav, Reema; Sharma, Preeti; Gurjar, Mohan; Poddar, Banani; Singh, Ratender Kumar

2017-01-01

Background: The aim of this study was to conduct external validation of risk prediction scores for invasive candidiasis. Methods: We conducted a prospective observational study in a 12-bedded adult medical/surgical Intensive Care Unit (ICU) to evaluate Candida score >3, colonization index (CI) >0.5, corrected CI >0.4 (CCI), and Ostrosky's clinical prediction rule (CPR). Patients' characteristics and risk factors for invasive candidiasis were noted. Patients were divided into two groups; invasive candidiasis and no-invasive candidiasis. Results: Of 198 patients, 17 developed invasive candidiasis. Discriminatory power (area under receiver operator curve [AUROC]) for Candida score, CI, CCI, and CPR were 0.66, 0.67, 0.63, and 0.62, respectively. A large number of patients in the no-invasive candidiasis group (114 out of 181) were exposed to antifungal agents during their stay in ICU. Subgroup analysis was carried out after excluding such patients from no-invasive candidiasis group. AUROC of Candida score, CI, CCI, and CPR were 0.7, 0.7, 0.65, and 0.72, respectively, and positive predictive values (PPVs) were in the range of 25%–47%, along with negative predictive values (NPVs) in the range of 84%–96% in the subgroup analysis. Conclusion: Currently available risk prediction scores have good NPV but poor PPV. They are useful for selecting patients who are not likely to benefit from antifungal therapy. PMID:28904481

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

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.

Quantitative prediction of oral cancer risk in patients with oral leukoplakia.

Science.gov (United States)

Liu, Yao; Li, Yicheng; Fu, Yue; Liu, Tong; Liu, Xiaoyong; Zhang, Xinyan; Fu, Jie; Guan, Xiaobing; Chen, Tong; Chen, Xiaoxin; Sun, Zheng

2017-07-11

Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.

Risk Evaluation for CO{sub 2} Geosequestration in the Knox Supergroup

Energy Technology Data Exchange (ETDEWEB)

Leetaru, Hannes

2014-01-31

.e., persistently porous and permeable) injection depths within the overall formation. Less direct implications include the vertical position of the Potosi within the rock column and the absence of a laterally extensive shale caprock immediately overlying the Potosi. Based on modeling work done partly in association with this risk report, risks that should also be evaluated include the ability of available methods to predict and track the development of a CO{sub 2} plume as it migrates away from the injection point(s). The geologic and hydrodynamic uncertainties present risks that are compounded at the stage of acquiring necessary drilling and injection permits. It is anticipated that, in the future, a regional geologic study or CO{sub 2}-emitter request may identify a small specific area as a prospective CCS project site. At that point, the FEPs lists provided in this report should be evaluated by experts for their relative levels of risk. A procedure for this evaluation is provided. The higher-risk FEPs should then be used to write project-specific scenarios that may themselves be evaluated for risk. Then, actions to reduce and to manage risk can be described and undertaken. The FEPs lists provided as Appendix 2 should not be considered complete, as potentially the most important risks are ones that have not yet been thought of. But these lists are intended to include the most important risk elements pertinent to a Potosi-target CCS project, and they provide a good starting point for diligent risk identification, evaluation, and management.

A Bayesian framework for early risk prediction in traumatic brain injury

Science.gov (United States)

Chaganti, Shikha; Plassard, Andrew J.; Wilson, Laura; Smith, Miya A.; Patel, Mayur B.; Landman, Bennett A.

2016-03-01

Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.

Prediction of Adult Dyslipidemia Using Genetic and Childhood Clinical Risk Factors: The Cardiovascular Risk in Young Finns Study.

Science.gov (United States)

Nuotio, Joel; Pitkänen, Niina; Magnussen, Costan G; Buscot, Marie-Jeanne; Venäläinen, Mikko S; Elo, Laura L; Jokinen, Eero; Laitinen, Tomi; Taittonen, Leena; Hutri-Kähönen, Nina; Lyytikäinen, Leo-Pekka; Lehtimäki, Terho; Viikari, Jorma S; Juonala, Markus; Raitakari, Olli T

2017-06-01

Dyslipidemia is a major modifiable risk factor for cardiovascular disease. We examined whether the addition of novel single-nucleotide polymorphisms for blood lipid levels enhances the prediction of adult dyslipidemia in comparison to childhood lipid measures. Two thousand four hundred and twenty-two participants of the Cardiovascular Risk in Young Finns Study who had participated in 2 surveys held during childhood (in 1980 when aged 3-18 years and in 1986) and at least once in a follow-up study in adulthood (2001, 2007, and 2011) were included. We examined whether inclusion of a lipid-specific weighted genetic risk score based on 58 single-nucleotide polymorphisms for low-density lipoprotein cholesterol, 71 single-nucleotide polymorphisms for high-density lipoprotein cholesterol, and 40 single-nucleotide polymorphisms for triglycerides improved the prediction of adult dyslipidemia compared with clinical childhood risk factors. Adjusting for age, sex, body mass index, physical activity, and smoking in childhood, childhood lipid levels, and weighted genetic risk scores were associated with an increased risk of adult dyslipidemia for all lipids. Risk assessment based on 2 childhood lipid measures and the lipid-specific weighted genetic risk scores improved the accuracy of predicting adult dyslipidemia compared with the approach using only childhood lipid measures for low-density lipoprotein cholesterol (area under the receiver-operating characteristic curve 0.806 versus 0.811; P =0.01) and triglycerides (area under the receiver-operating characteristic curve 0.740 versus area under the receiver-operating characteristic curve 0.758; P dyslipidemia in adulthood. © 2017 American Heart Association, Inc.

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.

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....

Risk score prediction model for dementia in patients with type 2 diabetes.

Science.gov (United States)

Li, Chia-Ing; Li, Tsai-Chung; Liu, Chiu-Shong; Liao, Li-Na; Lin, Wen-Yuan; Lin, Chih-Hsueh; Yang, Sing-Yu; Chiang, Jen-Huai; Lin, Cheng-Chieh

2018-03-30

No study established a prediction dementia model in the Asian populations. This study aims to develop a prediction model for dementia in Chinese type 2 diabetes patients. This retrospective cohort study included 27,540 Chinese type 2 diabetes patients (aged 50-94 years) enrolled in Taiwan National Diabetes Care Management Program. Participants were randomly allocated into derivation and validation sets at 2:1 ratio. Cox proportional hazards regression models were used to identify risk factors for dementia in the derivation set. Steps proposed by Framingham Heart Study were used to establish a prediction model with a scoring system. The average follow-up was 8.09 years, with a total of 853 incident dementia cases in derivation set. Dementia risk score summed up the individual scores (from 0 to 20). The areas under curve of 3-, 5-, and 10-year dementia risks were 0.82, 0.79, and 0.76 in derivation set and 0.84, 0.80, and 0.75 in validation set, respectively. The proposed score system is the first dementia risk prediction model for Chinese type 2 diabetes patients in Taiwan. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Comparison of RISK-PCI, GRACE, TIMI risk scores for prediction of major adverse cardiac events in patients with acute coronary syndrome.

Science.gov (United States)

Jakimov, Tamara; Mrdović, Igor; Filipović, Branka; Zdravković, Marija; Djoković, Aleksandra; Hinić, Saša; Milić, Nataša; Filipović, Branislav

2017-12-31

To compare the prognostic performance of three major risk scoring systems including global registry for acute coronary events (GRACE), thrombolysis in myocardial infarction (TIMI), and prediction of 30-day major adverse cardiovascular events after primary percutaneous coronary intervention (RISK-PCI). This single-center retrospective study involved 200 patients with acute coronary syndrome (ACS) who underwent invasive diagnostic approach, ie, coronary angiography and myocardial revascularization if appropriate, in the period from January 2014 to July 2014. The GRACE, TIMI, and RISK-PCI risk scores were compared for their predictive ability. The primary endpoint was a composite 30-day major adverse cardiovascular event (MACE), which included death, urgent target-vessel revascularization (TVR), stroke, and non-fatal recurrent myocardial infarction (REMI). The c-statistics of the tested scores for 30-day MACE or area under the receiver operating characteristic curve (AUC) with confidence intervals (CI) were as follows: RISK-PCI (AUC=0.94; 95% CI 1.790-4.353), the GRACE score on admission (AUC=0.73; 95% CI 1.013-1.045), the GRACE score on discharge (AUC=0.65; 95% CI 0.999-1.033). The RISK-PCI score was the only score that could predict TVR (AUC=0.91; 95% CI 1.392-2.882). The RISK-PCI scoring system showed an excellent discriminative potential for 30-day death (AUC=0.96; 95% CI 1.339-3.548) in comparison with the GRACE scores on admission (AUC=0.88; 95% CI 1.018-1.072) and on discharge (AUC=0.78; 95% CI 1.000-1.058). In comparison with the GRACE and TIMI scores, RISK-PCI score showed a non-inferior ability to predict 30-day MACE and death in ACS patients. Moreover, RISK-PCI was the only scoring system that could predict recurrent ischemia requiring TVR.

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...

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.

Cutoff value of HbA1c for predicting diabetes and prediabetes in a Chinese high risk population aged over 45.

Science.gov (United States)

Zhang, Ruyi; Wang, Jiao; Luo, Jinhua; Yang, Xiaoyan; Yang, Rui; Cai, Dehong; Zhang, Hua

2015-01-01

To evaluate the cutoff value of HbA1c for predicting diabetes and prediabetes in a Chinese high risk population aged over 45. A total of 619 people aged over 45 without diabetes were randomly recruited to complete Finnish Diabetes Risk Score (FINDRISC) questionnaire. 208 high-risk individuals (defined by Diabetes Risk Score >=9) had OGTT and HbA1c determined at the same time. In a Chinese population aged over 45, the best cutoff value of HbA1c for detecting diabetes and prediabetes was 5.8% and 5.4% respectively. The area under the receiver operating characteristic (AUROC) curve of HbA1c for detecting diabetes was 0.85 (95% CI: 0.80-0.90) and prediabetes was 0.62 (95% CI: 0.54-0.70). The combined use of HbA1c and fasting blood glucose (FPG) had larger AUROC than HbA1c alone (0.88, 95%CI: 0.83-0.92 in detecting diabetes vs 0.75, 95% CI: 0.67-0.82 in prediabetes), and had a higher sensitivity in predicting diabetes and higher specificity and positive predictive value (PPV) in predicting prediabetes. However, the AUROC between HbA1c alone and combined use in predicting diabetes was not significantly different (p=0.173). FINDRISC is feasible tool to screen people who are at high risk of diabetes. The cutoff values of HbA1c to diagnose diabetes and prediabetes in a Chinese high risk population aged over 45 were 5.8% and 5.4%, respectively. The sensitivity and specificity of HbA1c for detecting diabetes and prediabetes was relatively low, so that the combined use of HbA1c and FPG may be more effective in prediction.

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)

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

Directory of Open Access Journals (Sweden)

Shaik Mohammad Naushad

2011-01-01

Full Text Available This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1 G80A, thymidylate synthase (TYMS 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR C677T and catecholamine-O-methyl transferase (COMT genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII C1561T and cytosolic serine hydroxymethyl transferase (cSHMT C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC curves were plotted and the area under ROC curve (C was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93 while the predictability was lost in subjects with one protective allele (C=0.60. However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles. The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.

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

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.

The PER (Preoperative Esophagectomy Risk) Score: A Simple Risk Score to Predict Short-Term and Long-Term Outcome in Patients with Surgically Treated Esophageal Cancer.

Science.gov (United States)

Reeh, Matthias; Metze, Johannes; Uzunoglu, Faik G; Nentwich, Michael; Ghadban, Tarik; Wellner, Ullrich; Bockhorn, Maximilian; Kluge, Stefan; Izbicki, Jakob R; Vashist, Yogesh K

2016-02-01

Esophageal resection in patients with esophageal cancer (EC) is still associated with high mortality and morbidity rates. We aimed to develop a simple preoperative risk score for the prediction of short-term and long-term outcomes for patients with EC treated by esophageal resection. In total, 498 patients suffering from esophageal carcinoma, who underwent esophageal resection, were included in this retrospective cohort study. Three preoperative esophagectomy risk (PER) groups were defined based on preoperative functional evaluation of different organ systems by validated tools (revised cardiac risk index, model for end-stage liver disease score, and pulmonary function test). Clinicopathological parameters, morbidity, and mortality as well as disease-free survival (DFS) and overall survival (OS) were correlated to the PER score. The PER score significantly predicted the short-term outcome of patients with EC who underwent esophageal resection. PER 2 and PER 3 patients had at least double the risk of morbidity and mortality compared to PER 1 patients. Furthermore, a higher PER score was associated with shorter DFS (P PER score was identified as an independent predictor of tumor recurrence (hazard ratio [HR] 2.1; P PER score allows preoperative objective allocation of patients with EC into different risk categories for morbidity, mortality, and long-term outcomes. Thus, multicenter studies are needed for independent validation of the PER score.

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

Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance.

Science.gov (United States)

Chang, Hsien-Yen; Richards, Thomas M; Shermock, Kenneth M; Elder Dalpoas, Stacy; J Kan, Hong; Alexander, G Caleb; Weiner, Jonathan P; Kharrazi, Hadi

2017-12-01

Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates-extracted by comparing electronic health record prescriptions and pharmacy claims fills-represent a novel measure of medication adherence and may improve the performance of risk adjustment models. We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization. We conducted a retrospective cohort study of 43,097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill rates: primary 0-7 days, primary 0-30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders. The overall, primary 0-7, and 0-30 days fill rates were 72.30%, 59.82%, and 67.33%. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R by 1.15% to 2.07%), followed by total costs (0.58% to 1.43%), and pharmacy costs (0.07% to 0.65%). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates. Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited.

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

Predicting Drug Safety and Communicating Risk: Benefits of a Bayesian Approach.

Science.gov (United States)

Lazic, Stanley E; Edmunds, Nicholas; Pollard, Christopher E

2018-03-01

Drug toxicity is a major source of attrition in drug discovery and development. Pharmaceutical companies routinely use preclinical data to predict clinical outcomes and continue to invest in new assays to improve predictions. However, there are many open questions about how to make the best use of available data, combine diverse data, quantify risk, and communicate risk and uncertainty to enable good decisions. The costs of suboptimal decisions are clear: resources are wasted and patients may be put at risk. We argue that Bayesian methods provide answers to all of these problems and use hERG-mediated QT prolongation as a case study. Benefits of Bayesian machine learning models include intuitive probabilistic statements of risk that incorporate all sources of uncertainty, the option to include diverse data and external information, and visualizations that have a clear link between the output from a statistical model and what this means for risk. Furthermore, Bayesian methods are easy to use with modern software, making their adoption for safety screening straightforward. We include R and Python code to encourage the adoption of these methods.

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.

Longitudinal Evaluation of Johns Hopkins Fall Risk Assessment Tool and Nurses' Experience.

Science.gov (United States)

Hur, Eun Young; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) is relatively new in Korea, and it has not been fully evaluated. This study revealed that the JHFRAT had good predictive validity throughout the hospitalization period. However, 2 items (fall history and elimination patterns) on the tool were not determinants of falls in this population. Interestingly, the nurses indicated those 2 items were the most difficult items to assess and needed further training to develop the assessment skills.

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

Development of a Korean Fracture Risk Score (KFRS for Predicting Osteoporotic Fracture Risk: Analysis of Data from the Korean National Health Insurance Service.

Directory of Open Access Journals (Sweden)

Ha Young Kim

Full Text Available Asian-specific prediction models for estimating individual risk of osteoporotic fractures are rare. We developed a Korean fracture risk prediction model using clinical risk factors and assessed validity of the final model.A total of 718,306 Korean men and women aged 50-90 years were followed for 7 years in a national system-based cohort study. In total, 50% of the subjects were assigned randomly to the development dataset and 50% were assigned to the validation dataset. Clinical risk factors for osteoporotic fracture were assessed at the biennial health check. Data on osteoporotic fractures during the follow-up period were identified by ICD-10 codes and the nationwide database of the National Health Insurance Service (NHIS.During the follow-up period, 19,840 osteoporotic fractures were reported (4,889 in men and 14,951 in women in the development dataset. The assessment tool called the Korean Fracture Risk Score (KFRS is comprised of a set of nine variables, including age, body mass index, recent fragility fracture, current smoking, high alcohol intake, lack of regular exercise, recent use of oral glucocorticoid, rheumatoid arthritis, and other causes of secondary osteoporosis. The KFRS predicted osteoporotic fractures over the 7 years. This score was validated using an independent dataset. A close relationship with overall fracture rate was observed when we compared the mean predicted scores after applying the KFRS with the observed risks after 7 years within each 10th of predicted risk.We developed a Korean specific prediction model for osteoporotic fractures. The KFRS was able to predict risk of fracture in the primary population without bone mineral density testing and is therefore suitable for use in both clinical setting and self-assessment. The website is available at http://www.nhis.or.kr.

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.

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

Predicting the risk of perioperative transfusion for patients undergoing elective hepatectomy.

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Sima, Camelia S; Jarnagin, William R; Fong, Yuman; Elkin, Elena; Fischer, Mary; Wuest, David; D'Angelica, Michael; DeMatteo, Ronald P; Blumgart, Leslie H; Gönen, Mithat

2009-12-01

To develop 2 instruments that predict the probability of perioperative red blood cell transfusion in patients undergoing elective liver resection for primary and secondary tumors. Hepatic resection is the most effective treatment for several benign and malign conditions, but may be accompanied by substantial blood loss and the need for perioperative transfusions. While blood conservation strategies such as autologous blood donation, acute normovolemic hemodilution, or cell saver systems are available, they are economically efficient only if directed toward patients with a high risk of transfusion. Using preoperative data from 1204 consecutive patients who underwent liver resection between 1995 and 2000 at Memorial Sloan- Kettering Cancer Center, we modeled the probability of perioperative red blood cell transfusion. We used the resulting model, validated on an independent dataset (n = 555 patients), to develop 2 prediction instruments, a nomogram and a transfusion score, which can be easily implemented into clinical practice. The planned number of liver segments resected, concomitant extrahepatic organ resection, a diagnosis of primary liver malignancy, as well as preoperative hemoglobin and platelets levels predicted the probability of perioperative red blood cell transfusion. The predictions of the model appeared accurate and with good discriminatory abilities, generating an area under the receiver operating characteristic curve of 0.71. Preoperative factors can be combined into risk profiles to predict the likelihood of transfusion during or after elective liver resection. These predictions, easy to calculate in the frame of a nomogram or of a transfusion score, can be used to identify patients who are at high risk for red cell transfusions and therefore most likely to benefit from blood conservation techniques.

Endogenous Information, Risk Characterization, and the Predictability of Average Stock Returns

Directory of Open Access Journals (Sweden)

Pradosh Simlai

2012-09-01

Full Text Available In this paper we provide a new type of risk characterization of the predictability of two widely known abnormal patterns in average stock returns: momentum and reversal. The purpose is to illustrate the relative importance of common risk factors and endogenous information. Our results demonstrates that in the presence of zero-investment factors, spreads in average momentum and reversal returns correspond to spreads in the slopes of the endogenous information. The empirical findings support the view that various classes of firms react differently to volatility risk, and endogenous information harbor important sources of potential risk loadings. Taken together, our results suggest that returns are influenced by random endogenous information flow, which is asymmetric in nature, and can be used as a performance attribution factor. If one fails to incorporate the existing asymmetric endogenous information hidden in the historical behavior, any attempt to explore average stock return predictability will be subject to an unquantified specification bias.

Developing an objective evaluation method to estimate diabetes risk in community-based settings.

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Kenya, Sonjia; He, Qing; Fullilove, Robert; Kotler, Donald P

2011-05-01

Exercise interventions often aim to affect abdominal obesity and glucose tolerance, two significant risk factors for type 2 diabetes. Because of limited financial and clinical resources in community and university-based environments, intervention effects are often measured with interviews or questionnaires and correlated with weight loss or body fat indicated by body bioimpedence analysis (BIA). However, self-reported assessments are subject to high levels of bias and low levels of reliability. Because obesity and body fat are correlated with diabetes at different levels in various ethnic groups, data reflecting changes in weight or fat do not necessarily indicate changes in diabetes risk. To determine how exercise interventions affect diabetes risk in community and university-based settings, improved evaluation methods are warranted. We compared a noninvasive, objective measurement technique--regional BIA--with whole-body BIA for its ability to assess abdominal obesity and predict glucose tolerance in 39 women. To determine regional BIA's utility in predicting glucose, we tested the association between the regional BIA method and blood glucose levels. Regional BIA estimates of abdominal fat area were significantly correlated (r = 0.554, P < 0.003) with fasting glucose. When waist circumference and family history of diabetes were added to abdominal fat in multiple regression models, the association with glucose increased further (r = 0.701, P < 0.001). Regional BIA estimates of abdominal fat may predict fasting glucose better than whole-body BIA as well as provide an objective assessment of changes in diabetes risk achieved through physical activity interventions in community settings.

The Predictive Value of Preendoscopic Risk Scores to Predict Adverse Outcomes in Emergency Department Patients With Upper Gastrointestinal Bleeding: A Systematic Review.

Science.gov (United States)

Ramaekers, Rosa; Mukarram, Muhammad; Smith, Christine A M; Thiruganasambandamoorthy, Venkatesh

2016-11-01

Risk stratification of emergency department (ED) patients with upper gastrointestinal bleeding (UGIB) using preendoscopic risk scores can aid ED physicians in disposition decision-making. We conducted a systematic review to assess the predictive value of preendoscopic risk scores for 30-day serious adverse events. We searched MEDLINE, PubMed, Embase, and the Cochrane Database of Systematic Reviews from inception to March 2015. We included studies involving adult ED UGIB patients evaluating preendoscopic risk scores and excluded reviews, case reports, and animal studies. The composite outcome included 30-day mortality, recurrent bleeding, and need for intervention. In two phases (screening and full review), two reviewers independently screened articles for inclusion and extracted patient-level data. The consensus data were used for analysis. We reported sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratios with 95% confidence intervals. We identified 3,173 articles, of which 16 were included: three studied Glasgow Blatchford score (GBS); one studied clinical Rockall score (cRockall); two studied AIMS65; six compared GBS and cRockall; three compared GBS, a modification of the GBS, and cRockall; and one compared the GBS and AIMS65. Overall, the sensitivity and specificity of the GBS were 0.98 and 0.16, respectively; for the cRockall they were 0.93 and 0.24, respectively; and for the AIMS65 they were 0.79 and 0.61, respectively. The GBS with a cutoff point of 0 had a sensitivity of 0.99 and a specificity of 0.08. The GBS with a cutoff point of 0 was superior over other cutoff points and risk scores for identifying low-risk patients but had a very low specificity. None of the risk scores identified by our systematic review were robust and, hence, cannot be recommended for use in clinical practice. Future prospective studies are needed to develop robust new scores for use in ED patients with UGIB. © 2016 by the

A spatial assessment framework for evaluating flood risk under extreme climates.

Science.gov (United States)

Chen, Yun; Liu, Rui; Barrett, Damian; Gao, Lei; Zhou, Mingwei; Renzullo, Luigi; Emelyanova, Irina

2015-12-15

Australian coal mines have been facing a major challenge of increasing risk of flooding caused by intensive rainfall events in recent years. In light of growing climate change concerns and the predicted escalation of flooding, estimating flood inundation risk becomes essential for understanding sustainable mine water management in the Australian mining sector. This research develops a spatial multi-criteria decision making prototype for the evaluation of flooding risk at a regional scale using the Bowen Basin and its surroundings in Queensland as a case study. Spatial gridded data, including climate, hydrology, topography, vegetation and soils, were collected and processed in ArcGIS. Several indices were derived based on time series of observations and spatial modeling taking account of extreme rainfall, evapotranspiration, stream flow, potential soil water retention, elevation and slope generated from a digital elevation model (DEM), as well as drainage density and proximity extracted from a river network. These spatial indices were weighted using the analytical hierarchy process (AHP) and integrated in an AHP-based suitability assessment (AHP-SA) model under the spatial risk evaluation framework. A regional flooding risk map was delineated to represent likely impacts of criterion indices at different risk levels, which was verified using the maximum inundation extent detectable by a time series of remote sensing imagery. The result provides baseline information to help Bowen Basin coal mines identify and assess flooding risk when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in this research offers the Australian mining industry, and social and environmental studies around the world, an effective way to produce reliable assessment on flood risk for managing uncertainty in water availability under climate change. Copyright © 2015. Published by Elsevier B.V.

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.

Early-onset Conduct Problems: Predictions from daring temperament and risk taking behavior.

Science.gov (United States)

Bai, Sunhye; Lee, Steve S

2017-12-01

Given its considerable public health significance, identifying predictors of early expressions of conduct problems is a priority. We examined the predictive validity of daring, a key dimension of temperament, and the Balloon Analog Risk Task (BART), a laboratory-based measure of risk taking behavior, with respect to two-year change in parent, teacher-, and youth self-reported oppositional defiant disorder (ODD), conduct disorder (CD), and antisocial behavior. At baseline, 150 ethnically diverse 6- to 10-year old (M=7.8, SD=1.1; 69.3% male) youth with ( n =82) and without ( n =68) DSM-IV ADHD completed the BART whereas parents rated youth temperament (i.e., daring); parents and teachers also independently rated youth ODD and CD symptoms. Approximately 2 years later, multi-informant ratings of youth ODD, CD, and antisocial behavior were gathered from rating scales and interviews. Whereas risk taking on the BART was unrelated to conduct problems, individual differences in daring prospectively predicted multi-informant rated conduct problems, independent of baseline risk taking, conduct problems, and ADHD diagnostic status. Early differences in the propensity to show positive socio-emotional responses to risky or novel experiences uniquely predicted escalating conduct problems in childhood, even with control of other potent clinical correlates. We consider the role of temperament in the origins and development of significant conduct problems from childhood to adolescence, including possible explanatory mechanisms underlying these predictions.

A Risk Prediction Model for Sporadic CRC Based on Routine Lab Results.

Science.gov (United States)

Boursi, Ben; Mamtani, Ronac; Hwang, Wei-Ting; Haynes, Kevin; Yang, Yu-Xiao

2016-07-01

Current risk scores for colorectal cancer (CRC) are based on demographic and behavioral factors and have limited predictive values. To develop a novel risk prediction model for sporadic CRC using clinical and laboratory data in electronic medical records. We conducted a nested case-control study in a UK primary care database. Cases included those with a diagnostic code of CRC, aged 50-85. Each case was matched with four controls using incidence density sampling. CRC predictors were examined using univariate conditional logistic regression. Variables with p value CRC prediction models which included age, sex, height, obesity, ever smoking, alcohol dependence, and previous screening colonoscopy had an AUC of 0.58 (0.57-0.59) with poor goodness of fit. A laboratory-based model including hematocrit, MCV, lymphocytes, and neutrophil-lymphocyte ratio (NLR) had an AUC of 0.76 (0.76-0.77) and a McFadden's R2 of 0.21 with a NRI of 47.6 %. A combined model including sex, hemoglobin, MCV, white blood cells, platelets, NLR, and oral hypoglycemic use had an AUC of 0.80 (0.79-0.81) with a McFadden's R2 of 0.27 and a NRI of 60.7 %. Similar results were shown in an internal validation set. A laboratory-based risk model had good predictive power for sporadic CRC risk.

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.

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.

The Functional Movement Screen and Injury Risk: Association and Predictive Value in Active Men.

Science.gov (United States)

Bushman, Timothy T; Grier, Tyson L; Canham-Chervak, Michelle; Anderson, Morgan K; North, William J; Jones, Bruce H

2016-02-01

The Functional Movement Screen (FMS) is a series of 7 tests used to assess the injury risk in active populations. To determine the association of the FMS with the injury risk, assess predictive values, and identify optimal cut points using 3 injury types. Cohort study; Level of evidence, 2. Physically active male soldiers aged 18 to 57 years (N = 2476) completed the FMS. Demographic and fitness data were collected by survey. Medical record data for overuse injuries, traumatic injuries, and any injury 6 months after the FMS assessment were obtained. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated along with the receiver operating characteristic (ROC) to determine the area under the curve (AUC) and identify optimal cut points for the risk assessment. Risks, risk ratios (RRs), odds ratios (ORs), and 95% CIs were calculated to assess injury risks. Soldiers who scored ≤14 were at a greater risk for injuries compared with those who scored >14 using the composite score for overuse injuries (RR, 1.84; 95% CI, 1.63-2.09), traumatic injuries (RR, 1.26; 95% CI, 1.03-1.54), and any injury (RR, 1.60; 95% CI, 1.45-1.77). When controlling for other known injury risk factors, multivariate logistic regression analysis identified poor FMS performance (OR [score ≤14/19-21], 2.00; 95% CI, 1.42-2.81) as an independent risk factor for injuries. A cut point of ≤14 registered low measures of predictive value for all 3 injury types (sensitivity, 28%-37%; PPV, 19%-52%; AUC, 54%-61%). Shifting the injury risk cut point of ≤14 to the optimal cut points indicated by the ROC did not appreciably improve sensitivity or the PPV. Although poor FMS performance was associated with a higher risk of injuries, it displayed low sensitivity, PPV, and AUC. On the basis of these findings, the use of the FMS to screen for the injury risk is not recommended in this population because of the low predictive value and misclassification of the

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…

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.

Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

Science.gov (United States)

Wang, Shengfeng; Ogundiran, Temidayo O; Ademola, Adeyinka; Oluwasola, Olayiwola A; Adeoye, Adewunmi O; Sofoluwe, Adenike; Morhason-Bello, Imran; Odedina, Stella O; Agwai, Imaria; Adebamowo, Clement; Obajimi, Millicent; Ojengbede, Oladosu; Olopade, Olufunmilayo I; Huo, Dezheng

2018-04-20

Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aim to develop a model for absolute breast cancer risk prediction for Nigerian women. A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998~2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. The NBCS model included age, age at menarche, parity, duration of breast feeding, family history of breast cancer, height, body mass index, benign breast diseases and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model (area under ROC curve [AUC]=0.703, 95% confidence interval [CI]: 0.687-0.719) was better than the Black Women's Health Study (BWHS) model (AUC=0.605, 95% CI: 0.586-0.624), Gail model for White population (AUC=0.551, 95% CI: 0.531-0.571), and Gail model for Black population (AUC=0.545, 95% CI: 0.525-0.565). Compared to the BWHS, two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45% and 14.19%, respectively. We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in SSA populations. Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high-risk for breast cancer screening. Copyright ©2018, American Association for Cancer Research.