Developing points-based risk-scoring systems in the presence of competing risks.

PubMed

Austin, Peter C; Lee, Douglas S; D'Agostino, Ralph B; Fine, Jason P

2016-09-30

Predicting the occurrence of an adverse event over time is an important issue in clinical medicine. Clinical prediction models and associated points-based risk-scoring systems are popular statistical methods for summarizing the relationship between a multivariable set of patient risk factors and the risk of the occurrence of an adverse event. Points-based risk-scoring systems are popular amongst physicians as they permit a rapid assessment of patient risk without the use of computers or other electronic devices. The use of such points-based risk-scoring systems facilitates evidence-based clinical decision making. There is a growing interest in cause-specific mortality and in non-fatal outcomes. However, when considering these types of outcomes, one must account for competing risks whose occurrence precludes the occurrence of the event of interest. We describe how points-based risk-scoring systems can be developed in the presence of competing events. We illustrate the application of these methods by developing risk-scoring systems for predicting cardiovascular mortality in patients hospitalized with acute myocardial infarction. Code in the R statistical programming language is provided for the implementation of the described methods. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

PubMed

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome.

PubMed

Ambler, Gareth; Omar, Rumana Z; Royston, Patrick

2007-06-01

Risk models that aim to predict the future course and outcome of disease processes are increasingly used in health research, and it is important that they are accurate and reliable. Most of these risk models are fitted using routinely collected data in hospitals or general practices. Clinical outcomes such as short-term mortality will be near-complete, but many of the predictors may have missing values. A common approach to dealing with this is to perform a complete-case analysis. However, this may lead to overfitted models and biased estimates if entire patient subgroups are excluded. The aim of this paper is to investigate a number of methods for imputing missing data to evaluate their effect on risk model estimation and the reliability of the predictions. Multiple imputation methods, including hotdecking and multiple imputation by chained equations (MICE), were investigated along with several single imputation methods. A large national cardiac surgery database was used to create simulated yet realistic datasets. The results suggest that complete case analysis may produce unreliable risk predictions and should be avoided. Conditional mean imputation performed well in our scenario, but may not be appropriate if using variable selection methods. MICE was amongst the best performing multiple imputation methods with regards to the quality of the predictions. Additionally, it produced the least biased estimates, with good coverage, and hence is recommended for use in practice.

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

PubMed Central

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-01-01

Background Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. Methods In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Results Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Conclusion Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided. PMID:19036144

A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

PubMed

Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

2014-01-01

Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

PubMed Central

Sun, Jimeng; Hu, Jianying; Luo, Dijun; Markatou, Marianthi; Wang, Fei; Edabollahi, Shahram; Steinhubl, Steven E.; Daar, Zahra; Stewart, Walter F.

2012-01-01

Background: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. Methods: We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. Results: The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003–2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. Conclusion: We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors. PMID:23304365

A variable structure fuzzy neural network model of squamous dysplasia and esophageal squamous cell carcinoma based on a global chaotic optimization algorithm.

PubMed

Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Malekzadeh, Reza

2013-02-07

Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

PubMed Central

Xiao, Lei; Liu, Ya; Wang, Zuoguang; Li, Chuang; Jin, Yongxin; Zhao, Qiong

2017-01-01

Background 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. Methods 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. Results 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%. Conclusions 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. PMID:29084293

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

PubMed

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.

A time series modeling approach in risk appraisal of violent and sexual recidivism.

PubMed

Bani-Yaghoub, Majid; Fedoroff, J Paul; Curry, Susan; Amundsen, David E

2010-10-01

For over half a century, various clinical and actuarial methods have been employed to assess the likelihood of violent recidivism. Yet there is a need for new methods that can improve the accuracy of recidivism predictions. This study proposes a new time series modeling approach that generates high levels of predictive accuracy over short and long periods of time. The proposed approach outperformed two widely used actuarial instruments (i.e., the Violence Risk Appraisal Guide and the Sex Offender Risk Appraisal Guide). Furthermore, analysis of temporal risk variations based on specific time series models can add valuable information into risk assessment and management of violent offenders.

Integrative genetic risk prediction using non-parametric empirical Bayes classification.

PubMed

Zhao, Sihai Dave

2017-06-01

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa. © 2016, The International Biometric Society.

Paradigm of pretest risk stratification before coronary computed tomography.

PubMed

Jensen, Jesper Møller; Ovrehus, Kristian A; Nielsen, Lene H; Jensen, Jesper K; Larsen, Henrik M; Nørgaard, Bjarne L

2009-01-01

The optimal method of determining the pretest risk of coronary artery disease as a patient selection tool before coronary multidetector computed tomography (MDCT) is unknown. We investigated the ability of 3 different clinical risk scores to predict the outcome of coronary MDCT. This was a retrospective study of 551 patients consecutively referred for coronary MDCT on a suspicion of coronary artery disease. Diamond-Forrester, Duke, and Morise risk models were used to predict coronary artery stenosis (>50%) as assessed by coronary MDCT. The models were compared by receiver operating characteristic analysis. The distribution of low-, intermediate-, and high-risk persons, respectively, was established and compared for each of the 3 risk models. Overall, all risk prediction models performed equally well. However, the Duke risk model classified the low-risk patients more correctly than did the other models (P < 0.01). In patients without coronary artery calcification (CAC), the predictive value of the Duke risk model was superior to the other risk models (P < 0.05). Currently available risk prediction models seem to perform better in patients without CAC. Between the risk prediction models, there was a significant discrepancy in the distribution of patients at low, intermediate, or high risk (P < 0.01). The 3 risk prediction models perform equally well, although the Duke risk score may have advantages in subsets of patients. The choice of risk prediction model affects the referral pattern to MDCT. Copyright (c) 2009 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Risk prediction model: Statistical and artificial neural network approach

NASA Astrophysics Data System (ADS)

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.

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

PubMed

Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

2016-01-01

Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

PubMed

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Body Composition Indices and Predicted Cardiovascular Disease Risk Profile among Urban Dwellers in Malaysia

PubMed Central

Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Dahlui, Maznah; Majid, Hazreen Abdul

2015-01-01

Objectives. This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD) risk profile in an urban population in Kuala Lumpur, Malaysia. Methods. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Results. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. Conclusions. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions. PMID:25710002

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.

PubMed

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-11-26

Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.

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

PubMed

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.

Predicting Institutional Aggression in Offenders with Intellectual Disabilities Using the Violence Risk Appraisal Guide

ERIC Educational Resources Information Center

Pouls, Claudia; Jeandarme, Inge

2018-01-01

Background: One of the most extensively tested risk assessment instruments in offenders with an intellectual disability (OIDs) is the Violence Risk Appraisal Guide (VRAG). The purpose of this prospective study was to test the ability of this instrument to predict institutional aggression in OIDs. Method: VRAG scores were collected for 52 OIDs, and…

Using genetic prediction from known complex disease Loci to guide the design of next-generation sequencing experiments.

PubMed

Jostins, Luke; Levine, Adam P; Barrett, Jeffrey C

2013-01-01

A central focus of complex disease genetics after genome-wide association studies (GWAS) is to identify low frequency and rare risk variants, which may account for an important fraction of disease heritability unexplained by GWAS. A profusion of studies using next-generation sequencing are seeking such risk alleles. We describe how already-known complex trait loci (largely from GWAS) can be used to guide the design of these new studies by selecting cases, controls, or families who are most likely to harbor undiscovered risk alleles. We show that genetic risk prediction can select unrelated cases from large cohorts who are enriched for unknown risk factors, or multiply-affected families that are more likely to harbor high-penetrance risk alleles. We derive the frequency of an undiscovered risk allele in selected cases and controls, and show how this relates to the variance explained by the risk score, the disease prevalence and the population frequency of the risk allele. We also describe a new method for informing the design of sequencing studies using genetic risk prediction in large partially-genotyped families using an extension of the Inside-Outside algorithm for inference on trees. We explore several study design scenarios using both simulated and real data, and show that in many cases genetic risk prediction can provide significant increases in power to detect low-frequency and rare risk alleles. The same approach can also be used to aid discovery of non-genetic risk factors, suggesting possible future utility of genetic risk prediction in conventional epidemiology. Software implementing the methods in this paper is available in the R package Mangrove.

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

PubMed

Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh

2016-05-01

Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for Cancer Research.

Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

PubMed Central

2011-01-01

Background Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models. Methods We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants. Results We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures. Conclusions The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC. PMID:21797996

Prediction of Prostate Cancer Recurrence Using Quantitative Phase Imaging

NASA Astrophysics Data System (ADS)

Sridharan, Shamira; Macias, Virgilia; Tangella, Krishnarao; Kajdacsy-Balla, André; Popescu, Gabriel

2015-05-01

The risk of biochemical recurrence of prostate cancer among individuals who undergo radical prostatectomy for treatment is around 25%. Current clinical methods often fail at successfully predicting recurrence among patients at intermediate risk for recurrence. We used a label-free method, spatial light interference microscopy, to perform localized measurements of light scattering in prostatectomy tissue microarrays. We show, for the first time to our knowledge, that anisotropy of light scattering in the stroma immediately adjoining cancerous glands can be used to identify patients at higher risk for recurrence. The data show that lower value of anisotropy corresponds to a higher risk for recurrence, meaning that the stroma adjoining the glands of recurrent patients is more fractionated than in non-recurrent patients. Our method outperformed the widely accepted clinical tool CAPRA-S in the cases we interrogated irrespective of Gleason grade, prostate-specific antigen (PSA) levels and pathological tumor-node-metastasis (pTNM) stage. These results suggest that QPI shows promise in assisting pathologists to improve prediction of prostate cancer recurrence.

Dynamic Relationships Between Parental Monitoring, Peer Risk Involvement and Sexual Risk Behavior Among Bahamian Mid-Adolescents

PubMed Central

Wang, Bo; Stanton, Bonita; Deveaux, Lynette; Li, Xiaoming; Lunn, Sonja

2015-01-01

CONTEXT Considerable research has examined reciprocal relationships between parenting, peers and adolescent problem behavior; however, such studies have largely considered the influence of peers and parents separately. It is important to examine simultaneously the relationships between parental monitoring, peer risk involvement and adolescent sexual risk behavior, and whether increases in peer risk involvement and changes in parental monitoring longitudinally predict adolescent sexual risk behavior. METHODS Four waves of sexual behavior data were collected between 2008/2009 and 2011 from high school students aged 13–17 in the Bahamas. Structural equation and latent growth curve modeling were used to examine reciprocal relationships between parental monitoring, perceived peer risk involvement and adolescent sexual risk behavior. RESULTS For both male and female youth, greater perceived peer risk involvement predicted higher sexual risk behavior index scores, and greater parental monitoring predicted lower scores. Reciprocal relationships were found between parental monitoring and sexual risk behavior for males and between perceived peer risk involvement and sexual risk behavior for females. For males, greater sexual risk behavior predicted lower parental monitoring; for females, greater sexual risk behavior predicted higher perceived peer risk involvement. According to latent growth curve models, a higher initial level of parental monitoring predicted decreases in sexual risk behavior, whereas both a higher initial level and a higher growth rate of peer risk involvement predicted increases in sexual risk behavior. CONCLUSION Results highlight the important influence of peer risk involvement on youths’ sexual behavior and gender differences in reciprocal relationships between parental monitoring, peer influence and adolescent sexual risk behavior. PMID:26308261

Development and Preliminary Performance of a Risk Factor Screen to Predict Posttraumatic Psychological Disorder After Trauma Exposure

PubMed Central

Carlson, Eve B.; Palmieri, Patrick A.; Spain, David A.

2017-01-01

Objective We examined data from a prospective study of risk factors that increase vulnerability or resilience, exacerbate distress, or foster recovery to determine whether risk factors accurately predict which individuals will later have high posttraumatic (PT) symptom levels and whether brief measures of risk factors also accurately predict later symptom elevations. Method Using data from 129 adults exposed to traumatic injury of self or a loved one, we conducted receiver operating characteristic (ROC) analyses of 14 risk factors assessed by full-length measures, determined optimal cutoff scores and calculated predictive performance for the nine that were most predictive. For five risk factors, we identified sets of items that accounted for 90% of variance in total scores and calculated predictive performance for sets of brief risk measures. Results A set of nine risk factors assessed by full measures identified 89% of those who later had elevated PT symptoms (sensitivity) and 78% of those who did not (specificity). A set of four brief risk factor measures assessed soon after injury identified 86% of those who later had elevated PT symptoms and 72% of those who did not. Conclusions Use of sets of brief risk factor measures shows promise of accurate prediction of PT psychological disorder and probable PTSD or depression. Replication of predictive accuracy is needed in a new and larger sample. PMID:28622811

Application of Deep Learning and Supervised Learning Methods to Recognize Nonlinear Hidden Pattern in Water Stress Levels from Spatiotemporal Datasets across Rural and Urban US Counties

NASA Astrophysics Data System (ADS)

Eisenhart, T.; Josset, L.; Rising, J. A.; Devineni, N.; Lall, U.

2017-12-01

In the wake of recent water crises, the need to understand and predict the risk of water stress in urban and rural areas has grown. This understanding has the potential to improve decision making in public resource management, policy making, risk management and investment decisions. Assuming an underlying relationship between urban and rural water stress and observable features, we apply Deep Learning and Supervised Learning models to uncover hidden nonlinear patterns from spatiotemporal datasets. Results of interest includes prediction accuracy on extreme categories (i.e. urban areas highly prone to water stress) and not solely the average risk for urban or rural area, which adds complexity to the tuning of model parameters. We first label urban water stressed counties using annual water quality violations and compile a comprehensive spatiotemporal dataset that captures the yearly evolution of climatic, demographic and economic factors of more than 3,000 US counties over the 1980-2010 period. As county-level data reporting is not done on a yearly basis, we test multiple imputation methods to get around the issue of missing data. Using Python libraries, TensorFlow and scikit-learn, we apply and compare the ability of, amongst other methods, Recurrent Neural Networks (testing both LSTM and GRU cells), Convolutional Neural Networks and Support Vector Machines to predict urban water stress. We evaluate the performance of those models over multiple time spans and combine methods to diminish the risk of overfitting and increase prediction power on test sets. This methodology seeks to identify hidden nonlinear patterns to assess the predominant data features that influence urban and rural water stress. Results from this application at the national scale will assess the performance of deep learning models to predict water stress risk areas across all US counties and will highlight a predominant Machine Learning method for modeling water stress risk using spatiotemporal data.

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

NASA Technical Reports Server (NTRS)

Hatfield, Glen S.; Hark, Frank; Stott, James

2016-01-01

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account system integration risks such as those attributable to manufacturing and assembly. These sources often dominate component level risk. While consequence of failure is often understood, using predicted values in a risk model to estimate the probability of occurrence may underestimate the actual risk. Managers and decision makers use the probability of occurrence to influence the determination whether to accept the risk or require a design modification. The actual risk threshold for acceptance may not be fully understood due to the absence of system level test data or operational data. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

Predicting high risk births with contraceptive prevalence and contraceptive method-mix in an ecologic analysis.

PubMed

Perin, Jamie; Amouzou, Agbessi; Walker, Neff

2017-11-07

Increased contraceptive use has been associated with a decrease in high parity births, births that occur close together in time, and births to very young or to older women. These types of births are also associated with high risk of under-five mortality. Previous studies have looked at the change in the level of contraception use and the average change in these types of high-risk births. We aim to predict the distribution of births in a specific country when there is a change in the level and method of modern contraception. We used data from full birth histories and modern contraceptive use from 207 nationally representative Demographic and Health Surveys covering 71 countries to describe the distribution of births in each survey based on birth order, preceding birth space, and mother's age at birth. We estimated the ecologic associations between the prevalence and method-mix of modern contraceptives and the proportion of births in each category. Hierarchical modelling was applied to these aggregated cross sectional proportions, so that random effects were estimated for countries with multiple surveys. We use these results to predict the change in type of births associated with scaling up modern contraception in three different scenarios. We observed marked differences between regions, in the absolute rates of contraception, the types of contraceptives in use, and in the distribution of type of birth. Contraceptive method-mix was a significant determinant of proportion of high-risk births, especially for birth spacing, but also for mother's age and parity. Increased use of modern contraceptives is especially predictive of reduced parity and more births with longer preceding space. However, increased contraception alone is not associated with fewer births to women younger than 18 years or a decrease in short-spaced births. Both the level and the type of contraception are important factors in determining the effects of family planning on changes in distribution of high-risk births. The best predictions for how birth risk changes with increased modern contraception and for different contraception methods allow for more nuanced predictions specific to each country and can aid better planning for the scaling up of modern contraception.

Aquatic predicted no-effect concentrations of 16 polycyclic aromatic hydrocarbons and their ecological risks in surface seawater of Liaodong Bay, China.

PubMed

Wang, Ying; Wang, Juying; Mu, Jingli; Wang, Zhen; Cong, Yi; Yao, Ziwei; Lin, Zhongsheng

2016-06-01

Polycyclic aromatic hydrocarbons (PAHs), a class of ubiquitous pollutants in marine environments, exhibit moderate to high adverse effects on aquatic organisms and humans. However, the lack of PAH toxicity data for aquatic organism has limited evaluation of their ecological risks. In the present study, aquatic predicted no-effect concentrations (PNECs) of 16 priority PAHs were derived based on species sensitivity distribution models, and their probabilistic ecological risks in seawater of Liaodong Bay, Bohai Sea, China, were assessed. A quantitative structure-activity relationship method was adopted to achieve the predicted chronic toxicity data for the PNEC derivation. Good agreement for aquatic PNECs of 8 PAHs based on predicted and experimental chronic toxicity data was observed (R(2)  = 0.746), and the calculated PNECs ranged from 0.011 µg/L to 205.3 µg/L. A significant log-linear relationship also existed between the octanol-water partition coefficient and PNECs derived from experimental toxicity data (R(2)  = 0.757). A similar order of ecological risks for the 16 PAH species in seawater of Liaodong Bay was found by probabilistic risk quotient and joint probability curve methods. The individual high ecological risk of benzo[a]pyrene, benzo[b]fluoranthene, and benz[a]anthracene needs to be determined. The combined ecological risk of PAHs in seawater of Liaodong Bay calculated by the joint probability curve method was 13.9%, indicating a high risk as a result of co-exposure to PAHs. Environ Toxicol Chem 2016;35:1587-1593. © 2015 SETAC. © 2015 SETAC.

Incorporating Truncating Variants in PALB2, CHEK2 and ATM into the BOADICEA Breast Cancer Risk Model

PubMed Central

Lee, Andrew J.; Cunningham, Alex P.; Tischkowitz, Marc; Simard, Jacques; Pharoah, Paul D.; Easton, Douglas F.; Antoniou, Antonis C.

2016-01-01

Purpose The proliferation of gene-panel testing precipitates the need for a breast cancer (BC) risk model that incorporates the effects of mutations in several genes and family history (FH). We extended the BOADICEA model to incorporate the effects of truncating variants in PALB2, CHEK2 and ATM. Methods The BC incidence was modelled via the explicit effects of truncating variants in BRCA1/2, PALB2, CHEK2 and ATM and other unobserved genetic effects using segregation analysis methods. Results The predicted average BC risk by age 80 for an ATM mutation carrier is 28%, 30% for CHEK2, 50% for PALB2, 74% for BRCA1 and BRCA2. However, the BC risks are predicted to increase with FH-burden. In families with mutations, predicted risks for mutation-negative members depend on both FH and the specific mutation. The reduction in BC risk after negative predictive-testing is greatest when a BRCA1 mutation is identified in the family, but for women whose relatives carry a CHEK2 or ATM mutation, the risks decrease slightly. Conclusions The model may be a valuable tool for counselling women who have undergone gene-panel testing for providing consistent risks and harmonizing their clinical management. A web-application can be used to obtain BC- risks in clinical practice (http://ccge.medschl.cam.ac.uk/boadicea/). PMID:27464310

Developing a java android application of KMV-Merton default rate model

NASA Astrophysics Data System (ADS)

Yusof, Norliza Muhamad; Anuar, Aini Hayati; Isa, Norsyaheeda Natasha; Zulkafli, Sharifah Nursyuhada Syed; Sapini, Muhamad Luqman

2017-11-01

This paper presents a developed java android application for KMV-Merton model in predicting the defaut rate of a firm. Predicting default rate is essential in the risk management area as default risk can be immediately transmitted from one entity to another entity. This is the reason default risk is known as a global risk. Although there are several efforts, instruments and methods used to manage the risk, it is said to be insufficient. To the best of our knowledge, there has been limited innovation in developing the default risk mathematical model into a mobile application. Therefore, through this study, default risk is predicted quantitatively using the KMV-Merton model. The KMV-Merton model has been integrated in the form of java program using the Android Studio Software. The developed java android application is tested by predicting the levels of default risk of the three different rated companies. It is found that the levels of default risk are equivalent to the ratings of the respective companies. This shows that the default rate predicted by the KMV-Merton model using the developed java android application can be a significant tool to the risk mangement field. The developed java android application grants users an alternative to predict level of default risk within less procedure.

Proposal for a recovery prediction method for patients affected by acute mediastinitis

PubMed Central

2012-01-01

Background An attempt to find a prediction method of death risk in patients affected by acute mediastinitis. There is not such a tool described in available literature for that serious disease. Methods The study comprised 44 consecutive cases of acute mediastinitis. General anamnesis and biochemical data were included. Factor analysis was used to extract the risk characteristic for the patients. The most valuable results were obtained for 8 parameters which were selected for further statistical analysis (all collected during few hours after admission). Three factors reached Eigenvalue >1. Clinical explanations of these combined statistical factors are: Factor1 - proteinic status (serum total protein, albumin, and hemoglobin level), Factor2 - inflammatory status (white blood cells, CRP, procalcitonin), and Factor3 - general risk (age, number of coexisting diseases). Threshold values of prediction factors were estimated by means of statistical analysis (factor analysis, Statgraphics Centurion XVI). Results The final prediction result for the patients is constructed as simultaneous evaluation of all factor scores. High probability of death should be predicted if factor 1 value decreases with simultaneous increase of factors 2 and 3. The diagnostic power of the proposed method was revealed to be high [sensitivity =90%, specificity =64%], for Factor1 [SNC = 87%, SPC = 79%]; for Factor2 [SNC = 87%, SPC = 50%] and for Factor3 [SNC = 73%, SPC = 71%]. Conclusion The proposed prediction method seems a useful emergency signal during acute mediastinitis control in affected patients. PMID:22574625

A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

PubMed

Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data

PubMed Central

Seok, Junhee; Davis, Ronald W.; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge. PMID:25933378

Identifying Causal Risk Factors for Violence among Discharged Patients

PubMed Central

Coid, Jeremy W.; Kallis, Constantinos; Doyle, Mike; Shaw, Jenny; Ullrich, Simone

2015-01-01

Background Structured Professional Judgement (SPJ) is routinely administered in mental health and criminal justice settings but cannot identify violence risk above moderate accuracy. There is no current evidence that violence can be prevented using SPJ. This may be explained by routine application of predictive instead of causal statistical models when standardising SPJ instruments. Methods We carried out a prospective cohort study of 409 male and female patients discharged from medium secure services in England and Wales to the community. Measures were taken at baseline (pre-discharge), 6 and 12 months post-discharge using the Historical, Clinical and Risk-20 items version 3 (HCR-20v3) and Structural Assessment of Protective Factors (SAPROF). Information on violence was obtained via the McArthur community violence instrument and the Police National Computer. Results In a lagged model, HCR-20v3 and SAPROF items were poor predictors of violence. Eight items of the HCR-20v3 and 4 SAPROF items did not predict violent behaviour better than chance. In re-analyses considering temporal proximity of risk/ protective factors (exposure) on violence (outcome), risk was elevated due to violent ideation (OR 6.98, 95% CI 13.85–12.65, P<0.001), instability (OR 5.41, 95% CI 3.44–8.50, P<0.001), and poor coping/ stress (OR 8.35, 95% CI 4.21–16.57, P<0.001). All 3 risk factors were explanatory variables which drove the association with violent outcome. Self-control (OR 0.13, 95% CI 0.08–0.24, P<0.001) conveyed protective effects and explained the association of other protective factors with violence. Conclusions Using two standardised SPJ instruments, predictive (lagged) methods could not identify risk and protective factors which must be targeted in interventions for discharged patients with severe mental illness. Predictive methods should be abandoned if the aim is to progress from risk assessment to effective risk management and replaced by methods which identify factors causally associated with violence. PMID:26554711

Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data.

PubMed

Gim, Jungsoo; Kim, Wonji; Kwak, Soo Heon; Choi, Hosik; Park, Changyi; Park, Kyong Soo; Kwon, Sunghoon; Park, Taesung; Won, Sungho

2017-11-01

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance. Copyright © 2017 by the Genetics Society of America.

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

PubMed

Mei, Suyu; Zhu, Hao

2015-01-26

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

Probabalistic Risk Assessment of a Turbine Disk

NASA Astrophysics Data System (ADS)

Carter, Jace A.; Thomas, Michael; Goswami, Tarun; Fecke, Ted

Current Federal Aviation Administration (FAA) rotor design certification practices risk assessment using a probabilistic framework focused on only the life-limiting defect location of a component. This method generates conservative approximations of the operational risk. The first section of this article covers a discretization method, which allows for a transition from this relative risk to an absolute risk where the component is discretized into regions called zones. General guidelines were established for the zone-refinement process based on the stress gradient topology in order to reach risk convergence. The second section covers a risk assessment method for predicting the total fatigue life due to fatigue induced damage. The total fatigue life incorporates a dual mechanism approach including the crack initiation life and propagation life while simultaneously determining the associated initial flaw sizes. A microstructure-based model was employed to address uncertainties in material response and relate crack initiation life with crack size, while propagation life was characterized large crack growth laws. The two proposed methods were applied to a representative Inconel 718 turbine disk. The zone-based method reduces the conservative approaches, while showing effects of feature-based inspection on the risk assessment. In the fatigue damage assessment, the predicted initial crack distribution was found to be the most sensitive probabilistic parameter and can be used to establish an enhanced inspection planning.

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

NASA Technical Reports Server (NTRS)

Hatfield, Glen S.; Hark, Frank; Stott, James

2016-01-01

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account risks attributable to manufacturing, assembly, and process controls. These sources often dominate component level reliability or risk of failure probability. While consequences of failure is often understood in assessing risk, using predicted values in a risk model to estimate the probability of occurrence will likely underestimate the risk. Managers and decision makers often use the probability of occurrence in determining whether to accept the risk or require a design modification. Due to the absence of system level test and operational data inherent in aerospace applications, the actual risk threshold for acceptance may not be appropriately characterized for decision making purposes. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

The predictive performance of a path-dependent exotic-option credit risk model in the emerging market

NASA Astrophysics Data System (ADS)

Chen, Dar-Hsin; Chou, Heng-Chih; Wang, David; Zaabar, Rim

2011-06-01

Most empirical research of the path-dependent, exotic-option credit risk model focuses on developed markets. Taking Taiwan as an example, this study investigates the bankruptcy prediction performance of the path-dependent, barrier option model in the emerging market. We adopt Duan's (1994) [11], (2000) [12] transformed-data maximum likelihood estimation (MLE) method to directly estimate the unobserved model parameters, and compare the predictive ability of the barrier option model to the commonly adopted credit risk model, Merton's model. Our empirical findings show that the barrier option model is more powerful than Merton's model in predicting bankruptcy in the emerging market. Moreover, we find that the barrier option model predicts bankruptcy much better for highly-leveraged firms. Finally, our findings indicate that the prediction accuracy of the credit risk model can be improved by higher asset liquidity and greater financial transparency.

Mortality risk score prediction in an elderly population using machine learning.

PubMed

Rose, Sherri

2013-03-01

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.

Obesity as a risk factor for developing functional limitation among older adults: A conditional inference tree analysis

USDA-ARS?s Scientific Manuscript database

Objective: To examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors. Methods: The conditional inference tree analysis, a data mining approach, was used to con...

Genetic Risk by Experience Interaction for Childhood Internalizing Problems: Converging Evidence across Multiple Methods

ERIC Educational Resources Information Center

Vendlinski, Matthew K.; Lemery-Chalfant, Kathryn; Essex, Marilyn J.; Goldsmith, H. Hill

2011-01-01

Background: Identifying how genetic risk interacts with experience to predict psychopathology is an important step toward understanding the etiology of mental health problems. Few studies have examined genetic risk by experience interaction (GxE) in the development of childhood psychopathology. Methods: We used both co-twin and parent mental…

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

PubMed

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.

Early Diagnosis and Intervention Strategies for Post-Traumatic Heterotopic Ossification in Severely Injured Extremities

DTIC Science & Technology

2013-10-01

study will recruit wounded warriors with severe extremity trauma, which places them at high risk for heterotopic ossification (HO); bone formation at...involved in HO; 2) to define accurate and practical methods to predict where HO will develop; and 3) to define potential therapies for prevention or...elicit HO. These tools also need to provide effective methods for early diagnosis or risk assessment (prediction) so that therapies for prevention or

A new approach to applying feedforward neural networks to the prediction of musculoskeletal disorder risk.

PubMed

Chen, C L; Kaber, D B; Dempsey, P G

2000-06-01

A new and improved method to feedforward neural network (FNN) development for application to data classification problems, such as the prediction of levels of low-back disorder (LBD) risk associated with industrial jobs, is presented. Background on FNN development for data classification is provided along with discussions of previous research and neighborhood (local) solution search methods for hard combinatorial problems. An analytical study is presented which compared prediction accuracy of a FNN based on an error-back propagation (EBP) algorithm with the accuracy of a FNN developed by considering results of local solution search (simulated annealing) for classifying industrial jobs as posing low or high risk for LBDs. The comparison demonstrated superior performance of the FNN generated using the new method. The architecture of this FNN included fewer input (predictor) variables and hidden neurons than the FNN developed based on the EBP algorithm. Independent variable selection methods and the phenomenon of 'overfitting' in FNN (and statistical model) generation for data classification are discussed. The results are supportive of the use of the new approach to FNN development for applications to musculoskeletal disorders and risk forecasting in other domains.

Developing Risk Prediction Models for Kidney Injury and Assessing Incremental Value for Novel Biomarkers

PubMed Central

Kerr, Kathleen F.; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G.

2014-01-01

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. PMID:24855282

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods

PubMed Central

Stone, Bryan L; Sakaguchi, Farrant; Sheng, Xiaoming; Murtaugh, Maureen A

2015-01-01

Background Chronic diseases affect 52% of Americans and consume 86% of health care costs. A small portion of patients consume most health care resources and costs. More intensive patient management strategies, such as case management, are usually more effective at improving health outcomes, but are also more expensive. To use limited resources efficiently, risk stratification is commonly used in managing patients with chronic diseases, such as asthma, chronic obstructive pulmonary disease, diabetes, and heart disease. Patients are stratified based on predicted risk with patients at higher risk given more intensive care. The current risk-stratified patient management approach has 3 limitations resulting in many patients not receiving the most appropriate care, unnecessarily increased costs, and suboptimal health outcomes. First, using predictive models for health outcomes and costs is currently the best method for forecasting individual patient’s risk. Yet, accuracy of predictive models remains poor causing many patients to be misstratified. If an existing model were used to identify candidate patients for case management, enrollment would miss more than half of those who would benefit most, but include others unlikely to benefit, wasting limited resources. Existing models have been developed under the assumption that patient characteristics primarily influence outcomes and costs, leaving physician characteristics out of the models. In reality, both characteristics have an impact. Second, existing models usually give neither an explanation why a particular patient is predicted to be at high risk nor suggestions on interventions tailored to the patient’s specific case. As a result, many high-risk patients miss some suitable interventions. Third, thresholds for risk strata are suboptimal and determined heuristically with no quality guarantee. Objective The purpose of this study is to improve risk-stratified patient management so that more patients will receive the most appropriate care. Methods This study will (1) combine patient, physician profile, and environmental variable features to improve prediction accuracy of individual patient health outcomes and costs; (2) develop the first algorithm to explain prediction results and suggest tailored interventions; (3) develop the first algorithm to compute optimal thresholds for risk strata; and (4) conduct simulations to estimate outcomes of risk-stratified patient management for various configurations. The proposed techniques will be demonstrated on a test case of asthma patients. Results We are currently in the process of extracting clinical and administrative data from an integrated health care system’s enterprise data warehouse. We plan to complete this study in approximately 5 years. Conclusions Methods developed in this study will help transform risk-stratified patient management for better clinical outcomes, higher patient satisfaction and quality of life, reduced health care use, and lower costs. PMID:26503357

Problems With Risk Reclassification Methods for Evaluating Prediction Models

PubMed Central

Pepe, Margaret S.

2011-01-01

For comparing the performance of a baseline risk prediction model with one that includes an additional predictor, a risk reclassification analysis strategy has been proposed. The first step is to cross-classify risks calculated according to the 2 models for all study subjects. Summary measures including the percentage of reclassification and the percentage of correct reclassification are calculated, along with 2 reclassification calibration statistics. The author shows that interpretations of the proposed summary measures and P values are problematic. The author's recommendation is to display the reclassification table, because it shows interesting information, but to use alternative methods for summarizing and comparing model performance. The Net Reclassification Index has been suggested as one alternative method. The author argues for reporting components of the Net Reclassification Index because they are more clinically relevant than is the single numerical summary measure. PMID:21555714

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

PubMed

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.

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

PubMed

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.

The ACTA PORT-score for predicting perioperative risk of blood transfusion for adult cardiac surgery.

PubMed

Klein, A A; Collier, T; Yeates, J; Miles, L F; Fletcher, S N; Evans, C; Richards, T

2017-09-01

A simple and accurate scoring system to predict risk of transfusion for patients undergoing cardiac surgery is lacking. We identified independent risk factors associated with transfusion by performing univariate analysis, followed by logistic regression. We then simplified the score to an integer-based system and tested it using the area under the receiver operator characteristic (AUC) statistic with a Hosmer-Lemeshow goodness-of-fit test. Finally, the scoring system was applied to the external validation dataset and the same statistical methods applied to test the accuracy of the ACTA-PORT score. Several factors were independently associated with risk of transfusion, including age, sex, body surface area, logistic EuroSCORE, preoperative haemoglobin and creatinine, and type of surgery. In our primary dataset, the score accurately predicted risk of perioperative transfusion in cardiac surgery patients with an AUC of 0.76. The external validation confirmed accuracy of the scoring method with an AUC of 0.84 and good agreement across all scores, with a minor tendency to under-estimate transfusion risk in very high-risk patients. The ACTA-PORT score is a reliable, validated tool for predicting risk of transfusion for patients undergoing cardiac surgery. This and other scores can be used in research studies for risk adjustment when assessing outcomes, and might also be incorporated into a Patient Blood Management programme. © The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Assessing Breast Cancer Risk with an Artificial Neural Network

PubMed

Sepandi, Mojtaba; Taghdir, Maryam; Rezaianzadeh, Abbas; Rahimikazerooni, Salar

2018-04-25

Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk. Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer. This study aimed to establish a model to aid radiologists in breast cancer risk estimation. This incorporated imaging methods and fine needle aspiration biopsy (FNAB) for cyto-pathological diagnosis. Methods: An artificial neural network (ANN) technique was used on a retrospectively collected dataset including mammographic results, risk factors, and clinical findings to accurately predict the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: The network incorporating the selected features performed best (AUC = 0.955). Sensitivity and specificity of the ANN were respectively calculated as 0.82 and 0.90. In addition, negative and positive predictive values were respectively computed as 0.90 and 0.80. Conclusion: ANN has potential applications as a decision-support tool to help underperforming practitioners to improve the positive predictive value of biopsy recommendations. Creative Commons Attribution License

Method of Breast Reconstruction Determines Venous Thromboembolism Risk Better Than Current Prediction Models

PubMed Central

Patel, Niyant V.; Wagner, Douglas S.

2015-01-01

Background: Venous thromboembolism (VTE) risk models including the Davison risk score and the 2005 Caprini risk assessment model have been validated in plastic surgery patients. However, their utility and predictive value in breast reconstruction has not been well described. We sought to determine the utility of current VTE risk models in this population and the VTE rate observed in various methods of breast reconstruction. Methods: A retrospective review of breast reconstructions by a single surgeon was performed. One hundred consecutive transverse rectus abdominis myocutaneous (TRAM) patients, 100 consecutive implant patients, and 100 consecutive latissimus dorsi patients were identified over a 10-year period. Patient demographics and presence of symptomatic VTE were collected. 2005 Caprini risk scores and Davison risk scores were calculated for each patient. Results: The TRAM reconstruction group was found to have a higher VTE rate (6%) than the implant (0%) and latissimus (0%) reconstruction groups (P < 0.01). Mean Davison risk scores and 2005 Caprini scores were similar across all reconstruction groups (P > 0.1). The vast majority of patients were stratified as high risk (87.3%) by the VTE risk models. However, only TRAM reconstruction patients demonstrated significant VTE risk. Conclusions: TRAM reconstruction appears to have a significantly higher risk of VTE than both implant and latissimus reconstruction. Current risk models do not effectively stratify breast reconstruction patients at risk for VTE. The method of breast reconstruction appears to have a significant role in patients’ VTE risk. PMID:26090287

Risky business: Addressing the consequences of predicting violence.

PubMed

Miller, Sarah L; Brodsky, Stanley L

2011-01-01

Despite major advances in the prediction of violence and risk management, risk evaluations are necessarily imperfect. This article focuses on the role of the evaluator in such assessments and addresses the consequences incurred by clinicians involved in conducting risk assessments. We discuss the risks of overpredicting and underpredicting violence with respect to how these risks can influence an evaluator's opinion. Then, we review how law and psychology inform the clinician's work. Finally, in response to the compelling tendency toward overprediction of violence, we propose a preliminary self-assessment guide as a method of examining influences on the evaluator in assessing violence risk.

Assessing Risk for Sexual Offenders in New Zealand: Development and Validation of a Computer-Scored Risk Measure

ERIC Educational Resources Information Center

Skelton, Alexander; Riley, David; Wales, David; Vess, James

2006-01-01

A growing research base supports the predictive validity of actuarial methods of risk assessment with sexual offenders. These methods use clearly defined variables with demonstrated empirical association with re-offending. The advantages of actuarial measures for screening large numbers of offenders quickly and economically are further enhanced…

Unpacking Trauma Exposure Risk Factors and Differential Pathways of Influence: Predicting Postwar Mental Distress in Bosnian Adolescents

ERIC Educational Resources Information Center

Layne, Christopher M.; Olsen, Joseph A.; Baker, Aaron; Legerski, John-Paul; Isakson, Brian; Pasalic, Alma; Durakovic-Belko, Elvira; Dapo, Nermin; Campara, Nihada; Arslanagic, Berina; Saltzman, William R.; Pynoos, Robert S.

2010-01-01

Methods are needed for quantifying the potency and differential effects of risk factors to identify at-risk groups for theory building and intervention. Traditional methods for constructing war exposure measures are poorly suited to "unpack" differential relations between specific types of exposure and specific outcomes. This study of…

An Ensemble Multilabel Classification for Disease Risk Prediction

PubMed Central

Liu, Wei; Zhao, Hongling; Zhang, Chaoyang

2017-01-01

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance. PMID:29065647

Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.

PubMed

Liang, Yong; Chai, Hua; Liu, Xiao-Ying; Xu, Zong-Ben; Zhang, Hai; Leung, Kwong-Sak

2016-03-01

One of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients' gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients' clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified. To try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease. The results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets. The advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients' survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi-supervised learning model is one more appropriate tool for survival analysis in clinical cancer research.

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

PubMed Central

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

2017-01-01

Background Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. Aim 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. Design and setting Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Method 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. Results 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. Conclusion 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. PMID:28360074

Using the weighted area under the net benefit curve for decision curve analysis.

PubMed

Talluri, Rajesh; Shete, Sanjay

2016-07-18

Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.

Multi-Risk Infants: Predicting Attachment Security from Sociodemographic, Psychosocial, and Health Risk among African-American Preterm Infants

ERIC Educational Resources Information Center

Candelaria, Margo; Teti, Douglas M.; Black, Maureen M.

2011-01-01

Background: Ecological and transactional theories link child outcomes to accumulated risk. This study hypothesized that cumulative risk was negatively related to attachment, and that maternal sensitivity mediated linkages between risk and attachment. Methods: One hundred and twelve high-risk African-American premature infant-mother dyads…

A Reactive Balance Rating Method that Correlates with Kinematics after Trip-Like Perturbations on a Treadmill and Fall Risk Among Residents of Older Adult Congregate Housing.

PubMed

Madigan, Michael L; Aviles, Jessica; Allin, Leigh J; Nussbaum, Maury A; Alexander, Neil B

2018-04-16

A growing number of studies are using modified treadmills to train reactive balance after trip-like perturbations that require multiple steps to recover balance. The goal of this study was thus to develop and validate a low-tech reactive balance rating method in the context of trip-like treadmill perturbations to facilitate the implementation of this training outside the research setting. Thirty-five residents of five senior congregate housing facilities participated in the study. Subjects completed a series of reactive balance tests on a modified treadmill from which the reactive balance rating was determined, along with a battery of standard clinical balance and mobility tests that predict fall risk. We investigated the strength of correlation between the reactive balance rating and reactive balance kinematics. We compared the strength of correlation between the reactive balance rating and clinical tests predictive of fall risk, with the strength of correlation between reactive balance kinematics and the same clinical tests. We also compared the reactive balance rating between subjects predicted to be at a high or low risk of falling. The reactive balance rating was correlated with reactive balance kinematics (Spearman's rho squared = .04 - .30), exhibited stronger correlations with clinical tests than most kinematic measures (Spearman's rho squared = .00 - .23), and was 42-60% lower among subjects predicted to be at a high risk for falling. The reactive balance rating method may provide a low-tech, valid measure of reactive balance kinematics, and an indicator of fall risk, after trip-like postural perturbations.

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

PubMed Central

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. PMID:28376093

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

PubMed

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.

Predicting landslides related to clearcut logging, northwestern California, U.S.A.

Treesearch

David J. Furbish; Raymond M. Rice

1983-01-01

Abstract - Landslides related to clearcut logging are a significant source of erosion in the mountains of northwestern California. Forest managers, therefore, frequently must include assessments of landslide risk in their land-use plans. A quantitative method is needed to predict such risk over large areas of rugged mountainous terrain. From air photographs, data...

Location of Food Stores Near Schools Does Not Predict the Weight Status of Maine High School Students

ERIC Educational Resources Information Center

Harris, David E.; Blum, Janet Whatley; Bampton, Matthew; O'Brien, Liam M.; Beaudoin, Christina M.; Polacsek, Michele; O'Rourke, Karen A.

2011-01-01

Objective: To examine the relationship between stores selling calorie-dense food near schools and student obesity risk, with the hypothesis that high availability predicts increased risk. Methods: Mail surveys determined height, weight, and calorie-dense food consumption for 552 students at 11 Maine high schools. Driving distance from all food…

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

PubMed

Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

2014-12-01

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The Predictive Validity of the Short-Term Assessment of Risk and Treatability (START) for Multiple Adverse Outcomes in a Secure Psychiatric Inpatient Setting.

PubMed

O'Shea, Laura E; Picchioni, Marco M; Dickens, Geoffrey L

2016-04-01

The Short-Term Assessment of Risk and Treatability (START) aims to assist mental health practitioners to estimate an individual's short-term risk for a range of adverse outcomes via structured consideration of their risk ("Vulnerabilities") and protective factors ("Strengths") in 20 areas. It has demonstrated predictive validity for aggression but this is less established for other outcomes. We collated START assessments for N = 200 adults in a secure mental health hospital and ascertained 3-month risk event incidence using the START Outcomes Scale. The specific risk estimates, which are the tool developers' suggested method of overall assessment, predicted aggression, self-harm/suicidality, and victimization, and had incremental validity over the Strength and Vulnerability scales for these outcomes. The Strength scale had incremental validity over the Vulnerability scale for aggressive outcomes; therefore, consideration of protective factors had demonstrable value in their prediction. Further evidence is required to support use of the START for the full range of outcomes it aims to predict. © The Author(s) 2015.

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

PubMed

Eley, John G; Friedrich, Thomas; Homann, Kenneth L; Howell, Rebecca M; Scholz, Michael; Durante, Marco; Newhauser, Wayne D

2016-05-01

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. 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. 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). Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy. Copyright © 2016 Elsevier Inc. All rights reserved.

Reader performance in visual assessment of breast density using visual analogue scales: Are some readers more predictive of breast cancer?

NASA Astrophysics Data System (ADS)

Rayner, Millicent; Harkness, Elaine F.; Foden, Philip; Wilson, Mary; Gadde, Soujanya; Beetles, Ursula; Lim, Yit Y.; Jain, Anil; Bundred, Sally; Barr, Nicky; Evans, D. Gareth; Howell, Anthony; Maxwell, Anthony; Astley, Susan M.

2018-03-01

Mammographic breast density is one of the strongest risk factors for breast cancer, and is used in risk prediction and for deciding appropriate imaging strategies. In the Predicting Risk Of Cancer At Screening (PROCAS) study, percent density estimated by two readers on Visual Analogue Scales (VAS) has shown a strong relationship with breast cancer risk when assessed against automated methods. However, this method suffers from reader variability. This study aimed to assess the performance of PROCAS readers using VAS, and to identify those most predictive of breast cancer. We selected the seven readers who had estimated density on over 6,500 women including at least 100 cancer cases, analysing their performance using multivariable logistic regression and Receiver Operator Characteristic (ROC) analysis. All seven readers showed statistically significant odds ratios (OR) for cancer risk according to VAS score after adjusting for classical risk factors. The OR was greatest for reader 18 at 1.026 (95% Cl 1.018-1.034). Adjusted Area Under the ROC Curves (AUCs) were statistically significant for all readers, but greatest for reader 14 at 0.639. Further analysis of the VAS scores for these two readers showed reader 14 had higher sensitivity (78.0% versus 42.2%), whereas reader 18 had higher specificity (78.0% versus 46.0%). Our results demonstrate individual differences when assigning VAS scores; one better identified those with increased risk, whereas another better identified low risk individuals. However, despite their different strengths, both readers showed similar predictive abilities overall. Standardised training for VAS may improve reader variability and consistency of VAS scoring.

Population-based absolute risk estimation with survey data

PubMed Central

Kovalchik, Stephanie A.; Pfeiffer, Ruth M.

2013-01-01

Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level. PMID:23686614

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

PubMed Central

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

Early Adolescent Relationship Predictors of Emerging Adult Outcomes: Youth with and without Type 1 Diabetes

PubMed Central

Helgeson, Vicki S.; Palladino, Dianne K.; Reynolds, Kerry A.; Becker, Dorothy; Escobar, Oscar; Siminerio, Linda

2013-01-01

Background Emerging adulthood is a high-risk period for mental health problems and risk behaviors for youth generally and for physical health problems among those with type 1 diabetes. Purpose To examine whether adolescents’ relationships with parents and friends predict health and risk behaviors during emerging adulthood. Method Youth with and without diabetes were enrolled at average age 12 and followed for 7 years. Parent and friend relationship variables, measured during adolescence, were used to predict emerging adulthood outcomes: depression, risk behavior, and, for those with diabetes, diabetes outcomes. Results Parent relationship quality predicted decreased depressive symptoms and, for those with diabetes, decreased alcohol use. Parent control predicted increased smoking, reduced college attendance, and, for control participants, increased depressive symptoms. For those with diabetes, parent control predicted decreased depressive symptoms and better self-care. Friend relationship variables predicted few outcomes. Conclusions Adolescent parent relationships remain an important influence on emerging adults’ lives. PMID:24178509

Methods and Techniques for Risk Prediction of Space Shuttle Upgrades

NASA Technical Reports Server (NTRS)

Hoffman, Chad R.; Pugh, Rich; Safie, Fayssal

1998-01-01

Since the Space Shuttle Accident in 1986, NASA has been trying to incorporate probabilistic risk assessment (PRA) in decisions concerning the Space Shuttle and other NASA projects. One major study NASA is currently conducting is in the PRA area in establishing an overall risk model for the Space Shuttle System. The model is intended to provide a tool to predict the Shuttle risk and to perform sensitivity analyses and trade studies including evaluation of upgrades. Marshall Space Flight Center (MSFC) and its prime contractors including Pratt and Whitney (P&W) are part of the NASA team conducting the PRA study. MSFC responsibility involves modeling the External Tank (ET), the Solid Rocket Booster (SRB), the Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME). A major challenge that faced the PRA team is modeling the shuttle upgrades. This mainly includes the P&W High Pressure Fuel Turbopump (HPFTP) and the High Pressure Oxidizer Turbopump (HPOTP). The purpose of this paper is to discuss the various methods and techniques used for predicting the risk of the P&W redesigned HPFTP and HPOTP.

Osteoporosis risk prediction using machine learning and conventional methods.

PubMed

Kim, Sung Kean; Yoo, Tae Keun; Oh, Ein; Kim, Deok Won

2013-01-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

PAUSE: Predictive Analytics Using SPARQL-Endpoints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas R; Ainsworth, Keela; Bond, Nathaniel

2014-07-11

This invention relates to the medical industry and more specifically to methods of predicting risks. With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.

Expected number of asbestos-related lung cancers in the Netherlands in the next two decades: a comparison of methods.

PubMed

Van der Bij, Sjoukje; Vermeulen, Roel C H; Portengen, Lützen; Moons, Karel G M; Koffijberg, Hendrik

2016-05-01

Exposure to asbestos fibres increases the risk of mesothelioma and lung cancer. Although the vast majority of mesothelioma cases are caused by asbestos exposure, the number of asbestos-related lung cancers is less clear. This number cannot be determined directly as lung cancer causes are not clinically distinguishable but may be estimated using varying modelling methods. We applied three different modelling methods to the Dutch population supplemented with uncertainty ranges (UR) due to uncertainty in model input values. The first method estimated asbestos-related lung cancer cases directly from observed and predicted mesothelioma cases in an age-period-cohort analysis. The second method used evidence on the fraction of lung cancer cases attributable (population attributable risk (PAR)) to asbestos exposure. The third method incorporated risk estimates and population exposure estimates to perform a life table analysis. The three methods varied substantially in incorporated evidence. Moreover, the estimated number of asbestos-related lung cancer cases in the Netherlands between 2011 and 2030 depended crucially on the actual method applied, as the mesothelioma method predicts 17 500 expected cases (UR 7000-57 000), the PAR method predicts 12 150 cases (UR 6700-19 000), and the life table analysis predicts 6800 cases (UR 6800-33 850). The three different methods described resulted in absolute estimates varying by a factor of ∼2.5. These results show that accurate estimation of the impact of asbestos exposure on the lung cancer burden remains a challenge. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation - A Systematic Review.

PubMed

Tomaselli Muensterman, Elena; Tisdale, James E

2018-06-08

Prolongation of the heart rate-corrected QT (QTc) interval increases the risk for torsades de pointes (TdP), a potentially fatal arrhythmia. The likelihood of TdP is higher in patients with risk factors, which include female sex, older age, heart failure with reduced ejection fraction, hypokalemia, hypomagnesemia, concomitant administration of ≥ 2 QTc interval-prolonging medications, among others. Assessment and quantification of risk factors may facilitate prediction of patients at highest risk for developing QTc interval prolongation and TdP. Investigators have utilized the field of predictive analytics, which generates predictions using techniques including data mining, modeling, machine learning, and others, to develop methods of risk quantification and prediction of QTc interval prolongation. Predictive analytics have also been incorporated into clinical decision support (CDS) tools to alert clinicians regarding patients at increased risk of developing QTc interval prolongation. The objectives of this paper are to assess the effectiveness of predictive analytics for identification of patients at risk of drug-induced QTc interval prolongation, and to discuss the efficacy of incorporation of predictive analytics into CDS tools in clinical practice. A systematic review of English language articles (human subjects only) was performed, yielding 57 articles, with an additional 4 articles identified from other sources; a total of 10 articles were included in this review. Risk scores for QTc interval prolongation have been developed in various patient populations including those in cardiac intensive care units (ICUs) and in broader populations of hospitalized or health system patients. One group developed a risk score that includes information regarding genetic polymorphisms; this score significantly predicted TdP. Development of QTc interval prolongation risk prediction models and incorporation of these models into CDS tools reduces the risk of QTc interval prolongation in cardiac ICUs and identifies health-system patients at increased risk for mortality. The impact of these QTc interval prolongation predictive analytics on overall patient safety outcomes, such as TdP and sudden cardiac death relative to the cost of development and implementation, requires further study. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor

PubMed Central

Parmar, Priya; Krishnamurthi, Rita; Ikram, M Arfan; Hofman, Albert; Mirza, Saira S; Varakin, Yury; Kravchenko, Michael; Piradov, Michael; Thrift, Amanda G; Norrving, Bo; Wang, Wenzhi; Mandal, Dipes Kumar; Barker-Collo, Suzanne; Sahathevan, Ramesh; Davis, Stephen; Saposnik, Gustavo; Kivipelto, Miia; Sindi, Shireen; Bornstein, Natan M; Giroud, Maurice; Béjot, Yannick; Brainin, Michael; Poulton, Richie; Narayan, K M Venkat; Correia, Manuel; Freire, António; Kokubo, Yoshihiro; Wiebers, David; Mensah, George; BinDhim, Nasser F; Barber, P Alan; Pandian, Jeyaraj Durai; Hankey, Graeme J; Mehndiratta, Man Mohan; Azhagammal, Shobhana; Ibrahim, Norlinah Mohd; Abbott, Max; Rush, Elaine; Hume, Patria; Hussein, Tasleem; Bhattacharjee, Rohit; Purohit, Mitali; Feigin, Valery L

2015-01-01

Background The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the ‘mass’ approach), the ‘high risk’ approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer™, has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer™) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results The Stroke Riskometer™ performed well against the FSRS five-year AUROC for both males (FSRS = 75·0% (95% CI 72·3%–77·6%), Stroke Riskometer™ = 74·0(95% CI 71·3%–76·7%) and females [FSRS = 70·3% (95% CI 67·9%–72·8%, Stroke Riskometer™ = 71·5% (95% CI 69·0%–73·9%)], and better than QStroke [males – 59·7% (95% CI 57·3%–62·0%) and comparable to females = 71·1% (95% CI 69·0%–73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51–0·56, D-statistic ranging from 0·01–0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P < 0·006). Conclusions The Stroke Riskometer™ is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer™ will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors. PMID:25491651

Advances in the assessment and prediction of interpersonal violence.

PubMed

Mills, Jeremy F

2005-02-01

This article underscores the weakness of clinical judgment as a mechanism for prediction with examples from other areas in the psychological literature. Clinical judgment has as its Achilles'heel the reliance on a person to incorporate multiple pieces of information while overcoming human judgment errors--a feat insurmountable thus far. The actuarial approach to risk assessment has overcome many of the weaknesses of clinical judgment and has been shown to be a much superior method. Nonetheless, the static/historical nature of the risk factors associated with most actuarial approaches is limiting. Advances in risk prediction will be found in part in the development of dynamic actuarial instruments that will measure both static/historical and changeable risk factors. The dynamic risk factors can be reevaluated on an ongoing basis, and it is proposed that the level of change in dynamic factors necessary to represent a significant change in overall risk will be an interactive function with static risk factors.

A Community-based Cross-sectional Study of Cardiovascular Risk in a Rural Community of Puducherry.

PubMed

Shrivastava, Saurabh R; Ghorpade, Arun G; Shrivastava, Prateek S

2015-01-01

The World Health Organization (WHO) / International Society of Hypertension (ISH) risk prediction chart can predict the risk of cardiovascular events in any population. To assess the prevalence of cardiovascular risk factors and to estimate the cardiovascular risk using the WHO/ISH risk charts. A cross-sectional study was done from November 2011 to January 2012 in a rural area of Puducherry. Method of sampling was a single stage cluster random sampling, and subjects were enrolled depending on their suitability with the inclusion and exclusion criteria. The data collection tool was a piloted and semi-structured questionnaire, while WHO/ISH cardiovascular risk prediction charts for the South-East Asian region was used to predict the cardiovascular risk. Institutional Ethics committee permission was obtained before the start of the study. Statistical analysis was done using SPSS version 16 and appropriate statistical tests were applied. The mean age in years was 54.2 (±11.1) years with 46.7% of the participants being male. On application of the WHO/ISH risk prediction charts, almost 17% of the study subjects had moderate or high risk for a cardiovascular event. Additionally, high salt diet, alcohol use and low HDL levels, were identified as the major CVD risk factors. To conclude, stratification of people on the basis of risk prediction chart is a major step to have a clear idea about the magnitude of the problem. The findings of the current study revealed that there is a high burden of CVD risk in the rural Puducherry.

Robust human body model injury prediction in simulated side impact crashes.

PubMed

Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

2016-01-01

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

Impact of Hydrogeological Uncertainty on Estimation of Environmental Risks Posed by Hydrocarbon Transportation Networks

NASA Astrophysics Data System (ADS)

Ciriello, V.; Lauriola, I.; Bonvicini, S.; Cozzani, V.; Di Federico, V.; Tartakovsky, Daniel M.

2017-11-01

Ubiquitous hydrogeological uncertainty undermines the veracity of quantitative predictions of soil and groundwater contamination due to accidental hydrocarbon spills from onshore pipelines. Such predictions, therefore, must be accompanied by quantification of predictive uncertainty, especially when they are used for environmental risk assessment. We quantify the impact of parametric uncertainty on quantitative forecasting of temporal evolution of two key risk indices, volumes of unsaturated and saturated soil contaminated by a surface spill of light nonaqueous-phase liquids. This is accomplished by treating the relevant uncertain parameters as random variables and deploying two alternative probabilistic models to estimate their effect on predictive uncertainty. A physics-based model is solved with a stochastic collocation method and is supplemented by a global sensitivity analysis. A second model represents the quantities of interest as polynomials of random inputs and has a virtually negligible computational cost, which enables one to explore any number of risk-related contamination scenarios. For a typical oil-spill scenario, our method can be used to identify key flow and transport parameters affecting the risk indices, to elucidate texture-dependent behavior of different soils, and to evaluate, with a degree of confidence specified by the decision-maker, the extent of contamination and the correspondent remediation costs.

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

PubMed

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.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models' with and without novel biomarkers. Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham's "general CVD risk" algorithm. The command is addpred for logistic regression models. The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.

GRECOS project. The use of genetics to predict the vascular recurrence after stroke

PubMed Central

Fernández-Cadenas, Israel; Mendióroz, Maite; Giralt, Dolors; Nafria, Cristina; Garcia, Elena; Carrera, Caty; Gallego-Fabrega, Cristina; Domingues-Montanari, Sophie; Delgado, Pilar; Ribó, Marc; Castellanos, Mar; Martínez, Sergi; Freijo, Mari Mar; Jiménez-Conde, Jordi; Rubiera, Marta; Alvarez-Sabín, José; Molina, Carlos A.; Font, Maria Angels; Olivares, Marta Grau; Palomeras, Ernest; de la Ossa, Natalia Perez; Martinez-Zabaleta, Maite; Masjuan, Jaime; Moniche, Francisco; Canovas, David; Piñana, Carlos; Purroy, Francisco; Cocho, Dolores; Navas, Inma; Tejero, Carlos; Aymerich, Nuria; Cullell, Natalia; Muiño, Elena; Serena, Joaquín; Rubio, Francisco; Davalos, Antoni; Roquer, Jaume; Arenillas, Juan Francisco; Martí-Fábregas, Joan; Keene, Keith; Chen, Wei-Min; Worrall, Bradford; Sale, Michele; Arboix, Adrià; Krupinski, Jerzy; Montaner, Joan

2017-01-01

Background and Purpose Vascular recurrence occurs in 11% of patients during the first year after ischemic stroke (IS) or transient ischemic attack (TIA). Clinical scores do not predict the whole vascular recurrence risk, therefore we aimed to find genetic variants associated with recurrence that might improve the clinical predictive models in IS. Methods We analyzed 256 polymorphisms from 115 candidate genes in three patient cohorts comprising 4,482 IS or TIA patients. The discovery cohort was prospectively recruited and included 1,494 patients, 6.2% of them developed a new IS during the first year of follow-up. Replication analysis was performed in 2,988 patients using SNPlex or HumanOmni1-Quad technology. We generated a predictive model using Cox regression (GRECOS score), and generated risk groups using a classification tree method. Results The analyses revealed that rs1800801 in the MGP gene (HR: 1.33, p= 9×10−03), a gene related to artery calcification, was associated with new IS during the first year of follow-up. This polymorphism was replicated in a Spanish cohort (n=1.305), however it was not significantly associated in a North American cohort (n=1.683). The GRECOS score predicted new IS (p= 3.2×10−09) and could classify patients, from low risk of stroke recurrence (1.9%) to high risk (12.6%). Moreover, the addition of genetic risk factors to the GRECOS score improves the prediction compared to previous SPI-II score (p=0.03). Conclusions The use of genetics could be useful to estimate vascular recurrence risk after IS. Genetic variability in the MGP gene was associated with vascular recurrence in the Spanish population. PMID:28411264

The PRONE score: an algorithm for predicting doctors’ risks of formal patient complaints using routinely collected administrative data

PubMed Central

Spittal, Matthew J; Bismark, Marie M; Studdert, David M

2015-01-01

Background Medicolegal agencies—such as malpractice insurers, medical boards and complaints bodies—are mostly passive regulators; they react to episodes of substandard care, rather than intervening to prevent them. At least part of the explanation for this reactive role lies in the widely recognised difficulty of making robust predictions about medicolegal risk at the individual clinician level. We aimed to develop a simple, reliable scoring system for predicting Australian doctors’ risks of becoming the subject of repeated patient complaints. Methods Using routinely collected administrative data, we constructed a national sample of 13 849 formal complaints against 8424 doctors. The complaints were lodged by patients with state health service commissions in Australia over a 12-year period. We used multivariate logistic regression analysis to identify predictors of subsequent complaints, defined as another complaint occurring within 2 years of an index complaint. Model estimates were then used to derive a simple predictive algorithm, designed for application at the doctor level. Results The PRONE (Predicted Risk Of New Event) score is a 22-point scoring system that indicates a doctor's future complaint risk based on four variables: a doctor's specialty and sex, the number of previous complaints and the time since the last complaint. The PRONE score performed well in predicting subsequent complaints, exhibiting strong validity and reliability and reasonable goodness of fit (c-statistic=0.70). Conclusions The PRONE score appears to be a valid method for assessing individual doctors’ risks of attracting recurrent complaints. Regulators could harness such information to target quality improvement interventions, and prevent substandard care and patient dissatisfaction. The approach we describe should be replicable in other agencies that handle large numbers of patient complaints or malpractice claims. PMID:25855664

Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort

PubMed Central

Gaziano, Thomas A; Young, Cynthia R; Fitzmaurice, Garrett; Atwood, Sidney; Gaziano, J Michael

2008-01-01

Summary Background Around 80% of all cardiovascular deaths occur in developing countries. Assessment of those patients at high risk is an important strategy for prevention. Since developing countries have limited resources for prevention strategies that require laboratory testing, we assessed if a risk prediction method that did not require any laboratory tests could be as accurate as one requiring laboratory information. Methods The National Health and Nutrition Examination Survey (NHANES) was a prospective cohort study of 14 407 US participants aged between 25–74 years at the time they were first examined (between 1971 and 1975). Our follow-up study population included participants with complete information on these surveys who did not report a history of cardiovascular disease (myocardial infarction, heart failure, stroke, angina) or cancer, yielding an analysis dataset N=6186. We compared how well either method could predict first-time fatal and non-fatal cardiovascular disease events in this cohort. For the laboratory-based model, which required blood testing, we used standard risk factors to assess risk of cardiovascular disease: age, systolic blood pressure, smoking status, total cholesterol, reported diabetes status, and current treatment for hypertension. For the non-laboratory-based model, we substituted body-mass index for cholesterol. Findings In the cohort of 6186, there were 1529 first-time cardiovascular events and 578 (38%) deaths due to cardiovascular disease over 21 years. In women, the laboratory-based model was useful for predicting events, with a c statistic of 0·829. The c statistic of the non-laboratory-based model was 0·831. In men, the results were similar (0·784 for the laboratory-based model and 0·783 for the non-laboratory-based model). Results were similar between the laboratory-based and non-laboratory-based models in both men and women when restricted to fatal events only. Interpretation A method that uses non-laboratory-based risk factors predicted cardiovascular events as accurately as one that relied on laboratory-based values. This approach could simplify risk assessment in situations where laboratory testing is inconvenient or unavailable. PMID:18342687

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

PubMed

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 problem that warrants further studies.

Monitoring late-onset toxicities in phase I trials using predicted risks

PubMed Central

Bekele, B. Nebiyou; Ji, Yuan; Shen, Yu; Thall, Peter F.

2008-01-01

Late-onset (LO) toxicities are a serious concern in many phase I trials. Since most dose-limiting toxicities occur soon after therapy begins, most dose-finding methods use a binary indicator of toxicity occurring within a short initial time period. If an agent causes LO toxicities, however, an undesirably large number of patients may be treated at toxic doses before any toxicities are observed. A method addressing this problem is the time-to-event continual reassessment method (TITE-CRM, Cheung and Chappell, 2000). We propose a Bayesian dose-finding method similar to the TITE-CRM in which doses are chosen using time-to-toxicity data. The new aspect of our method is a set of rules, based on predictive probabilities, that temporarily suspend accrual if the risk of toxicity at prospective doses for future patients is unacceptably high. If additional follow-up data reduce the predicted risk of toxicity to an acceptable level, then accrual is restarted, and this process may be repeated several times during the trial. A simulation study shows that the proposed method provides a greater degree of safety than the TITE-CRM, while still reliably choosing the preferred dose. This advantage increases with accrual rate, but the price of this additional safety is that the trial takes longer to complete on average. PMID:18084008

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

PubMed Central

Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

2015-01-01

Introduction 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. Methods 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. Results 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). Discussion 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. Conclusions 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. PMID:26468369

Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

PubMed

Schummers, Laura; Himes, Katherine P; Bodnar, Lisa M; Hutcheon, Jennifer A

2016-09-21

Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r 2 ) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

Predicting the Risk of Clostridium difficile Infection upon Admission: A Score to Identify Patients for Antimicrobial Stewardship Efforts.

PubMed

Kuntz, Jennifer L; Smith, David H; Petrik, Amanda F; Yang, Xiuhai; Thorp, Micah L; Barton, Tracy; Barton, Karen; Labreche, Matthew; Spindel, Steven J; Johnson, Eric S

2016-01-01

Increasing morbidity and health care costs related to Clostridium difficile infection (CDI) have heightened interest in methods to identify patients who would most benefit from interventions to mitigate the likelihood of CDI. To develop a risk score that can be calculated upon hospital admission and used by antimicrobial stewards, including pharmacists and clinicians, to identify patients at risk for CDI who would benefit from enhanced antibiotic review and patient education. We assembled a cohort of Kaiser Permanente Northwest patients with a hospital admission from July 1, 2005, through December 30, 2012, and identified CDI in the six months following hospital admission. Using Cox regression, we constructed a score to identify patients at high risk for CDI on the basis of preadmission characteristics. We calculated and plotted the observed six-month CDI risk for each decile of predicted risk. We identified 721 CDIs following 54,186 hospital admissions-a 6-month incidence of 13.3 CDIs/1000 patient admissions. Patients with the highest predicted risk of CDI had an observed incidence of 53 CDIs/1000 patient admissions. The score differentiated between patients who do and do not develop CDI, with values for the extended C-statistic of 0.75. Predicted risk for CDI agreed closely with observed risk. Our risk score accurately predicted six-month risk for CDI using preadmission characteristics. Accurate predictions among the highest-risk patient subgroups allow for the identification of patients who could be targeted for and who would likely benefit from review of inpatient antibiotic use or enhanced educational efforts at the time of discharge planning.

New methods for fall risk prediction.

PubMed

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.

Development and Validation of a Risk Model for Prediction of Hazardous Alcohol Consumption in General Practice Attendees: The PredictAL Study

PubMed Central

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

Background 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. Methods 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. Results 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). Conclusions 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. PMID:21853028

Prediction of Anxiety Symptoms in Preschool-Aged Children: Examination of Maternal and Paternal Perspectives

ERIC Educational Resources Information Center

Edwards, Susan L.; Rapee, Ronald M.; Kennedy, Susan

2010-01-01

Background: Little is known about risk factors for anxiety in young children. The current study investigated the value of a set of theoretically derived risk factors to predict symptoms of anxiety in a sample of preschool-aged children. Methods: Mothers (n = 632) and fathers (n = 249) completed questionnaires twice, 12 months apart. Measures were…

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

PubMed Central

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

Configurations of Common Childhood Psychosocial Risk Factors

ERIC Educational Resources Information Center

Copeland, William; Shanahan, Lilly; Costello, E. Jane; Angold, Adrian

2009-01-01

Background: Co-occurrence of psychosocial risk factors is commonplace, but little is known about psychiatrically-predictive configurations of psychosocial risk factors. Methods: Latent class analysis (LCA) was applied to 17 putative psychosocial risk factors in a representative population sample of 920 children ages 9 to 17. The resultant class…

A Tissue Systems Pathology Assay for High-Risk Barrett's Esophagus.

PubMed

Critchley-Thorne, Rebecca J; Duits, Lucas C; Prichard, Jeffrey W; Davison, Jon M; Jobe, Blair A; Campbell, Bruce B; Zhang, Yi; Repa, Kathleen A; Reese, Lia M; Li, Jinhong; Diehl, David L; Jhala, Nirag C; Ginsberg, Gregory; DeMarshall, Maureen; Foxwell, Tyler; Zaidi, Ali H; Lansing Taylor, D; Rustgi, Anil K; Bergman, Jacques J G H M; Falk, Gary W

2016-06-01

Better methods are needed to predict risk of progression for Barrett's esophagus. We aimed to determine whether a tissue systems pathology approach could predict progression in patients with nondysplastic Barrett's esophagus, indefinite for dysplasia, or low-grade dysplasia. We performed a nested case-control study to develop and validate a test that predicts progression of Barrett's esophagus to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC), based upon quantification of epithelial and stromal variables in baseline biopsies. Data were collected from Barrett's esophagus patients at four institutions. Patients who progressed to HGD or EAC in ≥1 year (n = 79) were matched with patients who did not progress (n = 287). Biopsies were assigned randomly to training or validation sets. Immunofluorescence analyses were performed for 14 biomarkers and quantitative biomarker and morphometric features were analyzed. Prognostic features were selected in the training set and combined into classifiers. The top-performing classifier was assessed in the validation set. A 3-tier, 15-feature classifier was selected in the training set and tested in the validation set. The classifier stratified patients into low-, intermediate-, and high-risk classes [HR, 9.42; 95% confidence interval, 4.6-19.24 (high-risk vs. low-risk); P < 0.0001]. It also provided independent prognostic information that outperformed predictions based on pathology analysis, segment length, age, sex, or p53 overexpression. We developed a tissue systems pathology test that better predicts risk of progression in Barrett's esophagus than clinicopathologic variables. The test has the potential to improve upon histologic analysis as an objective method to risk stratify Barrett's esophagus patients. Cancer Epidemiol Biomarkers Prev; 25(6); 958-68. ©2016 AACR. ©2016 American Association for Cancer Research.

Neuroprediction, Violence, and the Law: Setting the Stage.

PubMed

Nadelhoffer, Thomas; Bibas, Stephanos; Grafton, Scott; Kiehl, Kent A; Mansfield, Andrew; Sinnott-Armstrong, Walter; Gazzaniga, Michael

2012-04-01

In this paper, our goal is to (a) survey some of the legal contexts within which violence risk assessment already plays a prominent role, (b) explore whether developments in neuroscience could potentially be used to improve our ability to predict violence, and (c) discuss whether neuropredictive models of violence create any unique legal or moral problems above and beyond the well worn problems already associated with prediction more generally. In "Violence Risk Assessment and the Law", we briefly examine the role currently played by predictions of violence in three high stakes legal contexts: capital sentencing ("Violence Risk Assessment and Capital Sentencing"), civil commitment hearings ("Violence Risk Assessment and Civil Commitment"), and "sexual predator" statutes ("Violence Risk Assessment and Sexual Predator Statutes"). In "Clinical vs. Actuarial Violence Risk Assessment", we briefly examine the distinction between traditional clinical methods of predicting violence and more recently developed actuarial methods, exemplified by the Classification of Violence Risk (COVR) software created by John Monahan and colleagues as part of the MacArthur Study of Mental Disorder and Violence [1]. In "The Neural Correlates of Psychopathy", we explore what neuroscience currently tells us about the neural correlates of violence, using the recent neuroscientific research on psychopathy as our focus. We also discuss some recent advances in both data collection ("Cutting-Edge Data Collection: Genetically Informed Neuroimaging") and data analysis ("Cutting-Edge Data Analysis: Pattern Classification") that we believe will play an important role when it comes to future neuroscientific research on violence. In "The Potential Promise of Neuroprediction", we discuss whether neuroscience could potentially be used to improve our ability to predict future violence. Finally, in "The Potential Perils of Neuroprediction", we explore some potential evidentiary ("Evidentiary Issues"), constitutional ("Constitutional Issues"), and moral ("Moral Issues") issues that may arise in the context of the neuroprediction of violence.

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

PubMed Central

2012-01-01

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 diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting. PMID:22417403

Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.

PubMed

Nateghi, Roshanak; Guikema, Seth D; Quiring, Steven M

2011-12-01

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. © 2011 Society for Risk Analysis.

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

PubMed Central

Choudhry, Shahid A.; Li, Jing; Davis, Darcy; Erdmann, Cole; Sikka, Rishi; Sutariya, Bharat

2013-01-01

Introduction: Preventing the occurrence of hospital readmissions is needed to improve quality of care and foster population health across the care continuum. Hospitals are being held accountable for improving transitions of care to avert unnecessary readmissions. Advocate Health Care in Chicago and Cerner (ACC) collaborated to develop all-cause, 30-day hospital readmission risk prediction models to identify patients that need interventional resources. Ideally, prediction models should encompass several qualities: they should have high predictive ability; use reliable and clinically relevant data; use vigorous performance metrics to assess the models; be validated in populations where they are applied; and be scalable in heterogeneous populations. However, a systematic review of prediction models for hospital readmission risk determined that most performed poorly (average C-statistic of 0.66) and efforts to improve their performance are needed for widespread usage. Methods: The ACC team incorporated electronic health record data, utilized a mixed-method approach to evaluate risk factors, and externally validated their prediction models for generalizability. Inclusion and exclusion criteria were applied on the patient cohort and then split for derivation and internal validation. Stepwise logistic regression was performed to develop two predictive models: one for admission and one for discharge. The prediction models were assessed for discrimination ability, calibration, overall performance, and then externally validated. Results: The ACC Admission and Discharge Models demonstrated modest discrimination ability during derivation, internal and external validation post-recalibration (C-statistic of 0.76 and 0.78, respectively), and reasonable model fit during external validation for utility in heterogeneous populations. Conclusions: The ACC Admission and Discharge Models embody the design qualities of ideal prediction models. The ACC plans to continue its partnership to further improve and develop valuable clinical models. PMID:24224068

Validation of prediction models: examining temporal and geographic stability of baseline risk and estimated covariate effects

PubMed Central

Austin, Peter C.; van Klaveren, David; Vergouwe, Yvonne; Nieboer, Daan; Lee, Douglas S.; Steyerberg, Ewout W.

2018-01-01

Background Stability in baseline risk and estimated predictor effects both geographically and temporally is a desirable property of clinical prediction models. However, this issue has received little attention in the methodological literature. Our objective was to examine methods for assessing temporal and geographic heterogeneity in baseline risk and predictor effects in prediction models. Methods We studied 14,857 patients hospitalized with heart failure at 90 hospitals in Ontario, Canada, in two time periods. We focussed on geographic and temporal variation in baseline risk (intercept) and predictor effects (regression coefficients) of the EFFECT-HF mortality model for predicting 1-year mortality in patients hospitalized for heart failure. We used random effects logistic regression models for the 14,857 patients. Results The baseline risk of mortality displayed moderate geographic variation, with the hospital-specific probability of 1-year mortality for a reference patient lying between 0.168 and 0.290 for 95% of hospitals. Furthermore, the odds of death were 11% lower in the second period than in the first period. However, we found minimal geographic or temporal variation in predictor effects. Among 11 tests of differences in time for predictor variables, only one had a modestly significant P value (0.03). Conclusions This study illustrates how temporal and geographic heterogeneity of prediction models can be assessed in settings with a large sample of patients from a large number of centers at different time periods. PMID:29350215

Prediction of psychosis across protocols and risk cohorts using automated language analysis

PubMed Central

Corcoran, Cheryl M.; Carrillo, Facundo; Fernández‐Slezak, Diego; Bedi, Gillinder; Klim, Casimir; Javitt, Daniel C.; Bearden, Carrie E.; Cecchi, Guillermo A.

2018-01-01

Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer‐based natural language processing analyses, we previously showed that, among English‐speaking clinical (e.g., ultra) high‐risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross‐validate these automated linguistic analytic methods in a second larger risk cohort, also English‐speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine‐learning speech classifier – comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns – that had an 83% accuracy in predicting psychosis onset (intra‐protocol), a cross‐validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross‐protocol), and a 72% accuracy in discriminating the speech of recent‐onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at‐risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry. PMID:29352548

Prediction of psychosis across protocols and risk cohorts using automated language analysis.

PubMed

Corcoran, Cheryl M; Carrillo, Facundo; Fernández-Slezak, Diego; Bedi, Gillinder; Klim, Casimir; Javitt, Daniel C; Bearden, Carrie E; Cecchi, Guillermo A

2018-02-01

Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier - comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns - that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry. © 2018 World Psychiatric Association.

Updating Risk Prediction Tools: A Case Study in Prostate Cancer

PubMed Central

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.

2013-01-01

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. PMID:22095849

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

PubMed

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 analysis for veterinary biologicals released into the environment.

PubMed

Silva, S V; Samagh, B S; Morley, R S

1995-12-01

All veterinary biologicals licensed in Canada must be shown to be pure, potent, safe and effective. A risk-based approach is used to evaluate the safety of all biologicals, whether produced by conventional methods or by molecular biological techniques. Traditionally, qualitative risk assessment methods have been used for this purpose. More recently, quantitative risk assessment has become available for complex issues. The quantitative risk assessment method uses "scenario tree analysis' to predict the likelihood of various outcomes and their respective impacts. The authors describe the quantitative risk assessment approach which is used within the broader context of risk analysis (i.e. risk assessment, risk management and risk communication) to develop recommendations for the field release of veterinary biologicals. The general regulatory framework for the licensing of veterinary biologicals in Canada is also presented.

A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.

PubMed

Wolfson, Julian; Bandyopadhyay, Sunayan; Elidrisi, Mohamed; Vazquez-Benitez, Gabriela; Vock, David M; Musgrove, Donald; Adomavicius, Gediminas; Johnson, Paul E; O'Connor, Patrick J

2015-09-20

Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system. Copyright © 2015 John Wiley & Sons, Ltd.

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

PubMed Central

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

Risk Assessment Stability: A Revalidation Study of the Arizona Risk/Needs Assessment Instrument

ERIC Educational Resources Information Center

Schwalbe, Craig S.

2009-01-01

The actuarial method is the gold standard for risk assessment in child welfare, juvenile justice, and criminal justice. It produces risk classifications that are highly predictive and that may be robust to sampling error. This article reports a revalidation study of the Arizona Risk/Needs Assessment instrument, an actuarial instrument for juvenile…

Estimating community health needs against a Triple Aim background: What can we learn from current predictive risk models?

PubMed

Elissen, Arianne M J; Struijs, Jeroen N; Baan, Caroline A; Ruwaard, Dirk

2015-05-01

To support providers and commissioners in accurately assessing their local populations' health needs, this study produces an overview of Dutch predictive risk models for health care, focusing specifically on the type, combination and relevance of included determinants for achieving the Triple Aim (improved health, better care experience, and lower costs). We conducted a mixed-methods study combining document analyses, interviews and a Delphi study. Predictive risk models were identified based on a web search and expert input. Participating in the study were Dutch experts in predictive risk modelling (interviews; n=11) and experts in healthcare delivery, insurance and/or funding methodology (Delphi panel; n=15). Ten predictive risk models were analysed, comprising 17 unique determinants. Twelve were considered relevant by experts for estimating community health needs. Although some compositional similarities were identified between models, the combination and operationalisation of determinants varied considerably. Existing predictive risk models provide a good starting point, but optimally balancing resources and targeting interventions on the community level will likely require a more holistic approach to health needs assessment. Development of additional determinants, such as measures of people's lifestyle and social network, may require policies pushing the integration of routine data from different (healthcare) sources. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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.

Predicting the 10-year risk of hip and major osteoporotic fracture in rheumatoid arthritis and in the general population: an independent validation and update of UK FRAX without bone mineral density

PubMed Central

Klop, Corinne; de Vries, Frank; Bijlsma, Johannes W J; Leufkens, Hubert G M; Welsing, Paco M J

2016-01-01

Objectives FRAX incorporates rheumatoid arthritis (RA) as a dichotomous predictor for predicting the 10-year risk of hip and major osteoporotic fracture (MOF). However, fracture risk may deviate with disease severity, duration or treatment. Aims were to validate, and if needed to update, UK FRAX for patients with RA and to compare predictive performance with the general population (GP). Methods Cohort study within UK Clinical Practice Research Datalink (CPRD) (RA: n=11 582, GP: n=38 755), also linked to hospital admissions for hip fracture (CPRD-Hospital Episode Statistics, HES) (RA: n=7221, GP: n=24 227). Predictive performance of UK FRAX without bone mineral density was assessed by discrimination and calibration. Updating methods included recalibration and extension. Differences in predictive performance were assessed by the C-statistic and Net Reclassification Improvement (NRI) using the UK National Osteoporosis Guideline Group intervention thresholds. Results UK FRAX significantly overestimated fracture risk in patients with RA, both for MOF (mean predicted vs observed 10-year risk: 13.3% vs 8.4%) and hip fracture (CPRD: 5.5% vs 3.1%, CPRD-HES: 5.5% vs 4.1%). Calibration was good for hip fracture in the GP (CPRD-HES: 2.7% vs 2.4%). Discrimination was good for hip fracture (RA: 0.78, GP: 0.83) and moderate for MOF (RA: 0.69, GP: 0.71). Extension of the recalibrated UK FRAX using CPRD-HES with duration of RA disease, glucocorticoids (>7.5 mg/day) and secondary osteoporosis did not improve the NRI (0.01, 95% CI −0.04 to 0.05) or C-statistic (0.78). Conclusions UK FRAX overestimated fracture risk in RA, but performed well for hip fracture in the GP after linkage to hospitalisations. Extension of the recalibrated UK FRAX did not improve predictive performance. PMID:26984006

Recent ecological responses to climate change support predictions of high extinction risk

PubMed Central

Maclean, Ilya M. D.; Wilson, Robert J.

2011-01-01

Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity. PMID:21746924

Recent ecological responses to climate change support predictions of high extinction risk.

PubMed

Maclean, Ilya M D; Wilson, Robert J

2011-07-26

Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity.

Different type 2 diabetes risk assessments predict dissimilar numbers at ‘high risk’: a retrospective analysis of diabetes risk-assessment tools

PubMed Central

Gray, Benjamin J; Bracken, Richard M; Turner, Daniel; Morgan, Kerry; Thomas, Michael; Williams, Sally P; Williams, Meurig; Rice, Sam; Stephens, Jeffrey W

2015-01-01

Background Use of a validated risk-assessment tool to identify individuals at high risk of developing type 2 diabetes is currently recommended. It is under-reported, however, whether a different risk tool alters the predicted risk of an individual. Aim This study explored any differences between commonly used validated risk-assessment tools for type 2 diabetes. Design and setting Cross-sectional analysis of individuals who participated in a workplace-based risk assessment in Carmarthenshire, South Wales. Method Retrospective analysis of 676 individuals (389 females and 287 males) who participated in a workplace-based diabetes risk-assessment initiative. Ten-year risk of type 2 diabetes was predicted using the validated QDiabetes®, Leicester Risk Assessment (LRA), FINDRISC, and Cambridge Risk Score (CRS) algorithms. Results Differences between the risk-assessment tools were apparent following retrospective analysis of individuals. CRS categorised the highest proportion (13.6%) of individuals at ‘high risk’ followed by FINDRISC (6.6%), QDiabetes (6.1%), and, finally, the LRA was the most conservative risk tool (3.1%). Following further analysis by sex, over one-quarter of males were categorised at high risk using CRS (25.4%), whereas a greater percentage of females were categorised as high risk using FINDRISC (7.8%). Conclusion The adoption of a different valid risk-assessment tool can alter the predicted risk of an individual and caution should be used to identify those individuals who really are at high risk of type 2 diabetes. PMID:26541180

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

PubMed

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.

Comparison of Enzootic Risk Measures for Predicting West Nile Disease, Los Angeles, California, USA, 2004–2010

PubMed Central

Kwan, Jennifer L.; Park, Bborie K.; Carpenter, Tim E.; Ngo, Van; Civen, Rachel

2012-01-01

In Los Angeles, California, USA, 2 epidemics of West Nile virus (WNV) disease have occurred since WNV was recognized in 2003. To assess which measure of risk was most predictive of human cases, we compared 3 measures: the California Mosquito-Borne Virus Surveillance and Response Plan Assessment, the vector index, and the Dynamic Continuous-Area Space-Time system. A case–crossover study was performed by using symptom onset dates from 384 persons with WNV infection to determine their relative environmental exposure to high-risk conditions as measured by each method. Receiver-operating characteristic plots determined thresholds for each model, and the area under the curve was used to compare methods. We found that the best risk assessment model for human WNV cases included surveillance data from avian, mosquito, and climate sources. PMID:22840314

Web-based decision support system to predict risk level of long term rice production

NASA Astrophysics Data System (ADS)

Mukhlash, Imam; Maulidiyah, Ratna; Sutikno; Setiyono, Budi

2017-09-01

Appropriate decision making in risk management of rice production is very important in agricultural planning, especially for Indonesia which is an agricultural country. Good decision would be obtained if the supporting data required are satisfied and using appropriate methods. This study aims to develop a Decision Support System that can be used to predict the risk level of rice production in some districts which are central of rice production in East Java. Web-based decision support system is constructed so that the information can be easily accessed and understood. Components of the system are data management, model management, and user interface. This research uses regression models of OLS and Copula. OLS model used to predict rainfall while Copula model used to predict harvested area. Experimental results show that the models used are successfully predict the harvested area of rice production in some districts which are central of rice production in East Java at any given time based on the conditions and climate of a region. Furthermore, it can predict the amount of rice production with the level of risk. System generates prediction of production risk level in the long term for some districts that can be used as a decision support for the authorities.

[Application of Competing Risks Model in Predicting Smoking Relapse Following Ischemic Stroke].

PubMed

Hou, Li-Sha; Li, Ji-Jie; Du, Xu-Dong; Yan, Pei-Jing; Zhu, Cai-Rong

2017-07-01

To determine factors associated with smoking relapse in men who survived from their first stroke. Data were collected through face to face interviews with stroke patients in the hospital, and then repeated every three months via telephone over the period from 2010 to 2014. Kaplan-Meier method and competing risk model were adopted to estimate and predict smoking relapse rates. The Kaplan-Meier method estimated a higher relapse rate than the competing risk model. The four-year relapse rate was 43.1% after adjustment of competing risk. Exposure to environmental tobacco smoking outside of home and workplace (such as bars and restaurants) ( P =0.01), single ( P <0.01), and prior history of smoking at least 20 cigarettes per day ( P =0.02) were significant predictors of smoking relapse. When competing risks exist, competing risks model should be used in data analyses. Smoking interventions should give priorities to those without a spouse and those with a heavy smoking history. Smoking ban in public settings can reduce smoking relapse in stroke patients.

Review of fall risk assessment in geriatric populations using inertial sensors

PubMed Central

2013-01-01

Background Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population. Methods Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes. Results Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%). Conclusions Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls. PMID:23927446

Bayesian averaging over Decision Tree models for trauma severity scoring.

PubMed

Schetinin, V; Jakaite, L; Krzanowski, W

2018-01-01

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery.

PubMed

Hickey, Graeme L; Blackstone, Eugene H

2016-08-01

Clinical risk-prediction models serve an important role in healthcare. They are used for clinical decision-making and measuring the performance of healthcare providers. To establish confidence in a model, external model validation is imperative. When designing such an external model validation study, thought must be given to patient selection, risk factor and outcome definitions, missing data, and the transparent reporting of the analysis. In addition, there are a number of statistical methods available for external model validation. Execution of a rigorous external validation study rests in proper study design, application of suitable statistical methods, and transparent reporting. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Machine learning derived risk prediction of anorexia nervosa.

PubMed

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.

Applying under-sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer.

PubMed

Hsu, Jia-Lien; Hung, Ping-Cheng; Lin, Hung-Yen; Hsieh, Chung-Ho

2015-04-01

Breast cancer is one of the most common cause of cancer mortality. Early detection through mammography screening could significantly reduce mortality from breast cancer. However, most of screening methods may consume large amount of resources. We propose a computational model, which is solely based on personal health information, for breast cancer risk assessment. Our model can be served as a pre-screening program in the low-cost setting. In our study, the data set, consisting of 3976 records, is collected from Taipei City Hospital starting from 2008.1.1 to 2008.12.31. Based on the dataset, we first apply the sampling techniques and dimension reduction method to preprocess the testing data. Then, we construct various kinds of classifiers (including basic classifiers, ensemble methods, and cost-sensitive methods) to predict the risk. The cost-sensitive method with random forest classifier is able to achieve recall (or sensitivity) as 100 %. At the recall of 100 %, the precision (positive predictive value, PPV), and specificity of cost-sensitive method with random forest classifier was 2.9 % and 14.87 %, respectively. In our study, we build a breast cancer risk assessment model by using the data mining techniques. Our model has the potential to be served as an assisting tool in the breast cancer screening.

Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

PubMed

Zhang, Hui; Ding, Lan; Zou, Yi; Hu, Shui-Qing; Huang, Hai-Guo; Kong, Wei-Bao; Zhang, Ji

2016-10-01

Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a U.K. tri-ethnic prospective cohort study (SABRE--Southall And Brent REvisited).

PubMed

Tillin, Therese; Hughes, Alun D; Whincup, Peter; Mayet, Jamil; Sattar, Naveed; McKeigue, Paul M; Chaturvedi, Nish

2014-01-01

To evaluate QRISK2 and Framingham cardiovascular disease (CVD) risk scores in a tri-ethnic U.K. population. Cohort study. West London. Randomly selected from primary care lists. Follow-up data were available for 87% of traced participants, comprising 1866 white Europeans, 1377 South Asians, and 578 African Caribbeans, aged 40-69 years at baseline (1998-1991). First CVD events: myocardial infarction, coronary revascularisation, angina, transient ischaemic attack or stroke reported by participant, primary care or hospital records or death certificate. During follow-up, 387 CVD events occurred in men (14%) and 78 in women (8%). Both scores underestimated risk in European and South Asian women (ratio of predicted to observed risk: European women: QRISK2: 0.73, Framingham: 0.73; South Asian women: QRISK2: 0.52, Framingham: 0.43). In African Caribbeans, Framingham over-predicted in men and women and QRISK2 over-predicted in women. Framingham classified 28% of participants as high risk, predicting 54% of all such events. QRISK2 classified 19% as high risk, predicting 42% of all such events. Both scores performed poorly in identifying high risk African Caribbeans; QRISK2 and Framingham identified as high risk only 10% and 24% of those who experienced events. Neither score performed consistently well in all ethnic groups. Further validation of QRISK2 in other multi-ethnic datasets, and better methods for identifying high risk African Caribbeans and South Asian women, are required.

Predicting readmission risk with institution-specific prediction models.

PubMed

Yu, Shipeng; Farooq, Faisal; van Esbroeck, Alexander; Fung, Glenn; Anand, Vikram; Krishnapuram, Balaji

2015-10-01

The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. We propose a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and, optionally, for a specific condition. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We have experimented with classification methods such as support vector machines, and prognosis methods such as the Cox regression. We compared our methods with industry-standard methods such as the LACE model, and showed the proposed framework is not only more flexible but also more effective. We applied our framework to patient data from three hospitals, and obtained some initial results for heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN) patients as well as patients with all conditions. On Hospital 2, the LACE model yielded AUC 0.57, 0.56, 0.53 and 0.55 for AMI, HF, PN and All Cause readmission prediction, respectively, while the proposed model yielded 0.66, 0.65, 0.63, 0.74 for the corresponding conditions, all significantly better than the LACE counterpart. The proposed models that leverage all features at discharge time is more accurate than the models that only leverage features at admission time (0.66 vs. 0.61 for AMI, 0.65 vs. 0.61 for HF, 0.63 vs. 0.56 for PN, 0.74 vs. 0.60 for All Cause). Furthermore, the proposed admission-time models already outperform the performance of LACE, which is a discharge-time model (0.61 vs. 0.57 for AMI, 0.61 vs. 0.56 for HF, 0.56 vs. 0.53 for PN, 0.60 vs. 0.55 for All Cause). Similar conclusions can be drawn from other hospitals as well. The same performance comparison also holds for precision and recall at top-decile predictions. Most of the performance improvements are statistically significant. The institution-specific readmission risk prediction framework is more flexible and more effective than the one-size-fit-all models like the LACE, sometimes twice and three-time more effective. The admission-time models are able to give early warning signs compared to the discharge-time models, and may be able to help hospital staff intervene early while the patient is still in the hospital. Copyright © 2015 Elsevier B.V. All rights reserved.

Influence of Injury Risk Thresholds on the Performance of an Algorithm to Predict Crashes with Serious Injuries

PubMed Central

Bahouth, George; Digges, Kennerly; Schulman, Carl

2012-01-01

This paper presents methods to estimate crash injury risk based on crash characteristics captured by some passenger vehicles equipped with Advanced Automatic Crash Notification technology. The resulting injury risk estimates could be used within an algorithm to optimize rescue care. Regression analysis was applied to the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) to determine how variations in a specific injury risk threshold would influence the accuracy of predicting crashes with serious injuries. The recommended thresholds for classifying crashes with severe injuries are 0.10 for frontal crashes and 0.05 for side crashes. The regression analysis of NASS/CDS indicates that these thresholds will provide sensitivity above 0.67 while maintaining a positive predictive value in the range of 0.20. PMID:23169132

Psychosis prediction and clinical utility in familial high-risk studies: Selective review, synthesis, and implications for early detection and intervention

PubMed Central

Shah, Jai L.; Tandon, Neeraj; Keshavan, Matcheri S.

2016-01-01

Aim Accurate prediction of which individuals will go on to develop psychosis would assist early intervention and prevention paradigms. We sought to review investigations of prospective psychosis prediction based on markers and variables examined in longitudinal familial high-risk (FHR) studies. Methods We performed literature searches in MedLine, PubMed and PsycINFO for articles assessing performance characteristics of predictive clinical tests in FHR studies of psychosis. Studies were included if they reported one or more predictive variables in subjects at FHR for psychosis. We complemented this search strategy with references drawn from articles, reviews, book chapters and monographs. Results Across generations of familial high-risk projects, predictive studies have investigated behavioral, cognitive, psychometric, clinical, neuroimaging, and other markers. Recent analyses have incorporated multivariate and multi-domain approaches to risk ascertainment, although with still generally modest results. Conclusions While a broad range of risk factors has been identified, no individual marker or combination of markers can at this time enable accurate prospective prediction of emerging psychosis for individuals at FHR. We outline the complex and multi-level nature of psychotic illness, the myriad of factors influencing its development, and methodological hurdles to accurate and reliable prediction. Prospects and challenges for future generations of FHR studies are discussed in the context of early detection and intervention strategies. PMID:23693118

The East London glaucoma prediction score: web-based validation of glaucoma risk screening tool

PubMed Central

Stephen, Cook; Benjamin, Longo-Mbenza

2013-01-01

AIM It is difficult for Optometrists and General Practitioners to know which patients are at risk. The East London glaucoma prediction score (ELGPS) is a web based risk calculator that has been developed to determine Glaucoma risk at the time of screening. Multiple risk factors that are available in a low tech environment are assessed to provide a risk assessment. This is extremely useful in settings where access to specialist care is difficult. Use of the calculator is educational. It is a free web based service. Data capture is user specific. METHOD The scoring system is a web based questionnaire that captures and subsequently calculates the relative risk for the presence of Glaucoma at the time of screening. Three categories of patient are described: Unlikely to have Glaucoma; Glaucoma Suspect and Glaucoma. A case review methodology of patients with known diagnosis is employed to validate the calculator risk assessment. RESULTS Data from the patient records of 400 patients with an established diagnosis has been captured and used to validate the screening tool. The website reports that the calculated diagnosis correlates with the actual diagnosis 82% of the time. Biostatistics analysis showed: Sensitivity = 88%; Positive predictive value = 97%; Specificity = 75%. CONCLUSION Analysis of the first 400 patients validates the web based screening tool as being a good method of screening for the at risk population. The validation is ongoing. The web based format will allow a more widespread recruitment for different geographic, population and personnel variables. PMID:23550097

Multi-analytical Approaches Informing the Risk of Sepsis

NASA Astrophysics Data System (ADS)

Gwadry-Sridhar, Femida; Lewden, Benoit; Mequanint, Selam; Bauer, Michael

Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit (ICU) and hospital stay. The economic burden associated with sepsis is huge. With advances in medicine, there are now aggressive goal oriented treatments that can be used to help these patients. If we were able to predict which patients may be at risk for sepsis we could start treatment early and potentially reduce the risk of mortality and morbidity. Analytic methods currently used in clinical research to determine the risk of a patient developing sepsis may be further enhanced by using multi-modal analytic methods that together could be used to provide greater precision. Researchers commonly use univariate and multivariate regressions to develop predictive models. We hypothesized that such models could be enhanced by using multiple analytic methods that together could be used to provide greater insight. In this paper, we analyze data about patients with and without sepsis using a decision tree approach and a cluster analysis approach. A comparison with a regression approach shows strong similarity among variables identified, though not an exact match. We compare the variables identified by the different approaches and draw conclusions about the respective predictive capabilities,while considering their clinical significance.

Improving risk estimates of runoff producing areas: formulating variable source areas as a bivariate process.

PubMed

Cheng, Xiaoya; Shaw, Stephen B; Marjerison, Rebecca D; Yearick, Christopher D; DeGloria, Stephen D; Walter, M Todd

2014-05-01

Predicting runoff producing areas and their corresponding risks of generating storm runoff is important for developing watershed management strategies to mitigate non-point source pollution. However, few methods for making these predictions have been proposed, especially operational approaches that would be useful in areas where variable source area (VSA) hydrology dominates storm runoff. The objective of this study is to develop a simple approach to estimate spatially-distributed risks of runoff production. By considering the development of overland flow as a bivariate process, we incorporated both rainfall and antecedent soil moisture conditions into a method for predicting VSAs based on the Natural Resource Conservation Service-Curve Number equation. We used base-flow immediately preceding storm events as an index of antecedent soil wetness status. Using nine sub-basins of the Upper Susquehanna River Basin, we demonstrated that our estimated runoff volumes and extent of VSAs agreed with observations. We further demonstrated a method for mapping these areas in a Geographic Information System using a Soil Topographic Index. The proposed methodology provides a new tool for watershed planners for quantifying runoff risks across watersheds, which can be used to target water quality protection strategies. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

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

2016-03-01

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

Genetic Predisposition to Ischemic Stroke

PubMed Central

Kamatani, Yoichiro; Takahashi, Atsushi; Hata, Jun; Furukawa, Ryohei; Shiwa, Yuh; Yamaji, Taiki; Hara, Megumi; Tanno, Kozo; Ohmomo, Hideki; Ono, Kanako; Takashima, Naoyuki; Matsuda, Koichi; Wakai, Kenji; Sawada, Norie; Iwasaki, Motoki; Yamagishi, Kazumasa; Ago, Tetsuro; Ninomiya, Toshiharu; Fukushima, Akimune; Hozawa, Atsushi; Minegishi, Naoko; Satoh, Mamoru; Endo, Ryujin; Sasaki, Makoto; Sakata, Kiyomi; Kobayashi, Seiichiro; Ogasawara, Kuniaki; Nakamura, Motoyuki; Hitomi, Jiro; Kita, Yoshikuni; Tanaka, Keitaro; Iso, Hiroyasu; Kitazono, Takanari; Kubo, Michiaki; Tanaka, Hideo; Tsugane, Shoichiro; Kiyohara, Yutaka; Yamamoto, Masayuki; Sobue, Kenji; Shimizu, Atsushi

2017-01-01

Background and Purpose— The prediction of genetic predispositions to ischemic stroke (IS) may allow the identification of individuals at elevated risk and thereby prevent IS in clinical practice. Previously developed weighted multilocus genetic risk scores showed limited predictive ability for IS. Here, we investigated the predictive ability of a newer method, polygenic risk score (polyGRS), based on the idea that a few strong signals, as well as several weaker signals, can be collectively informative to determine IS risk. Methods— We genotyped 13 214 Japanese individuals with IS and 26 470 controls (derivation samples) and generated both multilocus genetic risk scores and polyGRS, using the same derivation data set. The predictive abilities of each scoring system were then assessed using 2 independent sets of Japanese samples (KyushuU and JPJM data sets). Results— In both validation data sets, polyGRS was shown to be significantly associated with IS, but weighted multilocus genetic risk scores was not. Comparing the highest with the lowest polyGRS quintile, the odds ratios for IS were 1.75 (95% confidence interval, 1.33–2.31) and 1.99 (95% confidence interval, 1.19–3.33) in the KyushuU and JPJM samples, respectively. Using the KyushuU samples, the addition of polyGRS to a nongenetic risk model resulted in a significant improvement of the predictive ability (net reclassification improvement=0.151; P<0.001). Conclusions— The polyGRS was shown to be superior to weighted multilocus genetic risk scores as an IS prediction model. Thus, together with the nongenetic risk factors, polyGRS will provide valuable information for individual risk assessment and management of modifiable risk factors. PMID:28034966

Body composition indices and predicted cardiovascular disease risk profile among urban dwellers in Malaysia.

PubMed

Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Dahlui, Maznah; Majid, Hazreen Abdul

2015-01-01

This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD) risk profile in an urban population in Kuala Lumpur, Malaysia. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

PubMed

Zhao, Di; Weng, Chunhua

2011-10-01

In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

Combining PubMed Knowledge and EHR Data to Develop a Weighted Bayesian Network for Pancreatic Cancer Prediction

PubMed Central

Zhao, Di; Weng, Chunhua

2011-01-01

In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. PMID:21642013

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

PubMed Central

Jang, Eun Jin; Park, ByeongJu; Kim, Tae-Young; Shin, Soon-Ae

2016-01-01

Background 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. Methods 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). Results 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. Conclusion 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. PMID:27399597

["Screening" in special situations. Assessing predictive genetic screening for hereditary breast and colorectal cancer].

PubMed

Jonas, Susanna; Wild, Claudia; Schamberger, Chantal

2003-02-01

The aim of this health technology assessment was to analyse the current scientific and genetic counselling on predictive genetic testing for hereditary breast and colorectal cancer. Predictive genetic testing will be available for several common diseases in the future and questions related to financial issues and quality standards will be raised. This report is based on a systematic/nonsystematic literature search in several databases (e.g. EmBase, Medline, Cochrane Library) and on a specific health technology assessment report (CCOHTA) and review (American Gastroenterological Ass.), respectively. Laboratory test methods, early detection methods and the benefit from prophylactic interventions were analysed and social consequences interpreted. Breast and colorectal cancer are counted among the most frequently cancer diseases. Most of them are based on random accumulation of risk factors, 5-10% show a familial determination. A hereditary modified gene is responsible for the increased cancer risk. In these families, high tumour frequency, young age at diagnosis and multiple primary tumours are remarkable. GENETIC DIAGNOSIS: Sequence analysis is the gold standard. Denaturing high performance liquid chromatography is a quick alternative method. The identification of the responsible gene defect in an affected family member is important. If the test result is positive there is an uncertainty whether the disease will develop or not, when and in which degree, which is founded in the geno-/phenotype correlation. The individual risk estimation is based upon empirical evidence. The test results affect the whole family. Currently, primary prevention is possible for familial adenomatous polyposis (celecoxib, prophylactic colectomy) and for hereditary mamma carcinoma (prophylactic mastectomy). The so-called preventive medical check-ups are early detection examinations. The evidence about early detection methods for colorectal cancer is better than for breast cancer. Prophylactic mastectomy (PM) reduces the relative breast cancer risk by approximately 90%. The question is if PM has an impact on mortality. The acceptance of PM is culture-dependent. Colectomy can be used as a prophylactic (FAP) and therapeutic method. After surgery, the cancer risk remains high and so early detection examinations are still necessary. EVIDENCE-BASED STATEMENTS: The evidence is often fragmentary and of limited quality. For objective test result presentation information about sensitivity, specificity, positive predictive value, and number needed to screen and treat, respectively, are necessary. New identification of mutations and demand will lead to an increase of predictive genetic counselling and testing. There is a gap between predictive genetic diagnosis and prediction, prevention, early detection and surgical interventions. These circumstances require a basic strategy. Since predictive genetic diagnosis is a very sensitive issue it is important to deal with it carefully in order to avoid inappropriate hopes. Thus, media, experts and politicians need to consider opportunities and limitations in their daily decision-making processes.

Applying adverse outcome pathways and species sensitivity-weighted distribution to predicted-no-effect concentration derivation and quantitative ecological risk assessment for bisphenol A and 4-nonylphenol in aquatic environments: A case study on Tianjin City, China.

PubMed

Wang, Ying; Na, Guangshui; Zong, Humin; Ma, Xindong; Yang, Xianhai; Mu, Jingli; Wang, Lijun; Lin, Zhongsheng; Zhang, Zhifeng; Wang, Juying; Zhao, Jinsong

2018-02-01

Adverse outcome pathways (AOPs) are a novel concept that effectively considers the toxic modes of action and guides the ecological risk assessment of chemicals. To better use toxicity data including biochemical or molecular responses and mechanistic data, we further developed a species sensitivity-weighted distribution (SSWD) method for bisphenol A and 4-nonylphenol. Their aquatic predicted-no-effect concentrations (PNECs) were derived using the log-normal statistical extrapolation method. We calculated aquatic PNECs of bisphenol A and 4-nonylphenol with values of 4.01 and 0.721 µg/L, respectively. The ecological risk of each chemical in different aquatic environments near Tianjin, China, a coastal municipality along the Bohai Sea, was characterized by hazard quotient and probabilistic risk quotient assessment techniques. Hazard quotients of 7.02 and 5.99 at 2 municipal sewage sites using all of the endpoints were observed for 4-nonylphenol, which indicated high ecological risks posed by 4-nonylphenol to aquatic organisms, especially endocrine-disrupting effects. Moreover, a high ecological risk of 4-nonylphenol was indicated based on the probabilistic risk quotient method. The present results show that combining the SSWD method and the AOP concept could better protect aquatic organisms from adverse effects such as endocrine disruption and could decrease uncertainty in ecological risk assessment. Environ Toxicol Chem 2018;37:551-562. © 2017 SETAC. © 2017 SETAC.

Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality.

PubMed

Li, Pengxiang; Kim, Michelle M; Doshi, Jalpa A

2010-08-20

The Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-HCC) model to risk adjust Medicare capitation payments. This study intends to assess the performance of the CMS-HCC risk adjustment method and to compare it to the Charlson and Elixhauser comorbidity measures in predicting in-hospital and six-month mortality in Medicare beneficiaries. The study used the 2005-2006 Chronic Condition Data Warehouse (CCW) 5% Medicare files. The primary study sample included all community-dwelling fee-for-service Medicare beneficiaries with a hospital admission between January 1st, 2006 and June 30th, 2006. Additionally, four disease-specific samples consisting of subgroups of patients with principal diagnoses of congestive heart failure (CHF), stroke, diabetes mellitus (DM), and acute myocardial infarction (AMI) were also selected. Four analytic files were generated for each sample by extracting inpatient and/or outpatient claims for each patient. Logistic regressions were used to compare the methods. Model performance was assessed using the c-statistic, the Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and their 95% confidence intervals estimated using bootstrapping. The CMS-HCC had statistically significant higher c-statistic and lower AIC and BIC values than the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality across all samples in analytic files that included claims from the index hospitalization. Exclusion of claims for the index hospitalization generally led to drops in model performance across all methods with the highest drops for the CMS-HCC method. However, the CMS-HCC still performed as well or better than the other two methods. The CMS-HCC method demonstrated better performance relative to the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality. The CMS-HCC model is preferred over the Charlson and Elixhauser methods if information about the patient's diagnoses prior to the index hospitalization is available and used to code the risk adjusters. However, caution should be exercised in studies evaluating inpatient processes of care and where data on pre-index admission diagnoses are unavailable.

Electronic health record-based cardiac risk assessment and identification of unmet preventive needs.

PubMed

Persell, Stephen D; Dunne, Alexis P; Lloyd-Jones, Donald M; Baker, David W

2009-04-01

Cardiac risk assessment may not be routinely performed. Electronic health records (EHRs) offer the potential to automate risk estimation. We compared EHR-based assessment with manual chart review to determine the accuracy of automated cardiac risk estimation and determination of candidates for antiplatelet or lipid-lowering interventions. We performed an observational retrospective study of 23,111 adults aged 20 to 79 years, seen in a large urban primary care group practice. Automated assessments classified patients into 4 cardiac risk groups or as unclassifiable and determined candidates for antiplatelet or lipid-lowering interventions based on current guidelines. A blinded physician manually reviewed 100 patients from each risk group and the unclassifiable group. We determined the agreement between full review and automated assessments for cardiac risk estimation and identification of which patients were candidates for interventions. By automated methods, 9.2% of the population were candidates for lipid-lowering interventions, and 8.0% were candidates for antiplatelet medication. Agreement between automated risk classification and manual review was high (kappa = 0.91; 95% confidence interval [CI], 0.88-0.93). Automated methods accurately identified candidates for antiplatelet therapy [sensitivity, 0.81 (95% CI, 0.73-0.89); specificity, 0.98 (95% CI, 0.96-0.99); positive predictive value, 0.86 (95% CI, 0.78-0.94); and negative predictive value, 0.98 (95% CI, 0.97-0.99)] and lipid lowering [sensitivity, 0.92 (95% CI, 0.87-0.96); specificity, 0.98 (95% CI, 0.97-0.99); positive predictive value, 0.94 (95% CI, 0.89-0.99); and negative predictive value, 0.99 (95% CI, 0.98-> or =0.99)]. EHR data can be used to automatically perform cardiovascular risk stratification and identify patients in need of risk-lowering interventions. This could improve detection of high-risk patients whom physicians would otherwise be unaware.

A Systematic Evaluation of Field-Based Screening Methods for the Assessment of Anterior Cruciate Ligament (ACL) Injury Risk.

PubMed

Fox, Aaron S; Bonacci, Jason; McLean, Scott G; Spittle, Michael; Saunders, Natalie

2016-05-01

Laboratory-based measures provide an accurate method to identify risk factors for anterior cruciate ligament (ACL) injury; however, these methods are generally prohibitive to the wider community. Screening methods that can be completed in a field or clinical setting may be more applicable for wider community use. Examination of field-based screening methods for ACL injury risk can aid in identifying the most applicable method(s) for use in these settings. The objective of this systematic review was to evaluate and compare field-based screening methods for ACL injury risk to determine their efficacy of use in wider community settings. An electronic database search was conducted on the SPORTDiscus™, MEDLINE, AMED and CINAHL databases (January 1990-July 2015) using a combination of relevant keywords. A secondary search of the same databases, using relevant keywords from identified screening methods, was also undertaken. Studies identified as potentially relevant were independently examined by two reviewers for inclusion. Where consensus could not be reached, a third reviewer was consulted. Original research articles that examined screening methods for ACL injury risk that could be undertaken outside of a laboratory setting were included for review. Two reviewers independently assessed the quality of included studies. Included studies were categorized according to the screening method they examined. A description of each screening method, and data pertaining to the ability to prospectively identify ACL injuries, validity and reliability, recommendations for identifying 'at-risk' athletes, equipment and training required to complete screening, time taken to screen athletes, and applicability of the screening method across sports and athletes were extracted from relevant studies. Of 1077 citations from the initial search, a total of 25 articles were identified as potentially relevant, with 12 meeting all inclusion/exclusion criteria. From the secondary search, eight further studies met all criteria, resulting in 20 studies being included for review. Five ACL-screening methods-the Landing Error Scoring System (LESS), Clinic-Based Algorithm, Observational Screening of Dynamic Knee Valgus (OSDKV), 2D-Cam Method, and Tuck Jump Assessment-were identified. There was limited evidence supporting the use of field-based screening methods in predicting ACL injuries across a range of populations. Differences relating to the equipment and time required to complete screening methods were identified. Only screening methods for ACL injury risk were included for review. Field-based screening methods developed for lower-limb injury risk in general may also incorporate, and be useful in, screening for ACL injury risk. Limited studies were available relating to the OSDKV and 2D-Cam Method. The LESS showed predictive validity in identifying ACL injuries, however only in a youth athlete population. The LESS also appears practical for community-wide use due to the minimal equipment and set-up/analysis time required. The Clinic-Based Algorithm may have predictive value for ACL injury risk as it identifies athletes who exhibit high frontal plane knee loads during a landing task, but requires extensive additional equipment and time, which may limit its application to wider community settings.

Estimating the impact of grouping misclassification on risk prediction when using the relative potency factors method to assess mixtures risk

EPA Science Inventory

Environmental health risk assessments of chemical mixtures that rely on component approaches often begin by grouping the chemicals of concern according to toxicological similarity. Approaches that assume dose addition typically are used for groups of similarly-acting chemicals an...

Review of methods for developing probabilistic risk assessments

Treesearch

D. A. Weinstein; P.B. Woodbury

2010-01-01

We describe methodologies currently in use or those under development containing features for estimating fire occurrence risk assessment. We describe two major categories of fire risk assessment tools: those that predict fire under current conditions, assuming that vegetation, climate, and the interactions between them and fire remain relatively similar to their...

Sun Protection Motivational Stages and Behavior: Skin Cancer Risk Profiles

ERIC Educational Resources Information Center

Pagoto, Sherry L.; McChargue, Dennis E.; Schneider, Kristin; Cook, Jessica Werth

2004-01-01

Objective: To create skin cancer risk profiles that could be used to predict sun protection among Midwest beachgoers. Method: Cluster analysis was used with study participants (N=239), who provided information about sun protection motivation and behavior, perceived risk, burn potential, and tan importance. Participants were clustered according to…

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study.

PubMed

AbdelRahman, Samir E; Zhang, Mingyuan; Bray, Bruce E; Kawamoto, Kensaku

2014-05-27

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time. Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak. The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%. The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

Research on the Risk Early Warning Method of Material Supplier Performance in Power Industry

NASA Astrophysics Data System (ADS)

Chen, Peng; Zhang, Xi

2018-01-01

The early warning of supplier performance risk is still in the initial stage interiorly, and research on the early warning mechanism to identify, analyze and prevent the performance risk is few. In this paper, a new method aiming at marerial supplier performance risk in power industry is proposed, firstly, establishing a set of risk early warning indexes, Then use the ECM method to classify the indexes to form different risk grades. Then, improving Crock Ford risk quantization model by considering three indicators, including the stability of power system, economic losses and successful bid ratio to form the predictive risk grade, and ultimately using short board effect principle to form the ultimate risk grade to truly reflect the supplier performance risk. Finally, making empirical analysis on supplier performance and putting forward the counter measures and prevention strategies for different risks.

Model estimation of claim risk and premium for motor vehicle insurance by using Bayesian method

NASA Astrophysics Data System (ADS)

Sukono; Riaman; Lesmana, E.; Wulandari, R.; Napitupulu, H.; Supian, S.

2018-01-01

Risk models need to be estimated by the insurance company in order to predict the magnitude of the claim and determine the premiums charged to the insured. This is intended to prevent losses in the future. In this paper, we discuss the estimation of risk model claims and motor vehicle insurance premiums using Bayesian methods approach. It is assumed that the frequency of claims follow a Poisson distribution, while a number of claims assumed to follow a Gamma distribution. The estimation of parameters of the distribution of the frequency and amount of claims are made by using Bayesian methods. Furthermore, the estimator distribution of frequency and amount of claims are used to estimate the aggregate risk models as well as the value of the mean and variance. The mean and variance estimator that aggregate risk, was used to predict the premium eligible to be charged to the insured. Based on the analysis results, it is shown that the frequency of claims follow a Poisson distribution with parameter values λ is 5.827. While a number of claims follow the Gamma distribution with parameter values p is 7.922 and θ is 1.414. Therefore, the obtained values of the mean and variance of the aggregate claims respectively are IDR 32,667,489.88 and IDR 38,453,900,000,000.00. In this paper the prediction of the pure premium eligible charged to the insured is obtained, which amounting to IDR 2,722,290.82. The prediction of the claims and premiums aggregate can be used as a reference for the insurance company’s decision-making in management of reserves and premiums of motor vehicle insurance.

Predictive and Prognostic Factors in Definition of Risk Groups in Endometrial Carcinoma

PubMed Central

Sorbe, Bengt

2012-01-01

Background. The aim was to evaluate predictive and prognostic factors in a large consecutive series of endometrial carcinomas and to discuss pre- and postoperative risk groups based on these factors. Material and Methods. In a consecutive series of 4,543 endometrial carcinomas predictive and prognostic factors were analyzed with regard to recurrence rate and survival. The patients were treated with primary surgery and adjuvant radiotherapy. Two preoperative and three postoperative risk groups were defined. DNA ploidy was included in the definitions. Eight predictive or prognostic factors were used in multivariate analyses. Results. The overall recurrence rate of the complete series was 11.4%. Median time to relapse was 19.7 months. In a multivariate logistic regression analysis, FIGO grade, myometrial infiltration, and DNA ploidy were independent and statistically predictive factors with regard to recurrence rate. The 5-year overall survival rate was 73%. Tumor stage was the single most important factor with FIGO grade on the second place. DNA ploidy was also a significant prognostic factor. In the preoperative risk group definitions three factors were used: histology, FIGO grade, and DNA ploidy. Conclusions. DNA ploidy was an important and significant predictive and prognostic factor and should be used both in preoperative and postoperative risk group definitions. PMID:23209924

The factor-of-risk biomechanical approach predicts hip fracture in men and women: the Framingham Study.

PubMed

Dufour, A B; Roberts, B; Broe, K E; Kiel, D P; Bouxsein, M L; Hannan, M T

2012-02-01

We examined the relation between a biomechanical measure, factor-of-risk, and hip fracture risk in 1,100 men and women from the Framingham Study and found that it predicted hip fracture (men, ORs of 1.8; women, 1.2-1.4). Alternative methods of predicting hip fracture are needed since 50% of adults who fracture do not have osteoporosis by bone mineral density (BMD) measurements. One method, factor-of-risk (Φ), computes the ratio of force on the hip in a fall to femoral strength. We examined the relation between Φ and hip fracture in 1,100 subjects from the Framingham Study with measured hip BMD, along with weight, height, and age, collected in 1988-1989. We estimated both peak and attenuated force applied to the hip in a sideways fall from standing height, where attenuated force incorporated cushioning effects of trochanteric soft tissue. Femoral strength was estimated from femoral neck BMD, using cadaveric femoral strength data. Sex-specific, age-adjusted survival models were used to calculate hazard ratios (HR) and 95% confidence intervals for the relation between Φ (peak), Φ (attenuated), and their components with hip fracture. In 425 men and 675 women (mean age, 76 years), 136 hip fractures occurred over median follow-up of 11.3 years. Factor-of-risk, Φ, was associated with increased age-adjusted risk for hip fracture. One standard deviation increase in Φ (peak) and Φ (attenuated) was associated with HR of 1.88 and 1.78 in men and 1.23 and 1.41 in women, respectively. Examining components of Φ, in women, we found fall force and soft tissue thickness were predictive of hip fracture independent of femoral strength (was estimated from BMD). Thus, both Φ (peak) and Φ (attenuated) predict hip fracture in men and women. These findings suggest additional studies of Φ predicting hip fracture using direct measurements of trochanteric soft tissue.

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

PubMed

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.

Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Saxena, Abhinav; Daigle, Matthew; Goebel, Kai

2013-01-01

This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.

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

PubMed

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 < 0.0001 for 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 prediction for type 2 diabetes using readily available administrative data is feasible and has better prediction performance than classical diabetes risk prediction algorithms on very large populations with missing data. The new model enables intervention allocation at national scale quickly and accurately and recovers potentially novel risk factors at different stages before the disease onset.

Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data.

PubMed

Bernecker, Samantha L; Rosellini, Anthony J; Nock, Matthew K; Chiu, Wai Tat; Gutierrez, Peter M; Hwang, Irving; Joiner, Thomas E; Naifeh, James A; Sampson, Nancy A; Zaslavsky, Alan M; Stein, Murray B; Ursano, Robert J; Kessler, Ronald C

2018-04-03

High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median = 26.0%). Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.

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

PubMed Central

Pfeiffer, Ruth M.; Miglioretti, Diana L.; Kerlikowske, Karla; Tice, Jeffery; Vacek, Pamela M.; Gierach, Gretchen L.

2016-01-01

Purpose 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. Methods 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. Results 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. Conclusions 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 predicted risk of developing breast cancer does not imply that if cancer develops it will behave aggressively. PMID:27560501

Development and validation of a gene profile predicting benefit of postmastectomy radiotherapy in patients with high-risk breast cancer: a study of gene expression in the DBCG82bc cohort.

PubMed

Tramm, Trine; Mohammed, Hayat; Myhre, Simen; Kyndi, Marianne; Alsner, Jan; Børresen-Dale, Anne-Lise; Sørlie, Therese; Frigessi, Arnoldo; Overgaard, Jens

2014-10-15

To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into "high LRR risk" and "low LRR risk" groups. PMRT significantly reduced risk of LRR in "high LRR risk" patients, whereas "low LRR risk" patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. ©2014 American Association for Cancer Research.

Prospective Dynamic Assessment of Risk of Sexual Reoffending in Individuals with an Intellectual Disability and a History of Sexual Offending Behaviour

ERIC Educational Resources Information Center

Lofthouse, Rachael E.; Lindsay, William R.; Totsika, Vasiliki; Hastings, Richard P.; Boer, Douglas P.; Haaven, James L.

2013-01-01

Background: The purpose of the present study was to add to the literature on the predictive accuracy of a dynamic intellectual disability specific risk assessment tool. Method: A dynamic risk assessment for sexual reoffending (ARMIDILO-S), a static risk assessment for sexual offending (STATIC-99), and a static risk assessment for violence…

Predicting landslides in clearcut patches

Treesearch

Raymond M. Rice; Norman H. Pillsbury

1982-01-01

Abstract - Accelerated erosion in the form of landslides can be an undesirable consequence of clearcut logging on steep slopes. Forest managers need a method of predicting the risk of such erosion. Data collected after logging in a granitic area of northwestern California were used to develop a predictive equation. A linear discriminant function was developed that...

Evaluation of the DAVROS (Development And Validation of Risk-adjusted Outcomes for Systems of emergency care) risk-adjustment model as a quality indicator for healthcare

PubMed Central

Wilson, Richard; Goodacre, Steve W; Klingbajl, Marcin; Kelly, Anne-Maree; Rainer, Tim; Coats, Tim; Holloway, Vikki; Townend, Will; Crane, Steve

2014-01-01

Background and objective Risk-adjusted mortality rates can be used as a quality indicator if it is assumed that the discrepancy between predicted and actual mortality can be attributed to the quality of healthcare (ie, the model has attributional validity). The Development And Validation of Risk-adjusted Outcomes for Systems of emergency care (DAVROS) model predicts 7-day mortality in emergency medical admissions. We aimed to test this assumption by evaluating the attributional validity of the DAVROS risk-adjustment model. Methods We selected cases that had the greatest discrepancy between observed mortality and predicted probability of mortality from seven hospitals involved in validation of the DAVROS risk-adjustment model. Reviewers at each hospital assessed hospital records to determine whether the discrepancy between predicted and actual mortality could be explained by the healthcare provided. Results We received 232/280 (83%) completed review forms relating to 179 unexpected deaths and 53 unexpected survivors. The healthcare system was judged to have potentially contributed to 10/179 (8%) of the unexpected deaths and 26/53 (49%) of the unexpected survivors. Failure of the model to appropriately predict risk was judged to be responsible for 135/179 (75%) of the unexpected deaths and 2/53 (4%) of the unexpected survivors. Some 10/53 (19%) of the unexpected survivors died within a few months of the 7-day period of model prediction. Conclusions We found little evidence that deaths occurring in patients with a low predicted mortality from risk-adjustment could be attributed to the quality of healthcare provided. PMID:23605036

Real-data comparison of data mining methods in prediction of diabetes in iran.

PubMed

Tapak, Lily; Mahjub, Hossein; Hamidi, Omid; Poorolajal, Jalal

2013-09-01

Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

Do causal concentration-response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality.

PubMed

Cox, Louis Anthony Tony

2017-08-01

Concentration-response (C-R) functions relating concentrations of pollutants in ambient air to mortality risks or other adverse health effects provide the basis for many public health risk assessments, benefits estimates for clean air regulations, and recommendations for revisions to existing air quality standards. The assumption that C-R functions relating levels of exposure and levels of response estimated from historical data usefully predict how future changes in concentrations would change risks has seldom been carefully tested. This paper critically reviews literature on C-R functions for fine particulate matter (PM2.5) and mortality risks. We find that most of them describe historical associations rather than valid causal models for predicting effects of interventions that change concentrations. The few papers that explicitly attempt to model causality rely on unverified modeling assumptions, casting doubt on their predictions about effects of interventions. A large literature on modern causal inference algorithms for observational data has been little used in C-R modeling. Applying these methods to publicly available data from Boston and the South Coast Air Quality Management District around Los Angeles shows that C-R functions estimated for one do not hold for the other. Changes in month-specific PM2.5 concentrations from one year to the next do not help to predict corresponding changes in average elderly mortality rates in either location. Thus, the assumption that estimated C-R relations predict effects of pollution-reducing interventions may not be true. Better causal modeling methods are needed to better predict how reducing air pollution would affect public health.

Screening for potential child maltreatment in parents of a newborn baby: The predictive validity of an Instrument for early identification of Parents At Risk for child Abuse and Neglect (IPARAN).

PubMed

van der Put, Claudia E; Bouwmeester-Landweer, Merian B R; Landsmeer-Beker, Eleonore A; Wit, Jan M; Dekker, Friedo W; Kousemaker, N Pieter J; Baartman, Herman E M

2017-08-01

For preventive purposes it is important to be able to identify families with a high risk of child maltreatment at an early stage. Therefore we developed an actuarial instrument for screening families with a newborn baby, the Instrument for identification of Parents At Risk for child Abuse and Neglect (IPARAN). The aim of this study was to assess the predictive validity of the IPARAN and to examine whether combining actuarial and clinical methods leads to an improvement of the predictive validity. We examined the predictive validity by calculating several performance indicators (i.e., sensitivity, specificity and the Area Under the receiver operating characteristic Curve [AUC]) in a sample of 4692 Dutch families with newborns. The outcome measure was a report of child maltreatment at Child Protection Services during a follow-up of 3 years. For 17 children (.4%) a report of maltreatment was registered. The predictive validity of the IPARAN was significantly better than chance (AUC=.700, 95% CI [.567-.832]), in contrast to a low value for clinical judgement of nurses of the Youth Health Care Centers (AUC=.591, 95% CI [.422-.759]). The combination of the IPARAN and clinical judgement resulted in the highest predictive validity (AUC=.720, 95% CI [.593-.847]), however, the difference between the methods did not reach statistical significance. The good predictive validity of the IPARAN in combination with clinical judgment of the nurse enables professionals to assess risks at an early stage and to make referrals to early intervention programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Study on Re-entry Predictions of Uncontrolled Space Objects for Space Situational Awareness

NASA Astrophysics Data System (ADS)

Choi, Eun-Jung; Cho, Sungki; Lee, Deok-Jin; Kim, Siwoo; Jo, Jung Hyun

2017-12-01

The key risk analysis technologies for the re-entry of space objects into Earth’s atmosphere are divided into four categories: cataloguing and databases of the re-entry of space objects, lifetime and re-entry trajectory predictions, break-up models after re-entry and multiple debris distribution predictions, and ground impact probability models. In this study, we focused on re- entry prediction, including orbital lifetime assessments, for space situational awareness systems. Re-entry predictions are very difficult and are affected by various sources of uncertainty. In particular, during uncontrolled re-entry, large spacecraft may break into several pieces of debris, and the surviving fragments can be a significant hazard for persons and properties on the ground. In recent years, specific methods and procedures have been developed to provide clear information for predicting and analyzing the re-entry of space objects and for ground-risk assessments. Representative tools include object reentry survival analysis tool (ORSAT) and debris assessment software (DAS) developed by National Aeronautics and Space Administration (NASA), spacecraft atmospheric re-entry and aerothermal break-up (SCARAB) and debris risk assessment and mitigation analysis (DRAMA) developed by European Space Agency (ESA), and semi-analytic tool for end of life analysis (STELA) developed by Centre National d’Etudes Spatiales (CNES). In this study, various surveys of existing re-entry space objects are reviewed, and an efficient re-entry prediction technique is suggested based on STELA, the life-cycle analysis tool for satellites, and DRAMA, a re-entry analysis tool. To verify the proposed method, the re-entry of the Tiangong-1 Space Lab, which is expected to re-enter Earth’s atmosphere shortly, was simulated. Eventually, these results will provide a basis for space situational awareness risk analyses of the re-entry of space objects.

An integrated computational approach can classify VHL missense mutations according to risk of clear cell renal carcinoma

PubMed Central

Gossage, Lucy; Pires, Douglas E. V.; Olivera-Nappa, Álvaro; Asenjo, Juan; Bycroft, Mark; Blundell, Tom L.; Eisen, Tim

2014-01-01

Mutations in the von Hippel–Lindau (VHL) gene are pathogenic in VHL disease, congenital polycythaemia and clear cell renal carcinoma (ccRCC). pVHL forms a ternary complex with elongin C and elongin B, critical for pVHL stability and function, which interacts with Cullin-2 and RING-box protein 1 to target hypoxia-inducible factor for polyubiquitination and proteasomal degradation. We describe a comprehensive database of missense VHL mutations linked to experimental and clinical data. We use predictions from in silico tools to link the functional effects of missense VHL mutations to phenotype. The risk of ccRCC in VHL disease is linked to the degree of destabilization resulting from missense mutations. An optimized binary classification system (symphony), which integrates predictions from five in silico methods, can predict the risk of ccRCC associated with VHL missense mutations with high sensitivity and specificity. We use symphony to generate predictions for risk of ccRCC for all possible VHL missense mutations and present these predictions, in association with clinical and experimental data, in a publically available, searchable web server. PMID:24969085

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

PubMed Central

Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

2016-01-01

Background: Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. Method: A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study. Results: Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. Conclusions: The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced. PMID:28077893

Risk stratification for arrhythmic death in an emergency department cohort: a new method of nonlinear PD2i analysis of the ECG

PubMed Central

Skinner, James E; Meyer, Michael; Dalsey, William C; Nester, Brian A; Ramalanjaona, George; O’Neil, Brian J; Mangione, Antoinette; Terregino, Carol; Moreyra, Abel; Weiss, Daniel N; Anchin, Jerry M; Geary, Una; Taggart, Pamela

2008-01-01

Heart rate variability (HRV) reflects both cardiac autonomic function and risk of sudden arrhythmic death (AD). Indices of HRV based on linear stochastic models are independent risk factors for AD in postmyocardial infarction (MI) cohorts. Indices based on nonlinear deterministic models have a higher sensitivity and specificity for predicting AD in retrospective data. A new nonlinear deterministic model, the automated Point Correlation Dimension (PD2i), was prospectively evaluated for prediction of AD. Patients were enrolled (N = 918) in 6 emergency departments (EDs) upon presentation with chest pain and being determined to be at risk of acute MI (AMI) >7%. Brief digital ECGs (>1000 heartbeats, ∼15 min) were recorded and automated PD2i results obtained. Out-of-hospital AD was determined by modified Hinkle-Thaler criteria. All-cause mortality at 1 year was 6.2%, with 3.5% being ADs. Of the AD fatalities, 34% were without previous history of MI or diagnosis of AMI. The PD2i prediction of AD had sensitivity = 96%, specificity = 85%, negative predictive value = 99%, and relative risk >24.2 (p ≤ 0.001). HRV analysis by the time-dependent nonlinear PD2i algorithm can accurately predict risk of AD in an ED cohort and may have both life-saving and resource-saving implications for individual risk assessment. PMID:19209249

Subsea release of oil from a riser: an ecological risk assessment.

PubMed

Nazir, Muddassir; Khan, Faisal; Amyotte, Paul; Sadiq, Rehan

2008-10-01

This study illustrates a newly developed methodology, as a part of the U.S. EPA ecological risk assessment (ERA) framework, to predict exposure concentrations in a marine environment due to underwater release of oil and gas. It combines the hydrodynamics of underwater blowout, weathering algorithms, and multimedia fate and transport to measure the exposure concentration. Naphthalene and methane are used as surrogate compounds for oil and gas, respectively. Uncertainties are accounted for in multimedia input parameters in the analysis. The 95th percentile of the exposure concentration (EC(95%)) is taken as the representative exposure concentration for the risk estimation. A bootstrapping method is utilized to characterize EC(95%) and associated uncertainty. The toxicity data of 19 species available in the literature are used to calculate the 5th percentile of the predicted no observed effect concentration (PNEC(5%)) by employing the bootstrapping method. The risk is characterized by transforming the risk quotient (RQ), which is the ratio of EC(95%) to PNEC(5%), into a cumulative risk distribution. This article describes a probabilistic basis for the ERA, which is essential from risk management and decision-making viewpoints. Two case studies of underwater oil and gas mixture release, and oil release with no gaseous mixture are used to show the systematic implementation of the methodology, elements of ERA, and the probabilistic method in assessing and characterizing the risk.

AUTONOMY AND RELATEDNESS IN MOTHER-TEEN INTERACTIONS AS PREDICTORS OF INVOLVEMENT IN ADOLESCENT DATING AGGRESSION

PubMed Central

Niolon, Phyllis Holditch; Kuperminc, Gabriel P.; Allen, Joseph P.

2015-01-01

Objective This multi-method, longitudinal study examines the negotiation of autonomy and relatedness between teens and their mothers as etiologic predictors of perpetration and victimization of dating aggression two years later. Method Observations of 88 mid-adolescents and their mothers discussing a topic of disagreement were coded for each individual’s demonstrations of autonomy and relatedness using a validated coding system. Adolescents self-reported on perpetration and victimization of physical and psychological dating aggression two years later. We hypothesized that mother’s and adolescents’ behaviors supporting autonomy and relatedness would longitudinally predict lower reporting of dating aggression, and that their behaviors inhibiting autonomy and relatedness would predict higher reporting of dating aggression. Results Hypotheses were not supported; main findings were characterized by interactions of sex and risk status with autonomy. Maternal behaviors supporting autonomy predicted higher reports of perpetration and victimization of physical dating aggression for girls, but not for boys. Adolescent behaviors supporting autonomy predicted higher reports of perpetration of physical dating aggression for high-risk adolescents, but not for low-risk adolescents. Conclusions Results indicate that autonomy is a dynamic developmental process, operating differently as a function of social contexts in predicting dating aggression. Examination of these and other developmental processes within parent-child relationships is important in predicting dating aggression, but may depend on social context. PMID:25914852

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

PubMed Central

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

Early Prediction of Cancer Progression by Depth-Resolved Nanoscale Mapping of Nuclear Architecture from Unstained Tissue Specimens.

PubMed

Uttam, Shikhar; Pham, Hoa V; LaFace, Justin; Leibowitz, Brian; Yu, Jian; Brand, Randall E; Hartman, Douglas J; Liu, Yang

2015-11-15

Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Therefore, frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy. Here, we present a new method to predict cancer progression risk via nanoscale nuclear architecture mapping (nanoNAM) of unstained tissue sections based on the intrinsic density alteration of nuclear structure rather than the amount of stain uptake. We demonstrate that nanoNAM detects a gradual increase in the density alteration of nuclear architecture during malignant transformation in animal models of colon carcinogenesis and in human patients with ulcerative colitis, even in tissue that appears histologically normal according to pathologists. We evaluated the ability of nanoNAM to predict "future" cancer progression in patients with ulcerative colitis who did and did not develop colon cancer up to 13 years after their initial colonoscopy. NanoNAM of the initial biopsies correctly classified 12 of 15 patients who eventually developed colon cancer and 15 of 18 who did not, with an overall accuracy of 85%. Taken together, our findings demonstrate great potential for nanoNAM in predicting cancer progression risk and suggest that further validation in a multicenter study with larger cohorts may eventually advance this method to become a routine clinical test. ©2015 American Association for Cancer Research.

Neuroprediction, Violence, and the Law: Setting the Stage

PubMed Central

Bibas, Stephanos; Grafton, Scott; Kiehl, Kent A.; Mansfield, Andrew; Sinnott-Armstrong, Walter; Gazzaniga, Michael

2014-01-01

In this paper, our goal is to (a) survey some of the legal contexts within which violence risk assessment already plays a prominent role, (b) explore whether developments in neuroscience could potentially be used to improve our ability to predict violence, and (c) discuss whether neuropredictive models of violence create any unique legal or moral problems above and beyond the well worn problems already associated with prediction more generally. In “Violence Risk Assessment and the Law”, we briefly examine the role currently played by predictions of violence in three high stakes legal contexts: capital sentencing (“Violence Risk Assessment and Capital Sentencing”), civil commitment hearings (“Violence Risk Assessment and Civil Commitment”), and “sexual predator” statutes (“Violence Risk Assessment and Sexual Predator Statutes”). In “Clinical vs. Actuarial Violence Risk Assessment”, we briefly examine the distinction between traditional clinical methods of predicting violence and more recently developed actuarial methods, exemplified by the Classification of Violence Risk (COVR) software created by John Monahan and colleagues as part of the MacArthur Study of Mental Disorder and Violence [1]. In “The Neural Correlates of Psychopathy”, we explore what neuroscience currently tells us about the neural correlates of violence, using the recent neuroscientific research on psychopathy as our focus. We also discuss some recent advances in both data collection (“Cutting-Edge Data Collection: Genetically Informed Neuroimaging”) and data analysis (“Cutting-Edge Data Analysis: Pattern Classification”) that we believe will play an important role when it comes to future neuroscientific research on violence. In “The Potential Promise of Neuroprediction”, we discuss whether neuroscience could potentially be used to improve our ability to predict future violence. Finally, in “The Potential Perils of Neuroprediction”, we explore some potential evidentiary (“Evidentiary Issues”), constitutional (“Constitutional Issues”), and moral (“Moral Issues”) issues that may arise in the context of the neuroprediction of violence. PMID:25083168

Predicting Future Suicide Attempts Among Adolescent and Emerging Adult Psychiatric Emergency Patients

PubMed Central

Horwitz, Adam G.; Czyz, Ewa K.; King, Cheryl A.

2014-01-01

Objective The purpose of this study was to longitudinally examine specific characteristics of suicidal ideation in combination with histories of suicide attempts and non-suicidal self-injury (NSSI) to best evaluate risk for a future attempt among high-risk adolescents and emerging adults. Method Participants in this retrospective medical record review study were 473 (53% female; 69% Caucasian) consecutive patients, ages 15–24 years (M = 19.4 years) who presented for psychiatric emergency (PE) services during a 9-month period. These patients’ medical records, including a clinician-administered Columbia-Suicide Severity Rating Scale, were coded at the index visit and at future visits occurring within the next 18 months. Logistic regression models were used to predict suicide attempts during this period. Results SES, suicidal ideation severity (i.e., intent, method), suicidal ideation intensity (i.e., frequency, controllability), a lifetime history of suicide attempt, and a lifetime history of NSSI were significant independent predictors of a future suicide attempt. Suicidal ideation added incremental validity to the prediction of future suicide attempts above and beyond the influence of a past suicide attempt, whereas a lifetime history of NSSI did not. Sex moderated the relationship between the duration of suicidal thoughts and future attempts (predictive for males, but not females). Conclusions Results suggest value in incorporating both past behaviors and current thoughts into suicide risk formulation. Furthermore, suicidal ideation duration warrants additional examination as a potential critical factor for screening assessments evaluating suicide risk among high-risk samples, particularly for males. PMID:24871489

Absolute fracture risk assessment using lumbar spine and femoral neck bone density measurements: derivation and validation of a hybrid system.

PubMed

Leslie, William D; Lix, Lisa M

2011-03-01

The World Health Organization (WHO) Fracture Risk Assessment Tool (FRAX) computes 10-year probability of major osteoporotic fracture from multiple risk factors, including femoral neck (FN) T-scores. Lumbar spine (LS) measurements are not currently part of the FRAX formulation but are used widely in clinical practice, and this creates confusion when there is spine-hip discordance. Our objective was to develop a hybrid 10-year absolute fracture risk assessment system in which nonvertebral (NV) fracture risk was assessed from the FN and clinical vertebral (V) fracture risk was assessed from the LS. We identified 37,032 women age 45 years and older undergoing baseline FN and LS dual-energy X-ray absorptiometry (DXA; 1990-2005) from a population database that contains all clinical DXA results for the Province of Manitoba, Canada. Results were linked to longitudinal health service records for physician billings and hospitalizations to identify nontrauma vertebral and nonvertebral fracture codes after bone mineral density (BMD) testing. The population was randomly divided into equal-sized derivation and validation cohorts. Using the derivation cohort, three fracture risk prediction systems were created from Cox proportional hazards models (adjusted for age and multiple FRAX risk factors): FN to predict combined all fractures, FN to predict nonvertebral fractures, and LS to predict vertebral (without nonvertebral) fractures. The hybrid system was the sum of nonvertebral risk from the FN model and vertebral risk from the LS model. The FN and hybrid systems were both strongly predictive of overall fracture risk (p < .001). In the validation cohort, ROC analysis showed marginally better performance of the hybrid system versus the FN system for overall fracture prediction (p = .24) and significantly better performance for vertebral fracture prediction (p < .001). In a discordance subgroup with FN and LS T-score differences greater than 1 SD, there was a significant improvement in overall fracture prediction with the hybrid method (p = .025). Risk reclassification under the hybrid system showed better alignment with observed fracture risk, with 6.4% of the women reclassified to a different risk category. In conclusion, a hybrid 10-year absolute fracture risk assessment system based on combining FN and LS information is feasible. The improvement in fracture risk prediction is small but supports clinical interest in a system that integrates LS in fracture risk assessment. Copyright © 2011 American Society for Bone and Mineral Research.

Premorbid social adjustment and association with attenuated psychotic symptoms in clinical high-risk and help-seeking youth.

PubMed

Tarbox-Berry, S I; Perkins, D O; Woods, S W; Addington, J

2018-04-01

Attenuated positive symptom syndrome (APSS), characterized by 'putatively prodromal' attenuated psychotic-like pathology, indicates increased risk for psychosis. Poor premorbid social adjustment predicts severity of APSS symptoms and predicts subsequent psychosis in APSS-diagnosed individuals, suggesting application for improving detection of 'true' prodromal youth who will transition to psychosis. However, these predictive associations have not been tested in controls and therefore may be independent of the APSS diagnosis, negating utility for improving prediction in APSS-diagnosed individuals. Association between premorbid social maladjustment and severity of positive, negative, disorganized, and general APSS symptoms was tested in 156 individuals diagnosed with APSS and 76 help-seeking (non-APSS) controls enrolled in the Enhancing the Prospective Prediction of Psychosis (PREDICT) study using prediction analysis. Premorbid social maladjustment was associated with social anhedonia, reduced expression of emotion, restricted ideational richness, and deficits in occupational functioning, independent of the APSS diagnosis. Associations between social maladjustment and suspiciousness, unusual thought content, avolition, dysphoric mood, and impaired tolerance to normal stress were uniquely present in participants meeting APSS criteria. Social maladjustment was associated with odd behavior/appearance and diminished experience of emotions and self only in participants who did not meet APSS criteria. Predictive associations between poor premorbid social adjustment and attenuated psychotic-like pathology were identified, a subset of which were indicative of high risk for psychosis. This study offers a method for improving risk identification while ruling out low-risk individuals.

Autonomous Aerobraking Using Thermal Response Surface Analysis

NASA Technical Reports Server (NTRS)

Prince, Jill L.; Dec, John A.; Tolson, Robert H.

2007-01-01

Aerobraking is a proven method of significantly increasing the science payload that can be placed into low Mars orbits when compared to an all propulsive capture. However, the aerobraking phase is long and has mission cost and risk implications. The main cost benefit is that aerobraking permits the use of a smaller and cheaper launch vehicle, but additional operational costs are incurred during the long aerobraking phase. Risk is increased due to the repeated thermal loading of spacecraft components and the multiple attitude and propulsive maneuvers required for successful aerobraking. Both the cost and risk burdens can be significantly reduced by automating the aerobraking operations phase. All of the previous Mars orbiter missions that have utilized aerobraking have increasingly relied on onboard calculations during aerobraking. Even though the temperature of spacecraft components has been the limiting factor, operational methods have relied on using a surrogate variable for mission control. This paper describes several methods, based directly on spacecraft component maximum temperature, for autonomously predicting the subsequent aerobraking orbits and prescribing apoapsis propulsive maneuvers to maintain the spacecraft within specified temperature limits. Specifically, this paper describes the use of thermal response surface analysis in predicting the temperature of the spacecraft components and the corresponding uncertainty in this temperature prediction.

Accuracy of the Atherosclerotic Cardiovascular Risk Equation in a Large Contemporary, Multiethnic Real-World Population

PubMed Central

Rana, Jamal S.; Tabada, Grace H.; Solomon, Matthew D.; Lo, Joan C.; Jaffe, Marc G.; Sung, Sue Hee; Ballantyne, Christie M.; Go, Alan S.

2016-01-01

Background The accuracy of the 2013 American College of Cardiology/American Heart Association (ACC/AHA) risk equation for atherosclerotic cardiovascular disease (ASCVD) events in contemporary and ethnically diverse populations is not well understood. Objectives We sought to evaluate the accuracy of the 2013 ACC/AHA risk equation within a large, multiethnic population in clinical care. Methods The target population for consideration of cholesterol-lowering therapy in a large, integrated health care delivery system population was identified in 2008 and followed through 2013. The main analyses excluded those with known ASCVD, diabetes mellitus, low-density lipoprotein cholesterol levels <70 or ≥190 mg/dl, prior statin use, or incomplete 5-year follow-up. Patient characteristics were obtained from electronic medical records and ASCVD events were ascertained using validated algorithms for hospitalization databases and death certificates. We compared predicted versus observed 5-year ASCVD risk, overall and by sex and race/ethnicity. We additionally examined predicted versus observed risk in patients with diabetes mellitus. Results Among 307,591 eligible adults without diabetes between 40 and 75 years of age, 22,283 were black, 52,917 Asian/Pacific Islander, and 18,745 Hispanic. We observed 2,061 ASCVD events during 1,515,142 person-years. In each 5-year predicted ASCVD risk category, observed 5-year ASCVD risk was substantially lower: 0.20% for predicted risk <2.50%; 0.65% for predicted risk 2.50 to 3.74%; 0.90% for predicted risk 3.75 to 4.99%; and 1.85% for predicted risk ≥5.00%, with C: 0.74. Similar ASCVD risk overestimation and poor calibration with moderate discrimination (C: 0.68 to 0.74) was observed in sex, racial/ethnic, and socioeconomic status subgroups, and in sensitivity analyses among patients receiving statins for primary prevention. Calibration among 4,242 eligible adults with diabetes was improved, but discrimination was worse (C: 0.64). Conclusions In a large, contemporary “real-world” population, the ACC/AHA Pooled Cohort risk equation substantially overestimated actual 5-year risk in adults without diabetes, overall and across sociodemographic subgroups. PMID:27151343

Predicting Sexual Risk Behaviors among Adolescent and Young Women Using a Prospective Diary Method

PubMed Central

Morrison-Beedy, Dianne; Carey, Michael P.; Feng, Changyong; Tu, Xin M.

2008-01-01

We describe the sexual risk behaviors, psychological distress, and substance use of 102 late adolescent girls and identify predictors of protected and unprotected vaginal sex. Participants completed questionnaires assessing hypothesized predictors and then daily behavioral diaries for 12 weeks. Protected intercourse was predicted by baseline sexual behavior, greater knowledge, positive condom attitudes, lower perceived condom-use difficulty, greater condom-use intentions, more drinking days, less binge drinking, less Ecstasy use, and lower psychological distress. Unprotected intercourse was predicted by baseline sexual behavior, binge drinking, Ecstasy and opiate use, fewer drinking days, and fewer daily drinks. These findings suggest that psychological distress, substance use, and sexual risk behavior are interconnected and should be considered collectively in interventions for adolescent females. PMID:18231976

Predictive Modeling of Risk Factors and Complications of Cataract Surgery

PubMed Central

Gaskin, Gregory L; Pershing, Suzann; Cole, Tyler S; Shah, Nigam H

2016-01-01

Purpose To quantify the relationship between aggregated preoperative risk factors and cataract surgery complications, as well as to build a model predicting outcomes on an individual-level—given a constellation of demographic, baseline, preoperative, and intraoperative patient characteristics. Setting Stanford Hospital and Clinics between 1994 and 2013. Design Retrospective cohort study Methods Patients age 40 or older who received cataract surgery between 1994 and 2013. Risk factors, complications, and demographic information were extracted from the Electronic Health Record (EHR), based on International Classification of Diseases, 9th edition (ICD-9) codes, Current Procedural Terminology (CPT) codes, drug prescription information, and text data mining using natural language processing. We used a bootstrapped least absolute shrinkage and selection operator (LASSO) model to identify highly-predictive variables. We built random forest classifiers for each complication to create predictive models. Results Our data corroborated existing literature on postoperative complications—including the association of intraoperative complications, complex cataract surgery, black race, and/or prior eye surgery with an increased risk of any postoperative complications. We also found a number of other, less well-described risk factors, including systemic diabetes mellitus, young age (<60 years old), and hyperopia as risk factors for complex cataract surgery and intra- and post-operative complications. Our predictive models based on aggregated outperformed existing published models. Conclusions The constellations of risk factors and complications described here can guide new avenues of research and provide specific, personalized risk assessment for a patient considering cataract surgery. The predictive capacity of our models can enable risk stratification of patients, which has utility as a teaching tool as well as informing quality/value-based reimbursements. PMID:26692059

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

PubMed Central

McCarthy, John F.; Katz, Ira R.; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M.; Nielson, Christopher; Schoenbaum, Michael

2015-01-01

Objectives. 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. Methods. 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. Results. 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%. Conclusions. 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. PMID:26066914

School Mobility during Childhood Predicts Psychotic Symptoms in Late Adolescence

ERIC Educational Resources Information Center

Winsper, Catherine; Wolke, Dieter; Bryson, Alex; Thompson, Andrew; Singh, Swaran P.

2016-01-01

Background: Recently, school mobility was identified as a risk factor for psychotic symptoms in early adolescence. The extent to which this risk continues into late adolescence and the trajectories via which this risk manifests remain unexplored. Methods: Psychotic symptoms in 4,720 adolescents aged 18 were ascertained by trained psychologists…

Estimating the impact of grouping misclassification on risk prediction when using the relative potency factors method to assess mixtures risk -Presentation

EPA Science Inventory

Environmental health risk assessments of chemical mixtures that rely on component approaches often begin by grouping the chemicals of concern according to toxicological similarity. Approaches that assume dose addition typically are used for groups of similarly-acting chemicals an...

Developmental Dyslexia: Predicting Individual Risk

ERIC Educational Resources Information Center

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

2015-01-01

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

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product.

PubMed

Poussin, Carine; Belcastro, Vincenzo; Martin, Florian; Boué, Stéphanie; Peitsch, Manuel C; Hoeng, Julia

2017-04-17

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.

Cost-effective HLA typing with tagging SNPs predicts celiac disease risk haplotypes in the Finnish, Hungarian, and Italian populations.

PubMed

Koskinen, Lotta; Romanos, Jihane; Kaukinen, Katri; Mustalahti, Kirsi; Korponay-Szabo, Ilma; Barisani, Donatella; Bardella, Maria Teresa; Ziberna, Fabiana; Vatta, Serena; Széles, György; Pocsai, Zsuzsa; Karell, Kati; Haimila, Katri; Adány, Róza; Not, Tarcisio; Ventura, Alessandro; Mäki, Markku; Partanen, Jukka; Wijmenga, Cisca; Saavalainen, Päivi

2009-04-01

Human leukocyte antigen (HLA) genes, located on chromosome 6p21.3, have a crucial role in susceptibility to various autoimmune and inflammatory diseases, such as celiac disease and type 1 diabetes. Certain HLA heterodimers, namely DQ2 (encoded by the DQA1*05 and DQB1*02 alleles) and DQ8 (DQA1*03 and DQB1*0302), are necessary for the development of celiac disease. Traditional genotyping of HLA genes is laborious, time-consuming, and expensive. A novel HLA-genotyping method, using six HLA-tagging single-nucleotide polymorphisms (SNPs) and suitable for high-throughput approaches, was described recently. Our aim was to validate this method in the Finnish, Hungarian, and Italian populations. The six previously reported HLA-tagging SNPs were genotyped in patients with celiac disease and in healthy individuals from Finland, Hungary, and two distinct regions of Italy. The potential of this method was evaluated in analyzing how well the tag SNP results correlate with the HLA genotypes previously determined using traditional HLA-typing methods. Using the tagging SNP method, it is possible to determine the celiac disease risk haplotypes accurately in Finnish, Hungarian, and Italian populations, with specificity and sensitivity ranging from 95% to 100%. In addition, it predicts homozygosity and heterozygosity for a risk haplotype, allowing studies on genotypic risk effects. The method is transferable between populations and therefore suited for large-scale research studies and screening of celiac disease among high-risk individuals or at the population level.

Guidelines for reporting and using prediction tools for genetic variation analysis.

PubMed

Vihinen, Mauno

2013-02-01

Computational prediction methods are widely used for the analysis of human genome sequence variants and their effects on gene/protein function, splice site aberration, pathogenicity, and disease risk. New methods are frequently developed. We believe that guidelines are essential for those writing articles about new prediction methods, as well as for those applying these tools in their research, so that the necessary details are reported. This will enable readers to gain the full picture of technical information, performance, and interpretation of results, and to facilitate comparisons of related methods. Here, we provide instructions on how to describe new methods, report datasets, and assess the performance of predictive tools. We also discuss what details of predictor implementation are essential for authors to understand. Similarly, these guidelines for the use of predictors provide instructions on what needs to be delineated in the text, as well as how researchers can avoid unwarranted conclusions. They are applicable to most prediction methods currently utilized. By applying these guidelines, authors will help reviewers, editors, and readers to more fully comprehend prediction methods and their use. © 2012 Wiley Periodicals, Inc.

Systemic Approach for Health Risk Assessment of Ambient Air Concentrations of Benzene in Petrochemical Environments: Integration of Fuzzy Logic, Artificial Neural Network, and IRIS Toxicity Method

PubMed Central

NOVIN, Vahid; GIVEHCHI, Saeed; HOVEIDI, Hassan

2016-01-01

Background: Reliable methods are crucial to cope with uncertainties in the risk analysis process. The aim of this study is to develop an integrated approach to assessing risks of benzene in the petrochemical plant that produces benzene. We offer an integrated system to contribute imprecise variables into the health risk calculation. Methods: The project was conducted in Asaluyeh, southern Iran during the years from 2013 to 2014. Integrated method includes fuzzy logic and artificial neural networks. Each technique had specific computational properties. Fuzzy logic was used for estimation of absorption rate. Artificial neural networks can decrease the noise of the data so applied for prediction of benzene concentration. First, the actual exposure was calculated then it combined with Integrated Risk Information System (IRIS) toxicity factors to assess real health risks. Results: High correlation between the measured and predicted benzene concentration was achieved (R2= 0.941). As for variable distribution, the best estimation of risk in a population implied 33% of workers exposed less than 1×10−5 and 67% inserted between 1.0×10−5 to 9.8×10−5 risk levels. The average estimated risk of exposure to benzene for entire work zones is equal to 2.4×10−5, ranging from 1.5×10−6 to 6.9×10−5. Conclusion: The integrated model is highly flexible as well as the rules possibly will be changed according to the necessities of the user in a different circumstance. The measured exposures can be duplicated well through proposed model and realistic risk assessment data will be produced. PMID:27957464

Percent Mammographic Density and Dense Area as Risk Factors for Breast Cancer.

PubMed

Rauh, C; Hack, C C; Häberle, L; Hein, A; Engel, A; Schrauder, M G; Fasching, P A; Jud, S M; Ekici, A B; Loehberg, C R; Meier-Meitinger, M; Ozan, S; Schulz-Wendtland, R; Uder, M; Hartmann, A; Wachter, D L; Beckmann, M W; Heusinger, K

2012-08-01

Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. Methods: We conducted a case control study with hospital-based patients with a diagnosis of invasive breast cancer and healthy women as controls. A total of 561 patients and 376 controls with available mammographic density were included into this study. We describe the differences concerning the common risk factors BMI, parital status, use of hormone replacement therapy (HRT) and menopause between cases and controls and estimate the odds ratios for PMD and DA, adjusted for the mentioned risk factors. Furthermore we compare the prediction models with each other to find out whether the addition of DA improves the model. Results: Mammographic density and DA were highly correlated with each other. Both variables were as well correlated to the commonly known risk factors with an expected direction and strength, however PMD (ρ = -0.56) was stronger correlated to BMI than DA (ρ = -0.11). The group of women within the highest quartil of PMD had an OR of 2.12 (95 % CI: 1.25-3.62). This could not be seen for the fourth quartile concerning DA. However the assessment of breast cancer risk could be improved by including DA in a prediction model in addition to common risk factors and PMD. Conclusions: The inclusion of the parameter DA into a prediction model for breast cancer in addition to established risk factors and PMD could improve the breast cancer risk assessment. As DA is measured together with PMD in the process of computer-assisted assessment of PMD it might be considered to include it as one additional breast cancer risk factor that is obtained from breast imaging.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong

PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy ofmore » this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.« less

Incorporating truncating variants in PALB2, CHEK2, and ATM into the BOADICEA breast cancer risk model.

PubMed

Lee, Andrew J; Cunningham, Alex P; Tischkowitz, Marc; Simard, Jacques; Pharoah, Paul D; Easton, Douglas F; Antoniou, Antonis C

2016-12-01

The proliferation of gene panel testing precipitates the need for a breast cancer (BC) risk model that incorporates the effects of mutations in several genes and family history (FH). We extended the BOADICEA model to incorporate the effects of truncating variants in PALB2, CHEK2, and ATM. The BC incidence was modeled via the explicit effects of truncating variants in BRCA1/2, PALB2, CHEK2, and ATM and other unobserved genetic effects using segregation analysis methods. The predicted average BC risk by age 80 for an ATM mutation carrier is 28%, 30% for CHEK2, 50% for PALB2, and 74% for BRCA1 and BRCA2. However, the BC risks are predicted to increase with FH burden. In families with mutations, predicted risks for mutation-negative members depend on both FH and the specific mutation. The reduction in BC risk after negative predictive testing is greatest when a BRCA1 mutation is identified in the family, but for women whose relatives carry a CHEK2 or ATM mutation, the risks decrease slightly. The model may be a valuable tool for counseling women who have undergone gene panel testing for providing consistent risks and harmonizing their clinical management. A Web application can be used to obtain BC risks in clinical practice (http://ccge.medschl.cam.ac.uk/boadicea/).Genet Med 18 12, 1190-1198.

Direct comparison of risk-adjusted and non-risk-adjusted CUSUM analyses of coronary artery bypass surgery outcomes.

PubMed

Novick, Richard J; Fox, Stephanie A; Stitt, Larry W; Forbes, Thomas L; Steiner, Stefan

2006-08-01

We previously applied non-risk-adjusted cumulative sum methods to analyze coronary bypass outcomes. The objective of this study was to assess the incremental advantage of risk-adjusted cumulative sum methods in this setting. Prospective data were collected in 793 consecutive patients who underwent coronary bypass grafting performed by a single surgeon during a period of 5 years. The composite occurrence of an "adverse outcome" included mortality or any of 10 major complications. An institutional logistic regression model for adverse outcome was developed by using 2608 contemporaneous patients undergoing coronary bypass. The predicted risk of adverse outcome in each of the surgeon's 793 patients was then calculated. A risk-adjusted cumulative sum curve was then generated after specifying control limits and odds ratio. This risk-adjusted curve was compared with the non-risk-adjusted cumulative sum curve, and the clinical significance of this difference was assessed. The surgeon's adverse outcome rate was 96 of 793 (12.1%) versus 270 of 1815 (14.9%) for all the other institution's surgeons combined (P = .06). The non-risk-adjusted curve reached below the lower control limit, signifying excellent outcomes between cases 164 and 313, 323 and 407, and 667 and 793, but transgressed the upper limit between cases 461 and 478. The risk-adjusted cumulative sum curve never transgressed the upper control limit, signifying that cases preceding and including 461 to 478 were at an increased predicted risk. Furthermore, if the risk-adjusted cumulative sum curve was reset to zero whenever a control limit was reached, it still signaled a decrease in adverse outcome at 166, 653, and 782 cases. Risk-adjusted cumulative sum techniques provide incremental advantages over non-risk-adjusted methods by not signaling a decrement in performance when preoperative patient risk is high.

Parturition prediction and timing of canine pregnancy

PubMed Central

Kim, YeunHee; Travis, Alexander J.; Meyers-Wallen, Vicki N.

2007-01-01

An accurate method of predicting the date of parturition in the bitch is clinically useful to minimize or prevent reproductive losses by timely intervention. Similarly, an accurate method of timing canine ovulation and gestation is critical for development of assisted reproductive technologies, e.g. estrous synchronization and embryo transfer. This review discusses present methods for accurately timing canine gestational age and outlines their use in clinical management of high-risk pregnancies and embryo transfer research. PMID:17904630

Utility of genetic and non-genetic risk factors in predicting coronary heart disease in Singaporean Chinese.

PubMed

Chang, Xuling; Salim, Agus; Dorajoo, Rajkumar; Han, Yi; Khor, Chiea-Chuen; van Dam, Rob M; Yuan, Jian-Min; Koh, Woon-Puay; Liu, Jianjun; Goh, Daniel Yt; Wang, Xu; Teo, Yik-Ying; Friedlander, Yechiel; Heng, Chew-Kiat

2017-01-01

Background Although numerous phenotype based equations for predicting risk of 'hard' coronary heart disease are available, data on the utility of genetic information for such risk prediction is lacking in Chinese populations. Design Case-control study nested within the Singapore Chinese Health Study. Methods A total of 1306 subjects comprising 836 men (267 incident cases and 569 controls) and 470 women (128 incident cases and 342 controls) were included. A Genetic Risk Score comprising 156 single nucleotide polymorphisms that have been robustly associated with coronary heart disease or its risk factors ( p < 5 × 10 -8 ) in at least two independent cohorts of genome-wide association studies was built. For each gender, three base models were used: recalibrated Adult Treatment Panel III (ATPIII) Model (M 1 ); ATP III model fitted using Singapore Chinese Health Study data (M 2 ) and M 3 : M 2 + C-reactive protein + creatinine. Results The Genetic Risk Score was significantly associated with incident 'hard' coronary heart disease ( p for men: 1.70 × 10 -10 -1.73 × 10 -9 ; p for women: 0.001). The inclusion of the Genetic Risk Score in the prediction models improved discrimination in both genders (c-statistics: 0.706-0.722 vs. 0.663-0.695 from base models for men; 0.788-0.790 vs. 0.765-0.773 for women). In addition, the inclusion of the Genetic Risk Score also improved risk classification with a net gain of cases being reclassified to higher risk categories (men: 12.4%-16.5%; women: 10.2% (M 3 )), while not significantly reducing the classification accuracy in controls. Conclusions The Genetic Risk Score is an independent predictor for incident 'hard' coronary heart disease in our ethnic Chinese population. Inclusion of genetic factors into coronary heart disease prediction models could significantly improve risk prediction performance.

Risk prediction models of breast cancer: a systematic review of model performances.

PubMed

Anothaisintawee, Thunyarat; Teerawattananon, Yot; Wiratkapun, Chollathip; Kasamesup, Vijj; Thakkinstian, Ammarin

2012-05-01

The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87-1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53-0.66) and in external validation (concordance statistics: 0.56-0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.

Comparing Mammography Abnormality Features and Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

PubMed Central

Burnside, Elizabeth S.; Liu, Jie; Wu, Yirong; Onitilo, Adedayo A.; McCarty, Catherine; Page, C. David; Peissig, Peggy; Trentham-Dietz, Amy; Kitchner, Terrie; Fan, Jun; Yuan, Ming

2015-01-01

Rationale and Objectives The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors. Materials and Methods Our IRB-approved, HIPAA-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature including: demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to BI-RADS. We developed predictive models using logistic regression to determine the predictive ability of: 1) demographic variables, 2) 10 selected genetic variants, or 3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross validation; used this risk estimate to construct ROC curves; and compared the AUC of each using the DeLong method. Results The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (p=0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; p<0.001) and the genetic model (AUC = .601; p<0.001). Conclusion BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy. PMID:26514439

Using multiscale texture and density features for near-term breast cancer risk analysis

PubMed Central

Sun, Wenqing; Tseng, Tzu-Liang (Bill); Qian, Wei; Zhang, Jianying; Saltzstein, Edward C.; Zheng, Bin; Lure, Fleming; Yu, Hui; Zhou, Shi

2015-01-01

Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors’ dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the “prior” screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. PMID:26127038

Quantified degree of eccentricity of aortic valve calcification predicts risk of paravalvular regurgitation and response to balloon post-dilation after self-expandable transcatheter aortic valve replacement.

PubMed

Park, Jun-Bean; Hwang, In-Chang; Lee, Whal; Han, Jung-Kyu; Kim, Chi-Hoon; Lee, Seung-Pyo; Yang, Han-Mo; Park, Eun-Ah; Kim, Hyung-Kwan; Chiam, Paul T L; Kim, Yong-Jin; Koo, Bon-Kwon; Sohn, Dae-Won; Ahn, Hyuk; Kang, Joon-Won; Park, Seung-Jung; Kim, Hyo-Soo

2018-05-15

Limited data exist regarding the impact of aortic valve calcification (AVC) eccentricity on the risk of paravalvular regurgitation (PVR) and response to balloon post-dilation (BPD) after transcatheter aortic valve replacement (TAVR). We investigated the prognostic value of AVC eccentricity in predicting the risk of PVR and response to BPD in patients undergoing TAVR. We analyzed 85 patients with severe aortic stenosis who underwent self-expandable TAVR (43 women; 77.2±7.1years). AVC was quantified as the total amount of calcification (total AVC load) and as the eccentricity of calcium (EoC) using calcium volume scoring with contrast computed tomography angiography (CTA). The EoC was defined as the maximum absolute difference in calcium volume scores between 2 adjacent sectors (bi-partition method) or between sectors based on leaflets (leaflet-based method). Total AVC load and bi-partition EoC, but not leaflet-based EoC, were significant predictors for the occurrence of ≥moderate PVR, and bi-partition EoC had a better predictive value than total AVC load (area under the curve [AUC]=0.863 versus 0.760, p for difference=0.006). In multivariate analysis, bi-partition EoC was an independent predictor for the risk of ≥moderate PVR regardless of perimeter oversizing index. The greater bi-partition EoC was the only significant parameter to predict poor response to BPD (AUC=0.775, p=0.004). Pre-procedural assessment of AVC eccentricity using CTA as "bi-partition EoC" provides useful predictive information on the risk of significant PVR and response to BPD in patients undergoing TAVR with self-expandable valves. Copyright © 2017 Elsevier B.V. All rights reserved.

Preexposure Prophylaxis and Predicted Condom Use Among High-Risk Men Who Have Sex With Men

PubMed Central

Golub, Sarit A.; Kowalczyk, William; Weinberger, Corina L.; Parsons, Jeffrey T.

2010-01-01

Objectives Preexposure prophylaxis (PREP) is an emerging HIV prevention strategy; however, many fear it may lead to neglect of traditional risk reduction practices through behavioral disinhibition or risk compensation. Methods Participants were 180 HIV-negative high-risk men who have sex with men recruited in New York City, who completed an Audio Computer Assisted Self Interview-administered survey between September 2007 and July 2009. Bivariate and multivariate logistic regression models were used to predict intention to use PREP and perceptions that PREP would decrease condom use. Results Almost 70% (n = 124) of participants reported that they would be likely to use PREP if it were at least 80% effective in preventing HIV. Of those who would use PREP, over 35% reported that they would be likely to decrease condom use while on PREP. In multivariate analyses, arousal/pleasure barriers to condom use significantly predicted likelihood of PREP use (odds ratio = 1.71, P < 0.05) and risk perception motivations for condom use significantly predicted decreased condom use on PREP (odds ratio = 2.48, P < 0.05). Discussion These data provide support for both behavioral disinhibition and risk compensation models and underscore the importance of developing behavioral interventions to accompany any wide-scale provision of PREP to high-risk populations. PMID:20512046

Association of Lipid Accumulation Product with Cardio-Metabolic Risk Factors in Postmenopausal Women.

PubMed

Namazi Shabestari, Alireza; Asadi, Mojgan; Jouyandeh, Zahra; Qorbani, Mostafa; Kelishadi, Roya

2016-06-01

The lipid accumulation product is a novel, safe and inexpensive index of central lipid over accumulation based on waist circumference and fasting concentration of circulating triglycerides. This study was designed to investigate the ability of lipid accumulation product to predict Cardio-metabolic risk factors in postmenopausal women. In this Cross-sectional study, 264 postmenopausal women by using convenience sampling method were selected from menopause clinic in Tehran. Cardio-metabolic risk factors were measured, and lipid accumulation product (waist-58×triglycerides [nmol/L]) was calculated. Optimal cut-off point of lipid accumulation product for predicting metabolic syndrome was estimated by ROC (Receiver-operating characteristic) curve analysis. Metabolic syndrome was diagnosed in 41.2% of subjects. Optimal cut-off point of lipid accumulation product for predicting metabolic syndrome was 47.63 (sensitivity:75%; specificity:77.9%). High lipid accumulation product increases risk of all Cardio-metabolic risk factors except overweight, high Total Cholesterol, high Low Density Lipoprotein Cholesterol and high Fasting Blood Sugar in postmenopausal women. Our findings show that lipid accumulation product is associated with metabolic syndrome and some Cardio-metabolic risk factors Also lipid accumulation product may have been a useful tool for predicting cardiovascular disease and metabolic syndrome risk in postmenopausal women.

An Empiric HIV Risk Scoring Tool to Predict HIV-1 Acquisition in African Women.

PubMed

Balkus, Jennifer E; Brown, Elizabeth; Palanee, Thesla; Nair, Gonasagrie; Gafoor, Zakir; Zhang, Jingyang; Richardson, Barbra A; Chirenje, Zvavahera M; Marrazzo, Jeanne M; Baeten, Jared M

2016-07-01

To develop and validate an HIV risk assessment tool to predict HIV acquisition among African women. Data were analyzed from 3 randomized trials of biomedical HIV prevention interventions among African women (VOICE, HPTN 035, and FEM-PrEP). We implemented standard methods for the development of clinical prediction rules to generate a risk-scoring tool to predict HIV acquisition over the course of 1 year. Performance of the score was assessed through internal and external validations. The final risk score resulting from multivariable modeling included age, married/living with a partner, partner provides financial or material support, partner has other partners, alcohol use, detection of a curable sexually transmitted infection, and herpes simplex virus 2 serostatus. Point values for each factor ranged from 0 to 2, with a maximum possible total score of 11. Scores ≥5 were associated with HIV incidence >5 per 100 person-years and identified 91% of incident HIV infections from among only 64% of women. The area under the curve (AUC) for predictive ability of the score was 0.71 (95% confidence interval [CI]: 0.68 to 0.74), indicating good predictive ability. Risk score performance was generally similar with internal cross-validation (AUC = 0.69; 95% CI: 0.66 to 0.73) and external validation in HPTN 035 (AUC = 0.70; 95% CI: 0.65 to 0.75) and FEM-PrEP (AUC = 0.58; 95% CI: 0.51 to 0.65). A discrete set of characteristics that can be easily assessed in clinical and research settings was predictive of HIV acquisition over 1 year. The use of a validated risk score could improve efficiency of recruitment into HIV prevention research and inform scale-up of HIV prevention strategies in women at highest risk.

Utility of different cardiovascular disease prediction models in rheumatoid arthritis

PubMed Central

Purcarea, A; Sovaila, S; Udrea, G; Rezus, E; Gheorghe, A; Tiu, C; Stoica, V

2014-01-01

Background. Rheumatoid arthritis comes with a 30% higher probability for cardiovascular disease than the general population. Current guidelines advocate for early and aggressive primary prevention and treatment of risk factors in high-risk populations but this excess risk is under-addressed in RA in real life. This is mainly due to difficulties met in the correct risk evaluation. This study aims to underline the differences in results of the main cardiovascular risk screening models in the real life rheumatoid arthritis population. Methods. In a cross-sectional study, patients addressed to a tertiary care center in Romania for an biannual follow-up of rheumatoid arthritis and the ones who were considered free of any cardiovascular disease were assessed for subclinical atherosclerosis. Clinical, biological and carotidal ultrasound evaluations were performed. A number of cardiovascular disease prediction scores were performed and differences between tests were noted in regard to subclinical atherosclerosis as defined by the existence of carotid intima media thickness over 0,9 mm or carotid plaque. Results. In a population of 29 Romanian rheumatoid arthritis patients free of cardiovascular disease, the performance of Framingham Risk Score, HeartSCORE, ARIC cardiovascular disease prediction score, Reynolds Risk Score, PROCAM risk score and Qrisk2 score were compared. All the scores under-diagnosed subclinical atherosclerosis. With an AUROC of 0,792, the SCORE model was the only one that could partially stratify patients in low, intermediate and high-risk categories. The use of the EULAR recommended modifier did not help to reclassify patients. Conclusion. The only score that showed a statistically significant prediction capacity for subclinical atherosclerosis in a Romanian rheumatoid arthritis population was SCORE. The additional calibration or the use of imaging techniques in CVD risk prediction for the intermediate risk category might be warranted. PMID:25713628

One vs. Two Breast Density Measures to Predict 5- and 10- Year Breast Cancer Risk

PubMed Central

Kerlikowske, Karla; Gard, Charlotte C.; Sprague, Brian L.; Tice, Jeffrey A.; Miglioretti, Diana L.

2015-01-01

Background One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined if two BI-RADS density measures improves the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared to one measure. Methods We included 722,654 women aged 35–74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000–2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. Results The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC=0.640 vs. 0.635). Of 18.6% of women (134,404/722,654) who decreased density categories, 15.4% (20,741/134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. Conclusion The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. Impact A two-density model should be considered for women whose density decreases when calculating breast cancer risk. PMID:25824444

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-08-03

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Prediction of the Possibility a Right-Turn Driving Behavior at Intersection Leads to an Accident by Detecting Deviation of the Situation from Usual when the Behavior is Observed

NASA Astrophysics Data System (ADS)

Hayashi, Toshinori; Yamada, Keiichi

Deviation of driving behavior from usual could be a sign of human error that increases the risk of traffic accidents. This paper proposes a novel method for predicting the possibility a driving behavior leads to an accident from the information on the driving behavior and the situation. In a previous work, a method of predicting the possibility by detecting the deviation of driving behavior from usual one in that situation has been proposed. In contrast, the method proposed in this paper predicts the possibility by detecting the deviation of the situation from usual one when the behavior is observed. An advantage of the proposed method is the number of the required models is independent of the variety of the situations. The method was applied to a problem of predicting accidents by right-turn driving behavior at an intersection, and the performance of the method was evaluated by experiments on a driving simulator.

A preliminary approach to quantifying the overall environmental risks posed by development projects during environmental impact assessment

PubMed Central

Chadès, Iadine

2017-01-01

Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection. PMID:28686651

A preliminary approach to quantifying the overall environmental risks posed by development projects during environmental impact assessment.

PubMed

Nicol, Sam; Chadès, Iadine

2017-01-01

Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection.

Inability to predict postpartum hemorrhage: insights from Egyptian intervention data

PubMed Central

2011-01-01

Background Knowledge on how well we can predict primary postpartum hemorrhage (PPH) can help policy makers and health providers design current delivery protocols and PPH case management. The purpose of this paper is to identify risk factors and determine predictive probabilities of those risk factors for primary PPH among women expecting singleton vaginal deliveries in Egypt. Methods From a prospective cohort study, 2510 pregnant women were recruited over a six-month period in Egypt in 2004. PPH was defined as blood loss ≥ 500 ml. Measures of blood loss were made every 20 minutes for the first 4 hours after delivery using a calibrated under the buttocks drape. Using all variables available in the patients' charts, we divided them in ante-partum and intra-partum factors. We employed logistic regression to analyze socio-demographic, medical and past obstetric history, and labor and delivery outcomes as potential PPH risk factors. Post-model predicted probabilities were estimated using the identified risk factors. Results We found a total of 93 cases of primary PPH. In multivariate models, ante-partum hemoglobin, history of previous PPH, labor augmentation and prolonged labor were significantly associated with PPH. Post model probability estimates showed that even among women with three or more risk factors, PPH could only be predicted in 10% of the cases. Conclusions The predictive probability of ante-partum and intra-partum risk factors for PPH is very low. Prevention of PPH to all women is highly recommended. PMID:22123123

Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24 827 people: systematic review and meta-analysis

PubMed Central

Singh, Jay P; Doll, Helen; Grann, Martin

2012-01-01

Objective To investigate the predictive validity of tools commonly used to assess the risk of violence, sexual, and criminal behaviour. Design Systematic review and tabular meta-analysis of replication studies following PRISMA guidelines. Data sources PsycINFO, Embase, Medline, and United States Criminal Justice Reference Service Abstracts. Review methods We included replication studies from 1 January 1995 to 1 January 2011 if they provided contingency data for the offending outcome that the tools were designed to predict. We calculated the diagnostic odds ratio, sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, the number needed to detain to prevent one offence, as well as a novel performance indicator—the number safely discharged. We investigated potential sources of heterogeneity using metaregression and subgroup analyses. Results Risk assessments were conducted on 73 samples comprising 24 847 participants from 13 countries, of whom 5879 (23.7%) offended over an average of 49.6 months. When used to predict violent offending, risk assessment tools produced low to moderate positive predictive values (median 41%, interquartile range 27-60%) and higher negative predictive values (91%, 81-95%), and a corresponding median number needed to detain of 2 (2-4) and number safely discharged of 10 (4-18). Instruments designed to predict violent offending performed better than those aimed at predicting sexual or general crime. Conclusions Although risk assessment tools are widely used in clinical and criminal justice settings, their predictive accuracy varies depending on how they are used. They seem to identify low risk individuals with high levels of accuracy, but their use as sole determinants of detention, sentencing, and release is not supported by the current evidence. Further research is needed to examine their contribution to treatment and management. PMID:22833604

Predicting the Onset of Anxiety Syndromes at 12 Months in Primary Care Attendees. The PredictA-Spain Study

PubMed Central

Moreno-Peral, Patricia; Luna, Juan de Dios; Marston, Louise; 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; Muñoz-Bravo, Carlos; Bellón, Juan Ángel

2014-01-01

Background There are no risk algorithms for the onset of anxiety syndromes at 12 months in primary care. We aimed to develop and validate internally a risk algorithm to predict the onset of anxiety syndromes at 12 months. Methods A prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multilevel logistic regression and inverse probability weighting to build the risk algorithm. Our main outcome was generalized anxiety, panic and other non-specific anxiety syndromes as measured by the Primary Care Evaluation of Mental Disorders, Patient Health Questionnaire (PRIME-MD-PHQ). We recruited 3,564 adult primary care attendees without anxiety syndromes from 174 family physicians and 32 health centers in 6 Spanish provinces. Results The cumulative 12-month incidence of anxiety syndromes was 12.2%. The predictA-Spain risk algorithm included the following predictors of anxiety syndromes: province; sex (female); younger age; taking medicines for anxiety, depression or stress; worse physical and mental quality of life (SF-12); dissatisfaction with paid and unpaid work; perception of financial strain; and the interactions sex*age, sex*perception of financial strain, and age*dissatisfaction with paid work. The C-index was 0.80 (95% confidence interval = 0.78–0.83) and the Hedges' g = 1.17 (95% confidence interval = 1.04–1.29). The Copas shrinkage factor was 0.98 and calibration plots showed an accurate goodness of fit. Conclusions The predictA-Spain risk algorithm is valid to predict anxiety syndromes at 12 months. Although external validation is required, the predictA-Spain is available for use as a predictive tool in the prevention of anxiety syndromes in primary care. PMID:25184313

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

PubMed Central

2011-01-01

Background Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. Methods In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. Results Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. Conclusions Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach. PMID:21711504

A fuzzy linguistic model for the prediction of carpal tunnel syndrome risks in an occupational environment.

PubMed

Bell, P M; Crumpton, L

1997-08-01

This research presents the development and evaluation of a fuzzy linguistic model designated to predict the risk of carpal tunnel syndrome (CTS) in an occupational setting. CTS has become one of the largest problems facing ergonomists and the medical community because it is developing in epidemic proportions within the occupational environment. In addition, practitioners are interested in identifying accurate methods for evaluating the risk of CTS in an occupational setting. It is hypothesized that many factors impact an individual's likelihood of developing CTS and the eventual development of CTS. This disparity in the occurrence of CTS for workers with similar backgrounds and work activities has confused researchers and has been a stumbling block in the development of a model for widespread use in evaluating the development of CTS. Thus this research is an attempt to develop a method that can be used to predict the likelihood of CTS risk in a variety of environments. The intent is that this model will be applied eventually in an occupational setting, thus model development was focused on a method that provided a usable interface and the desired system inputs can also be obtained without the benefit of a medical practitioner. The methodology involves knowledge acquisition to identify and categorize a holistic set of risk factors that include task-related, personal, and organizational categories. The determination of relative factor importance was accomplished using analytic hierarchy processing (AHP) analysis. Finally a mathematical representation of the CTS risk was accomplished by utilizing fuzzy set theory in order to quantify linguistic input parameters. An evaluation of the model including determination of sensitivity and specificity is conducted and the results of the model indicate that the results are fairly accurate and this method has the potential for widespread use. A significant aspect of this research is the comparison of this technique to other methods for assessing presence of CTS. The results of this evaluation technique are compared with more traditional methods for assessing the presence of CTS.

GRECOS Project (Genotyping Recurrence Risk of Stroke): The Use of Genetics to Predict the Vascular Recurrence After Stroke.

PubMed

Fernández-Cadenas, Israel; Mendióroz, Maite; Giralt, Dolors; Nafria, Cristina; Garcia, Elena; Carrera, Caty; Gallego-Fabrega, Cristina; Domingues-Montanari, Sophie; Delgado, Pilar; Ribó, Marc; Castellanos, Mar; Martínez, Sergi; Freijo, Marimar; Jiménez-Conde, Jordi; Rubiera, Marta; Alvarez-Sabín, José; Molina, Carlos A; Font, Maria Angels; Grau Olivares, Marta; Palomeras, Ernest; Perez de la Ossa, Natalia; Martinez-Zabaleta, Maite; Masjuan, Jaime; Moniche, Francisco; Canovas, David; Piñana, Carlos; Purroy, Francisco; Cocho, Dolores; Navas, Inma; Tejero, Carlos; Aymerich, Nuria; Cullell, Natalia; Muiño, Elena; Serena, Joaquín; Rubio, Francisco; Davalos, Antoni; Roquer, Jaume; Arenillas, Juan Francisco; Martí-Fábregas, Joan; Keene, Keith; Chen, Wei-Min; Worrall, Bradford; Sale, Michele; Arboix, Adrià; Krupinski, Jerzy; Montaner, Joan

2017-05-01

Vascular recurrence occurs in 11% of patients during the first year after ischemic stroke (IS) or transient ischemic attack. Clinical scores do not predict the whole vascular recurrence risk; therefore, we aimed to find genetic variants associated with recurrence that might improve the clinical predictive models in IS. We analyzed 256 polymorphisms from 115 candidate genes in 3 patient cohorts comprising 4482 IS or transient ischemic attack patients. The discovery cohort was prospectively recruited and included 1494 patients, 6.2% of them developed a new IS during the first year of follow-up. Replication analysis was performed in 2988 patients using SNPlex or HumanOmni1-Quad technology. We generated a predictive model using Cox regression (GRECOS score [Genotyping Reurrence Risk of Stroke]) and generated risk groups using a classification tree method. The analyses revealed that rs1800801 in the MGP gene (hazard ratio, 1.33; P =9×10 - 03 ), a gene related to artery calcification, was associated with new IS during the first year of follow-up. This polymorphism was replicated in a Spanish cohort (n=1.305); however, it was not significantly associated in a North American cohort (n=1.683). The GRECOS score predicted new IS ( P =3.2×10 - 09 ) and could classify patients, from low risk of stroke recurrence (1.9%) to high risk (12.6%). Moreover, the addition of genetic risk factors to the GRECOS score improves the prediction compared with previous Stroke Prognosis Instrument-II score ( P =0.03). The use of genetics could be useful to estimate vascular recurrence risk after IS. Genetic variability in the MGP gene was associated with vascular recurrence in the Spanish population. © 2017 American Heart Association, Inc.

Making predictions of mangrove deforestation: a comparison of two methods in Kenya.

PubMed

Rideout, Alasdair J R; Joshi, Neha P; Viergever, Karin M; Huxham, Mark; Briers, Robert A

2013-11-01

Deforestation of mangroves is of global concern given their importance for carbon storage, biogeochemical cycling and the provision of other ecosystem services, but the links between rates of loss and potential drivers or risk factors are rarely evaluated. Here, we identified key drivers of mangrove loss in Kenya and compared two different approaches to predicting risk. Risk factors tested included various possible predictors of anthropogenic deforestation, related to population, suitability for land use change and accessibility. Two approaches were taken to modelling risk; a quantitative statistical approach and a qualitative categorical ranking approach. A quantitative model linking rates of loss to risk factors was constructed based on generalized least squares regression and using mangrove loss data from 1992 to 2000. Population density, soil type and proximity to roads were the most important predictors. In order to validate this model it was used to generate a map of losses of Kenyan mangroves predicted to have occurred between 2000 and 2010. The qualitative categorical model was constructed using data from the same selection of variables, with the coincidence of different risk factors in particular mangrove areas used in an additive manner to create a relative risk index which was then mapped. Quantitative predictions of loss were significantly correlated with the actual loss of mangroves between 2000 and 2010 and the categorical risk index values were also highly correlated with the quantitative predictions. Hence, in this case the relatively simple categorical modelling approach was of similar predictive value to the more complex quantitative model of mangrove deforestation. The advantages and disadvantages of each approach are discussed, and the implications for mangroves are outlined. © 2013 Blackwell Publishing Ltd.

Predicting treatment failure, death and drug resistance using a computed risk score among newly diagnosed TB patients in Tamaulipas, Mexico.

PubMed

Abdelbary, B E; Garcia-Viveros, M; Ramirez-Oropesa, H; Rahbar, M H; Restrepo, B I

2017-10-01

The purpose of this study was to develop a method for identifying newly diagnosed tuberculosis (TB) patients at risk for TB adverse events in Tamaulipas, Mexico. Surveillance data between 2006 and 2013 (8431 subjects) was used to develop risk scores based on predictive modelling. The final models revealed that TB patients failing their treatment regimen were more likely to have at most a primary school education, multi-drug resistance (MDR)-TB, and few to moderate bacilli on acid-fast bacilli smear. TB patients who died were more likely to be older males with MDR-TB, HIV, malnutrition, and reporting excessive alcohol use. Modified risk scores were developed with strong predictability for treatment failure and death (c-statistic 0·65 and 0·70, respectively), and moderate predictability for drug resistance (c-statistic 0·57). Among TB patients with diabetes, risk scores showed moderate predictability for death (c-statistic 0·68). Our findings suggest that in the clinical setting, the use of our risk scores for TB treatment failure or death will help identify these individuals for tailored management to prevent these adverse events. In contrast, the available variables in the TB surveillance dataset are not robust predictors of drug resistance, indicating the need for prompt testing at time of diagnosis.

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

PubMed Central

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

2017-01-01

Aim 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). Methods 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). Results 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). Conclusions 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. PMID:29308832

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

PubMed Central

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

2012-01-01

Purpose The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Patients and methods Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Results Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. Conclusion It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones. PMID:23226004

Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

PubMed Central

Pennells, Lisa; Kaptoge, Stephen; White, Ian R.; Thompson, Simon G.; Wood, Angela M.; Tipping, Robert W.; Folsom, Aaron R.; Couper, David J.; Ballantyne, Christie M.; Coresh, Josef; Goya Wannamethee, S.; Morris, Richard W.; Kiechl, Stefan; Willeit, Johann; Willeit, Peter; Schett, Georg; Ebrahim, Shah; Lawlor, Debbie A.; Yarnell, John W.; Gallacher, John; Cushman, Mary; Psaty, Bruce M.; Tracy, Russ; Tybjærg-Hansen, Anne; Price, Jackie F.; Lee, Amanda J.; McLachlan, Stela; Khaw, Kay-Tee; Wareham, Nicholas J.; Brenner, Hermann; Schöttker, Ben; Müller, Heiko; Jansson, Jan-Håkan; Wennberg, Patrik; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Vartiainen, Erkki; Woodward, Mark; D'Agostino, Ralph B.; Bladbjerg, Else-Marie; Jørgensen, Torben; Kiyohara, Yutaka; Arima, Hisatomi; Doi, Yasufumi; Ninomiya, Toshiharu; Dekker, Jacqueline M.; Nijpels, Giel; Stehouwer, Coen D. A.; Kauhanen, Jussi; Salonen, Jukka T.; Meade, Tom W.; Cooper, Jackie A.; Cushman, Mary; Folsom, Aaron R.; Psaty, Bruce M.; Shea, Steven; Döring, Angela; Kuller, Lewis H.; Grandits, Greg; Gillum, Richard F.; Mussolino, Michael; Rimm, Eric B.; Hankinson, Sue E.; Manson, JoAnn E.; Pai, Jennifer K.; Kirkland, Susan; Shaffer, Jonathan A.; Shimbo, Daichi; Bakker, Stephan J. L.; Gansevoort, Ron T.; Hillege, Hans L.; Amouyel, Philippe; Arveiler, Dominique; Evans, Alun; Ferrières, Jean; Sattar, Naveed; Westendorp, Rudi G.; Buckley, Brendan M.; Cantin, Bernard; Lamarche, Benoît; Barrett-Connor, Elizabeth; Wingard, Deborah L.; Bettencourt, Richele; Gudnason, Vilmundur; Aspelund, Thor; Sigurdsson, Gunnar; Thorsson, Bolli; Kavousi, Maryam; Witteman, Jacqueline C.; Hofman, Albert; Franco, Oscar H.; Howard, Barbara V.; Zhang, Ying; Best, Lyle; Umans, Jason G.; Onat, Altan; Sundström, Johan; Michael Gaziano, J.; Stampfer, Meir; Ridker, Paul M.; Michael Gaziano, J.; Ridker, Paul M.; Marmot, Michael; Clarke, Robert; Collins, Rory; Fletcher, Astrid; Brunner, Eric; Shipley, Martin; Kivimäki, Mika; Ridker, Paul M.; Buring, Julie; Cook, Nancy; Ford, Ian; Shepherd, James; Cobbe, Stuart M.; Robertson, Michele; Walker, Matthew; Watson, Sarah; Alexander, Myriam; Butterworth, Adam S.; Angelantonio, Emanuele Di; Gao, Pei; Haycock, Philip; Kaptoge, Stephen; Pennells, Lisa; Thompson, Simon G.; Walker, Matthew; Watson, Sarah; White, Ian R.; Wood, Angela M.; Wormser, David; Danesh, John

2014-01-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. PMID:24366051

Assessing risk prediction models using individual participant data from multiple studies.

PubMed

Pennells, Lisa; Kaptoge, Stephen; White, Ian R; Thompson, Simon G; Wood, Angela M

2014-03-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

External validation of Vascular Study Group of New England risk predictive model of mortality after elective abdominal aorta aneurysm repair in the Vascular Quality Initiative and comparison against established models.

PubMed

Eslami, Mohammad H; Rybin, Denis V; Doros, Gheorghe; Siracuse, Jeffrey J; Farber, Alik

2018-01-01

The purpose of this study is to externally validate a recently reported Vascular Study Group of New England (VSGNE) risk predictive model of postoperative mortality after elective abdominal aortic aneurysm (AAA) repair and to compare its predictive ability across different patients' risk categories and against the established risk predictive models using the Vascular Quality Initiative (VQI) AAA sample. The VQI AAA database (2010-2015) was queried for patients who underwent elective AAA repair. The VSGNE cases were excluded from the VQI sample. The external validation of a recently published VSGNE AAA risk predictive model, which includes only preoperative variables (age, gender, history of coronary artery disease, chronic obstructive pulmonary disease, cerebrovascular disease, creatinine levels, and aneurysm size) and planned type of repair, was performed using the VQI elective AAA repair sample. The predictive value of the model was assessed via the C-statistic. Hosmer-Lemeshow method was used to assess calibration and goodness of fit. This model was then compared with the Medicare, Vascular Governance Northwest model, and Glasgow Aneurysm Score for predicting mortality in VQI sample. The Vuong test was performed to compare the model fit between the models. Model discrimination was assessed in different risk group VQI quintiles. Data from 4431 cases from the VSGNE sample with the overall mortality rate of 1.4% was used to develop the model. The internally validated VSGNE model showed a very high discriminating ability in predicting mortality (C = 0.822) and good model fit (Hosmer-Lemeshow P = .309) among the VSGNE elective AAA repair sample. External validation on 16,989 VQI cases with an overall 0.9% mortality rate showed very robust predictive ability of mortality (C = 0.802). Vuong tests yielded a significant fit difference favoring the VSGNE over then Medicare model (C = 0.780), Vascular Governance Northwest (0.774), and Glasgow Aneurysm Score (0.639). Across the 5 risk quintiles, the VSGNE model predicted observed mortality significantly with great accuracy. This simple VSGNE AAA risk predictive model showed very high discriminative ability in predicting mortality after elective AAA repair among a large external independent sample of AAA cases performed by a diverse array of physicians nationwide. The risk score based on this simple VSGNE model can reliably stratify patients according to their risk of mortality after elective AAA repair better than other established models. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Longitudinal study of mammographic density measures that predict breast cancer risk

PubMed Central

Krishnan, Kavitha; Baglietto, Laura; Stone, Jennifer; Simpson, Julie A; Severi, Gianluca; Evans, Christopher F; MacInnis, Robert J; Giles, Graham G; Apicella, Carmel; Hopper, John L

2016-01-01

Background After adjusting for age and body mass index (BMI), mammographic measures - dense area (DA), percent dense area (PDA) and non-dense area (NDA) - are associated with breast cancer risk. Our aim was to use longitudinal data to estimate the extent to which these risk-predicting measures track over time. Methods We collected 4,320 mammograms (age range, 24-83 years) from 970 women in the Melbourne Collaborative Cohort Study and the Australian Breast Cancer Family Registry. Women had on average 4.5 mammograms (range, 1-14). DA, PDA and NDA were measured using the Cumulus software and normalised using the Box-Cox method. Correlations in the normalised risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modelling of Gompertz curves. Results Mean normalised DA and PDA were constant with age to the early 40s, decreased over the next two decades, and were almost constant from the mid 60s onwards. Mean normalised NDA increased non-linearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalised DA were 0.94, 0.93, 0.91, 0.91 and 0.91 for mammograms taken 2, 4, 6, 8 and 10 years apart, respectively. Similar correlations were estimated for the age and BMI adjusted normalized PDA and NDA. Conclusion The mammographic measures that predict breast cancer risk are highly correlated over time. Impact This has implications for etiologic research and clinical management whereby women at increased risk could be identified at a young age (e.g. early 40s or even younger) and recommended appropriate screening and prevention strategies. PMID:28062399

Optimal trading from minimizing the period of bankruptcy risk

NASA Astrophysics Data System (ADS)

Liehr, S.; Pawelzik, K.

2001-04-01

Assuming that financial markets behave similar to random walk processes we derive a trading strategy with variable investment which is based on the equivalence of the period of bankruptcy risk and the risk to profit ratio. We define a state dependent predictability measure which can be attributed to the deterministic and stochastic components of the price dynamics. The influence of predictability variations and especially of short term inefficiency structures on the optimal amount of investment is analyzed in the given context and a method for adaptation of a trading system to the proposed objective function is presented. Finally we show the performance of our trading strategy on the DAX and S&P 500 as examples for real world data using different types of prediction models in comparison.

Risk Factors for Internet Gaming Disorder: Psychological Factors and Internet Gaming Characteristics.

PubMed

Rho, Mi Jung; Lee, Hyeseon; Lee, Taek-Ho; Cho, Hyun; Jung, Dong Jin; Kim, Dai-Jin; Choi, In Young

2017-12-27

Background : Understanding the risk factors associated with Internet gaming disorder (IGD) is important to predict and diagnose the condition. The purpose of this study is to identify risk factors that predict IGD based on psychological factors and Internet gaming characteristics; Methods : Online surveys were conducted between 26 November and 26 December 2014. There were 3568 Korean Internet game users among a total of 5003 respondents. We identified 481 IGD gamers and 3087 normal Internet gamers, based on Diagnostic and Statistical Manual for Mental Disorders (DSM-5) criteria. Logistic regression analysis was applied to identify significant risk factors for IGD; Results : The following eight risk factors were found to be significantly associated with IGD: functional and dysfunctional impulsivity (odds ratio: 1.138), belief self-control (1.034), anxiety (1.086), pursuit of desired appetitive goals (1.105), money spent on gaming (1.005), weekday game time (1.081), offline community meeting attendance (2.060), and game community membership (1.393; p < 0.05 for all eight risk factors); Conclusions : These risk factors allow for the prediction and diagnosis of IGD. In the future, these risk factors could also be used to inform clinical services for IGD diagnosis and treatment.

Risk Factors for Internet Gaming Disorder: Psychological Factors and Internet Gaming Characteristics

PubMed Central

Lee, Hyeseon; Lee, Taek-Ho; Cho, Hyun; Kim, Dai-Jin; Choi, In Young

2017-01-01

Background: Understanding the risk factors associated with Internet gaming disorder (IGD) is important to predict and diagnose the condition. The purpose of this study is to identify risk factors that predict IGD based on psychological factors and Internet gaming characteristics; Methods: Online surveys were conducted between 26 November and 26 December 2014. There were 3568 Korean Internet game users among a total of 5003 respondents. We identified 481 IGD gamers and 3087 normal Internet gamers, based on Diagnostic and Statistical Manual for Mental Disorders (DSM-5) criteria. Logistic regression analysis was applied to identify significant risk factors for IGD; Results: The following eight risk factors were found to be significantly associated with IGD: functional and dysfunctional impulsivity (odds ratio: 1.138), belief self-control (1.034), anxiety (1.086), pursuit of desired appetitive goals (1.105), money spent on gaming (1.005), weekday game time (1.081), offline community meeting attendance (2.060), and game community membership (1.393; p < 0.05 for all eight risk factors); Conclusions: These risk factors allow for the prediction and diagnosis of IGD. In the future, these risk factors could also be used to inform clinical services for IGD diagnosis and treatment. PMID:29280953

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

PubMed

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.

Effectiveness of repeated examination to diagnose enterobiasis in nursery school groups.

PubMed

Remm, Mare; Remm, Kalle

2009-09-01

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions.

Effectiveness of Repeated Examination to Diagnose Enterobiasis in Nursery School Groups

PubMed Central

Remm, Kalle

2009-01-01

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions. PMID:19724696

Frailty Models for Familial Risk with Application to Breast Cancer.

PubMed

Gorfine, Malka; Hsu, Li; Parmigiani, Giovanni

2013-12-01

In evaluating familial risk for disease we have two main statistical tasks: assessing the probability of carrying an inherited genetic mutation conferring higher risk; and predicting the absolute risk of developing diseases over time, for those individuals whose mutation status is known. Despite substantial progress, much remains unknown about the role of genetic and environmental risk factors, about the sources of variation in risk among families that carry high-risk mutations, and about the sources of familial aggregation beyond major Mendelian effects. These sources of heterogeneity contribute substantial variation in risk across families. In this paper we present simple and efficient methods for accounting for this variation in familial risk assessment. Our methods are based on frailty models. We implemented them in the context of generalizing Mendelian models of cancer risk, and compared our approaches to others that do not consider heterogeneity across families. Our extensive simulation study demonstrates that when predicting the risk of developing a disease over time conditional on carrier status, accounting for heterogeneity results in a substantial improvement in the area under the curve of the receiver operating characteristic. On the other hand, the improvement for carriership probability estimation is more limited. We illustrate the utility of the proposed approach through the analysis of BRCA1 and BRCA2 mutation carriers in the Washington Ashkenazi Kin-Cohort Study of Breast Cancer.

Boomerang: A method for recursive reclassification.

PubMed

Devlin, Sean M; Ostrovnaya, Irina; Gönen, Mithat

2016-09-01

While there are many validated prognostic classifiers used in practice, often their accuracy is modest and heterogeneity in clinical outcomes exists in one or more risk subgroups. Newly available markers, such as genomic mutations, may be used to improve the accuracy of an existing classifier by reclassifying patients from a heterogenous group into a higher or lower risk category. The statistical tools typically applied to develop the initial classifiers are not easily adapted toward this reclassification goal. In this article, we develop a new method designed to refine an existing prognostic classifier by incorporating new markers. The two-stage algorithm called Boomerang first searches for modifications of the existing classifier that increase the overall predictive accuracy and then merges to a prespecified number of risk groups. Resampling techniques are proposed to assess the improvement in predictive accuracy when an independent validation data set is not available. The performance of the algorithm is assessed under various simulation scenarios where the marker frequency, degree of censoring, and total sample size are varied. The results suggest that the method selects few false positive markers and is able to improve the predictive accuracy of the classifier in many settings. Lastly, the method is illustrated on an acute myeloid leukemia data set where a new refined classifier incorporates four new mutations into the existing three category classifier and is validated on an independent data set. © 2016, The International Biometric Society.

Boomerang: A Method for Recursive Reclassification

PubMed Central

Devlin, Sean M.; Ostrovnaya, Irina; Gönen, Mithat

2016-01-01

Summary While there are many validated prognostic classifiers used in practice, often their accuracy is modest and heterogeneity in clinical outcomes exists in one or more risk subgroups. Newly available markers, such as genomic mutations, may be used to improve the accuracy of an existing classifier by reclassifying patients from a heterogenous group into a higher or lower risk category. The statistical tools typically applied to develop the initial classifiers are not easily adapted towards this reclassification goal. In this paper, we develop a new method designed to refine an existing prognostic classifier by incorporating new markers. The two-stage algorithm called Boomerang first searches for modifications of the existing classifier that increase the overall predictive accuracy and then merges to a pre-specified number of risk groups. Resampling techniques are proposed to assess the improvement in predictive accuracy when an independent validation data set is not available. The performance of the algorithm is assessed under various simulation scenarios where the marker frequency, degree of censoring, and total sample size are varied. The results suggest that the method selects few false positive markers and is able to improve the predictive accuracy of the classifier in many settings. Lastly, the method is illustrated on an acute myeloid leukemia dataset where a new refined classifier incorporates four new mutations into the existing three category classifier and is validated on an independent dataset. PMID:26754051

The Potential Utility of Urinary Biomarkers for Risk Prediction in Combat Casualties: A Prospective Observational Cohort Study

DTIC Science & Technology

2015-06-16

are associated with poor outcomes, including death and the need for renal replacement therapy. Methods : We conducted a prospective, observational study...penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 16 JUN 2015...2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE The Potential Utility of Urinary Biomarkers for Risk Prediction in Combat

Satellite voice broadcast. Volume 2: System study

NASA Technical Reports Server (NTRS)

Bachtell, E. E.; Bettadapur, S. S.; Coyner, J. V.; Farrell, C. E.

1985-01-01

The Technical Volume of the Satellite Broadcast System Study is presented. Designs are synthesized for direct sound broadcast satellite systems for HF-, VHF-, L-, and Ku-bands. Methods are developed and used to predict satellite weight, volume, and RF performance for the various concepts considered. Cost and schedule risk assessments are performed to predict time and cost required to implement selected concepts. Technology assessments and tradeoffs are made to identify critical enabling technologies that require development to bring technical risk to acceptable levels for full scale development.

Does the prediction of breast cancer improve using a combination of mammographic density measures compared to individual measures alone?

NASA Astrophysics Data System (ADS)

Wong Sik Hee, Joseph Ryan; Harkness, Elaine F.; Gadde, Soujanya; Lim, Yit Y.; Maxwell, Anthony J.; Evans, D. Gareth; Howell, Anthony; Astley, Susan M.

2017-03-01

High mammographic density is associated with an increased risk of breast cancer, however whether the association is stronger when there is agreement across measures is unclear. This study investigates whether a combination of density measures is a better predictor of breast cancer risk than individual methods alone. Women recruited to the Predicting Risk of Cancer At Screening (PROCAS) study and with mammographic density assessed using three different methods were included (n=33,304). Density was assessed visually using Visual Analogue Scales (VAS) and by two fully automated methods, Quantra and Volpara. Percentage breast density was divided into (high, medium and low) and combinations of measures were used to further categorise individuals (e.g. `all high'). A total of 667 breast cancers were identified and logistic regression was used to determine the relationship between breast density and breast cancer risk. In total, 44% of individuals were in the same tertile for all three measures, 8.6% were in non-adjacent (high and low) or mixed categories (high, medium and low). For individual methods the strongest association with breast cancer risk was for medium and high tertiles of VAS with odds ratios (OR) adjusted for age and BMI of 1.63 (95% CI 1.31-2.03) and 2.33 (1.87-2.90) respectively. For the combination of density methods the strongest association was for `all high' (OR 2.42, 1.77-3.31) followed by "two high" (OR 1.90, 1.35-3.31) and "two medium" (OR 1.88, 1.40-2.52). Combining density measures did not affect the magnitude of risk compared to using individual methods.

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

PubMed

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 produced complication predictions that underestimated complication occurrence: 3.4% in the low-risk group (observed 12.6%), 5.9% in the medium-risk group (observed 34.5%), and 12.5% in the high-risk group (observed 38.8%). The RAT was more accurate than the ACS NSQIP calculator (p = 0.0018). CONCLUSIONS While the RAT and ACS NSQIP calculator were both able to identify patients more likely to experience complications following spine surgery, both have substantial room for improvement. Risk stratification is feasible in spine surgery procedures; currently used measures have low accuracy.

Probabilistic Methods for Structural Reliability and Risk

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2007-01-01

A formal method is described to quantify structural reliability and risk in the presence of a multitude of uncertainties. The method is based on the materials behavior level where primitive variables with their respective scatters are used to describe that behavior. Computational simulation is then used to propagate those uncertainties to the structural scale where reliability and risk are usually specified. A sample case is described to illustrate the effectiveness, versatility, and maturity of the method. Typical results from this method demonstrate that the method is mature and that it can be used for future strategic projections and planning to assure better, cheaper, faster products for competitive advantages in world markets. The results also indicate that the methods are suitable for predicting remaining life in aging or deteriorating structures.

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

PubMed Central

2016-01-01

Background One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. PMID:27185366

Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction.

PubMed

Warner, Grace; Hoenig, Helen; Montez, Maria; Wang, Fei; Rosen, Amy

2004-02-01

To examine performance of models in predicting health care utilization for individuals with spinal cord dysfunction. Regression models compared 2 diagnosis-based risk-adjustment methods, the adjusted clinical groups (ACGs) and diagnostic cost groups (DCGs). To improve prediction, we added to our model: (1) spinal cord dysfunction-specific diagnostic information, (2) limitations in self-care function, and (3) both 1 and 2. Models were replicated in 3 populations. Samples from 3 populations: (1) 40% of veterans using Veterans Health Administration services in fiscal year 1997 (FY97) (N=1,046,803), (2) veteran sample with spinal cord dysfunction identified by codes from the International Statistical Classification of Diseases, 9th Revision, Clinical Modifications (N=7666), and (3) veteran sample identified in Veterans Affairs Spinal Cord Dysfunction Registry (N=5888). Not applicable. Inpatient, outpatient, and total days of care in FY97. The DCG models (R(2) range,.22-.38) performed better than ACG models (R(2) range,.04-.34) for all outcomes. Spinal cord dysfunction-specific diagnostic information improved prediction more in the ACG model than in the DCG model (R(2) range for ACG,.14-.34; R(2) range for DCG,.24-.38). Information on self-care function slightly improved performance (R(2) range increased from 0 to.04). The DCG risk-adjustment models predicted health care utilization better than ACG models. ACG model prediction was improved by adding information.

Combining the ASA Physical Classification System and Continuous Intraoperative Surgical Apgar Score Measurement in Predicting Postoperative Risk.

PubMed

Jering, Monika Zdenka; Marolen, Khensani N; Shotwell, Matthew S; Denton, Jason N; Sandberg, Warren S; Ehrenfeld, Jesse Menachem

2015-11-01

The surgical Apgar score predicts major 30-day postoperative complications using data assessed at the end of surgery. We hypothesized that evaluating the surgical Apgar score continuously during surgery may identify patients at high risk for postoperative complications. We retrospectively identified general, vascular, and general oncology patients at Vanderbilt University Medical Center. Logistic regression methods were used to construct a series of predictive models in order to continuously estimate the risk of major postoperative complications, and to alert care providers during surgery should the risk exceed a given threshold. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminative ability of a model utilizing a continuously measured surgical Apgar score relative to models that use only preoperative clinical factors or continuously monitored individual constituents of the surgical Apgar score (i.e. heart rate, blood pressure, and blood loss). AUROC estimates were validated internally using a bootstrap method. 4,728 patients were included. Combining the ASA PS classification with continuously measured surgical Apgar score demonstrated improved discriminative ability (AUROC 0.80) in the pooled cohort compared to ASA (0.73) and the surgical Apgar score alone (0.74). To optimize the tradeoff between inadequate and excessive alerting with future real-time notifications, we recommend a threshold probability of 0.24. Continuous assessment of the surgical Apgar score is predictive for major postoperative complications. In the future, real-time notifications might allow for detection and mitigation of changes in a patient's accumulating risk of complications during a surgical procedure.

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

PubMed Central

Brenton, Ashley; Richeimer, Steven; Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Blanchard, John; Meshkin, Brian

2017-01-01

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. PMID:28572737

Twelve Month Prevalence of and Risk Factors for Suicide Attempts in the WHO World Mental Health Surveys

PubMed Central

Borges, Guilherme; Nock, Matthew K.; Haro Abad, Josep M.; Hwang, Irving; Sampson, Nancy A.; Alonso, Jordi; Andrade, Laura Helena; Angermeyer, Matthias C.; Beautrais, Annette; Bromet, Evelyn; Bruffaerts, Ronny; de Girolamo, Giovanni; Florescu, Silvia; Gureje, Oye; Hu, Chiyi; Karam, Elie G; Kovess-Masfety, Viviane; Lee, Sing; Levinson, Daphna; Medina-Mora, Maria Elena; Ormel, Johan; Posada-Villa, Jose; Sagar, Rajesh; Tomov, Toma; Uda, Hidenori; Williams, David R.; Kessler, Ronald C.

2009-01-01

Objective Although suicide is a leading cause of death worldwide, clinicians and researchers lack a data-driven method to assess the risk of suicide attempts. This study reports the results of an analysis of a large cross-national epidemiological survey database that estimates the 12-month prevalence of suicidal behaviors, identifies risk factors for suicide attempts, and combines these factors to create a risk index for 12-month suicide attempts separately for developed and developing countries. Method Data come from the WHO World Mental Health (WMH) Surveys (conducted 2001–2007) in which 108,705 adults from 21 countries were interviewed using the WHO Composite International Diagnostic Interview (CIDI). The survey assessed suicidal behaviors and potential risk factors across multiple domains including: socio-demographics, parent psychopathology, childhood adversities, DSM-IV disorders, and history of suicidal behavior. Results Twelve-month prevalence estimates of suicide ideation, plans and attempts are 2.0%, 0.6% and 0.3% respectively for developed countries and 2.1%, 0.7% and 0.4% for developing countries. Risk factors for suicidal behaviors in both developed and developing countries include: female sex, younger age, lower education and income, unmarried status, unemployment, parent psychopathology, childhood adversities, and presence of diverse 12-month DSM-IV mental disorders. Combining risk factors from multiple domains produced risk indices that accurately predicted 12-month suicide attempts in both developed and developing countries (AUC=.74–.80). Conclusion Suicidal behaviors occur at similar rates in both developed and developing countries. Risk indices assessing multiple domains can predict suicide attempts with fairly good accuracy and may be useful in aiding clinicians in the prediction of these behaviors. PMID:20816034

Modeling Individual Patient Preferences for Colorectal Cancer Screening Based on Their Tolerance for Complications Risk.

PubMed

Taksler, Glen B; Perzynski, Adam T; Kattan, Michael W

2017-04-01

Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies.

External Validation of the Garvan Nomograms for Predicting Absolute Fracture Risk: The Tromsø Study

PubMed Central

Ahmed, Luai A.; Nguyen, Nguyen D.; Bjørnerem, Åshild; Joakimsen, Ragnar M.; Jørgensen, Lone; Størmer, Jan; Bliuc, Dana; Center, Jacqueline R.; Eisman, John A.; Nguyen, Tuan V.; Emaus, Nina

2014-01-01

Background Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. Methods The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. Results The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. Conclusions The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction. PMID:25255221

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.

PubMed

Schoop, Rotraut; Beyersmann, Jan; Schumacher, Martin; Binder, Harald

2011-02-01

Prognostic models for time-to-event data play a prominent role in therapy assignment, risk stratification and inter-hospital quality assurance. The assessment of their prognostic value is vital not only for responsible resource allocation, but also for their widespread acceptance. The additional presence of competing risks to the event of interest requires proper handling not only on the model building side, but also during assessment. Research into methods for the evaluation of the prognostic potential of models accounting for competing risks is still needed, as most proposed methods measure either their discrimination or calibration, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (Statistics in Medicine 1999, 18, 2529–2545) and Gerds and Schumacher (Biometrical Journal 2006, 48, 1029–1040) to handle models with competing risks, i.e. more than one possible event type, and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows.

Advances in Imaging Approaches to Fracture Risk Evaluation

PubMed Central

Manhard, Mary Kate; Nyman, Jeffry S.; Does, Mark D.

2016-01-01

Fragility fractures are a growing problem worldwide, and current methods for diagnosing osteoporosis do not always identify individuals who require treatment to prevent a fracture and may misidentify those not a risk. Traditionally, fracture risk is assessed using dual-energy X-ray absorptiometry, which provides measurements of areal bone mineral density (BMD) at sites prone to fracture. Recent advances in imaging show promise in adding new information that could improve the prediction of fracture risk in the clinic. As reviewed herein, advances in quantitative computed tomography (QCT) predict hip and vertebral body strength; high resolution HR-peripheral QCT (HR-pQCT) and micro-magnetic resonance imaging (μMRI) assess the micro-architecture of trabecular bone; quantitative ultrasound (QUS) measures the modulus or tissue stiffness of cortical bone; and quantitative ultra-short echo time MRI methods quantify the concentrations of bound water and pore water in cortical bone, which reflect a variety of mechanical properties of bone. Each of these technologies provides unique characteristics of bone and may improve fracture risk diagnoses and reduce prevalence of fractures by helping to guide treatment decisions. PMID:27816505

Epidemiology and Long-term Clinical and Biologic Risk Factors for Pneumonia in Community-Dwelling Older Americans

PubMed Central

Alvarez, Karina; Loehr, Laura; Folsom, Aaron R.; Newman, Anne B.; Weissfeld, Lisa A.; Wunderink, Richard G.; Kritchevsky, Stephen B.; Mukamal, Kenneth J.; London, Stephanie J.; Harris, Tamara B.; Bauer, Doug C.; Angus, Derek C.

2013-01-01

Background: Preventing pneumonia requires better understanding of incidence, mortality, and long-term clinical and biologic risk factors, particularly in younger individuals. Methods: This was a cohort study in three population-based cohorts of community-dwelling individuals. A derivation cohort (n = 16,260) was used to determine incidence and survival and develop a risk prediction model. The prediction model was validated in two cohorts (n = 8,495). The primary outcome was 10-year risk of pneumonia hospitalization. Results: The crude and age-adjusted incidences of pneumonia were 6.71 and 9.43 cases/1,000 person-years (10-year risk was 6.15%). The 30-day and 1-year mortality were 16.5% and 31.5%. Although age was the most important risk factor (range of crude incidence rates, 1.69-39.13 cases/1,000 person-years for each 5-year increment from 45-85 years), 38% of pneumonia cases occurred in adults < 65 years of age. The 30-day and 1-year mortality were 12.5% and 25.7% in those < 65 years of age. Although most comorbidities were associated with higher risk of pneumonia, reduced lung function was the most important risk factor (relative risk = 6.61 for severe reduction based on FEV1 by spirometry). A clinical risk prediction model based on age, smoking, and lung function predicted 10-year risk (area under curve [AUC] = 0.77 and Hosmer-Lemeshow [HL] C statistic = 0.12). Model discrimination and calibration were similar in the internal validation cohort (AUC = 0.77; HL C statistic, 0.65) but lower in the external validation cohort (AUC = 0.62; HL C statistic, 0.45). The model also calibrated well in blacks and younger adults. C-reactive protein and IL-6 were associated with higher pneumonia risk but did not improve model performance. Conclusions: Pneumonia hospitalization is common and associated with high mortality, even in younger healthy adults. Long-term risk of pneumonia can be predicted in community-dwelling adults with a simple clinical risk prediction model. PMID:23744106

Pathway index models for construction of patient-specific risk profiles.

PubMed

Eng, Kevin H; Wang, Sijian; Bradley, William H; Rader, Janet S; Kendziorski, Christina

2013-04-30

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd.

Pupillary reactivity to negative stimuli prospectively predicts recurrence of major depressive disorder in women.

PubMed

Kudinova, Anastacia Y; Burkhouse, Katie L; Siegle, Greg; Owens, Max; Woody, Mary L; Gibb, Brandon E

2016-12-01

There is a large body of research supporting the association between disrupted physiological reactivity to negative stimuli and depression. The present study aimed to examine whether physiological reactivity to emotional stimuli, assessed via pupil dilation, served as a biological marker of risk for depression recurrence among individuals who are known to be at a higher risk due to having previous history of depression. Participants were 57 women with a history of major depressive disorder (MDD). Pupil dilation to angry, happy, sad, and neutral faces was recorded. Participants' diagnoses and symptoms were assessed 24 months after the initial assessment. We found that women's pupillary reactivity to negative (sad or angry faces) but not positive stimuli prospectively predicted MDD recurrence. Additionally, we found that both hyper- and hypopupillary reactivity to angry faces predicted risk for MDD recurrence. These findings suggest that disrupted physiological response to negative stimuli indexed via pupillary dilation could serve as a physiological marker of MDD risk, thus presenting clinicians with a convenient and inexpensive method to predict which of the at-risk women are more likely to experience depression recurrence. © 2016 Society for Psychophysiological Research.

Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer

NASA Astrophysics Data System (ADS)

Ginsburg, Shoshana B.; Rusu, Mirabela; Kurhanewicz, John; Madabhushi, Anant

2014-03-01

In this study we explore the ability of a novel machine learning approach, in conjunction with computer-extracted features describing prostate cancer morphology on pre-treatment MRI, to predict whether a patient will develop biochemical recurrence within ten years of radiation therapy. Biochemical recurrence, which is characterized by a rise in serum prostate-specific antigen (PSA) of at least 2 ng/mL above the nadir PSA, is associated with increased risk of metastasis and prostate cancer-related mortality. Currently, risk of biochemical recurrence is predicted by the Kattan nomogram, which incorporates several clinical factors to predict the probability of recurrence-free survival following radiation therapy (but has limited prediction accuracy). Semantic attributes on T2w MRI, such as the presence of extracapsular extension and seminal vesicle invasion and surrogate measure- ments of tumor size, have also been shown to be predictive of biochemical recurrence risk. While the correlation between biochemical recurrence and factors like tumor stage, Gleason grade, and extracapsular spread are well- documented, it is less clear how to predict biochemical recurrence in the absence of extracapsular spread and for small tumors fully contained in the capsule. Computer{extracted texture features, which quantitatively de- scribe tumor micro-architecture and morphology on MRI, have been shown to provide clues about a tumor's aggressiveness. However, while computer{extracted features have been employed for predicting cancer presence and grade, they have not been evaluated in the context of predicting risk of biochemical recurrence. This work seeks to evaluate the role of computer-extracted texture features in predicting risk of biochemical recurrence on a cohort of sixteen patients who underwent pre{treatment 1.5 Tesla (T) T2w MRI. We extract a combination of first-order statistical, gradient, co-occurrence, and Gabor wavelet features from T2w MRI. To identify which of these T2w MRI texture features are potential independent prognostic markers of PSA failure, we implement a partial least squares (PLS) method to embed the data in a low{dimensional space and then use the variable importance in projections (VIP) method to quantify the contributions of individual features to classification on the PLS embedding. In spite of the poor resolution of the 1.5 T MRI data, we are able to identify three Gabor wavelet features that, in conjunction with a logistic regression classifier, yield an area under the receiver operating characteristic curve of 0.83 for predicting the probability of biochemical recurrence following radiation therapy. In comparison to both the Kattan nomogram and semantic MRI attributes, the ability of these three computer-extracted features to predict biochemical recurrence risk is demonstrated.

Predicting non-familial major physical violent crime perpetration in the U.S. Army from administrative data

PubMed Central

Rosellini, Anthony J.; Monahan, John; Street, Amy E.; Heeringa, Steven G.; Hill, Eric D.; Petukhova, Maria; Reis, Ben Y.; Sampson, Nancy A.; Bliese, Paul; Schoenbaum, Michael; Stein, Murray B.; Ursano, Robert; Kessler, Ronald C.

2016-01-01

BACKGROUND Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among U.S. Army soldiers. METHODS A consolidated administrative database for all 975,057 soldiers in the U.S. Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). 5,771 of these soldiers committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression; random forests; penalized regressions). The model was then validated in an independent 2011-2013 sample. RESULTS Key predictors were indicators of disadvantaged social/socio-economic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver operating characteristic curve was .80-.82 in 2004-2009 and .77 in a 2011-2013 validation sample. 36.2-33.1% (male-female) of all administratively-recorded crimes were committed by the 5% of soldiers having highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. CONCLUSIONS Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks. PMID:26436603

Significant SNPs have limited prediction ability for thyroid cancer

PubMed Central

Guo, Shicheng; Wang, Yu-Long; Li, Yi; Jin, Li; Xiong, Momiao; Ji, Qing-Hai; Wang, Jiucun

2014-01-01

Recently, five thyroid cancer significantly associated genetic variants (rs965513, rs944289, rs116909374, rs966423, and rs2439302) have been discovered and validated in two independent GWAS and numerous case–control studies, which were conducted in different populations. We genotyped the above five single nucleotide polymorphisms (SNPs) in Han Chinese populations and performed thyroid cancer-risk predictions with nine machine learning methods. We found that four SNPs were significantly associated with thyroid cancer in Han Chinese population, while no polymorphism was observed for rs116909374. Small familial relative risks (1.02–1.05) and limited power to predict thyroid cancer (AUCs: 0.54–0.60) indicate limited clinical potential. Four significant SNPs have limited prediction ability for thyroid cancer. PMID:24591304

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

Aim of the present study is to evaluate the potential ecological risk and predict the 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 an order of E(Cd) > E(Pb) > E(Cu) > E(Cr) > E(Zn) have been obtained, which showed that Cd was the most important factor led to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, and the fixed number of years exceeding 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 metal, and the relationship between sampling points and variables. These findings provide some useful insights for making appropriate management strategies to prevent and decrease heavy metal pollution around coal gangue dump in Yangcaogou coal mine and other similar areas elsewhere.

Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death

PubMed Central

Skinner, James E; Meyer, Michael; Nester, Brian A; Geary, Una; Taggart, Pamela; Mangione, Antoinette; Ramalanjaona, George; Terregino, Carol; Dalsey, William C

2009-01-01

Objective: Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD). Background: Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data. Methods: Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle–Thaler criteria. Results: All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p ≤0.001). The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p ≤0.001); sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p ≤0.001). Conclusions: Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test. PMID:19707283

Validity of cardiovascular risk prediction models in Latin America and among Hispanics in the United States of America: a systematic review.

PubMed

Cortes-Bergoderi, Mery; Thomas, Randal J; Albuquerque, Felipe N; Batsis, John A; Burdiat, Gerard; Perez-Terzic, Carmen; Trejo-Gutierrez, Jorge; Lopez-Jimenez, Francisco

2012-08-01

To assess the use and validity of prediction models to estimate the risk of cardiovascular disease (CVD) in Latin America and among Hispanic populations in the United States of America. This was a systematic review of three databases: Ovid MEDLINE (1 January 1950-15 April 2010), LILACS (1 January 1988-15 April 2010), and EMBASE (1 January 1988-15 April 2010). MeSH search terms and domains were related to CVD, prediction rules, Latin America (including the Caribbean), and Hispanics in the United States. Database searches were supplemented by correspondence with experts in the field. A total of 1 655 abstracts were identified, of which five cohorts with a total of 13 142 subjects met inclusion criteria. A Mexican cohort showed that the predicted/observed event-rate ratio for coronary heart disease (CHD) according to the Framingham risk score (FRS) was 1.68 (95% CI, 1.26-2.11); incident myocardial infarction, 1.36 (95% CI, 0.90-1.83); and CHD death, 1.21 (95% CI, 0.43-2.00). In Ecuador, a prediction model for CVD and total deaths in hypertensive patients had an area under the curve (AUC) of 0.79 (95% CI, 0.72-0.86), while the World Health Organization method had an AUC of 0.74 (95% CI, 0.67-0.82). A study predicting mortality risk in people with Chagas' disease had an AUC of 0.81 (95% CI, 0.72-0.90). Among a United State s cohort that included Hispanics, FRS overestimated CVD risk for Hispanics with an AUC of 0.69. Another study in the United States that assessed FRS factors predicting CVD death among Mexican-Americans had an AUC of 0.78. The evidence regarding CVD risk prediction rules in Latin America or among Hispanics in the United States is modest at best. It is likely that the FRS overestimates CVD risk in Hispanics when not properly recalibrated.

Ethics Analysis of Neuroimaging in Alzheimer’s Disease

PubMed Central

Illes, J.; Rosen, A.; Greicius, M.; Racine, E.

2009-01-01

This article focuses on the prospects and ethics of using neuroimaging to predict Alzheimer’s disease (AD). It is motivated by consideration of the historical roles of science in medicine and society, and considerations specifically contemporary of capabilities in imaging and aging, and the benefits and hope they bring. A general consensus is that combinations of imaging methods will ultimately be most fruitful in predicting disease. Their roll-out into translational practice will not be free of complexity, however, as culture and values differ in terms of what defines benefit and risk, who will benefit and who is at risk, what methods must be in place to assure the maximum safety, comfort, and protection of subjects and patients, and educational and policy needs. Proactive planning for the ethical and societal implications of predicting diseases of the aging brain is critical and will benefit all stakeholders— researchers, patients and families, health care providers, and policy makers. PMID:17413029

Predicting the risk for colorectal cancer with personal characteristics and fecal immunochemical test.

PubMed

Li, Wen; Zhao, Li-Zhong; Ma, Dong-Wang; Wang, De-Zheng; Shi, Lei; Wang, Hong-Lei; Dong, Mo; Zhang, Shu-Yi; Cao, Lei; Zhang, Wei-Hua; Zhang, Xi-Peng; Zhang, Qing-Huai; Yu, Lin; Qin, Hai; Wang, Xi-Mo; Chen, Sam Li-Sheng

2018-05-01

We aimed to predict colorectal cancer (CRC) based on the demographic features and clinical correlates of personal symptoms and signs from Tianjin community-based CRC screening data.A total of 891,199 residents who were aged 60 to 74 and were screened in 2012 were enrolled. The Lasso logistic regression model was used to identify the predictors for CRC. Predictive validity was assessed by the receiver operating characteristic (ROC) curve. Bootstrapping method was also performed to validate this prediction model.CRC was best predicted by a model that included age, sex, education level, occupations, diarrhea, constipation, colon mucosa and bleeding, gallbladder disease, a stressful life event, family history of CRC, and a positive fecal immunochemical test (FIT). The area under curve (AUC) for the questionnaire with a FIT was 84% (95% CI: 82%-86%), followed by 76% (95% CI: 74%-79%) for a FIT alone, and 73% (95% CI: 71%-76%) for the questionnaire alone. With 500 bootstrap replications, the estimated optimism (<0.005) shows good discrimination in validation of prediction model.A risk prediction model for CRC based on a series of symptoms and signs related to enteric diseases in combination with a FIT was developed from first round of screening. The results of the current study are useful for increasing the awareness of high-risk subjects and for individual-risk-guided invitations or strategies to achieve mass screening for CRC.

The Lack of Utility of Circulating Biomarkers of Inflammation and Endothelial Dysfunction for Type 2 Diabetes Risk Prediction Among Postmenopausal Women

PubMed Central

Chao, Chun; Song, Yiqing; Cook, Nancy; Tseng, Chi-Hong; Manson, JoAnn E.; Eaton, Charles; Margolis, Karen L.; Rodriguez, Beatriz; Phillips, Lawrence S.; Tinker, Lesley F.; Liu, Simin

2011-01-01

Background Recent studies have linked plasma markers of inflammation and endothelial dysfunction to type 2 diabetes mellitus (DM) development. However, the utility of these novel biomarkers for type 2 DM risk prediction remains uncertain. Methods The Women’s Health Initiative Observational Study (WHIOS), a prospective cohort, and a nested case-control study within the WHIOS of 1584 incident type 2 DM cases and 2198 matched controls were used to evaluate the utility of plasma markers of inflammation and endothelial dysfunction for type 2 DM risk prediction. Between September 1994 and December 1998, 93 676 women aged 50 to 79 years were enrolled in the WHIOS. Fasting plasma levels of glucose, insulin, white blood cells, tumor necrosis factor receptor 2, interleukin 6, high-sensitivity C-reactive protein, E-selectin, soluble intercellular adhesion molecule 1, and vascular cell adhesion molecule 1 were measured using blood samples collected at baseline. A series of prediction models including traditional risk factors and novel plasma markers were evaluated on the basis of global model fit, model discrimination, net reclassification improvement, and positive and negative predictive values. Results Although white blood cell count and levels of interleukin 6, high-sensitivity C-reactive protein, and soluble intercellular adhesion molecule 1 significantly enhanced model fit, none of the inflammatory and endothelial dysfunction markers improved the ability of model discrimination (area under the receiver operating characteristic curve, 0.93 vs 0.93), net reclassification, or predictive values (positive, 0.22 vs 0.24; negative, 0.99 vs 0.99 [using 15% 6-year type 2 DM risk as the cutoff]) compared with traditional risk factors. Similar results were obtained in ethnic-specific analyses. Conclusion Beyond traditional risk factors, measurement of plasma markers of systemic inflammation and endothelial dysfunction contribute relatively little additional value in clinical type 2 DM risk prediction in a multiethnic cohort of postmenopausal women. PMID:20876407

Smoking Gun or Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants.

PubMed

Gagliano, Sarah A; Ravji, Reena; Barnes, Michael R; Weale, Michael E; Knight, Jo

2015-08-24

Although technology has triumphed in facilitating routine genome sequencing, new challenges have been created for the data-analyst. Genome-scale surveys of human variation generate volumes of data that far exceed capabilities for laboratory characterization. By incorporating functional annotations as predictors, statistical learning has been widely investigated for prioritizing genetic variants likely to be associated with complex disease. We compared three published prioritization procedures, which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding. We also explored different combinations of algorithm and annotation set. As an application, we tested which methodology performed best for prioritizing variants using data from a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71) in test set data, but there is more variability in the application to the schizophrenia GWAS. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome-scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved for risk variant prediction to address the impending bottleneck of the new generation of genome re-sequencing studies.

Application of the Rosner-Colditz Risk Prediction Model to Estimate Sexual Orientation Group Disparities in Breast Cancer Risk in a U.S. Cohort of Premenopausal Women

PubMed Central

Austin, S. Bryn; Pazaris, Mathew J.; Rosner, Bernard; Bowen, Deborah; Rich-Edwards, Janet; Spiegelman, Donna

2012-01-01

Background Lesbian and bisexual women may be at greater risk of breast cancer than heterosexual women during the premenopausal period due to disparities in risk factors. Methods With 16 years of prospective data from a large cohort of U.S. women ages 25–58 years, we conducted a breast cancer risk assessment for 87,392 premenopausal women by applying the Rosner-Colditz biomathematical risk-prediction model to estimate breast cancer risk based on known risk factors. Based on each woman’s comprehensive risk factor profile, we calculated the predicted one-year incidence rate (IR) per 100,000 person-years and estimated incidence rate ratios (IRR) and 95% confidence intervals (CI) for lesbian and bisexual women compared to heterosexual women. Results 87,392 premenopausal women provided 1,091,871 person-years of data included in analyses. Mean predicted one-year breast cancer IRs per 100,000 person-years for each sexual orientation group were: heterosexual 122.55, lesbian 131.61, and bisexual 131.72. IRs were significantly elevated in both lesbian (IRR 1.06; 95 CI 1.06, 1.06) and bisexual (IRR 1.10; 95% CI 1.10, 1.10) women compared to heterosexual women. Conclusions Our findings suggest both lesbian and bisexual women have slightly elevated predicted breast cancer incidence compared to heterosexual women throughout the premenopausal period. Impact Health professionals must ensure that breast cancer prevention efforts are reaching these women. As more health systems around the country collect data on patient sexual orientation, the National Cancer Institute’s SEER cancer registry should add this information to its data system to monitor progress in reducing sexual orientation-related disparities in cancer incidence and mortality. PMID:23035180

A Review on Methods of Risk Adjustment and their Use in Integrated Healthcare Systems

PubMed Central

Juhnke, Christin; Bethge, Susanne

2016-01-01

Introduction: Effective risk adjustment is an aspect that is more and more given weight on the background of competitive health insurance systems and vital healthcare systems. The objective of this review was to obtain an overview of existing models of risk adjustment as well as on crucial weights in risk adjustment. Moreover, the predictive performance of selected methods in international healthcare systems should be analysed. Theory and methods: A comprehensive, systematic literature review on methods of risk adjustment was conducted in terms of an encompassing, interdisciplinary examination of the related disciplines. Results: In general, several distinctions can be made: in terms of risk horizons, in terms of risk factors or in terms of the combination of indicators included. Within these, another differentiation by three levels seems reasonable: methods based on mortality risks, methods based on morbidity risks as well as those based on information on (self-reported) health status. Conclusions and discussion: After the final examination of different methods of risk adjustment it was shown that the methodology used to adjust risks varies. The models differ greatly in terms of their included morbidity indicators. The findings of this review can be used in the evaluation of integrated healthcare delivery systems and can be integrated into quality- and patient-oriented reimbursement of care providers in the design of healthcare contracts. PMID:28316544

Risk prediction models for graft failure in kidney transplantation: a systematic review.

PubMed

Kaboré, Rémi; Haller, Maria C; Harambat, Jérôme; Heinze, Georg; Leffondré, Karen

2017-04-01

Risk prediction models are useful for identifying kidney recipients at high risk of graft failure, thus optimizing clinical care. Our objective was to systematically review the models that have been recently developed and validated to predict graft failure in kidney transplantation recipients. We used PubMed and Scopus to search for English, German and French language articles published in 2005-15. We selected studies that developed and validated a new risk prediction model for graft failure after kidney transplantation, or validated an existing model with or without updating the model. Data on recipient characteristics and predictors, as well as modelling and validation methods were extracted. In total, 39 articles met the inclusion criteria. Of these, 34 developed and validated a new risk prediction model and 5 validated an existing one with or without updating the model. The most frequently predicted outcome was graft failure, defined as dialysis, re-transplantation or death with functioning graft. Most studies used the Cox model. There was substantial variability in predictors used. In total, 25 studies used predictors measured at transplantation only, and 14 studies used predictors also measured after transplantation. Discrimination performance was reported in 87% of studies, while calibration was reported in 56%. Performance indicators were estimated using both internal and external validation in 13 studies, and using external validation only in 6 studies. Several prediction models for kidney graft failure in adults have been published. Our study highlights the need to better account for competing risks when applicable in such studies, and to adequately account for post-transplant measures of predictors in studies aiming at improving monitoring of kidney transplant recipients. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network.

PubMed

Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

2016-10-01

Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study. Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced.

Using spatial information about recurrence risk for robust optimization of dose-painting prescription functions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bender, Edward T.

Purpose: To develop a robust method for deriving dose-painting prescription functions using spatial information about the risk for disease recurrence. Methods: Spatial distributions of radiobiological model parameters are derived from distributions of recurrence risk after uniform irradiation. These model parameters are then used to derive optimal dose-painting prescription functions given a constant mean biologically effective dose. Results: An estimate for the optimal dose distribution can be derived based on spatial information about recurrence risk. Dose painting based on imaging markers that are moderately or poorly correlated with recurrence risk are predicted to potentially result in inferior disease control when comparedmore » the same mean biologically effective dose delivered uniformly. A robust optimization approach may partially mitigate this issue. Conclusions: The methods described here can be used to derive an estimate for a robust, patient-specific prescription function for use in dose painting. Two approximate scaling relationships were observed: First, the optimal choice for the maximum dose differential when using either a linear or two-compartment prescription function is proportional to R, where R is the Pearson correlation coefficient between a given imaging marker and recurrence risk after uniform irradiation. Second, the predicted maximum possible gain in tumor control probability for any robust optimization technique is nearly proportional to the square of R.« less

Understanding outbreaks of waterborne infectious disease: quantitative microbial risk assessment vs. epidemiology

USDA-ARS?s Scientific Manuscript database

Drinking water contaminated with microbial pathogens can cause outbreaks of infectious disease, and these outbreaks are traditionally studied using epidemiologic methods. Quantitative microbial risk assessment (QMRA) can predict – and therefore help prevent – such outbreaks, but it has never been r...

The mortality risk score and the ADG score: two points-based scoring systems for the Johns Hopkins aggregated diagnosis groups to predict mortality in a general adult population cohort in Ontario, Canada.

PubMed

Austin, Peter C; Walraven, Carl van

2011-10-01

Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults. To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults. The ADG Score derives weights for the individual ADG diagnosis groups. : Retrospective cohort constructed using population-based administrative data. All 10,498,413 residents of Ontario, Canada, between the age of 20 and 100 years who were alive on their birthday in 2007, participated in this study. Participants were randomly divided into derivation and validation samples. : Death within 1 year. In the derivation cohort, the MRS ranged from -21 to 139 (median value 29, IQR 17 to 44). In the validation group, a logistic regression model with the MRS as the sole predictor significantly predicted the risk of 1-year mortality with a c-statistic of 0.917. A regression model with age, sex, and the ADG Score has similar performance. Both methods accurately predicted the risk of 1-year mortality across the 20 vigintiles of risk. The MRS combined values for a person's age, sex, and the John Hopkins ADGs to accurately predict 1-year mortality in adults. The ADG Score is a weighted score representing the presence or absence of the 32 ADG diagnosis groups. These scores will facilitate health services researchers conducting risk adjustment using administrative health care databases.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

Decision support systems and methods for complex networks

DOEpatents

Huang, Zhenyu [Richland, WA; Wong, Pak Chung [Richland, WA; Ma, Jian [Richland, WA; Mackey, Patrick S [Richland, WA; Chen, Yousu [Richland, WA; Schneider, Kevin P [Seattle, WA

2012-02-28

Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

Impacts of pesticide mixtures in European rivers as predicted by the Species Sensitivity Distribution (SSD) models and SPEAR bioindication

NASA Astrophysics Data System (ADS)

Jesenska, Sona; Liess, Mathias; Schäfer, Ralf; Beketov, Mikhail; Blaha, Ludek

2013-04-01

Species sensitivity distribution (SSD) is statistical method broadly used in the ecotoxicological risk assessment of chemicals. Originally it has been used for prospective risk assessment of single substances but nowadays it is becoming more important also in the retrospective risk assessment of mixtures, including the catchment scale. In the present work, SSD predictions (impacts of mixtures consisting of 25 pesticides; data from several catchments in Germany, France and Finland) were compared with SPEAR-pesticides, which a bioindicator index based on biological traits responsive to the effects of pesticides and post-contamination recovery. The results showed statistically significant correlations (Pearson's R, p<0.01) between SSD (predicted msPAF values) and values of SPEAR-pesticides (based on field biomonitoring observations). Comparisons of the thresholds established for the SSD and SPEAR approaches (SPEAR-pesticides=45%, i.e. LOEC level, and msPAF = 0.05 for SSD, i.e. HC5) showed that use of chronic toxicity data significantly improved the agreement between the two methods but the SPEAR-pesticides index was still more sensitive. Taken together, the validation study shows good potential of SSD models in predicting the real impacts of micropollutant mixtures on natural communities of aquatic biota.

Development and Validation of a qRT-PCR Classifier for Lung Cancer Prognosis

PubMed Central

Chen, Guoan; Kim, Sinae; Taylor, Jeremy MG; Wang, Zhuwen; Lee, Oliver; Ramnath, Nithya; Reddy, Rishindra M; Lin, Jules; Chang, Andrew C; Orringer, Mark B; Beer, David G

2011-01-01

Purpose This prospective study aimed to develop a robust and clinically-applicable method to identify high-risk early stage lung cancer patients and then to validate this method for use in future translational studies. Patients and Methods Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model and random survival forest (RSF) analysis. A final set of 91 genes was selected and tested as a predictor of survival using a qRT-PCR-based assay utilizing an independent cohort of 101 lung adenocarcinomas. Results The RSF model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index (MRI) was significantly related to survival (Cox model p < 0.00001) and separated all patients into low, medium, and high-risk groups (HR = 1.00, 2.82, 4.42). The MRI was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low, medium, and high-risk groups (HR = 1.00, 3.29, 3.77). Conclusions The development and validation of this robust qRT-PCR platform allows prediction of patient survival with early stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of lung cancer patients and improving patient survival. PMID:21792073

Application of the Rosner-Wei Risk-Prediction Model to Estimate Sexual Orientation Patterns in Colon Cancer Risk in a Prospective Cohort of U.S. Women

PubMed Central

Austin, S. Bryn; Pazaris, Mathew J.; Wei, Esther K.; Rosner, Bernard; Kennedy, Grace A.; Bowen, Deborah; Spiegelman, Donna

2014-01-01

Purpose We examined whether lesbian and bisexual women may be at greater risk of colon cancer (CC) than heterosexual women. Methods Working with a large cohort of U.S. women ages 25-64 years, we analyzed 20 years of prospective data to estimate CC incidence, based on known risk factors by applying the Rosner-Wei CC risk-prediction model. Comparing to heterosexual women, we calculated for lesbian and bisexual women the predicted one-year incidence rate (IR) per 100,000 person-years and estimated incidence rate ratios (IRR) and 95% confidence intervals (CI), based on each woman’s comprehensive risk factor profile. Results Analyses included 1,373,817 person-years of data from 66,257 women. For each sexual orientation group, mean predicted one-year CC IR per 100,000 person-years was slightly over 12 cases for each of the sexual orientation groups. After controlling for confounders in fully adjusted models and compared to heterosexuals, no significant differences in IRR were observed for lesbians (IRR 1.01; 95% CI 0.99, 1.04) or bisexuals (IRR 1.01; 95% CI 0.98, 1.04). Conclusions CC risk is similar across all sexual orientation subgroups, with all groups comparably affected. Health professionals must ensure that prevention, screening, and treatment programs are adequately reaching each of these communities. PMID:24852207

Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials

PubMed Central

Denton, Brian T.; Hayward, Rodney A.

2017-01-01

Background Intensive blood pressure (BP) treatment can avert cardiovascular disease (CVD) events but can cause some serious adverse events. We sought to develop and validate risk models for predicting absolute risk difference (increased risk or decreased risk) for CVD events and serious adverse events from intensive BP therapy. A secondary aim was to test if the statistical method of elastic net regularization would improve the estimation of risk models for predicting absolute risk difference, as compared to a traditional backwards variable selection approach. Methods and findings Cox models were derived from SPRINT trial data and validated on ACCORD-BP trial data to estimate risk of CVD events and serious adverse events; the models included terms for intensive BP treatment and heterogeneous response to intensive treatment. The Cox models were then used to estimate the absolute reduction in probability of CVD events (benefit) and absolute increase in probability of serious adverse events (harm) for each individual from intensive treatment. We compared the method of elastic net regularization, which uses repeated internal cross-validation to select variables and estimate coefficients in the presence of collinearity, to a traditional backwards variable selection approach. Data from 9,069 SPRINT participants with complete data on covariates were utilized for model development, and data from 4,498 ACCORD-BP participants with complete data were utilized for model validation. Participants were exposed to intensive (goal systolic pressure < 120 mm Hg) versus standard (<140 mm Hg) treatment. Two composite primary outcome measures were evaluated: (i) CVD events/deaths (myocardial infarction, acute coronary syndrome, stroke, congestive heart failure, or CVD death), and (ii) serious adverse events (hypotension, syncope, electrolyte abnormalities, bradycardia, or acute kidney injury/failure). The model for CVD chosen through elastic net regularization included interaction terms suggesting that older age, black race, higher diastolic BP, and higher lipids were associated with greater CVD risk reduction benefits from intensive treatment, while current smoking was associated with fewer benefits. The model for serious adverse events chosen through elastic net regularization suggested that male sex, current smoking, statin use, elevated creatinine, and higher lipids were associated with greater risk of serious adverse events from intensive treatment. SPRINT participants in the highest predicted benefit subgroup had a number needed to treat (NNT) of 24 to prevent 1 CVD event/death over 5 years (absolute risk reduction [ARR] = 0.042, 95% CI: 0.018, 0.066; P = 0.001), those in the middle predicted benefit subgroup had a NNT of 76 (ARR = 0.013, 95% CI: −0.0001, 0.026; P = 0.053), and those in the lowest subgroup had no significant risk reduction (ARR = 0.006, 95% CI: −0.007, 0.018; P = 0.71). Those in the highest predicted harm subgroup had a number needed to harm (NNH) of 27 to induce 1 serious adverse event (absolute risk increase [ARI] = 0.038, 95% CI: 0.014, 0.061; P = 0.002), those in the middle predicted harm subgroup had a NNH of 41 (ARI = 0.025, 95% CI: 0.012, 0.038; P < 0.001), and those in the lowest subgroup had no significant risk increase (ARI = −0.007, 95% CI: −0.043, 0.030; P = 0.72). In ACCORD-BP, participants in the highest subgroup of predicted benefit had significant absolute CVD risk reduction, but the overall ACCORD-BP participant sample was skewed towards participants with less predicted benefit and more predicted risk than in SPRINT. The models chosen through traditional backwards selection had similar ability to identify absolute risk difference for CVD as the elastic net models, but poorer ability to correctly identify absolute risk difference for serious adverse events. A key limitation of the analysis is the limited sample size of the ACCORD-BP trial, which expanded confidence intervals for ARI among persons with type 2 diabetes. Additionally, it is not possible to mechanistically explain the physiological relationships explaining the heterogeneous treatment effects captured by the models, since the study was an observational secondary data analysis. Conclusions We found that predictive models could help identify subgroups of participants in both SPRINT and ACCORD-BP who had lower versus higher ARRs in CVD events/deaths with intensive BP treatment, and participants who had lower versus higher ARIs in serious adverse events. PMID:29040268

Comparison of the predictive validity of diagnosis-based risk adjusters for clinical outcomes.

PubMed

Petersen, Laura A; Pietz, Kenneth; Woodard, LeChauncy D; Byrne, Margaret

2005-01-01

Many possible methods of risk adjustment exist, but there is a dearth of comparative data on their performance. We compared the predictive validity of 2 widely used methods (Diagnostic Cost Groups [DCGs] and Adjusted Clinical Groups [ACGs]) for 2 clinical outcomes using a large national sample of patients. We studied all patients who used Veterans Health Administration (VA) medical services in fiscal year (FY) 2001 (n = 3,069,168) and assigned both a DCG and an ACG to each. We used logistic regression analyses to compare predictive ability for death or long-term care (LTC) hospitalization for age/gender models, DCG models, and ACG models. We also assessed the effect of adding age to the DCG and ACG models. Patients in the highest DCG categories, indicating higher severity of illness, were more likely to die or to require LTC hospitalization. Surprisingly, the age/gender model predicted death slightly more accurately than the ACG model (c-statistic of 0.710 versus 0.700, respectively). The addition of age to the ACG model improved the c-statistic to 0.768. The highest c-statistic for prediction of death was obtained with a DCG/age model (0.830). The lowest c-statistics were obtained for age/gender models for LTC hospitalization (c-statistic 0.593). The c-statistic for use of ACGs to predict LTC hospitalization was 0.783, and improved to 0.792 with the addition of age. The c-statistics for use of DCGs and DCG/age to predict LTC hospitalization were 0.885 and 0.890, respectively, indicating the best prediction. We found that risk adjusters based upon diagnoses predicted an increased likelihood of death or LTC hospitalization, exhibiting good predictive validity. In this comparative analysis using VA data, DCG models were generally superior to ACG models in predicting clinical outcomes, although ACG model performance was enhanced by the addition of age.

Two-Step Approach for the Prediction of Future Type 2 Diabetes Risk

PubMed Central

Abdul-Ghani, Muhammad A.; Abdul-Ghani, Tamam; Stern, Michael P.; Karavic, Jasmina; Tuomi, Tiinamaija; Bo, Insoma; DeFronzo, Ralph A.; Groop, Leif

2011-01-01

OBJECTIVE To develop a model for the prediction of type 2 diabetes mellitus (T2DM) risk on the basis of a multivariate logistic model and 1-h plasma glucose concentration (1-h PG). RESEARCH DESIGN AND METHODS The model was developed in a cohort of 1,562 nondiabetic subjects from the San Antonio Heart Study (SAHS) and validated in 2,395 nondiabetic subjects in the Botnia Study. A risk score on the basis of anthropometric parameters, plasma glucose and lipid profile, and blood pressure was computed for each subject. Subjects with a risk score above a certain cut point were considered to represent high-risk individuals, and their 1-h PG concentration during the oral glucose tolerance test was used to further refine their future T2DM risk. RESULTS We used the San Antonio Diabetes Prediction Model (SADPM) to generate the initial risk score. A risk-score value of 0.065 was found to be an optimal cut point for initial screening and selection of high-risk individuals. A 1-h PG concentration >140 mg/dL in high-risk individuals (whose risk score was >0.065) was the optimal cut point for identification of subjects at increased risk. The two cut points had 77.8, 77.4, and 44.8% (for the SAHS) and 75.8, 71.6, and 11.9% (for the Botnia Study) sensitivity, specificity, and positive predictive value, respectively, in the SAHS and Botnia Study. CONCLUSIONS A two-step model, based on the combination of the SADPM and 1-h PG, is a useful tool for the identification of high-risk Mexican-American and Caucasian individuals. PMID:21788628

CRISP: Catheterization RISk score for Pediatrics: A Report from the Congenital Cardiac Interventional Study Consortium (CCISC).

PubMed

Nykanen, David G; Forbes, Thomas J; Du, Wei; Divekar, Abhay A; Reeves, Jaxk H; Hagler, Donald J; Fagan, Thomas E; Pedra, Carlos A C; Fleming, Gregory A; Khan, Danyal M; Javois, Alexander J; Gruenstein, Daniel H; Qureshi, Shakeel A; Moore, Phillip M; Wax, David H

2016-02-01

We sought to develop a scoring system that predicts the risk of serious adverse events (SAE's) for individual pediatric patients undergoing cardiac catheterization procedures. Systematic assessment of risk of SAE in pediatric catheterization can be challenging in view of a wide variation in procedure and patient complexity as well as rapidly evolving technology. A 10 component scoring system was originally developed based on expert consensus and review of the existing literature. Data from an international multi-institutional catheterization registry (CCISC) between 2008 and 2013 were used to validate this scoring system. In addition we used multivariate methods to further refine the original risk score to improve its predictive power of SAE's. Univariate analysis confirmed the strong correlation of each of the 10 components of the original risk score with SAE attributed to a pediatric cardiac catheterization (P < 0.001 for all variables). Multivariate analysis resulted in a modified risk score (CRISP) that corresponds to an increase in value of area under a receiver operating characteristic curve (AUC) from 0.715 to 0.741. The CRISP score predicts risk of occurrence of an SAE for individual patients undergoing pediatric cardiac catheterization procedures. © 2015 Wiley Periodicals, Inc.

A Review on Automatic Mammographic Density and Parenchymal Segmentation

PubMed Central

He, Wenda; Juette, Arne; Denton, Erika R. E.; Oliver, Arnau

2015-01-01

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models. PMID:26171249

Lysophosphatidic Acid and Apolipoprotein A1 Predict Increased Risk of Developing World Trade Center Lung Injury: A Nested Case-Control Study

PubMed Central

Tsukiji, Jun; Cho, Soo Jung; Echevarria, Ghislaine C.; Kwon, Sophia; Joseph, Phillip; Schenck, Edward J.; Naveed, Bushra; Prezant, David J.; Rom, William N.; Schmidt, Ann Marie; Weiden, Michael D.; Nolan, Anna

2014-01-01

Rationale Metabolic syndrome, inflammatory and vascular injury markers measured in serum after WTC exposures predict abnormal FEV1. We hypothesized that elevated LPA levels predict FEV1

Comparative Evaluation of Arabin Pessary and Cervical Cerclage for the Prevention of Preterm Labor in Asymptomatic Women with High Risk Factors

PubMed Central

Tsikouras, Panagiotis; Anastasopoulos, George; Maroulis, Vasileios; Bothou, Anastasia; Chalkidou, Anna; Deuteraiou, Dorelia; Anthoulaki, Xanthoula; Bourazan, Arzou Halil; Iatrakis, George; Zervoudis, Stefanos; Galazios, Georgios; Inagamova, Lola-Katerina; Csorba, Roland; Teichmann, Alexander-Tobias

2018-01-01

Objective: Preterm labor is one of the most significant obstetric problems associated with high rate of actual and long-term perinatal complications. Despite the creation of scoring systems, uterine activity monitoring, cervical ultrasound and several biochemical markers, the prediction and prevention of preterm labor is still a matter of concern. The aim of this study was to examine cervical findings for the prediction and the comparative use of Arabin pessary or cerclage for the prevention of preterm birth in asymptomatic women with high risk factors for preterm labor. Material and methods: The study group was composed of singleton pregnancies (spontaneously conceived) with high risk factors for preterm labor. Cervical length, dilatation of the internal cervical os and funneling, were estimated with transvaginal ultrasound during the first and the second trimesters of pregnancy. Results: Cervical funneling, during the second trimester of pregnancy, was the most significant factor for the prediction of preterm labor. The use of Arabin cervical pessary was found to be more effective than cerclage in the prolongation of pregnancy. Conclusion: In women at risk for preterm labor, the detection of cervical funneling in the second trimester of pregnancy may help to predict preterm labor and to apply the appropriate treatment for its prevention. Although the use of cervical pessary was found to be more effective than cerclage, more studies are needed to classify the effectiveness of different methods for such prevention. PMID:29670041

Comparison of risk classification between EndoPredict and MammaPrint in ER-positive/HER2-negative primary invasive breast cancer

PubMed Central

Peláez-García, Alberto; Yébenes, Laura; Berjón, Alberto; Angulo, Antonia; Zamora, Pilar; Sánchez-Méndez, José Ignacio; Espinosa, Enrique; Redondo, Andrés; Heredia-Soto, Victoria; Mendiola, Marta; Feliú, Jaime

2017-01-01

Purpose To compare the concordance in risk classification between the EndoPredict and the MammaPrint scores obtained for the same cancer samples on 40 estrogen-receptor positive/HER2-negative breast carcinomas. Methods Formalin-fixed, paraffin-embedded invasive breast carcinoma tissues that were previously analyzed with MammaPrint as part of routine care of the patients, and were classified as high-risk (20 patients) and low-risk (20 patients), were selected to be analyzed by the EndoPredict assay, a second generation gene expression test that combines expression of 8 genes (EP score) with two clinicopathological variables (tumor size and nodal status, EPclin score). Results The EP score classified 15 patients as low-risk and 25 patients as high-risk. EPclin re-classified 5 of the 25 EP high-risk patients into low-risk, resulting in a total of 20 high-risk and 20 low-risk tumors. EP score and MammaPrint score were significantly correlated (p = 0.008). Twelve of 20 samples classified as low-risk by MammaPrint were also low-risk by EP score (60%). 17 of 20 MammaPrint high-risk tumors were also high-risk by EP score. The overall concordance between EP score and MammaPrint was 72.5% (κ = 0.45, (95% CI, 0.182 to 0.718)). EPclin score also correlated with MammaPrint results (p = 0.004). Discrepancies between both tests occurred in 10 cases: 5 MammaPrint low-risk patients were classified as EPclin high-risk and 5 high-risk MammaPrint were classified as low-risk by EPclin and overall concordance of 75% (κ = 0.5, (95% CI, 0.232 to 0.768)). Conclusions This pilot study demonstrates a limited concordance between MammaPrint and EndoPredict. Differences in results could be explained by the inclusion of different gene sets in each platform, the use of different methodology, and the inclusion of clinicopathological parameters, such as tumor size and nodal status, in the EndoPredict test. PMID:28886093

The Predictive Validity of the Tilburg Frailty Indicator: Disability, Health Care Utilization, and Quality of Life in a Population at Risk

ERIC Educational Resources Information Center

Gobbens, Robbert J. J.; van Assen, Marcel A. L. M.; Luijkx, Katrien G.; Schols, Jos M. G. A.

2012-01-01

Purpose: To assess the predictive validity of frailty and its domains (physical, psychological, and social), as measured by the Tilburg Frailty Indicator (TFI), for the adverse outcomes disability, health care utilization, and quality of life. Design and Methods: The predictive validity of the TFI was tested in a representative sample of 484…

[Screening for risk of child abuse and neglect. A practicable method?].

PubMed

Kindler, H

2010-10-01

Selective primary prevention programs for child abuse and neglect depend on risk screening instruments that have the goal of systematically identifying families who can profit most from early help. Based on a systematic review of longitudinal studies, a set of established risk factors for early child abuse and neglect is presented. Nearly half of the items included in screening instruments can be seen as validated. Available studies indicate a high sensitivity of risk screening instruments. Positive predictive values, however, are low. Overall, the use of risk screening instruments in the area of primary prevention for families at risk represents a feasible method, as long as stigmatizing effects can be avoided and participating families also benefit beyond preventing endangerment.

Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods

NASA Astrophysics Data System (ADS)

Roos, Katarina; Hogner, Anders; Ogg, Derek; Packer, Martin J.; Hansson, Eva; Granberg, Kenneth L.; Evertsson, Emma; Nordqvist, Anneli

2015-12-01

In drug discovery, prediction of binding affinity ahead of synthesis to aid compound prioritization is still hampered by the low throughput of the more accurate methods and the lack of general pertinence of one method that fits all systems. Here we show the applicability of a method based on density functional theory using core fragments and a protein model with only the first shell residues surrounding the core, to predict relative binding affinity of a matched series of mineralocorticoid receptor (MR) antagonists. Antagonists of MR are used for treatment of chronic heart failure and hypertension. Marketed MR antagonists, spironolactone and eplerenone, are also believed to be highly efficacious in treatment of chronic kidney disease in diabetes patients, but is contra-indicated due to the increased risk for hyperkalemia. These findings and a significant unmet medical need among patients with chronic kidney disease continues to stimulate efforts in the discovery of new MR antagonist with maintained efficacy but low or no risk for hyperkalemia. Applied on a matched series of MR antagonists the quantum mechanical based method gave an R2 = 0.76 for the experimental lipophilic ligand efficiency versus relative predicted binding affinity calculated with the M06-2X functional in gas phase and an R2 = 0.64 for experimental binding affinity versus relative predicted binding affinity calculated with the M06-2X functional including an implicit solvation model. The quantum mechanical approach using core fragments was compared to free energy perturbation calculations using the full sized compound structures.

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.

PubMed

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 <0.0001) and validation data (AUC=0.769 versus AUC=0.747, P =0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P <0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P =0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index. WGRS19 improves prediction of adulthood 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.

Fatty acid amide hydrolase–morphine interaction influences ventilatory response to hypercapnia and postoperative opioid outcomes in children

PubMed Central

Chidambaran, Vidya; Pilipenko, Valentina; Spruance, Kristie; Venkatasubramanian, Raja; Niu, Jing; Fukuda, Tsuyoshi; Mizuno, Tomoyuki; Zhang, Kejian; Kaufman, Kenneth; Vinks, Alexander A; Martin, Lisa J; Sadhasivam, Senthilkumar

2017-01-01

Aim: Fatty acid amide hydrolase (FAAH) degrades anandamide, an endogenous cannabinoid. We hypothesized that FAAH variants will predict risk of morphine-related adverse outcomes due to opioid–endocannabinoid interactions. Patients & methods: In 101 postsurgical adolescents receiving morphine analgesia, we prospectively studied ventilatory response to 5% CO2 (HCVR), respiratory depression (RD) and vomiting. Blood was collected for genotyping and morphine pharmacokinetics. Results: We found significant FAAH–morphine interaction for missense (rs324420) and several regulatory variants, with HCVR (p < 0.0001) and vomiting (p = 0.0339). HCVR was more depressed in patients who developed RD compared with those who did not (p = 0.0034), thus FAAH–HCVR association predicts risk of impending RD from morphine use. Conclusion: FAAH genotypes predict risk for morphine-related adverse outcomes. PMID:27977335

Predictors of Depressive Symptoms among Foster Caregivers

ERIC Educational Resources Information Center

Cole, Susan A.; Eamon, Mary Keegan

2007-01-01

Objectives: The main purposes of this study were to determine (1) the prevalence of depressive symptoms among foster caregivers, (2) the social-demographics, risk factors, and social support predicting depressive symptoms, and (3) whether social support buffered the effects of the risk factors in the Illinois Foster Caregivers Study. Method:…

Independent data validation of an in vitro method for prediction of relative bioavailability of arsenic in contaminated soils

EPA Science Inventory

In vitro bioaccessibility assays (IVBA) estimate arsenic (As) relative bioavailability (RBA) in contaminated soils to improve the accuracy of site-specific human exposure assessments and risk calculations. For an IVBA assay to gain acceptance for use in risk assessment, it must ...

DEVELOPMENT AND EVALUATION OF NOVEL DOSE-RESPONSE MODELS FOR USE IN MICROBIAL RISK ASSESSMENT

EPA Science Inventory

This document contains a description of dose-response modeling methods designed to provide a robust approach under uncertainty for predicting human population risk from exposure to pathogens in drinking water.

The purpose of this document is to describe a body of literatu...

Emotion Regulation Predicts Attention Bias in Maltreated Children At-Risk for Depression

ERIC Educational Resources Information Center

Romens, Sarah E.; Pollak, Seth D.

2012-01-01

Background: Child maltreatment is associated with heightened risk for depression; however, not all individuals who experience maltreatment develop depression. Previous research indicates that maltreatment contributes to an attention bias for emotional cues, and that depressed individuals show attention bias for sad cues. Method: The present study…

The SAVRY Improves Prediction of Reoffending: A Naturalistic Longitudinal Comparative Study

ERIC Educational Resources Information Center

Åström, Therese; Gumpert, Clara Hellner; Andershed, Anna-Karin; Forster, Martin

2017-01-01

Purpose: This study investigated the utility of the risk assessment "Structured Assessment of Violence Risk in Youth" (SAVRY) within the social services in Stockholm County, Sweden. Method: SAVRY assessments of 56 adolescents were compared to assessments guided by another instrument (Adolescent Drug Abuse Diagnosis [ADAD]; n = 38) and…

Predicting the short-term risk of diabetes in HIV-positive patients: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study

PubMed Central

Petoumenos, Kathy; Worm, Signe W; Fontas, Eric; Weber, Rainer; De Wit, Stephane; Bruyand, Mathias; Reiss, Peter; El-Sadr, Wafaa; Monforte, Antonella D'Arminio; Friis-Møller, Nina; Lundgren, Jens D; Law, Matthew G

2012-01-01

Introduction HIV-positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM and other glucose-associated disorders among HIV-positive patients have been reported to range between 2 and 14%, and in an ageing HIV-positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short-term (six-month) risk of DM in HIV-positive populations and to compare the existing models developed in the general population. Methods All patients recruited to the Data Collection on Adverse events of Anti-HIV Drugs (D:A:D) study with follow-up data, without prior DM, myocardial infarction or other CVD events and with a complete DM risk factor profile were included. Conventional risk factors identified in the general population as well as key HIV-related factors were assessed using Poisson-regression methods. Expected probabilities of DM events were also determined based on the Framingham Offspring Study DM equation. The D:A:D and Framingham equations were then assessed using an internal-external validation process; area under the receiver operating characteristic (AUROC) curve and predicted DM events were determined. Results Of 33,308 patients, 16,632 (50%) patients were included, with 376 cases of new onset DM during 89,469 person-years (PY). Factors predictive of DM included higher glucose, body mass index (BMI) and triglyceride levels, and older age. Among HIV-related factors, recent CD4 counts of<200 cells/µL and lipodystrophy were predictive of new onset DM. The mean performance of the D:A:D and Framingham equations yielded AUROC of 0.894 (95% CI: 0.849, 0.940) and 0.877 (95% CI: 0.823, 0.932), respectively. The Framingham equation over-predicted DM events compared to D:A:D for lower glucose and lower triglycerides, and for BMI levels below 25 kg/m2. Conclusions The D:A:D equation performed well in predicting the short-term onset of DM in the validation dataset and for specific subgroups provided better estimates of DM risk than the Framingham. PMID:23078769

Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks.

PubMed

Blanche, Paul; Proust-Lima, Cécile; Loubère, Lucie; Berr, Claudine; Dartigues, Jean-François; Jacqmin-Gadda, Hélène

2015-03-01

Thanks to the growing interest in personalized medicine, joint modeling of longitudinal marker and time-to-event data has recently started to be used to derive dynamic individual risk predictions. Individual predictions are called dynamic because they are updated when information on the subject's health profile grows with time. We focus in this work on statistical methods for quantifying and comparing dynamic predictive accuracy of this kind of prognostic models, accounting for right censoring and possibly competing events. Dynamic area under the ROC curve (AUC) and Brier Score (BS) are used to quantify predictive accuracy. Nonparametric inverse probability of censoring weighting is used to estimate dynamic curves of AUC and BS as functions of the time at which predictions are made. Asymptotic results are established and both pointwise confidence intervals and simultaneous confidence bands are derived. Tests are also proposed to compare the dynamic prediction accuracy curves of two prognostic models. The finite sample behavior of the inference procedures is assessed via simulations. We apply the proposed methodology to compare various prediction models using repeated measures of two psychometric tests to predict dementia in the elderly, accounting for the competing risk of death. Models are estimated on the French Paquid cohort and predictive accuracies are evaluated and compared on the French Three-City cohort. © 2014, The International Biometric Society.

A New Method to Assess Asymmetry in Fingerprints Could Be Used as an Early Indicator of Type 2 Diabetes Mellitus

PubMed Central

Morris, Molly R.; Ludwar, Bjoern Ch.; Swingle, Evan; Mamo, Mahelet N.; Shubrook, Jay H.

2016-01-01

Background: Inexpensive screening tools are needed to identify individuals predisposed to developing diabetes mellitus (DM). Such early identification coupled with an effective intervention could help many people avoid the substantial health costs of this disease. We investigated the hypothesis that fluctuating asymmetry (FA) in fingerprints is an indicator of type 2 diabetes mellitus (T2DM). Methods: Participants with T2DM, with T1DM, and without any indication or known family history of diabetes were fingerprinted with a Crossmatch Verifier 320 LC scanner. Asymmetry scores for each finger pair were assessed using both pattern analysis (ridge counts), and a wavelet-based analysis. Results: Both methods for scoring asymmetry predicted risk of T2DM for finger pair IV, controlling for gender and age. AUC scores were significantly greater than the null for pattern asymmetry scores (finger IV AUC = 0.74), and wavelet asymmetry scores for finger pair IV (AUC = 0.73) and finger pair V (AUC = 0.73), for predicting T2DM. In addition, wavelet asymmetry scores for finger pair IV (AUC = 0.80) and finger pair V (AUC = 0.85) significantly predicted risk of T1DM. Conclusions: A diagnostic tool based on FA in the fingerprints of finger pair IV, measured using a wavelet analysis could be developed for predicting risk prior to associated health problems for both T2DM and T1DM. In addition, given that that the prints for fingers IV and V develop during the 14-17 weeks of gestation, we predict that interventions during this time period of pregnancy will be most successful. PMID:26830490

Risk assessment predictions of open dumping area after closure using Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Pauzi, Nur Irfah Mohd; Radhi, Mohd Shahril Mat; Omar, Husaini

2017-10-01

Currently, there are many abandoned open dumping areas that were left without any proper mitigation measures. These open dumping areas could pose serious hazard to human and pollute the environment. The objective of this paper is to determine the risk assessment at the open dumping area after they has been closed using Monte Carlo Simulation method. The risk assessment exercise is conducted at the Kuala Lumpur dumping area. The rapid urbanisation of Kuala Lumpur coupled with increase in population lead to increase in waste generation. It leads to more dumping/landfill area in Kuala Lumpur. The first stage of this study involve the assessment of the dumping area and samples collections. It followed by measurement of settlement of dumping area using oedometer. The risk of the settlement is predicted using Monte Carlo simulation method. Monte Carlo simulation calculates the risk and the long-term settlement. The model simulation result shows that risk level of the Kuala Lumpur open dumping area ranges between Level III to Level IV i.e. between medium risk to high risk. These settlement (ΔH) is between 3 meters to 7 meters. Since the risk is between medium to high, it requires mitigation measures such as replacing the top waste soil with new sandy gravel soil. This will increase the strength of the soil and reduce the settlement.

Optimizing protocols for risk prediction in asymptomatic carotid stenosis using embolic signal detection: the Asymptomatic Carotid Emboli Study.

PubMed

King, Alice; Shipley, Martin; Markus, Hugh

2011-10-01

Improved methods are required to identify patients with asymptomatic carotid stenosis at high risk for stroke. The Asymptomatic Carotid Emboli Study recently showed embolic signals (ES) detected by transcranial Doppler on 2 recordings that lasted 1-hour independently predict 2-year stroke risk. ES detection is time-consuming, and whether similar predictive information could be obtained from simpler recording protocols is unknown. In a predefined secondary analysis of Asymptomatic Carotid Emboli Study, we looked at the temporal variation of ES. We determined the predictive yield associated with different recording protocols and with the use of a higher threshold to indicate increased risk (≥2 ES). To compare the different recording protocols, sensitivity and specificity analyses were performed using analysis of receiver-operator characteristic curves. Of 477 patients, 467 had baseline recordings adequate for analysis; 77 of these had ES on 1 or both of the 2 recordings. ES status on the 2 recordings was significantly associated (P<0.0001), but there was poor agreement between ES positivity on the 2 recordings (κ=0.266). For the primary outcome of ipsilateral stroke or transient ischemic attack, the use of 2 baseline recordings lasting 1 hour had greater predictive accuracy than either the first baseline recording alone (P=0.0005), a single 30-minute (P<0.0001) recording, or 2 recordings lasting 30 minutes (P<0.0001). For the outcome of ipsilateral stroke alone, two recordings lasting 1 hour had greater predictive accuracy when compared to all other recording protocols (all P<0.0001). Our analysis demonstrates the relative predictive yield of different recording protocols that can be used in application of the technique in clinical practice. Two baseline recordings lasting 1 hour as used in Asymptomatic Carotid Emboli Study gave the best risk prediction.

Hyaluronic Acid Levels Predict Risk of Hepatic Encephalopathy and Liver-Related Death in HIV/Viral Hepatitis Coinfected Patients

PubMed Central

Peters, Lars; Mocroft, Amanda; Soriano, Vincent; Rockstroh, Jürgen; Rauch, Andri; Karlsson, Anders; Knysz, Brygida; Pradier, Christian; Zilmer, Kai; Lundgren, Jens D.

2013-01-01

Background Whereas it is well established that various soluble biomarkers can predict level of liver fibrosis, their ability to predict liver-related clinical outcomes is less clearly established, in particular among HIV/viral hepatitis co-infected persons. We investigated plasma hyaluronic acid’s (HA) ability to predict risk of liver-related events (LRE; hepatic coma or liver-related death) in the EuroSIDA study. Methods Patients included were positive for anti-HCV and/or HBsAg with at least one available plasma sample. The earliest collected plasma sample was tested for HA (normal range 0–75 ng/mL) and levels were associated with risk of LRE. Change in HA per year of follow-up was estimated after measuring HA levels in latest sample before the LRE for those experiencing this outcome (cases) and in a random selection of one sixth of the remaining patients (controls). Results During a median of 8.2 years of follow-up, 84/1252 (6.7%) patients developed a LRE. Baseline median (IQR) HA in those without and with a LRE was 31.8 (17.2–62.6) and 221.6 ng/mL (74.9–611.3), respectively (p<0.0001). After adjustment, HA levels predicted risk of contracting a LRE; incidence rate ratios for HA levels 75–250 or ≥250 vs. <75 ng/mL were 5.22 (95% CI 2.86–9.26, p<0.0007) and 28.22 (95% CI 14.95–46.00, p<0.0001), respectively. Median HA levels increased substantially prior to developing a LRE (107.6 ng/mL, IQR 0.8 to 251.1), but remained stable for controls (1.0 ng/mL, IQR –5.1 to 8.2), (p<0.0001 comparing cases and controls), and greater increases predicted risk of a LRE in adjusted models (p<0.001). Conclusions An elevated level of plasma HA, particularly if the level further increases over time, substantially increases the risk of contracting LRE over the next five years. HA is an inexpensive, standardized and non-invasive supplement to other methods aimed at identifying HIV/viral hepatitis co-infected patients at risk of hepatic complications. PMID:23724041

The Fatigue Approach to Vibration and Health: is it a Practical and Viable way of Predicting the Effects on People?

NASA Astrophysics Data System (ADS)

Sandover, J.

1998-08-01

The fatigue approach assumes that the vertebral end-plates are the weak link in the spine subjected to shock and vibration, and fail as a result of material fatigue. The theory assumes that end-plate damage leads to degeneration and pain in the lumbar spine. There is evidence for both the damage predicted and the fatigue mode of failure so that the approach may provide a basis for predictive methods for use in epidemiology and standards. An available data set from a variety of heavy vehicles in practical situations was used for predictions of spinal stress and fatigue life. Although there was some disparity between the predictive methods used, the more developed methods indicated fatigue lives that appeared reasonable, taking into account the vehicles tested and our knowledge of spinal degeneration. It is argued that the modelling and fatigue approaches combined offer a basis for estimating the effects of vibration and shock on health. Although the human variables are such that the approach, as yet, only offers rough estimates, it offers a good basis for understanding. The approach indicates that peak values are important and large peaks dominate risk. The method indicates that long term r.m.s. methods probably underestimate the risk of injury. The BS 6841Wband ISO 2631Wkweightings have shortcomings when used where peak values are important. A simple model may be more appropriate. The principle can be applied to continuous vibration as well as high acceleration events so that one method can be applied universally to continuous vibrations, high acceleration events and mixtures of these. An endurance limit can be hypothesised and, if this limit is sufficiently high, then the need for many measurements can be reduced.

Comparisons of survival predictions using survival risk ratios based on International Classification of Diseases, Ninth Revision and Abbreviated Injury Scale trauma diagnosis codes.

PubMed

Clarke, John R; Ragone, Andrew V; Greenwald, Lloyd

2005-09-01

We conducted a comparison of methods for predicting survival using survival risk ratios (SRRs), including new comparisons based on International Classification of Diseases, Ninth Revision (ICD-9) versus Abbreviated Injury Scale (AIS) six-digit codes. From the Pennsylvania trauma center's registry, all direct trauma admissions were collected through June 22, 1999. Patients with no comorbid medical diagnoses and both ICD-9 and AIS injury codes were used for comparisons based on a single set of data. SRRs for ICD-9 and then for AIS diagnostic codes were each calculated two ways: from the survival rate of patients with each diagnosis and when each diagnosis was an isolated diagnosis. Probabilities of survival for the cohort were calculated using each set of SRRs by the multiplicative ICISS method and, where appropriate, the minimum SRR method. These prediction sets were then internally validated against actual survival by the Hosmer-Lemeshow goodness-of-fit statistic. The 41,364 patients had 1,224 different ICD-9 injury diagnoses in 32,261 combinations and 1,263 corresponding AIS injury diagnoses in 31,755 combinations, ranging from 1 to 27 injuries per patient. All conventional ICD-9-based combinations of SRRs and methods had better Hosmer-Lemeshow goodness-of-fit statistic fits than their AIS-based counterparts. The minimum SRR method produced better calibration than the multiplicative methods, presumably because it did not magnify inaccuracies in the SRRs that might occur with multiplication. Predictions of survival based on anatomic injury alone can be performed using ICD-9 codes, with no advantage from extra coding of AIS diagnoses. Predictions based on the single worst SRR were closer to actual outcomes than those based on multiplying SRRs.

Prenatal diagnosis of hemoglobinopathies: evaluation of techniques for analysing globin-chain synthesis in blood samples obtained by fetoscopy.

PubMed Central

Congote, L. F.; Hamilton, E. F.; Chow, J. C.; Perry, T. B.

1982-01-01

Three techniques for analysing hemoglobin synthesis in blood samples obtained by fetoscopy were evaluated. Of the fetuses studied, 12 were not at risk of genetic disorders, 10 were at risk of beta-thalassemia, 2 were at risk of sickle cell anemia and 1 was at risk of both diseases. The conventional method of prenatal diagnosis of hemoglobinopathies, involving the separation of globin chains labelled with a radioactive isotope on carboxymethyl cellulose (CMC) columns, was compared with a method involving globin-chain separation by high-pressure liquid chromatography (HPLC) and with direct analysis of labelled hemoglobin tetramers obtained from cell lysates by chromatography on ion-exchange columns. The last method is technically the simplest and can be used for diagnosing beta-thalassemia and sickle cell anemia. However, it gives spuriously high levels of adult hemoglobin in samples containing nonlabelled adult hemoglobin. HPLC is the fastest method for prenatal diagnosis of beta-thalassemia and may prove as reliable as the CMC method. Of the 13 fetuses at risk for hemoglobinopathies, 1 was predicted to be affected, and the diagnosis was confirmed in the abortus. Of 12 predicted to be unaffected, 1 was aborted spontaneously and was unavailable for confirmatory studies, as were 3 of the infants; however, the diagnosis was confirmed in seven cases and is awaiting confirmation when the infant in 6 months old in one case. Couples at risk of bearing a child with a hemoglobinopathy should be referred for genetic counselling before pregnancy or, at the latest, by the 12th week of gestation so that prenatal diagnosis can be attempted by amniocentesis, safer procedure, with restriction endonuclease analysis of the amniotic fluid cells. PMID:7139502

Biomarkers for predicting type 2 diabetes development—Can metabolomics improve on existing biomarkers?

PubMed Central

Savolainen, Otto; Fagerberg, Björn; Vendelbo Lind, Mads; Sandberg, Ann-Sofie; Ross, Alastair B.; Bergström, Göran

2017-01-01

Aim The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. Methods Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. Results Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738–0.850]) and 0.808 [0.749–0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577–0.736]). Prediction based on non-blood based measures was 0.638 [0.565–0.711]). Conclusions Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model. PMID:28692646

Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients.

PubMed

Marschollek, Michael; Rehwald, Anja; Wolf, Klaus-Hendrik; Gietzelt, Matthias; Nemitz, Gerhard; zu Schwabedissen, Hubertus Meyer; Schulze, Mareike

2011-06-28

Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

Theory-Based Cartographic Risk Model Development and Application for Home Fire Safety.

PubMed

Furmanek, Stephen; Lehna, Carlee; Hanchette, Carol

There is a gap in the use of predictive risk models to identify areas at risk for home fires and burn injury. The purpose of this study was to describe the creation, validation, and application of such a model using a sample from an intervention study with parents of newborns in Jefferson County, KY, as an example. Performed was a literature search to identify risk factors for home fires and burn injury in the target population. Obtained from the American Community Survey at the census tract level and synthesized to create a predictive cartographic risk model was risk factor data. Model validation was performed through correlation, regression, and Moran's I with fire incidence data from open records. Independent samples t-tests were used to examine the model in relation to geocoded participant addresses. Participant risk level for fire rate was determined and proximity to fire station service areas and hospitals. The model showed high and severe risk clustering in the northwest section of the county. Strongly correlated with fire rate was modeled risk; the best predictive model for fire risk contained home value (low), race (black), and non high school graduates. Applying the model to the intervention sample, the majority of participants were at lower risk and mostly within service areas closest to a fire department and hospital. Cartographic risk models were useful in identifying areas at risk and analyzing participant risk level. The methods outlined in this study are generalizable to other public health issues.

Relationship Status Predicts Lower Restrictive Eating Pathology for Bisexual and Gay Men across 10-year Follow-up

PubMed Central

Brown, Tiffany A.; Keel, Pamela K.

2015-01-01

Objective Cross-sectional studies support that bisexual and gay (BG) men are at increased risk for eating pathology, and romantic relationships may buffer against risk; however, no studies have examined this association longitudinally. The current study examined how romantic relationships impact the trajectory of eating pathology in BG versus heterosexual men. Method BG (n=51) and heterosexual (n=522) men completed surveys of health and eating behaviors at baseline and 10-year follow-up. Results For BG men, being single at baseline prospectively predicted an increase in Drive for Thinness scores over 10-year follow-up. Additionally, for BG men in relationships at baseline, lower relationship satisfaction predicted an increase in Drive for Thinness scores over time. Conversely, these relationship variables did not predict trajectory of eating pathology for heterosexual men. Discussion Implications for theoretical models of risk, including objectification theory and sexual minority stress theory, and prevention, including peer-led cognitive dissonance based interventions, are discussed. PMID:26172055

Clusters of Behaviors and Beliefs Predicting Adolescent Depression: Implications for Prevention

PubMed Central

Paunesku, David; Ellis, Justin; Fogel, Joshua; Kuwabara, Sachiko A; Gollan, Jackie; Gladstone, Tracy; Reinecke, Mark; Van Voorhees, Benjamin W.

2009-01-01

OBJECTIVE Risk factors for various disorders are known to cluster. However, the factor structure for behaviors and beliefs predicting depressive disorder in adolescents is not known. Knowledge of this structure can facilitate prevention planning. METHODS We used the National Longitudinal Study of Adolescent Health (AddHealth) data set to conduct an exploratory factor analysis to identify clusters of behaviors/experiences predicting the onset of major depressive disorder (MDD) at 1-year follow-up (N=4,791). RESULTS Four factors were identified: family/interpersonal relations, self-emancipation, avoidant problem solving/low self-worth, and religious activity. Strong family/interpersonal relations were the most significantly protective against depression at one year follow-up. Avoidant problem solving/low self-worth was not predictive of MDD on its own, but significantly amplified the risks associated with delinquency. CONCLUSION Depression prevention interventions should consider giving family relationships a more central role in their efforts. Programs teaching problem solving skills may be most appropriate for reducing MDD risk in delinquent youth. PMID:20502621

Application of a predictive Bayesian model to environmental accounting.

PubMed

Anex, R P; Englehardt, J D

2001-03-30

Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.

Development of a Melanoma Risk Prediction Model Incorporating MC1R Genotype and Indoor Tanning Exposure: Impact of Mole Phenotype on Model Performance

PubMed Central

Penn, Lauren A.; Qian, Meng; Zhang, Enhan; Ng, Elise; Shao, Yongzhao; Berwick, Marianne; Lazovich, DeAnn; Polsky, David

2014-01-01

Background Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. Methods Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as “none”, “few”, “some” or “many” moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant (“none” and “few” moles) and nevus-prone (“some” and “many” moles)). Results Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71–0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. Conclusions These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals. PMID:25003831

Improving Global Vascular Risk Prediction with Behavioral and Anthropometric Factors: The Multi-ethnic Northern Manhattan Cohort Study

PubMed Central

Sacco, Ralph L.; Khatri, Minesh; Rundek, Tatjana; Xu, Qiang; Gardener, Hannah; Boden-Albala, Bernadette; Di Tullio, Marco R.; Homma, Shunichi; Elkind, Mitchell SV; Paik, Myunghee C

2010-01-01

Objective To improve global vascular risk prediction with behavioral and anthropometric factors. Background Few cardiovascular risk models are designed to predict the global vascular risk of MI, stroke, or vascular death in multi-ethnic individuals, and existing schemes do not fully include behavioral risk factors. Methods A randomly-derived, population-based, prospective cohort of 2737 community participants free of stroke and coronary artery disease were followed annually for a median of 9.0 years in the Northern Manhattan Study (mean age 69 years; 63.2% women; 52.7% Hispanic, 24.9% African-American, 19.9% white). A global vascular risk score (GVRS) predictive of stroke, myocardial infarction, or vascular death was developed by adding variables to the traditional Framingham cardiovascular variables based on the likelihood ratio criterion. Model utility was assessed through receiver operating characteristics, calibration, and effect on reclassification of subjects. Results Variables which significantly added to the traditional Framingham profile included waist circumference, alcohol consumption, and physical activity. Continuous measures for blood pressure and fasting blood sugar were used instead of hypertension and diabetes. Ten -year event-free probabilities were 0.95 for the first quartile of GVRS, 0.89 for the second quartile, 0.79 for the third quartile, and 0.56 for the fourth quartile. The addition of behavioral factors in our model improved prediction of 10 -year event rates compared to a model restricted to the traditional variables. Conclusion A global vascular risk score that combines both traditional, behavioral, and anthropometric risk factors, uses continuous variables for physiological parameters, and is applicable to non-white subjects could improve primary prevention strategies. PMID:19958966

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.

PubMed

Heidari, Morteza; Khuzani, Abolfazl Zargari; Hollingsworth, Alan B; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2018-01-30

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Hollingsworth, Alan B.; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2018-02-01

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

Assessing the Importance of Atlantic Basin Tropical Cyclone Steering Currents in Anticipating Landfall Risk

NASA Astrophysics Data System (ADS)

Truchelut, R.; Hart, R. E.

2013-12-01

While a number of research groups offer quantitative pre-seasonal assessments of aggregate annual Atlantic Basin tropical cyclone activity, the literature is comparatively thin concerning methods to meaningfully quantify seasonal U.S. landfall risks. As the example of Hurricane Andrew impacting Southeast Florida in the otherwise quiet 1992 season demonstrates, an accurate probabilistic assessment of seasonal tropical cyclone threat levels would be of immense public utility and economic value; however, the methods used to predict annual activity demonstrate little skill for predicting annual count of landfalling systems of any intensity bin. Therefore, while current models are optimized to predict cumulative seasonal tropical cyclone activity, they are not ideal tools for assessing the potential for sensible impacts of storms on populated areas. This research aims to bridge the utility gap in seasonal tropical cyclone forecasting by shifting the focus of seasonal modelling to the parameters that are most closely linked to creating conditions favorable for U.S. landfalls, particularly those of destructive and costly intense hurricanes. As it is clear from the initial findings of this study that overall activity has a limited influence on sensible outcomes, this project concentrates on detecting predictability and trends in cyclogenesis location and upper-level wind steering patterns. These metrics are demonstrated to have a relationship with landfall activity in the Atlantic Basin climatological record. By aggregating historic seasonally-averaged steering patterns using newly-available reanalysis model datasets, some atmospheric and oceanic precursors to an elevated risk of North American tropical cyclone landfall have been identified. Work is ongoing to quantify the variance, persistence, and predictability of such patterns over seasonal timescales, with the aim of yielding tools that could be incorporated into tropical cyclone risk mitigation strategies.

Development of a screening tool to predict malnutrition among children under two years old in Zambia

PubMed Central

Hasegawa, Junko; Ito, Yoichi M; Yamauchi, Taro

2017-01-01

ABSTRACT Background: Maternal and child undernutrition is an important issue, particularly in low- and middle-income countries. Children at high risk of malnutrition should be prioritized to receive necessary interventions to minimize such risk. Several risk factors have been proposed; however, until now, there has been no appropriate evaluation method to identify these children. In sub-Saharan Africa, children commonly receive regular check-ups from community health workers. A simple and easy nutrition assessment method is therefore needed for use by semi-professional health workers. Objectives: The aim of this study was to develop and test a practical screening tool for community use in predicting growth stunting in children under two years in rural Zambia. Methods: Field research was conducted from July to August 2014 in Southern Province, Zambia. Two hundred and sixty-four mother-child pairs participated in the study. Anthropometric measurements were performed on all children and mothers, and all mothers were interviewed. Risk factors for the screening test were estimated by using least absolute shrinkage and selection operator analysis. After re-evaluating all participants using the new screening tool, a receiver operating characteristic curve was drawn to set the cut-off value. Sensitivity and specificity were also calculated. Results: The screening tool included age, weight-for-age Z-score status, birth weight, feeding status, history of sibling death, multiple birth, and maternal education level. The total score ranged from 0 to 22, and the cut-off value was eight. Sensitivity and specificity were 0.963 and 0.697 respectively. Conclusions: A screening tool was developed to predict children at high risk of malnutrition living in Zambia. Further longitudinal studies are needed to confirm the test’s validity in detecting future stunting and to investigate the effectiveness of malnutrition treatment. PMID:28730929

Early identification of posttraumatic stress following military deployment: Application of machine learning methods to a prospective study of Danish soldiers.

PubMed

Karstoft, Karen-Inge; Statnikov, Alexander; Andersen, Søren B; Madsen, Trine; Galatzer-Levy, Isaac R

2015-09-15

Pre-deployment identification of soldiers at risk for long-term posttraumatic stress psychopathology after home coming is important to guide decisions about deployment. Early post-deployment identification can direct early interventions to those in need and thereby prevents the development of chronic psychopathology. Both hold significant public health benefits given large numbers of deployed soldiers, but has so far not been achieved. Here, we aim to assess the potential for pre- and early post-deployment prediction of resilience or posttraumatic stress development in soldiers by application of machine learning (ML) methods. ML feature selection and prediction algorithms were applied to a prospective cohort of 561 Danish soldiers deployed to Afghanistan in 2009 to identify unique risk indicators and forecast long-term posttraumatic stress responses. Robust pre- and early postdeployment risk indicators were identified, and included individual PTSD symptoms as well as total level of PTSD symptoms, previous trauma and treatment, negative emotions, and thought suppression. The predictive performance of these risk indicators combined was assessed by cross-validation. Together, these indicators forecasted long term posttraumatic stress responses with high accuracy (pre-deployment: AUC = 0.84 (95% CI = 0.81-0.87), post-deployment: AUC = 0.88 (95% CI = 0.85-0.91)). This study utilized a previously collected data set and was therefore not designed to exhaust the potential of ML methods. Further, the study relied solely on self-reported measures. Pre-deployment and early post-deployment identification of risk for long-term posttraumatic psychopathology are feasible and could greatly reduce the public health costs of war. Copyright © 2015 Elsevier B.V. All rights reserved.

Purposes and methods of scoring earthquake forecasts

NASA Astrophysics Data System (ADS)

Zhuang, J.

2010-12-01

There are two kinds of purposes in the studies on earthquake prediction or forecasts: one is to give a systematic estimation of earthquake risks in some particular region and period in order to give advice to governments and enterprises for the use of reducing disasters, the other one is to search for reliable precursors that can be used to improve earthquake prediction or forecasts. For the first case, a complete score is necessary, while for the latter case, a partial score, which can be used to evaluate whether the forecasts or predictions have some advantages than a well know model, is necessary. This study reviews different scoring methods for evaluating the performance of earthquake prediction and forecasts. Especially, the gambling scoring method, which is developed recently, shows its capacity in finding good points in an earthquake prediction algorithm or model that are not in a reference model, even if its overall performance is no better than the reference model.

Estimation and prediction under local volatility jump-diffusion model

NASA Astrophysics Data System (ADS)

Kim, Namhyoung; Lee, Younhee

2018-02-01

Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

Prediction of hip fracture risk by quantitative ultrasound in more than 7000 Swiss women > or =70 years of age: comparison of three technologically different bone ultrasound devices in the SEMOF study.

PubMed

Krieg, Marc-Antoine; Cornuz, Jacques; Ruffieux, Christiane; Van Melle, Guy; Büche, Daniel; Dambacher, Maximilian A; Hans, Didier; Hartl, Florian; Häuselmann, Hansjorg J; Kraenzlin, Marius; Lippuner, Kurt; Neff, Maurus; Pancaldi, Pierro; Rizzoli, Rene; Tanzi, Franco; Theiler, Robert; Tyndall, Alan; Wimpfheimer, Claus; Burckhardt, Peter

2006-09-01

To compare the prediction of hip fracture risk of several bone ultrasounds (QUS), 7062 Swiss women > or =70 years of age were measured with three QUSs (two of the heel, one of the phalanges). Heel QUSs were both predictive of hip fracture risk, whereas the phalanges QUS was not. As the number of hip fracture is expected to increase during these next decades, it is important to develop strategies to detect subjects at risk. Quantitative bone ultrasound (QUS), an ionizing radiation-free method, which is transportable, could be interesting for this purpose. The Swiss Evaluation of the Methods of Measurement of Osteoporotic Fracture Risk (SEMOF) study is a multicenter cohort study, which compared three QUSs for the assessment of hip fracture risk in a sample of 7609 elderly ambulatory women > or =70 years of age. Two QUSs measured the heel (Achilles+; GE-Lunar and Sahara; Hologic), and one measured the heel (DBM Sonic 1200; IGEA). The Cox proportional hazards regression was used to estimate the hazard of the first hip fracture, adjusted for age, BMI, and center, and the area under the ROC curves were calculated to compare the devices and their parameters. From the 7609 women who were included in the study, 7062 women 75.2 +/- 3.1 (SD) years of age were prospectively followed for 2.9 +/- 0.8 years. Eighty women reported a hip fracture. A decrease by 1 SD of the QUS variables corresponded to an increase of the hip fracture risk from 2.3 (95% CI, 1.7, 3.1) to 2.6 (95% CI, 1.9, 3.4) for the three variables of Achilles+ and from 2.2 (95% CI, 1.7, 3.0) to 2.4 (95% CI, 1.8, 3.2) for the three variables of Sahara. Risk gradients did not differ significantly among the variables of the two heel QUS devices. On the other hand, the phalanges QUS (DBM Sonic 1200) was not predictive of hip fracture risk, with an adjusted hazard risk of 1.2 (95% CI, 0.9, 1.5), even after reanalysis of the digitalized data and using different cut-off levels (1700 or 1570 m/s). In this elderly women population, heel QUS devices were both predictive of hip fracture risk, whereas the phalanges QUS device was not.

Microvolt T-Wave Alternans

PubMed Central

Verrier, Richard L.; Klingenheben, Thomas; Malik, Marek; El-Sherif, Nabil; Exner, Derek V.; Hohnloser, Stefan H.; Ikeda, Takanori; Martínez, Juan Pablo; Narayan, Sanjiv M.; Nieminen, Tuomo; Rosenbaum, David S.

2014-01-01

This consensus guideline was prepared on behalf of the International Society for Holter and Noninvasive Electrocardiology and is cosponsored by the Japanese Circulation Society, the Computers in Cardiology Working Group on e-Cardiology of the European Society of Cardiology, and the European Cardiac Arrhythmia Society. It discusses the electrocardiographic phenomenon of T-wave alternans (TWA) (i.e., a beat-to-beat alternation in the morphology and amplitude of the ST- segment or T-wave). This statement focuses on its physiological basis and measurement technologies and its clinical utility in stratifying risk for life-threatening ventricular arrhythmias. Signal processing techniques including the frequency-domain Spectral Method and the time-domain Modified Moving Average method have demonstrated the utility of TWA in arrhythmia risk stratification in prospective studies in >12,000 patients. The majority of exercise-based studies using both methods have reported high relative risks for cardiovascular mortality and for sudden cardiac death in patients with preserved as well as depressed left ventricular ejection fraction. Studies with ambulatory electrocardiogram-based TWA analysis with Modified Moving Average method have yielded significant predictive capacity. However, negative studies with the Spectral Method have also appeared, including 2 interventional studies in patients with implantable defibrillators. Meta-analyses have been performed to gain insights into this issue. Frontiers of TWA research include use in arrhythmia risk stratification of individuals with preserved ejection fraction, improvements in predictivity with quantitative analysis, and utility in guiding medical as well as device-based therapy. Overall, although TWA appears to be a useful marker of risk for arrhythmic and cardiovascular death, there is as yet no definitive evidence that it can guide therapy. PMID:21920259

A feasibility study in adapting Shamos Bickel and Hodges Lehman estimator into T-Method for normalization

NASA Astrophysics Data System (ADS)

Harudin, N.; Jamaludin, K. R.; Muhtazaruddin, M. Nabil; Ramlie, F.; Muhamad, Wan Zuki Azman Wan

2018-03-01

T-Method is one of the techniques governed under Mahalanobis Taguchi System that developed specifically for multivariate data predictions. Prediction using T-Method is always possible even with very limited sample size. The user of T-Method required to clearly understanding the population data trend since this method is not considering the effect of outliers within it. Outliers may cause apparent non-normality and the entire classical methods breakdown. There exist robust parameter estimate that provide satisfactory results when the data contain outliers, as well as when the data are free of them. The robust parameter estimates of location and scale measure called Shamos Bickel (SB) and Hodges Lehman (HL) which are used as a comparable method to calculate the mean and standard deviation of classical statistic is part of it. Embedding these into T-Method normalize stage feasibly help in enhancing the accuracy of the T-Method as well as analysing the robustness of T-method itself. However, the result of higher sample size case study shows that T-method is having lowest average error percentages (3.09%) on data with extreme outliers. HL and SB is having lowest error percentages (4.67%) for data without extreme outliers with minimum error differences compared to T-Method. The error percentages prediction trend is vice versa for lower sample size case study. The result shows that with minimum sample size, which outliers always be at low risk, T-Method is much better on that, while higher sample size with extreme outliers, T-Method as well show better prediction compared to others. For the case studies conducted in this research, it shows that normalization of T-Method is showing satisfactory results and it is not feasible to adapt HL and SB or normal mean and standard deviation into it since it’s only provide minimum effect of percentages errors. Normalization using T-method is still considered having lower risk towards outlier’s effect.

Development of Physics-Based Hurricane Wave Response Functions: Application to Selected Sites on the U.S. Gulf Coast

NASA Astrophysics Data System (ADS)

McLaughlin, P. W.; Kaihatu, J. M.; Irish, J. L.; Taylor, N. R.; Slinn, D.

2013-12-01

Recent hurricane activity in the Gulf of Mexico has led to a need for accurate, computationally efficient prediction of hurricane damage so that communities can better assess risk of local socio-economic disruption. This study focuses on developing robust, physics based non-dimensional equations that accurately predict maximum significant wave height at different locations near a given hurricane track. These equations (denoted as Wave Response Functions, or WRFs) were developed from presumed physical dependencies between wave heights and hurricane characteristics and fit with data from numerical models of waves and surge under hurricane conditions. After curve fitting, constraints which correct for fully developed sea state were used to limit the wind wave growth. When applied to the region near Gulfport, MS, back prediction of maximum significant wave height yielded root mean square errors between 0.22-0.42 (m) at open coast stations and 0.07-0.30 (m) at bay stations when compared to the numerical model data. The WRF method was also applied to Corpus Christi, TX and Panama City, FL with similar results. Back prediction errors will be included in uncertainty evaluations connected to risk calculations using joint probability methods. These methods require thousands of simulations to quantify extreme value statistics, thus requiring the use of reduced methods such as the WRF to represent the relevant physical processes.

A Revised Framingham Stroke Risk Profile to Reflect Temporal Trends

PubMed Central

Dufouil, Carole; Beiser, Alexa; McLure, Leslie A.; Wolf, Philip A.; Tzourio, Christophe; Howard, Virginia J; Westwood, Andrew J.; Himali, Jayandra J.; Sullivan, Lisa; Aparicio, Hugo J.; Kelly-Hayes, Margaret; Ritchie, Karen; Kase, Carlos S.; Pikula, Aleksandra; Romero, Jose R.; D’Agostino, Ralph B.; Samieri, Cécilia; Vasan, Ramachandran S.; Chêne, Genevieve; Howard, George; Seshadri, Sudha

2017-01-01

Background Age-adjusted stroke incidence has decreased over the past 50 years, likely due to changes in the prevalence and impact of various stroke risk factors. An updated version of the Framingham Stroke Risk Profile (FSRP) might better predict current risks in the Framingham Heart Study (FHS) and other cohorts. We compared the accuracy of the standard (Old), and of a revised (New) version of the FSRP in predicting the risk of all-stroke and ischemic stroke, and validated this new FSRP in two external cohorts, the 3 Cities (3C) and REGARDS studies. Methods We computed the old FSRP as originally described, and a new model that used the most recent epoch-specific risk factors' prevalence and hazard-ratios for persons ≥ 55 years and for the subsample ≥ 65 years (to match the age range in REGARDS and 3C studies respectively), and compared the efficacy of these models in predicting 5- and 10-year stroke risks. Results The new FSRP was a better predictor of current stroke risks in all three samples than the old FSRP (Calibration chi-squares of new/old FSRP: in men 64.0/12.1, 59.4/30.6 and 20.7/12.5; in women 42.5/4.1, 115.4/90.3 and 9.8/6.5 in FHS, REGARDS and 3C, respectively). In the REGARDS, the new FSRP was a better predictor among whites compared to blacks. Conclusions A more contemporaneous, new FSRP better predicts current risks in 3 large community samples and could serve as the basis for examining geographic and racial differences in stroke risk and the incremental diagnostic utility of novel stroke risk factors. PMID:28159800

Developmental dyslexia: predicting individual risk

PubMed Central

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

2015-01-01

Background Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 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. Results 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. Conclusions 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. PMID:25832320

External Validation of the HERNIAscore: An Observational Study.

PubMed

Cherla, Deepa V; Moses, Maya L; Mueck, Krislynn M; Hannon, Craig; Ko, Tien C; Kao, Lillian S; Liang, Mike K

2017-09-01

The HERNIAscore is a ventral incisional hernia (VIH) risk assessment tool that uses only preoperative variables and predictable intraoperative variables. The aim of this study was to validate and modify, if needed, the HERNIAscore in an external dataset. This was a retrospective observational study of all patients undergoing resection for gastrointestinal malignancy from 2011 through 2015 at a safety-net hospital. The primary end point was clinical postoperative VIH. Patients were stratified into low-risk, medium-risk, and high-risk groups based on HERNIAscore. A revised HERNIAscore was calculated with the addition of earlier abdominal operation as a categorical variable. Cox regression of incisional hernia with stratification by risk class was performed. Incidence rates of clinical VIH formation within each risk class were also calculated. Two hundred and forty-seven patents were enrolled. On Cox regression, in addition to the 3 variables of the HERNIAscore (BMI, COPD, and incision length), earlier abdominal operation was also predictive of VIH. The revised HERNIAscore demonstrated improved predictive accuracy for clinical VIH. Although the original HERNIAscore effectively stratified the risk of an incisional radiographic VIH developing, the revised HERNIAscore provided a statistically significant stratification for both clinical and radiographic VIHs in this patient cohort. We have externally validated and improved the HERNIAscore. The revised HERNIAscore uses BMI, incision length, COPD, and earlier abdominal operation to predict risk of postoperative incisional hernia. Future research should assess methods to prevent incisional hernias in moderate-to-high risk patients. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Does Maternal Depression Predict Young Children's Executive Function?--A 4-Year Longitudinal Study

ERIC Educational Resources Information Center

Hughes, Claire; Roman, Gabriela; Hart, Martha J.; Ensor, Rosie

2013-01-01

Background: Building on reports that parental maltreatment and neglect adversely affect young children's executive function (EF), this longitudinal study examined whether exposure to a more common risk factor, mothers' depressive symptoms, predicted individual differences in EF at school-age. Methods: We followed up at age 6 a socially diverse…

Perinatal Health Belief Scales: A Cost Effective Technique for Predicting Prenatal Appointment Keeping Rates amongst Pregnant Teenagers.

ERIC Educational Resources Information Center

Wells, Robert D.; And Others

Prenatal appointment keeping is an important predictor of birth outcomes, yet many pregnant adolescents miss an excessive number of appointments. Since effective strategies for increasing appointment keeping require costly staff time, methods to predict relative risk for noncompliance with appointments might help delineate a circumscribed…

Evaluating performance of risk identification methods through a large-scale simulation of observational data.

PubMed

Ryan, Patrick B; Schuemie, Martijn J

2013-10-01

There has been only limited evaluation of statistical methods for identifying safety risks of drug exposure in observational healthcare data. Simulations can support empirical evaluation, but have not been shown to adequately model the real-world phenomena that challenge observational analyses. To design and evaluate a probabilistic framework (OSIM2) for generating simulated observational healthcare data, and to use this data for evaluating the performance of methods in identifying associations between drug exposure and health outcomes of interest. Seven observational designs, including case-control, cohort, self-controlled case series, and self-controlled cohort design were applied to 399 drug-outcome scenarios in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively. Longitudinal data for 10 million simulated patients were generated using a model derived from an administrative claims database, with associated demographics, periods of drug exposure derived from pharmacy dispensings, and medical conditions derived from diagnoses on medical claims. Simulation validation was performed through descriptive comparison with real source data. Method performance was evaluated using Area Under ROC Curve (AUC), bias, and mean squared error. OSIM2 replicates prevalence and types of confounding observed in real claims data. When simulated data are injected with relative risks (RR) ≥ 2, all designs have good predictive accuracy (AUC > 0.90), but when RR < 2, no methods achieve 100 % predictions. Each method exhibits a different bias profile, which changes with the effect size. OSIM2 can support methodological research. Results from simulation suggest method operating characteristics are far from nominal properties.

Inhalation of expiratory droplets in aircraft cabins.

PubMed

Gupta, J K; Lin, C-H; Chen, Q

2011-08-01

Airliner cabins have high occupant density and long exposure time, so the risk of airborne infection transmission could be high if one or more passengers are infected with an airborne infectious disease. The droplets exhaled by an infected passenger may contain infectious agents. This study developed a method to predict the amount of expiratory droplets inhaled by the passengers in an airliner cabin for any flight duration. The spatial and temporal distribution of expiratory droplets for the first 3 min after the exhalation from the index passenger was obtained using the computational fluid dynamics simulations. The perfectly mixed model was used for beyond 3 min after the exhalation. For multiple exhalations, the droplet concentration in a zone can be obtained by adding the droplet concentrations for all the exhalations until the current time with a time shift via the superposition method. These methods were used to determine the amount of droplets inhaled by the susceptible passengers over a 4-h flight under three common scenarios. The method, if coupled with information on the viability and the amount of infectious agent in the droplet, can aid in evaluating the infection risk. The distribution of the infectious agents contained in the expiratory droplets of an infected occupant in an indoor environment is transient and non-uniform. The risk of infection can thus vary with time and space. The investigations developed methods to predict the spatial and temporal distribution of expiratory droplets, and the inhalation of these droplets in an aircraft cabin. The methods can be used in other indoor environments to assess the relative risk of infection in different zones, and suitable measures to control the spread of infection can be adopted. Appropriate treatment can be implemented for the zone identified as high-risk zones. © 2011 John Wiley & Sons A/S.

Comparison between Frailty Index of Deficit Accumulation and Phenotypic Model to Predict Risk of Falls: Data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) Hamilton Cohort

PubMed Central

Thabane, Lehana; Ioannidis, George; Kennedy, Courtney; Papaioannou, Alexandra

2015-01-01

Objectives To compare the predictive accuracy of the frailty index (FI) of deficit accumulation and the phenotypic frailty (PF) model in predicting risks of future falls, fractures and death in women aged ≥55 years. Methods Based on the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort (n = 3,985), we compared the predictive accuracy of the FI and PF in risks of falls, fractures and death using three strategies: (1) investigated the relationship with adverse health outcomes by increasing per one-fifth (i.e., 20%) of the FI and PF; (2) trichotomized the FI based on the overlap in the density distribution of the FI by the three groups (robust, pre-frail and frail) which were defined by the PF; (3) categorized the women according to a predicted probability function of falls during the third year of follow-up predicted by the FI. Logistic regression models were used for falls and death, while survival analyses were conducted for fractures. Results The FI and PF agreed with each other at a good level of consensus (correlation coefficients ≥ 0.56) in all the three strategies. Both the FI and PF approaches predicted adverse health outcomes significantly. The FI quantified the risks of future falls, fractures and death more precisely than the PF. Both the FI and PF discriminated risks of adverse outcomes in multivariable models with acceptable and comparable area under the curve (AUCs) for falls (AUCs ≥ 0.68) and death (AUCs ≥ 0.79), and c-indices for fractures (c-indices ≥ 0.69) respectively. Conclusions The FI is comparable with the PF in predicting risks of adverse health outcomes. These findings may indicate the flexibility in the choice of frailty model for the elderly in the population-based settings. PMID:25764521

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model.

PubMed

Emura, Takeshi; Nakatochi, Masahiro; Matsui, Shigeyuki; Michimae, Hirofumi; Rondeau, Virginie

2017-01-01

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.

BRCA1/2 Test Results Impact Risk Management Attitudes, Intentions and Uptake

PubMed Central

O’Neill, Suzanne C.; Valdimarsdottir, Heiddis B.; DeMarco, Tiffani A.; Peshkin, Beth N.; Graves, Kristi D.; Brown, Karen; Hurley, Karen E.; Isaacs, Claudine; Hecker, Sharon; Schwartz, Marc D.

2011-01-01

BACKGROUND Women who receive positive or uninformative BRCA1/2 test results face a number of decisions about how to manage their cancer risk. The purpose of this study was to prospectively examine the effect of receiving a positive vs. uninformative BRCA1/2 genetic test result on the perceived pros and cons of risk-reducing mastectomy (RRM) and risk-reducing oophorectomy (RRO) and breast cancer screening. We further examined how perceived pros and cons of surgery predict intention for and uptake of surgery. METHODS 308 women (146 positive, 162 uninformative) were included in RRM and breast cancer screening analyses. 276 women were included in RRO analyses. Participants completed questionnaires at pre-disclosure baseline and 1-, 6-and 12-months post-disclosure. We used linear multiple regression to assess whether test result contributed to change in pros and cons and logistic regression to predict intentions and surgery uptake. RESULTS Receipt of a positive BRCA1/2 test result predicted stronger pros for RRM and RRO (Ps < .001), but not perceived cons of RRM and RRO. Pros of surgery predicted RRM and RRO intentions in carriers and RRO intentions in uninformatives. Cons predicted RRM intentions in carriers. Pros and cons predicted carriers’ RRO uptake in the year after testing (Ps < .001). CONCLUSIONS Receipt of BRCA1/2 mutation test results impacts how carriers see the positive aspects of RRO and RRM and their surgical intentions. Both the positive and negative aspects predict uptake of surgery. PMID:20383578

BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface

PubMed Central

Lee, A J; Cunningham, A P; Kuchenbaecker, K B; Mavaddat, N; Easton, D F; Antoniou, A C

2014-01-01

Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction model that is used to compute probabilities of carrying mutations in the high-risk breast and ovarian cancer susceptibility genes BRCA1 and BRCA2, and to estimate the future risks of developing breast or ovarian cancer. In this paper, we describe updates to the BOADICEA model that extend its capabilities, make it easier to use in a clinical setting and yield more accurate predictions. Methods: We describe: (1) updates to the statistical model to include cancer incidences from multiple populations; (2) updates to the distributions of tumour pathology characteristics using new data on BRCA1 and BRCA2 mutation carriers and women with breast cancer from the general population; (3) improvements to the computational efficiency of the algorithm so that risk calculations now run substantially faster; and (4) updates to the model's web interface to accommodate these new features and to make it easier to use in a clinical setting. Results: We present results derived using the updated model, and demonstrate that the changes have a significant impact on risk predictions. Conclusion: All updates have been implemented in a new version of the BOADICEA web interface that is now available for general use: http://ccge.medschl.cam.ac.uk/boadicea/. PMID:24346285

Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

Imaging utilization has significantly increased over the last two decades, and is only recently showing signs of moderating. To help healthcare providers identify patients at risk for high imaging utilization, we developed a prediction model to recognize high imaging utilizers based on their initial imaging reports. The prediction model uses a machine learning text classification framework. In this study, we used radiology reports from 18,384 patients with at least one abdomen computed tomography study in their imaging record at Stanford Health Care as the training set. We modeled the radiology reports in a vector space and trained a support vector machine classifier for this prediction task. We evaluated our model on a separate test set of 4791 patients. In addition to high prediction accuracy, in our method, we aimed at achieving high specificity to identify patients at high risk for high imaging utilization. Our results (accuracy: 94.0%, sensitivity: 74.4%, specificity: 97.9%, positive predictive value: 87.3%, negative predictive value: 95.1%) show that a prediction model can enable healthcare providers to identify in advance patients who are likely to be high utilizers of imaging services. Machine learning classifiers developed from narrative radiology reports are feasible methods to predict imaging utilization. Such systems can be used to identify high utilizers, inform future image ordering behavior, and encourage judicious use of imaging. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation

PubMed Central

2012-01-01

Background Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation. Methods We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions. Results We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively). Conclusions We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application. Trial registration The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821. PMID:22453031

How to determine an optimal threshold to classify real-time crash-prone traffic conditions?

PubMed

Yang, Kui; Yu, Rongjie; Wang, Xuesong; Quddus, Mohammed; Xue, Lifang

2018-08-01

One of the proactive approaches in reducing traffic crashes is to identify hazardous traffic conditions that may lead to a traffic crash, known as real-time crash prediction. Threshold selection is one of the essential steps of real-time crash prediction. And it provides the cut-off point for the posterior probability which is used to separate potential crash warnings against normal traffic conditions, after the outcome of the probability of a crash occurring given a specific traffic condition on the basis of crash risk evaluation models. There is however a dearth of research that focuses on how to effectively determine an optimal threshold. And only when discussing the predictive performance of the models, a few studies utilized subjective methods to choose the threshold. The subjective methods cannot automatically identify the optimal thresholds in different traffic and weather conditions in real application. Thus, a theoretical method to select the threshold value is necessary for the sake of avoiding subjective judgments. The purpose of this study is to provide a theoretical method for automatically identifying the optimal threshold. Considering the random effects of variable factors across all roadway segments, the mixed logit model was utilized to develop the crash risk evaluation model and further evaluate the crash risk. Cross-entropy, between-class variance and other theories were employed and investigated to empirically identify the optimal threshold. And K-fold cross-validation was used to validate the performance of proposed threshold selection methods with the help of several evaluation criteria. The results indicate that (i) the mixed logit model can obtain a good performance; (ii) the classification performance of the threshold selected by the minimum cross-entropy method outperforms the other methods according to the criteria. This method can be well-behaved to automatically identify thresholds in crash prediction, by minimizing the cross entropy between the original dataset with continuous probability of a crash occurring and the binarized dataset after using the thresholds to separate potential crash warnings against normal traffic conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predicted 25(OH)D score and colorectal cancer risk according to vitamin D receptor expression.

PubMed

Jung, Seungyoun; Qian, Zhi Rong; Yamauchi, Mai; Bertrand, Kimberly A; Fitzgerald, Kathryn C; Inamura, Kentaro; Kim, Sun A; Mima, Kosuke; Sukawa, Yasutaka; Zhang, Xuehong; Wang, Molin; Smith-Warner, Stephanie A; Wu, Kana; Fuchs, Charles S; Chan, Andrew T; Giovannucci, Edward L; Ng, Kimmie; Cho, Eunyoung; Ogino, Shuji; Nishihara, Reiko

2014-08-01

Despite accumulating evidence for the preventive effect of vitamin D on colorectal carcinogenesis, its precise mechanisms remain unclear. We hypothesized that vitamin D was associated with a lower risk of colorectal cancer with high-level vitamin D receptor (VDR) expression, but not with risk of tumor with low-level VDR expression. Among 140,418 participants followed from 1986 through 2008 in the Nurses' Health Study and the Health Professionals' Follow-up Study, we identified 1,059 incident colorectal cancer cases with tumor molecular data. The predicted 25-hydroxyvitamin D [25(OH)D] score was developed using the known determinants of plasma 25(OH)D. We estimated the HR for cancer subtypes using the duplication method Cox proportional hazards model. A higher predicted 25(OH)D score was associated with a lower risk of colorectal cancer irrespective of VDR expression level (P(heterogeneity) for subtypes = 0.75). Multivariate HRs (95% confidence intervals) comparing the highest with the lowest quintile of predicted 25(OH)D scores were 0.48 (0.30-0.78) for VDR-negative tumor and 0.56 (0.42-0.75) for VDR-positive tumor. Similarly, the significant inverse associations of the predicted 25(OH)D score with colorectal cancer risk did not significantly differ by KRAS, BRAF, or PIK3CA status (P(heterogeneity) for subtypes ≥ 0.22). A higher predicted vitamin D score was significantly associated with a lower colorectal cancer risk, regardless of VDR status and other molecular features examined. The preventive effect of vitamin D on colorectal carcinogenesis may not totally depend on tumor factors. Host factors (such as local and systemic immunity) may need to be considered. ©2014 American Association for Cancer Research.

Pretreatment Nomogram to Predict the Risk of Acute Urinary Retention After I-125 Prostate Brachytherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roeloffzen, Ellen M., E-mail: e.m.a.roeloffzen@umcutrecht.nl; Vulpen, Marco van; Battermann, Jan J.

Purpose: Acute urinary retention (AUR) after iodine-125 (I-125) prostate brachytherapy negatively influences long-term quality of life and therefore should be prevented. We aimed to develop a nomogram to preoperatively predict the risk of AUR. Methods: Using the preoperative data of 714 consecutive patients who underwent I-125 prostate brachytherapy between 2005 and 2008 at our department, we modeled the probability of AUR. Multivariate logistic regression analysis was used to assess the predictive ability of a set of pretreatment predictors and the additional value of a new risk factor (the extent of prostate protrusion into the bladder). The performance of the finalmore » model was assessed with calibration and discrimination measures. Results: Of the 714 patients, 57 patients (8.0%) developed AUR after implantation. Multivariate analysis showed that the combination of prostate volume, IPSS score, neoadjuvant hormonal treatment and the extent of prostate protrusion contribute to the prediction of AUR. The discriminative value (receiver operator characteristic area, ROC) of the basic model (including prostate volume, International Prostate Symptom Score, and neoadjuvant hormonal treatment) to predict the development of AUR was 0.70. The addition of prostate protrusion significantly increased the discriminative power of the model (ROC 0.82). Calibration of this final model was good. The nomogram showed that among patients with a low sum score (<18 points), the risk of AUR was only 0%-5%. However, in patients with a high sum score (>35 points), the risk of AUR was more than 20%. Conclusion: This nomogram is a useful tool for physicians to predict the risk of AUR after I-125 prostate brachytherapy. The nomogram can aid in individualized treatment decision-making and patient counseling.« less

Predicting the Individual Risk of Acute Severe Colitis at Diagnosis

PubMed Central

Cesarini, Monica; Collins, Gary S.; Rönnblom, Anders; Santos, Antonieta; Wang, Lai Mun; Sjöberg, Daniel; Parkes, Miles; Keshav, Satish

2017-01-01

Abstract Background and Aims: Acute severe colitis [ASC] is associated with major morbidity. We aimed to develop and externally validate an index that predicted ASC within 3 years of diagnosis. Methods: The development cohort included patients aged 16–89 years, diagnosed with ulcerative colitis [UC] in Oxford and followed for 3 years. Primary outcome was hospitalization for ASC, excluding patients admitted within 1 month of diagnosis. Multivariable logistic regression examined the adjusted association of seven risk factors with ASC. Backwards elimination produced a parsimonious model that was simplified to create an easy-to-use index. External validation occurred in separate cohorts from Cambridge, UK, and Uppsala, Sweden. Results: The development cohort [Oxford] included 34/111 patients who developed ASC within a median 14 months [range 1–29]. The final model applied the sum of 1 point each for extensive disease, C-reactive protein [CRP] > 10mg/l, or haemoglobin < 12g/dl F or < 14g/dl M at diagnosis, to give a score from 0/3 to 3/3. This predicted a 70% risk of developing ASC within 3 years [score 3/3]. Validation cohorts included different proportions with ASC [Cambridge = 25/96; Uppsala = 18/298]. Of those scoring 3/3 at diagnosis, 18/18 [Cambridge] and 12/13 [Uppsala] subsequently developed ASC. Discriminant ability [c-index, where 1.0 = perfect discrimination] was 0.81 [Oxford], 0.95 [Cambridge], 0.97 [Uppsala]. Internal validation using bootstrapping showed good calibration, with similar predicted risk across all cohorts. A nomogram predicted individual risk. Conclusions: An index applied at diagnosis reliably predicts the risk of ASC within 3 years in different populations. Patients with a score 3/3 at diagnosis may merit early immunomodulator therapy. PMID:27647858

Validation of Open-Heart Intraoperative Risk score to predict a prolonged intensive care unit stay for adult patients undergoing cardiac surgery with cardiopulmonary bypass

PubMed Central

Tribuddharat, Sirirat; Sathitkarnmanee, Thepakorn; Ngamsaengsirisup, Kriangsak; Wongbuddha, Chawalit

2018-01-01

Background A prolonged stay in an intensive care unit (ICU) after cardiac surgery with cardiopulmonary bypass (CPB) increases the cost of care as well as morbidity and mortality. Several predictive models aim at identifying patients at risk of prolonged ICU stay after cardiac surgery with CPB, but almost all of them involve a preoperative assessment for proper resource management, while one – the Open-Heart Intraoperative Risk (OHIR) score – focuses on intra-operative manipulatable risk factors for improving anesthetic care and patient outcome. Objective We aimed to revalidate the OHIR score in a different context. Materials and methods The ability of the OHIR score to predict a prolonged ICU stay was assessed in 123 adults undergoing cardiac surgery (both coronary bypass graft and valvular surgery) with CPB at two tertiary university hospitals between January 2013 and December 2014. The criteria for a prolonged ICU stay matched a previous study (ie, a stay longer than the median). Results The area under the receiver operating characteristic curve of the OHIR score to predict a prolonged ICU stay was 0.95 (95% confidence interval 0.90–1.00). The respective sensitivity, specificity, positive predictive value, and accuracy of an OHIR score of ≥3 to discriminate a prolonged ICU stay was 93.10%, 98.46%, 98.18%, and 95.9%. Conclusion The OHIR score is highly predictive of a prolonged ICU stay among intraopera-tive patients undergoing cardiac surgery with CPB. The OHIR comprises of six risk factors, five of which are manipulatable intraoperatively. The OHIR can be used to identify patients at risk as well as to improve the outcome of those patients. PMID:29379295

Finite element based damage assessment of composite tidal turbine blades

NASA Astrophysics Data System (ADS)

Fagan, Edward M.; Leen, Sean B.; Kennedy, Ciaran R.; Goggins, Jamie

2015-07-01

With significant interest growing in the ocean renewables sector, horizontal axis tidal current turbines are in a position to dominate the marketplace. The test devices that have been placed in operation so far have suffered from premature failures, caused by difficulties with structural strength prediction. The goal of this work is to develop methods of predicting the damage level in tidal turbines under their maximum operating tidal velocity. The analysis was conducted using the finite element software package Abaqus; shell models of three representative tidal turbine blades are produced. Different construction methods will affect the damage level in the blade and for this study models were developed with varying hydrofoil profiles. In order to determine the risk of failure, a user material subroutine (UMAT) was created. The UMAT uses the failure criteria designed by Alfred Puck to calculate the risk of fibre and inter-fibre failure in the blades. The results show that degradation of the stiffness is predicted for the operating conditions, having an effect on the overall tip deflection. The failure criteria applied via the UMAT form a useful tool for analysis of high risk regions within the blade designs investigated.

Derivation and Validation of the Surgical Site Infections Risk Model Using Health Administrative Data.

PubMed

van Walraven, Carl; Jackson, Timothy D; Daneman, Nick

2016-04-01

OBJECTIVE Surgical site infections (SSIs) are common hospital-acquired infections. Tracking SSIs is important to monitor their incidence, and this process requires primary data collection. In this study, we derived and validated a method using health administrative data to predict the probability that a person who had surgery would develop an SSI within 30 days. METHODS All patients enrolled in the National Surgical Quality Improvement Program (NSQIP) from 2 sites were linked to population-based administrative datasets in Ontario, Canada. We derived a multivariate model, stratified by surgical specialty, to determine the independent association of SSI status with patient and hospitalization covariates as well as physician claim codes. This SSI risk model was validated in 2 cohorts. RESULTS The derivation cohort included 5,359 patients with a 30-day SSI incidence of 6.0% (n=118). The SSI risk model predicted the probability that a person had an SSI based on 7 covariates: index hospitalization diagnostic score; physician claims score; emergency visit diagnostic score; operation duration; surgical service; and potential SSI codes. More than 90% of patients had predicted SSI risks lower than 10%. In the derivation group, model discrimination and calibration was excellent (C statistic, 0.912; Hosmer-Lemeshow [H-L] statistic, P=.47). In the 2 validation groups, performance decreased slightly (C statistics, 0.853 and 0.812; H-L statistics, 26.4 [P=.0009] and 8.0 [P=.42]), but low-risk patients were accurately identified. CONCLUSION Health administrative data can effectively identify postoperative patients with a very low risk of surgical site infection within 30 days of their procedure. Records of higher-risk patients can be reviewed to confirm SSI status.

Fuzzy Regression Prediction and Application Based on Multi-Dimensional Factors of Freight Volume

NASA Astrophysics Data System (ADS)

Xiao, Mengting; Li, Cheng

2018-01-01

Based on the reality of the development of air cargo, the multi-dimensional fuzzy regression method is used to determine the influencing factors, and the three most important influencing factors of GDP, total fixed assets investment and regular flight route mileage are determined. The system’s viewpoints and analogy methods, the use of fuzzy numbers and multiple regression methods to predict the civil aviation cargo volume. In comparison with the 13th Five-Year Plan for China’s Civil Aviation Development (2016-2020), it is proved that this method can effectively improve the accuracy of forecasting and reduce the risk of forecasting. It is proved that this model predicts civil aviation freight volume of the feasibility, has a high practical significance and practical operation.

Usefulness of microvolt T-wave alternans for prediction of ventricular tachyarrhythmic events in patients with dilated cardiomyopathy: results from a prospective observational study

NASA Technical Reports Server (NTRS)

Hohnloser, Stefan H.; Klingenheben, Thomas; Bloomfield, Daniel; Dabbous, Omar; Cohen, Richard J.

2003-01-01

OBJECTIVES: This study was designed to evaluate the ability of microvolt-level T-wave alternans (MTWA) to identify prospectively patients with idiopathic dilated cardiomyopathy (DCM) at risk of ventricular tachyarrhythmic events and to compare its predictive accuracy with that of conventional risk stratifiers. BACKGROUND: Patients with DCM are at increased risk of sudden death from ventricular tachyarrhythmias. At present, there are no established methods of assessing this risk. METHODS: A total of 137 patients with DCM underwent risk stratification through assessment of MTWA, left ventricular ejection fraction, baroreflex sensitivity (BRS), heart rate variability, presence of nonsustained ventricular tachycardia (VT), signal-averaged electrocardiogram, and presence of intraventricular conduction defect. The study end point was either sudden death, resuscitated ventricular fibrillation, or documented hemodynamically unstable VT. RESULTS: During an average follow-up of 14 +/- 6 months, MTWA and BRS were significant univariate predictors of ventricular tachyarrhythmic events (p < 0.035 and p < 0.015, respectively). Multivariate Cox regression analysis revealed that only MTWA was a significant predictor. CONCLUSIONS: Microvolt-level T-wave alternans is a powerful independent predictor of ventricular tachyarrhythmic events in patients with DCM.

Big Data’s Role in Precision Public Health

PubMed Central

Dolley, Shawn

2018-01-01

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts. PMID:29594091

Big Data's Role in Precision Public Health.

PubMed

Dolley, Shawn

2018-01-01

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

US EPA - A*Star Partnership - Accelerating the Acceptance of Next-Generation Sciences and Their Application to Regulatory Risk Assessment (A*Star Symposium, Singapore)

EPA Science Inventory

The path for incorporating new alternative methods and technologies into quantitative chemical risk assessment poses a diverse set of scientific challenges. Some of these challenges include development of relevant and predictive test systems and computational models to integrate...

Mathematical model to estimate risk of calcium-containing renal stones

NASA Technical Reports Server (NTRS)

Pietrzyk, R. A.; Feiveson, A. H.; Whitson, P. A.

1999-01-01

BACKGROUND/AIMS: Astronauts exposed to microgravity during the course of spaceflight undergo physiologic changes that alter the urinary environment so as to increase the risk of renal stone formation. This study was undertaken to identify a simple method with which to evaluate the potential risk of renal stone development during spaceflight. METHOD: We used a large database of urinary risk factors obtained from 323 astronauts before and after spaceflight to generate a mathematical model with which to predict the urinary supersaturation of calcium stone forming salts. RESULT: This model, which involves the fewest possible analytical variables (urinary calcium, citrate, oxalate, phosphorus, and total volume), reliably and accurately predicted the urinary supersaturation of the calcium stone forming salts when compared to results obtained from a group of 6 astronauts who collected urine during flight. CONCLUSIONS: The use of this model will simplify both routine medical monitoring during spaceflight as well as the evaluation of countermeasures designed to minimize renal stone development. This model also can be used for Earth-based applications in which access to analytical resources is limited.

Prediction of muscle performance during dynamic repetitive movement

NASA Technical Reports Server (NTRS)

Byerly, D. L.; Byerly, K. A.; Sognier, M. A.; Squires, W. G.

2003-01-01

BACKGROUND: During long-duration spaceflight, astronauts experience progressive muscle atrophy and often perform strenuous extravehicular activities. Post-flight, there is a lengthy recovery period with an increased risk for injury. Currently, there is a critical need for an enabling tool to optimize muscle performance and to minimize the risk of injury to astronauts while on-orbit and during post-flight recovery. Consequently, these studies were performed to develop a method to address this need. METHODS: Eight test subjects performed a repetitive dynamic exercise to failure at 65% of their upper torso weight using a Lordex spinal machine. Surface electromyography (SEMG) data was collected from the erector spinae back muscle. The SEMG data was evaluated using a 5th order autoregressive (AR) model and linear regression analysis. RESULTS: The best predictor found was an AR parameter, the mean average magnitude of AR poles, with r = 0.75 and p = 0.03. This parameter can predict performance to failure as early as the second repetition of the exercise. CONCLUSION: A method for predicting human muscle performance early during dynamic repetitive exercise was developed. The capability to predict performance to failure has many potential applications to the space program including evaluating countermeasure effectiveness on-orbit, optimizing post-flight recovery, and potential future real-time monitoring capability during extravehicular activity.

Greater Exposure to Sexual Content in Popular Movies Predicts Earlier Sexual Debut and Increased Sexual Risk Taking

PubMed Central

O’Hara, Ross E.; Gibbons, Frederick X.; Gerrard, Meg; Li, Zhigang; Sargent, James D.

2013-01-01

Early sexual debut is associated with risky sexual behavior and an increased risk of unplanned pregnancy and sexually transmitted infections later in life. The relations among early movie sexual exposure (MSE), sexual debut, and risky sexual behavior in adulthood (i.e., multiple sexual partners and inconsistent condom use) were examined in a longitudinal study of U.S. adolescents. MSE was measured using the Beach method, a comprehensive procedure for media content coding. Controlling for characteristics of adolescents and their families, analyses showed that MSE predicted age of sexual debut, both directly and indirectly through changes in sensation seeking. MSE also predicted engagement in risky sexual behaviors both directly and indirectly via early sexual debut. These results suggest that MSE may promote sexual risk taking both by modifying sexual behavior and by accelerating the normal rise in sensation seeking during adolescence. PMID:22810165

Interpersonal violence and the prediction of short-term risk of repeat suicide attempt

PubMed Central

Haglund, Axel; Lindh, Åsa U.; Lysell, Henrik; Renberg, Ellinor Salander; Jokinen, Jussi; Waern, Margda; Runeson, Bo

2016-01-01

In this multi-center cohort study, suicide attempters presenting to hospital (N = 355, 63% women) were interviewed using the Karolinska Interpersonal Violence Scale (KIVS) and followed-up by medical record review. Main outcome was non-fatal or fatal repeat suicide attempt within six months. Also, repeat attempt using a violent method was used as an additional outcome in separate analyses. Data were analyzed for the total group and for men and women separately. Repeat attempts were observed within six months in 78 persons (22%) and 21 (6%) of these used a violent method. KIVS total score of 6 or more was associated with repeat suicide attempt within six months (OR = 1.81, CI 1.08–3.02) and predicted new attempts with a sensitivity of 62% and a specificity of 53%. A three-fold increase in odds ratio was observed for repeat attempt using a violent method (OR = 3.40, CI 1.22–9.49). An association between exposure to violence in adulthood and violent reattempt was seen in women (OR = 1.38, CI 1.06–1.82). The overall conclusions are that information about interpersonal violence may help predict short-term risk for repeat suicide attempt, and that structured assessment of interpersonal violence may be of value in risk assessment after attempted suicide. PMID:27841333

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling.

PubMed

Leger, Stefan; Zwanenburg, Alex; Pilz, Karoline; Lohaus, Fabian; Linge, Annett; Zöphel, Klaus; Kotzerke, Jörg; Schreiber, Andreas; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Rödel, Claus; Ganswindt, Ute; Belka, Claus; Pigorsch, Steffi; Combs, Stephanie E; Mönnich, David; Zips, Daniel; Krause, Mechthild; Baumann, Michael; Troost, Esther G C; Löck, Steffen; Richter, Christian

2017-10-16

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g. C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.

Diabetes and Hypertension Consistently Predict the Presence and Extent of Coronary Artery Calcification in Symptomatic Patients: A Systematic Review and Meta-Analysis

PubMed Central

Nicoll, Rachel; Zhao, Ying; Ibrahimi, Pranvera; Olivecrona, Gunilla; Henein, Michael

2016-01-01

Background: The relationship of conventional cardiovascular risk factors (age, gender, ethnicity, diabetes, dyslipidaemia, hypertension, obesity, exercise, and the number of risk factors) to coronary artery calcification (CAC) presence and extent has never before been assessed in a systematic review and meta-analysis. Methods: We included only English language studies that assessed at least three conventional risk factors apart from age, gender, and ethnicity, but excluded studies in which all patients had another confirmed condition such as renal disease. Results: In total, 10 studies, comprising 15,769 patients, were investigated in the systematic review and seven studies, comprising 12,682 patients, were included in the meta-analysis, which demonstrated the importance of diabetes and hypertension as predictors of CAC presence and extent, with age also predicting CAC presence. Male gender, dyslipidaemia, family history of coronary artery disease, obesity, and smoking were overall not predictive of either CAC presence or extent, despite dyslipidaemia being a key risk factor for coronary artery disease (CAD). Conclusion: Diabetes and hypertension consistently predict the presence and extent of CAC in symptomatic patients. PMID:27608015

Dynamic drought risk assessment using crop model and remote sensing techniques

NASA Astrophysics Data System (ADS)

Sun, H.; Su, Z.; Lv, J.; Li, L.; Wang, Y.

2017-02-01

Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

G6PD as a predictive marker for glioma risk, prognosis and chemosensitivity.

PubMed

Yang, Chin-An; Huang, Hsi-Yuan; Lin, Cheng-Li; Chang, Jan-Gowth

2018-05-29

Glucose-6-phosphate dehydrogenase (G6PD) is a key enzyme preventing cells from oxidative damage and has been reported to have tumor-promoting roles. This study aims to comprehensively evaluate the predictive values of G6PD on brain tumor risk, prognosis and chemo-resistance. A retrospective 13-year cohort study analyzing cancer risk using the Taiwan National Health Insurance Research Database (4066 G6PD deficiency patients and 16,264 controls) was conducted. Furthermore, RNAseq and clinical data of grade II-III glioma (LGG, n = 515) and glioblastoma (GBM, n = 155) were downloaded from The Cancer Genome Atlas (TCGA) and analyzed. Bioinformatics methods were applied to build a glioma prognostication model and to predict response to chemotherapy based on tumor G6PD-related gene expressions. The predicted results were validated in another glioma cohort GSE 16011 and in KALS1 cell line. G6PD-dificient patients were found to have an increased risk for cancers, especially for brain tumor (adjusted hazard ratio (HR) 10.5, 95% CI 1.03-7.60). Furthermore, higher tumor G6PD expression was associated with poor patient survival in LGG, but not in GBM. A prognostication model using expression levels of G6PD and 9 related genes (PSMA2, PSMB8, SHFM1, GSS, GSTK1, MGST2, POLD3, MSH2, MSH6) could independently predict LGG patient survival. Boosted decision tree analysis on 213 cancer cell line database revealed predictive values of G6PD expression on response to gemcitabine and bortezomib. Knockdown of G6PD in KALS1 cell line enhanced its sensitivity to both chemotherapeutic agents. Our study suggests that G6PD could be a marker predicting glioma risk, prognosis and chemo-sensitivity.

Population-based study of blood biomarkers in prediction of sub-acute recurrent stroke

PubMed Central

Segal, Helen C; Burgess, Annette I; Poole, Debbie L; Mehta, Ziyah; Silver, Louise E; Rothwell, Peter M

2017-01-01

Background and purpose Risk of recurrent stroke is high in the first few weeks after TIA or stroke and clinic risk prediction tools have only limited accuracy, particularly after the hyper-acute phase. Previous studies of the predictive value of biomarkers have been small, been done in selected populations and have not concentrated on the acute phase or on intensively treated populations. We aimed to determine the predictive value of a panel of blood biomarkers in intensively treated patients early after TIA and stroke. Methods We studied 14 blood biomarkers related to inflammation, thrombosis, atherogenesis and cardiac or neuronal cell damage in early TIA or ischaemic stroke in a population-based study (Oxford Vascular Study). Biomarker levels were related to 90-day risk of recurrent stroke as Hazard Ratio (95%CI) per decile increase, adjusted for age and sex. Results Among 1292 eligible patients there were 53 recurrent ischaemic strokes within 90 days. There were moderate correlations (r>0.40; p<0001) between the inflammatory biomarkers and between the cell damage and thrombotic subsets. However, associations with risk of early recurrent stroke were weak, with significant associations limited to Interleukin-6 (HR=1.12, 1.01-1.24; p=0.035) and C-reactive protein (1.16, 1.02-1.30; p=0.019). When stratified by type of presenting event, P-selectin predicted stroke after TIA (1.31, 1.03-1.66; p=0.028) and C-reactive protein predicted stroke after stroke (1.16, 1.01-1.34; p=0.042). These associations remained after fully adjusting for other vascular risk factors. Conclusion In the largest study to date, we found very limited predictive utility for early recurrent stroke for a panel of inflammatory, thrombotic and cell damage biomarkers. PMID:25158774

Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset.

PubMed

Hostetter, Jason M; Morrison, James J; Morris, Michael; Jeudy, Jean; Wang, Kenneth C; Siegel, Eliot

2017-11-01

To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset. An algorithm was used to categorize nodules found in the first screening year of the National Lung Screening Trial as malignant or nonmalignant. Risk of malignancy for nodules was calculated based on size criteria according to the Fleischner Society recommendations from 2005, along with the additional discriminators of pack-years smoking history, sex, and nodule location. Imaging follow-up recommendations were assigned according to Fleischner size category malignancy risk. Nodule size correlated with malignancy risk as predicted by the Fleischner Society recommendations. With the additional discriminators of smoking history, sex, and nodule location, significant risk stratification was observed. For example, men with ≥60 pack-years smoking history and upper lobe nodules measuring >4 and ≤6 mm demonstrated significantly increased risk of malignancy at 12.4% compared to the mean of 3.81% for similarly sized nodules (P < .0001). Based on personalized malignancy risk, 54% of nodules >4 and ≤6 mm were reclassified to longer-term follow-up than recommended by Fleischner. Twenty-seven percent of nodules ≤4 mm were reclassified to shorter-term follow-up. Using available clinical datasets such as the National Lung Screening Trial in conjunction with locally collected datasets can help clinicians provide more personalized malignancy risk predictions and follow-up recommendations. By incorporating 3 demographic data points, the risk of lung nodule malignancy within the Fleischner categories can be considerably stratified and more personalized follow-up recommendations can be made. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Clinical Utility of Five Genetic Variants for Predicting Prostate Cancer Risk and Mortality

PubMed Central

Salinas, Claudia A.; Koopmeiners, Joseph S.; Kwon, Erika M.; FitzGerald, Liesel; Lin, Daniel W.; Ostrander, Elaine A.; Feng, Ziding; Stanford, Janet L.

2009-01-01

Background A recent report suggests that the combination of five single-nucleotide polymorphisms (SNPs) at 8q24, 17q12, 17q24.3 and a family history of the disease may predict risk of prostate cancer. The present study tests the performance of these factors in prediction models for prostate cancer risk and prostate cancer-specific mortality. Methods SNPs were genotyped in population-based samples from Caucasians in King County, Washington. Incident cases (n=1308), aged 35–74, were compared to age-matched controls (n=1266) using logistic regression to estimate odds ratios (OR) associated with genotypes and family history. Cox proportional hazards models estimated hazard ratios for prostate cancer-specific mortality according to genotypes. Results The combination of SNP genotypes and family history was significantly associated with prostate cancer risk (ptrend=1.5 × 10−20). Men with ≥ five risk factors had an OR of 4.9 (95% CI 1.6 to 18.5) compared to men with none. However, this combination of factors did not improve the ROC curve after accounting for known risk predictors (i.e., age, serum PSA, family history). Neither the individual nor combined risk factors was associated with prostate cancer-specific mortality. Conclusion Genotypes for five SNPs plus family history are associated with a significant elevation in risk for prostate cancer and may explain up to 45% of prostate cancer in our population. However, they do not improve prediction models for assessing who is at risk of getting or dying from the disease, once known risk or prognostic factors are taken into account. Thus, this SNP panel may have limited clinical utility. PMID:19058137

Personalized Prediction of Psychosis: External validation of the NAPLS2 Psychosis Risk Calculator with the EDIPPP project

PubMed Central

Carrión, Ricardo E.; Cornblatt, Barbara A.; Burton, Cynthia Z.; Tso, Ivy F; Auther, Andrea; Adelsheim, Steven; Calkins, Roderick; Carter, Cameron S.; Niendam, Tara; Taylor, Stephan F.; McFarlane, William R.

2016-01-01

Objective In the current issue, Cannon and colleagues, as part of the second phase of the North American Prodrome Longitudinal Study (NAPLS2), report on a risk calculator for the individualized prediction of developing a psychotic disorder in a 2-year period. The present study represents an external validation of the NAPLS2 psychosis risk calculator using an independent sample of subjects at clinical high risk for psychosis collected as part of the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP). Methods 176 subjects with follow-up (from the total EDIPPP sample of 210) rated as clinical high-risk (CHR) based on the Structured Interview for Prodromal Syndromes were used to construct a new prediction model with the 6 significant predictor variables in the NAPLS2 psychosis risk calculator (unusual thoughts, suspiciousness, Symbol Coding, verbal learning, social functioning decline, baseline age, and family history). Discrimination performance was assessed with the area under the receiver operating curve (AUC). The NAPLS2 risk calculator was then used to generate a psychosis risk estimate for each case in the external validation sample. Results The external validation model showed good discrimination, with an AUC of 79% (95% CI 0.644–0.937). In addition, the personalized risk generated by the NAPLS calculator provided a solid estimation of the actual conversion outcome in the validation sample. Conclusions In the companion papers in this issue, two independent samples of CHR subjects converge to validate the NAPLS2 psychosis risk calculator. This prediction calculator represents a meaningful step towards early intervention and personalized treatment of psychotic disorders. PMID:27363511

Human Population Density and Extinction Risk in the World's Carnivores

PubMed Central

Purvis, Andy; Sechrest, Wes; Gittleman, John L; Bielby, Jon; Mace, Georgina M

2004-01-01

Understanding why some species are at high risk of extinction, while others remain relatively safe, is central to the development of a predictive conservation science. Recent studies have shown that a species' extinction risk may be determined by two types of factors: intrinsic biological traits and exposure to external anthropogenic threats. However, little is known about the relative and interacting effects of intrinsic and external variables on extinction risk. Using phylogenetic comparative methods, we show that extinction risk in the mammal order Carnivora is predicted more strongly by biology than exposure to high-density human populations. However, biology interacts with human population density to determine extinction risk: biological traits explain 80% of variation in risk for carnivore species with high levels of exposure to human populations, compared to 45% for carnivores generally. The results suggest that biology will become a more critical determinant of risk as human populations expand. We demonstrate how a model predicting extinction risk from biology can be combined with projected human population density to identify species likely to move most rapidly towards extinction by the year 2030. African viverrid species are particularly likely to become threatened, even though most are currently considered relatively safe. We suggest that a preemptive approach to species conservation is needed to identify and protect species that may not be threatened at present but may become so in the near future. PMID:15252445

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

PubMed

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 automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

Characterizing vaccine-associated risks using cubic smoothing splines.

PubMed

Brookhart, M Alan; Walker, Alexander M; Lu, Yun; Polakowski, Laura; Li, Jie; Paeglow, Corrie; Puenpatom, Tosmai; Izurieta, Hector; Daniel, Gregory W

2012-11-15

Estimating risks associated with the use of childhood vaccines is challenging. The authors propose a new approach for studying short-term vaccine-related risks. The method uses a cubic smoothing spline to flexibly estimate the daily risk of an event after vaccination. The predicted incidence rates from the spline regression are then compared with the expected rates under a log-linear trend that excludes the days surrounding vaccination. The 2 models are then used to estimate the excess cumulative incidence attributable to the vaccination during the 42-day period after vaccination. Confidence intervals are obtained using a model-based bootstrap procedure. The method is applied to a study of known effects (positive controls) and expected noneffects (negative controls) of the measles, mumps, and rubella and measles, mumps, rubella, and varicella vaccines among children who are 1 year of age. The splines revealed well-resolved spikes in fever, rash, and adenopathy diagnoses, with the maximum incidence occurring between 9 and 11 days after vaccination. For the negative control outcomes, the spline model yielded a predicted incidence more consistent with the modeled day-specific risks, although there was evidence of increased risk of diagnoses of congenital malformations after vaccination, possibly because of a "provider visit effect." The proposed approach may be useful for vaccine safety surveillance.

Brain injury prediction: assessing the combined probability of concussion using linear and rotational head acceleration.

PubMed

Rowson, Steven; Duma, Stefan M

2013-05-01

Recent research has suggested possible long term effects due to repetitive concussions, highlighting the importance of developing methods to accurately quantify concussion risk. This study introduces a new injury metric, the combined probability of concussion, which computes the overall risk of concussion based on the peak linear and rotational accelerations experienced by the head during impact. The combined probability of concussion is unique in that it determines the likelihood of sustaining a concussion for a given impact, regardless of whether the injury would be reported or not. The risk curve was derived from data collected from instrumented football players (63,011 impacts including 37 concussions), which was adjusted to account for the underreporting of concussion. The predictive capability of this new metric is compared to that of single biomechanical parameters. The capabilities of these parameters to accurately predict concussion incidence were evaluated using two separate datasets: the Head Impact Telemetry System (HITS) data and National Football League (NFL) data collected from impact reconstructions using dummies (58 impacts including 25 concussions). Receiver operating characteristic curves were generated, and all parameters were significantly better at predicting injury than random guessing. The combined probability of concussion had the greatest area under the curve for all datasets. In the HITS dataset, the combined probability of concussion and linear acceleration were significantly better predictors of concussion than rotational acceleration alone, but not different from each other. In the NFL dataset, there were no significant differences between parameters. The combined probability of concussion is a valuable method to assess concussion risk in a laboratory setting for evaluating product safety.

Predictive values of D-dimer assay, GRACE scores and TIMI scores for adverse outcome in patients with non-ST-segment elevation myocardial infarction

PubMed Central

Satilmisoglu, Muhammet Hulusi; Ozyilmaz, Sinem Ozbay; Gul, Mehmet; Ak Yildirim, Hayriye; Kayapinar, Osman; Gokturk, Kadir; Aksu, Huseyin; Erkanli, Korhan; Eksik, Abdurrahman

2017-01-01

Purpose To determine the predictive values of D-dimer assay, Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) risk scores for adverse outcome in patients with non-ST-segment elevation myocardial infarction (NSTEMI). Patients and methods A total of 234 patients (mean age: 57.2±11.7 years, 75.2% were males) hospitalized with NSTEMI were included. Data on D-dimer assay, GRACE and TIMI risk scores were recorded. Logistic regression analysis was conducted to determine the risk factors predicting increased mortality. Results Median D-dimer levels were 349.5 (48.0–7,210.0) ng/mL, the average TIMI score was 3.2±1.2 and the GRACE score was 90.4±27.6 with high GRACE scores (>118) in 17.5% of patients. The GRACE score was correlated positively with both the D-dimer assay (r=0.215, P=0.01) and TIMI scores (r=0.504, P=0.000). Multivariate logistic regression analysis revealed that higher creatinine levels (odds ratio =18.465, 95% confidence interval: 1.059–322.084, P=0.046) constituted the only significant predictor of increased mortality risk with no predictive values for age, D-dimer assay, ejection fraction, glucose, hemoglobin A1c, sodium, albumin or total cholesterol levels for mortality. Conclusion Serum creatinine levels constituted the sole independent determinant of mortality risk, with no significant values for D-dimer assay, GRACE or TIMI scores for predicting the risk of mortality in NSTEMI patients. PMID:28408834

Predicting Acute and Persistent Neuropathy Associated with Oxaliplatin

PubMed Central

Alejandro, Linh; Behrendt, Carolyn E.; Chen, Kim; Openshaw, Harry; Shibata, Stephen

2014-01-01

Objectives We sought to predict oxaliplatin-associated peripheral neuropathy during modified FOLFOX6 (mFOLFOX6) therapy. Methods In a 50% female sample, patients with previously untreated, primary or recurrent colorectal cancer were followed through a first course of mFOLFOX6 with oxaliplatin 85 mg/m2 every 2 weeks. Accounting for correlation among a subject's cycles, logistic regression estimated per-cycle risk of acute (under 14 days) and persistent (14 days or more) neuropathy. Proportional hazards regression predicted time to persistent neuropathy. Results Among mFOLFOX6 recipients (n=50, age 58.9 +10.1 years), 36% received concomitant bevacizumab. Of total cycles, 94.2% (422/448) were evaluable. Most (84%) subjects reported neuropathy at least once: 74% reported acute and 48% reported persistent symptoms. On multivariate analysis, risk factors shared by acute and persistent neuropathy were body-surface area >2.0, acute neuropathy in a past cycle, and lower body weight. In addition, risk of acute neuropathy decreased with age (adjusted for renal function and winter season), while risk of persistent neuropathy increased with cumulative dose of oxaliplatin and persistent neuropathy in a past cycle. Concomitant bevacizumab was not a risk factor when administered in Stage IV disease but was associated with persistent neuropathy when administered experimentally in Stage III. Females had no increased risk of either form of neuropathy. After 3 cycles, weight, body-surface area, and prior acute neuropathy predicted time to persistent neuropathy. Conclusions Routinely available clinical factors predict acute and persistent neuropathy associated with oxaliplatin. When validated, the proposed prognostic score for persistent neuropathy can help clinicians counsel patients about chemotherapy. PMID:22547012

Message framing with respect to decisions about vaccination: the roles of frame valence, frame method and perceived risk.

PubMed

Ferguson, Eamonn; Gallagher, Laura

2007-11-01

People respond differently when information is framed either positively or negatively (frame valence). Two prominent models propose that the effects of valence are moderated by (1) the method of framing (attributes vs. goals: Levin, Schneider, & Gaeth, 1998) and (2) perceived risk (Rothman & Salovey, 1997). This experiment (N=200) explores the joint influence of both of these moderators with respect to decisions about a flu vaccination. The study extends previous work by integrating these two models and exploring the moderating effects of two different aspects of perceived risk (personal outcome effectiveness and procedural risk). The results show that personal outcome effectiveness indirectly links frames to intentions. Procedural risk moderates the relationship between valence and method in a manner consistent with predictions from Levin et al.. Partial support for the model proposed by Rothman and Salovey are observed for goal frames only.

Predicting Family Burden Following Childhood Traumatic Brain Injury: A Cumulative Risk Approach

PubMed Central

Josie, Katherine Leigh; Peterson, Catherine Cant; Burant, Christopher; Drotar, Dennis; Stancin, Terry; Wade, Shari L.; Yeates, Keith; Taylor, H. Gerry

2015-01-01

Objective To examine the utility of a cumulative risk index (CRI) in predicting the family burden of injury (FBI) over time in families of children with traumatic brain injury (TBI). Participants One hundred eight children with severe or moderate TBI and their families participated in the study. Measures The measures used in the study include the Socioeconomic Composite Index, Life Stressors and Social Resources Inventory—Adult Form, Vineland Adaptive Behavior Scales, Child Behavior Checklist, Children’s Depression Inventory, McMaster Family Assessment Device, Brief Symptom Inventory, and Family Burden of Injury Interview. In addition, information on injury-related risk was obtained via medical charts. Methods Participants were assessed immediately, 6, and 12 months postinjury and at a 4-year extended follow-up. Results Risk variables were dichotomized (ie, high- or low-risk) and summed to create a CRI for each child. The CRI predicted the FBI at all assessments, even after accounting for autocorrelations across repeated assessments. Path coefficients between the outcome measures at each time point were significant, as were all path coefficients from the CRI to family burden at each time point. In addition, all fit indices were above the recommended guidelines, and the χ2 statistic indicated a good fit to the data. Conclusions The current study provides initial support for the utility of a CRI (ie, an index of accumulated risk factors) in predicting family outcomes over time for children with TBI. The time period immediately after injury best predicts the future levels of FBI; however, cumulative risk continues to influence the change across successive postinjury assessments. These results suggest that clinical interventions could be proactive or preventive by intervening with identified “at-risk” subgroups immediately following injury. PMID:19033828

Derivation of genetic biomarkers for cancer risk stratification in Barrett's oesophagus: a prospective cohort study

PubMed Central

Timmer, Margriet R.; Martinez, Pierre; Lau, Chiu T.; Westra, Wytske M.; Calpe, Silvia; Rygiel, Agnieszka M.; Rosmolen, Wilda D.; Meijer, Sybren L.; ten Kate, Fiebo J.W.; Dijkgraaf, Marcel G.W.; Mallant-Hent, Rosalie C.; Naber, Anton H.J.; van Oijen, Arnoud H.A.M.; Baak, Lubbertus C.; Scholten, Pieter; Böhmer, Clarisse J.M.; Fockens, Paul; Maley, Carlo C.; Graham, Trevor A.; Bergman, Jacques J.G.H.M.; Krishnadath, Kausilia K.

2016-01-01

Objective The risk of developing adenocarcinoma in non-dysplastic Barrett's oesophagus is low and difficult to predict. Accurate tools for risk stratification are needed to increase the efficiency of surveillance. We aimed to develop a prediction model for progression using clinical variables and genetic markers. Methods In a prospective cohort of patients with non-dysplastic Barrett's oesophagus, we evaluated six molecular markers: p16, p53, Her-2/neu, 20q, MYC, and aneusomy by DNA fluorescence in situ hybridisation on brush cytology specimens. Primary study outcomes were the development of high-grade dysplasia or oesophageal adenocarcinoma. The most predictive clinical variables and markers were determined using Cox proportional-hazards models, receiver-operating-characteristic curves and a leave-one-out analysis. Results A total of 428 patients participated (345 men; median age 60 years) with a cumulative follow-up of 2019 patient-years (median 45 months per patient). Of these patients, 22 progressed; nine developed high-grade dysplasia and 13 oesophageal adenocarcinoma. The clinical variables, age and circumferential Barrett's length, and the markers, p16 loss, MYC gain, and aneusomy, were significantly associated with progression on univariate analysis. We defined an ‘Abnormal Marker Count’ that counted abnormalities in p16, MYC and aneusomy, which significantly improved risk prediction beyond using just age and Barrett's length. In multivariate analysis, these three factors identified a high-risk group with an 8.7-fold (95% CI, 2.6 to 29.8) increased hazard ratio compared with the low-risk group, with an area under the curve of 0.76 (95% CI, 0.66 to 0.86). Conclusion A prediction model based on age, Barrett's length, and the markers p16, MYC, and aneusomy determines progression risk in non-dysplastic Barrett's oesophagus. PMID:26104750

Landscape structure and management alter the outcome of a pesticide ERA: Evaluating impacts of endocrine disruption using the ALMaSS European Brown Hare model.

PubMed

Topping, Chris J; Dalby, Lars; Skov, Flemming

2016-01-15

There is a gradual change towards explicitly considering landscapes in regulatory risk assessment. To realise the objective of developing representative scenarios for risk assessment it is necessary to know how detailed a landscape representation is needed to generate a realistic risk assessment, and indeed how to generate such landscapes. This paper evaluates the contribution of landscape and farming components to a model based risk assessment of a fictitious endocrine disruptor on hares. In addition, we present methods and code examples for generation of landscape structures and farming simulation from data collected primarily for EU agricultural subsidy support and GIS map data. Ten different Danish landscapes were generated and the ERA carried out for each landscape using two different assumed toxicities. The results showed negative impacts in all cases, but the extent and form in terms of impacts on abundance or occupancy differed greatly between landscapes. A meta-model was created, predicting impact from landscape and farming characteristics. Scenarios based on all combinations of farming and landscape for five landscapes representing extreme and middle impacts were created. The meta-models developed from the 10 real landscapes failed to predict impacts for these 25 scenarios. Landscape, farming, and the emergent density of hares all influenced the results of the risk assessment considerably. The study indicates that prediction of a reasonable worst case scenario is difficult from structural, farming or population metrics; rather the emergent properties generated from interactions between landscape, management and ecology are needed. Meta-modelling may also fail to predict impacts, even when restricting inputs to combinations of those used to create the model. Future ERA may therefore need to make use of multiple scenarios representing a wide range of conditions to avoid locally unacceptable risks. This approach could now be feasible Europe wide given the landscape generation methods presented.

Predicting stroke through genetic risk functions: The CHARGE risk score project

PubMed Central

Ibrahim-Verbaas, Carla A; Fornage, Myriam; Bis, Joshua C; Choi, Seung Hoan; Psaty, Bruce M; Meigs, James B; Rao, Madhu; Nalls, Mike; Fontes, Joao D; O’Donnell, Christopher J.; Kathiresan, Sekar; Ehret, Georg B.; Fox, Caroline S; Malik, Rainer; Dichgans, Martin; Schmidt, Helena; Lahti, Jari; Heckbert, Susan R; Lumley, Thomas; Rice, Kenneth; Rotter, Jerome I; Taylor, Kent D; Folsom, Aaron R; Boerwinkle, Eric; Rosamond, Wayne D; Shahar, Eyal; Gottesman, Rebecca F.; Koudstaal, Peter J; Amin, Najaf; Wieberdink, Renske G.; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G; DeStefano, Anita L.; Debette, Stephanie; Xue, Luting; Beiser, Alexa; Wolf, Philip A.; DeCarli, Charles; Ikram, M. Arfan; Seshadri, Sudha; Mosley, Thomas H; Longstreth, WT; van Duijn, Cornelia M; Launer, Lenore J

2014-01-01

Background and Purpose Beyond the Framingham Stroke Risk Score (FSRS), prediction of future stroke may improve with a genetic risk score (GRS) based on Single nucleotide polymorphisms (SNPs) associated with stroke and its risk factors. Methods The study includes four population-based cohorts with 2,047 first incident strokes from 22,720 initially stroke-free European origin participants aged 55 years and older, who were followed for up to 20 years. GRS were constructed with 324 SNPs implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with Area under the curve (AUC) statistics comparing the GRS to age sex, and FSRS models, and with reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke (IS). Results In the meta-analysis, adding the GRS to the FSRS, age and sex model resulted in a significant improvement in discrimination (All stroke: Δjoint AUC =0.016, p-value=2.3*10-6; IS: Δ joint AUC =0.021, p-value=3.7*10−7), although the overall AUC remained low. In all studies there was a highly significantly improved net reclassification index (p-values <10−4). Conclusions The SNPs associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared to the classical epidemiological risk factors for stroke. PMID:24436238

Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics

PubMed Central

MacMillan, Denise K; Ford, Jermaine; Fennell, Timothy R; Black, Sherry R; Snyder, Rodney W; Sipes, Nisha S; Westerhout, Joost; Setzer, R Woodrow; Pearce, Robert G; Simmons, Jane Ellen; Thomas, Russell S

2018-01-01

Abstract Prioritizing the risk posed by thousands of chemicals potentially present in the environment requires exposure, toxicity, and toxicokinetic (TK) data, which are often unavailable. Relatively high throughput, in vitro TK (HTTK) assays and in vitro-to-in vivo extrapolation (IVIVE) methods have been developed to predict TK, but most of the in vivo TK data available to benchmark these methods are from pharmaceuticals. Here we report on new, in vivo rat TK experiments for 26 non-pharmaceutical chemicals with environmental relevance. Both intravenous and oral dosing were used to calculate bioavailability. These chemicals, and an additional 19 chemicals (including some pharmaceuticals) from previously published in vivo rat studies, were systematically analyzed to estimate in vivo TK parameters (e.g., volume of distribution [Vd], elimination rate). For each of the chemicals, rat-specific HTTK data were available and key TK predictions were examined: oral bioavailability, clearance, Vd, and uncertainty. For the non-pharmaceutical chemicals, predictions for bioavailability were not effective. While no pharmaceutical was absorbed at less than 10%, the fraction bioavailable for non-pharmaceutical chemicals was as low as 0.3%. Total clearance was generally more under-estimated for nonpharmaceuticals and Vd methods calibrated to pharmaceuticals may not be appropriate for other chemicals. However, the steady-state, peak, and time-integrated plasma concentrations of nonpharmaceuticals were predicted with reasonable accuracy. The plasma concentration predictions improved when experimental measurements of bioavailability were incorporated. In summary, HTTK and IVIVE methods are adequately robust to be applied to high throughput in vitro toxicity screening data of environmentally relevant chemicals for prioritizing based on human health risks. PMID:29385628

Noninvasive Prenatal Detection of Trisomy 21 by Targeted Semiconductor Sequencing: A Technical Feasibility Study.

PubMed

Xi, Yanwei; Arbabi, Aryan; McNaughton, Amy J M; Hamilton, Alison; Hull, Danna; Perras, Helene; Chiu, Tillie; Morrison, Shawna; Goldsmith, Claire; Creede, Emilie; Anger, Gregory J; Honeywell, Christina; Cloutier, Mireille; Macchio, Natasha; Kiss, Courtney; Liu, Xudong; Crocker, Susan; Davies, Gregory A; Brudno, Michael; Armour, Christine M

2017-01-01

To develop an alternate noninvasive prenatal testing method for the assessment of trisomy 21 (T21) using a targeted semiconductor sequencing approach. A customized AmpliSeq panel was designed with 1,067 primer pairs targeting specific regions on chromosomes 21, 18, 13, and others. A total of 235 samples, including 30 affected with T21, were sequenced with an Ion Torrent Proton sequencer, and a method was developed for assessing the probability of fetal aneuploidy via derivation of a risk score. Application of the derived risk score yields a bimodal distribution, with the affected samples clustering near 1.0 and the unaffected near 0. For a risk score cutoff of 0.345, above which all would be considered at "high risk," all 30 T21-positive pregnancies were correctly predicted to be affected, and 199 of the 205 non-T21 samples were correctly predicted. The average hands-on time spent on library preparation and sequencing was 19 h in total, and the average number of reads of sequence obtained was 3.75 million per sample. With the described targeted sequencing approach on the semiconductor platform using a custom-designed library and a probabilistic statistical approach, we have demonstrated the feasibility of an alternate method of assessment for fetal T21. © 2017 S. Karger AG, Basel.

B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

PubMed Central

Sinner, Moritz F.; Stepas, Katherine A.; Moser, Carlee B.; Krijthe, Bouwe P.; Aspelund, Thor; Sotoodehnia, Nona; Fontes, João D.; Janssens, A. Cecile J.W.; Kronmal, Richard A.; Magnani, Jared W.; Witteman, Jacqueline C.; Chamberlain, Alanna M.; Lubitz, Steven A.; Schnabel, Renate B.; Vasan, Ramachandran S.; Wang, Thomas J.; Agarwal, Sunil K.; McManus, David D.; Franco, Oscar H.; Yin, Xiaoyan; Larson, Martin G.; Burke, Gregory L.; Launer, Lenore J.; Hofman, Albert; Levy, Daniel; Gottdiener, John S.; Kääb, Stefan; Couper, David; Harris, Tamara B.; Astor, Brad C.; Ballantyne, Christie M.; Hoogeveen, Ron C.; Arai, Andrew E.; Soliman, Elsayed Z.; Ellinor, Patrick T.; Stricker, Bruno H.C.; Gudnason, Vilmundur; Heckbert, Susan R.; Pencina, Michael J.; Benjamin, Emelia J.; Alonso, Alvaro

2014-01-01

Aims B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information. Methods and results We ascertained AF incidence in 18 556 Whites and African Americans from the Atherosclerosis Risk in Communities Study (ARIC, n=10 675), Cardiovascular Health Study (CHS, n = 5043), and Framingham Heart Study (FHS, n = 2838), followed for 5 years (prediction horizon). We added BNP (ARIC/CHS: N-terminal pro-B-type natriuretic peptide; FHS: BNP), CRP, or both to a previously reported AF risk score, and assessed model calibration and predictive ability [C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI)]. We replicated models in two independent European cohorts: Age, Gene/Environment Susceptibility Reykjavik Study (AGES), n = 4467; Rotterdam Study (RS), n = 3203. B-type natriuretic peptide and CRP were significantly associated with AF incidence (n = 1186): hazard ratio per 1-SD ln-transformed biomarker 1.66 [95% confidence interval (CI), 1.56–1.76], P < 0.0001 and 1.18 (95% CI, 1.11–1.25), P < 0.0001, respectively. Model calibration was sufficient (BNP, χ2 = 17.0; CRP, χ2 = 10.5; BNP and CRP, χ2 = 13.1). B-type natriuretic peptide improved the C-statistic from 0.765 to 0.790, yielded an IDI of 0.027 (95% CI, 0.022–0.032), a relative IDI of 41.5%, and a continuous NRI of 0.389 (95% CI, 0.322–0.455). The predictive ability of CRP was limited (C-statistic increment 0.003). B-type natriuretic peptide consistently improved prediction in AGES and RS. Conclusion B-type natriuretic peptide, not CRP, substantially improved AF risk prediction beyond clinical factors in an independently replicated, heterogeneous population. B-type natriuretic peptide may serve as a benchmark to evaluate novel putative AF risk biomarkers. PMID:25037055

Evaluating predictive modeling’s potential to improve teleretinal screening participation in urban safety net clinics

PubMed Central

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Introduction Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Methods Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. Results The predictive models were modestly predictive with the best model having an AUC of 0.71. Discussion Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics. PMID:23920536

In vivo serial MRI-based models and statistical methods to quantify sensitivity and specificity of mechanical predictors for carotid plaque rupture: location and beyond.

PubMed

Wu, Zheyang; Yang, Chun; Tang, Dalin

2011-06-01

It has been hypothesized that mechanical risk factors may be used to predict future atherosclerotic plaque rupture. Truly predictive methods for plaque rupture and methods to identify the best predictor(s) from all the candidates are lacking in the literature. A novel combination of computational and statistical models based on serial magnetic resonance imaging (MRI) was introduced to quantify sensitivity and specificity of mechanical predictors to identify the best candidate for plaque rupture site prediction. Serial in vivo MRI data of carotid plaque from one patient was acquired with follow-up scan showing ulceration. 3D computational fluid-structure interaction (FSI) models using both baseline and follow-up data were constructed and plaque wall stress (PWS) and strain (PWSn) and flow maximum shear stress (FSS) were extracted from all 600 matched nodal points (100 points per matched slice, baseline matching follow-up) on the lumen surface for analysis. Each of the 600 points was marked "ulcer" or "nonulcer" using follow-up scan. Predictive statistical models for each of the seven combinations of PWS, PWSn, and FSS were trained using the follow-up data and applied to the baseline data to assess their sensitivity and specificity using the 600 data points for ulcer predictions. Sensitivity of prediction is defined as the proportion of the true positive outcomes that are predicted to be positive. Specificity of prediction is defined as the proportion of the true negative outcomes that are correctly predicted to be negative. Using probability 0.3 as a threshold to infer ulcer occurrence at the prediction stage, the combination of PWS and PWSn provided the best predictive accuracy with (sensitivity, specificity) = (0.97, 0.958). Sensitivity and specificity given by PWS, PWSn, and FSS individually were (0.788, 0.968), (0.515, 0.968), and (0.758, 0.928), respectively. The proposed computational-statistical process provides a novel method and a framework to assess the sensitivity and specificity of various risk indicators and offers the potential to identify the optimized predictor for plaque rupture using serial MRI with follow-up scan showing ulceration as the gold standard for method validation. While serial MRI data with actual rupture are hard to acquire, this single-case study suggests that combination of multiple predictors may provide potential improvement to existing plaque assessment schemes. With large-scale patient studies, this predictive modeling process may provide more solid ground for rupture predictor selection strategies and methods for image-based plaque vulnerability assessment.

Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory.

PubMed

Shi, X; Wong, Y D; Li, M Z F; Chai, C

2018-08-01

Accident events are generally unexpected and occur rarely. Pre-accident risk assessment by surrogate indicators is an effective way to identify risk levels and thus boost accident prediction. Herein, the concept of Key Risk Indicator (KRI) is proposed, which assesses risk exposures using hybrid indicators. Seven metrics are shortlisted as the basic indicators in KRI, with evaluation in terms of risk behaviour, risk avoidance, and risk margin. A typical real-world chain-collision accident and its antecedent (pre-crash) road traffic movements are retrieved from surveillance video footage, and a grid remapping method is proposed for data extraction and coordinates transformation. To investigate the feasibility of each indicator in risk assessment, a temporal-spatial case-control is designed. By comparison, Time Integrated Time-to-collision (TIT) performs better in identifying pre-accident risk conditions; while Crash Potential Index (CPI) is helpful in further picking out the severest ones (the near-accident). Based on TIT and CPI, the expressions of KRIs are developed, which enable us to evaluate risk severity with three levels, as well as the likelihood. KRI-based risk assessment also reveals predictive insights about a potential accident, including at-risk vehicles, locations and time. Furthermore, straightforward thresholds are defined flexibly in KRIs, since the impact of different threshold values is found not to be very critical. For better validation, another independent real-world accident sample is examined, and the two results are in close agreement. Hierarchical indicators such as KRIs offer new insights about pre-accident risk exposures, which is helpful for accident assessment and prediction. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predicting sun protection behaviors using protection motivation variables.

PubMed

Ch'ng, Joanne W M; Glendon, A Ian

2014-04-01

Protection motivation theory components were used to predict sun protection behaviors (SPBs) using four outcome measures: typical reported behaviors, previous reported behaviors, current sunscreen use as determined by interview, and current observed behaviors (clothing worn) to control for common method bias. Sampled from two SE Queensland public beaches during summer, 199 participants aged 18-29 years completed a questionnaire measuring perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation (PM). Personal perceived risk (similar to threat appraisal) and response likelihood (similar to coping appraisal) were derived from their respective PM components. Protection motivation predicted all four SPB criterion variables. Personal perceived risk and response likelihood predicted protection motivation. Protection motivation completely mediated the effect of response likelihood on all four criterion variables. Alternative models are considered. Strengths and limitations of the study are outlined and suggestions made for future research.

Informative priors on fetal fraction increase power of the noninvasive prenatal screen.

PubMed

Xu, Hanli; Wang, Shaowei; Ma, Lin-Lin; Huang, Shuai; Liang, Lin; Liu, Qian; Liu, Yang-Yang; Liu, Ke-Di; Tan, Ze-Min; Ban, Hao; Guan, Yongtao; Lu, Zuhong

2017-11-09

PurposeNoninvasive prenatal screening (NIPS) sequences a mixture of the maternal and fetal cell-free DNA. Fetal trisomy can be detected by examining chromosomal dosages estimated from sequencing reads. The traditional method uses the Z-test, which compares a subject against a set of euploid controls, where the information of fetal fraction is not fully utilized. Here we present a Bayesian method that leverages informative priors on the fetal fraction.MethodOur Bayesian method combines the Z-test likelihood and informative priors of the fetal fraction, which are learned from the sex chromosomes, to compute Bayes factors. Bayesian framework can account for nongenetic risk factors through the prior odds, and our method can report individual positive/negative predictive values.ResultsOur Bayesian method has more power than the Z-test method. We analyzed 3,405 NIPS samples and spotted at least 9 (of 51) possible Z-test false positives.ConclusionBayesian NIPS is more powerful than the Z-test method, is able to account for nongenetic risk factors through prior odds, and can report individual positive/negative predictive values.Genetics in Medicine advance online publication, 9 November 2017; doi:10.1038/gim.2017.186.

Comparing motor-vehicle crash risk of EU and US vehicles.

PubMed

Flannagan, Carol A C; Bálint, András; Klinich, Kathleen D; Sander, Ulrich; Manary, Miriam A; Cuny, Sophie; McCarthy, Michael; Phan, Vuthy; Wallbank, Caroline; Green, Paul E; Sui, Bo; Forsman, Åsa; Fagerlind, Helen

2018-08-01

This study examined the hypotheses that passenger vehicles meeting European Union (EU) safety standards have similar crashworthiness to United States (US) -regulated vehicles in the US driving environment, and vice versa. The first step involved identifying appropriate databases of US and EU crashes that include in-depth crash information, such as estimation of crash severity using Delta-V and injury outcome based on medical records. The next step was to harmonize variable definitions and sampling criteria so that the EU data could be combined and compared to the US data using the same or equivalent parameters. Logistic regression models of the risk of a Maximum injury according to the Abbreviated Injury Scale of 3 or greater, or fatality (MAIS3+F) in EU-regulated and US-regulated vehicles were constructed. The injury risk predictions of the EU model and the US model were each applied to both the US and EU standard crash populations. Frontal, near-side, and far-side crashes were analyzed together (termed "front/side crashes") and a separate model was developed for rollover crashes. For the front/side model applied to the US standard population, the mean estimated risk for the US-vehicle model is 0.035 (sd = 0.012), and the mean estimated risk for the EU-vehicle model is 0.023 (sd = 0.016). When applied to the EU front/side population, the US model predicted a 0.065 risk (sd = 0.027), and the EU model predicted a 0.052 risk (sd = 0.025). For the rollover model applied to the US standard population, the US model predicted a risk of 0.071 (sd = 0.024), and the EU model predicted 0.128 risk (sd = 0.057). When applied to the EU rollover standard population, the US model predicted a 0.067 risk (sd = 0.024), and the EU model predicted 0.103 risk (sd = 0.040). The results based on these methods indicate that EU vehicles most likely have a lower risk of MAIS3+F injury in front/side impacts, while US vehicles most likely have a lower risk of MAIS3+F injury in llroovers. These results should be interpreted with an understanding of the uncertainty of the estimates, the study limitations, and our recommendations for further study detailed in the report. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predicting Epidemic Risk from Past Temporal Contact Data

PubMed Central

Valdano, Eugenio; Poletto, Chiara; Giovannini, Armando; Palma, Diana; Savini, Lara; Colizza, Vittoria

2015-01-01

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. PMID:25763816

A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method.

PubMed

Papageorgiou, Elpiniki I; Jayashree Subramanian; Karmegam, Akila; Papandrianos, Nikolaos

2015-11-01

Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40-49 years (who have a family history of breast cancer or other related cancers) to assess their personal risk for developing familial breast cancer (FBC). Besides, as each individual woman possesses different levels of risk of developing breast cancer depending on their family history, genetic predispositions and personal medical history, individualized care setting mechanism needs to be identified so that appropriate risk assessment, counseling, screening, and prevention options can be determined by the health care professionals. The presented work aims at developing a soft computing based medical decision support system using Fuzzy Cognitive Map (FCM) that assists health care professionals in deciding the individualized care setting mechanisms based on the FBC risk level of the given women. The FCM based FBC risk management system uses NHL to learn causal weights from 40 patient records and achieves a 95% diagnostic accuracy. The results obtained from the proposed model are in concurrence with the comprehensive risk evaluation tool based on Tyrer-Cuzick model for 38/40 patient cases (95%). Besides, the proposed model identifies high risk women by calculating higher accuracy of prediction than the standard Gail and NSAPB models. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines as well as previous FCM-based risk prediction methods for BC. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Infant Maltreatment-Related Mortality in Alaska: Correcting the Count and Using Birth Certificates to Predict Mortality

ERIC Educational Resources Information Center

Parrish, Jared W.; Gessner, Bradford D.

2010-01-01

Objectives: To accurately count the number of infant maltreatment-related fatalities and to use information from the birth certificates to predict infant maltreatment-related deaths. Methods: A population-based retrospective cohort study of infants born in Alaska for the years 1992 through 2005 was conducted. Risk factor variables were ascertained…

Phonology and Handedness in Primary School: Predictions of the Right Shift Theory

ERIC Educational Resources Information Center

Smythe, Pamela; Annett, Marian

2006-01-01

Background: The right shift (RS) theory of handedness suggests that poor phonology may occur in the general population as a risk associated with absence of an agent of left cerebral speech, the hypothesised RS + gene. The theory predicts that poor phonology is associated with reduced bias to right-handedness. Methods: A representative cohort of…

Two-Step Screening for Depressive Symptoms and Prediction of Mortality in Patients With Heart Failure.

PubMed

Lee, Kyoung Suk; Moser, Debra K; Pelter, Michele; Biddle, Martha J; Dracup, Kathleen

2017-05-01

Comorbid depression in patients with heart failure is associated with increased risk for death. In order to effectively identify depressed patients with cardiac disease, the American Heart Association suggests a 2-step screening method: administering the 2-item Patient Health Questionnaire first and then the 9-item Patient Health Questionnaire. However, whether the 2-step method is better for predicting poor prognosis in heart failure than is either the 2-item or the 9-item tool alone is not known. To determine whether the 2-step method is better than either the 2-item or the 9-item questionnaire alone for predicting all-cause mortality in heart failure. During a 2-year period, 562 patients with heart failure were assessed for depression by using the 2-step method. With the 2-step method, results are considered positive if patients endorse either depressed mood or anhedonia on the 2-item screen and have scores of 10 or higher on the 9-item screen. Screening results with the 2-step method were not associated with all-cause mortality. Patients with scores positive for depression on either the 2-item or 9-item screen alone had 53% and 60% greater risk, respectively, for all-cause death than did patients with scores negative for depression after adjustments for covariates (hazard ratio, 1.530; 95% CI, 1.029-2.274 for the 2-item screen; hazard ratio, 1.603; 95% CI, 1.079-2.383 for the 9-item screen). The 2-step method has no clear advantages compared with the 2-item screen alone or the 9-item screen alone for predicting adverse prognostic effects of depressive symptoms in heart failure. ©2017 American Association of Critical-Care Nurses.

Cardiovascular risk estimation in women with a history of hypertensive pregnancy disorders at term: a longitudinal follow-up study

PubMed Central

2013-01-01

Background Cardiovascular disease is associated with major morbidity and mortality in women in the Western world. Prediction of an individual cardiovascular disease risk in young women is difficult. It is known that women with hypertensive pregnancy complications have an increased risk for developing cardiovascular disease in later life and pregnancy might be used as a cardiovascular stress test to identify women who are at high risk for cardiovascular disease. In this study we assess the possibility of long term cardiovascular risk prediction in women with a history of hypertensive pregnancy disorders at term. Methods In a longitudinal follow-up study, between June 2008 and November 2010, 300 women with a history of hypertensive pregnancy disorders at term (HTP cohort) and 94 women with a history of normotensive pregnancies at term (NTP cohort) were included. From the cardiovascular risk status that was known two years after index pregnancy we calculated individual (extrapolated) 10-and 30-year cardiovascular event risks using four different risk prediction models including the Framingham risk score, the SCORE score and the Reynolds risk score. Continuous data were analyzed using the Student’s T test and Mann–Whitney U test and categorical data by the Chi-squared test. A poisson regression analysis was performed to calculate the incidence risk ratios and corresponding 95% confidence intervals for the different cardiovascular risk estimation categories. Results After a mean follow-up of 2.5 years, HTP women had significantly higher mean (SD) extrapolated 10-year cardiovascular event risks (HTP 7.2% (3.7); NTP 4.4% (1.9) (p<.001, IRR 5.8, 95% CI 1.9 to 19)) and 30-year cardiovascular event risks (HTP 11% (7.6); NTP 7.3% (3.5) (p<.001, IRR 2.7, 95% CI 1.6 to 4.5)) as compared to NTP women calculated by the Framingham risk scores. The SCORE score and the Reynolds risk score showed similar significant results. Conclusions Women with a history of gestational hypertension or preeclampsia at term have higher predicted (extrapolated) 10-year and 30-year cardiovascular event risks as compared to women with a history of uncomplicated pregnancies. Further large prospective studies have to evaluate whether hypertensive pregnancy disorders have to be included as an independent variable in cardiovascular risk prediction models for women. PMID:23734952

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

PubMed

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

2017-12-01

Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862. Choice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiplier method may be unreliable to predict the timing of temporary hemiepiphysiodesis for coronal angular deformity.

PubMed

Wu, Zhenkai; Ding, Jing; Zhao, Dahang; Zhao, Li; Li, Hai; Liu, Jianlin

2017-07-10

The multiplier method was introduced by Paley to calculate the timing for temporary hemiepiphysiodesis. However, this method has not been verified in terms of clinical outcome measure. We aimed to (1) predict the rate of angular correction per year (ACPY) at the various corresponding ages by means of multiplier method and verify the reliability based on the data from the published studies and (2) screen out risk factors for deviation of prediction. A comprehensive search was performed in the following electronic databases: Cochrane, PubMed, and EMBASE™. A total of 22 studies met the inclusion criteria. If the actual value of ACPY from the collected date was located out of the range of the predicted value based on the multiplier method, it was considered as the deviation of prediction (DOP). The associations of patient characteristics with DOP were assessed with the use of univariate logistic regression. Only one article was evaluated as moderate evidence; the remaining articles were evaluated as poor quality. The rate of DOP was 31.82%. In the detailed individual data of included studies, the rate of DOP was 55.44%. The multiplier method is not reliable in predicting the timing for temporary hemiepiphysiodesis, even though it is prone to be more reliable for the younger patients with idiopathic genu coronal deformity.

Quantitative prediction of drug side effects based on drug-related features.

PubMed

Niu, Yanqing; Zhang, Wen

2017-09-01

Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

Realized volatility and absolute return volatility: a comparison indicating market risk.

PubMed

Zheng, Zeyu; Qiao, Zhi; Takaishi, Tetsuya; Stanley, H Eugene; Li, Baowen

2014-01-01

Measuring volatility in financial markets is a primary challenge in the theory and practice of risk management and is essential when developing investment strategies. Although the vast literature on the topic describes many different models, two nonparametric measurements have emerged and received wide use over the past decade: realized volatility and absolute return volatility. The former is strongly favored in the financial sector and the latter by econophysicists. We examine the memory and clustering features of these two methods and find that both enable strong predictions. We compare the two in detail and find that although realized volatility has a better short-term effect that allows predictions of near-future market behavior, absolute return volatility is easier to calculate and, as a risk indicator, has approximately the same sensitivity as realized volatility. Our detailed empirical analysis yields valuable guidelines for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods.

Realized Volatility and Absolute Return Volatility: A Comparison Indicating Market Risk

PubMed Central

Takaishi, Tetsuya; Stanley, H. Eugene; Li, Baowen

2014-01-01

Measuring volatility in financial markets is a primary challenge in the theory and practice of risk management and is essential when developing investment strategies. Although the vast literature on the topic describes many different models, two nonparametric measurements have emerged and received wide use over the past decade: realized volatility and absolute return volatility. The former is strongly favored in the financial sector and the latter by econophysicists. We examine the memory and clustering features of these two methods and find that both enable strong predictions. We compare the two in detail and find that although realized volatility has a better short-term effect that allows predictions of near-future market behavior, absolute return volatility is easier to calculate and, as a risk indicator, has approximately the same sensitivity as realized volatility. Our detailed empirical analysis yields valuable guidelines for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods. PMID:25054439

SU-E-T-628: Predicted Risk of Post-Irradiation Cerebral Necrosis in Pediatric Brain Cancer Patients: A Treatment Planning Comparison of Proton Vs. Photon Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freund, D; Zhang, R; Sanders, M

Purpose: Post-irradiation cerebral necrosis (PICN) is a severe late effect that can Result from brain cancers treatment using radiation therapy. The purpose of this study was to compare the treatment plans and predicted risk of PICN after volumetric modulated arc therapy (VMAT) to the risk after passively scattered proton therapy (PSPT) and intensity modulated proton therapy (IMPT) in a cohort of pediatric patients. Methods: Thirteen pediatric patients with varying age and sex were selected for this study. A clinical treatment volume (CTV) was constructed for 8 glioma patients and 5 ependymoma patients. Prescribed dose was 54 Gy over 30 fractionsmore » to the planning volume. Dosimetric endpoints were compared between VMAT and proton plans. The normal tissue complication probability (NTCP) following VMAT and proton therapy planning was also calculated using PICN as the biological endpoint. Sensitivity tests were performed to determine if predicted risk of PICN was sensitive to positional errors, proton range errors and selection of risk models. Results: Both PSPT and IMPT plans resulted in a significant increase in the maximum dose and reduction in the total brain volume irradiated to low doses compared with the VMAT plans. The average ratios of NTCP between PSPT and VMAT were 0.56 and 0.38 for glioma and ependymoma patients respectively and the average ratios of NTCP between IMPT and VMAT were 0.67 and 0.68 for glioma and ependymoma plans respectively. Sensitivity test revealed that predicted ratios of risk were insensitive to range and positional errors but varied with risk model selection. Conclusion: Both PSPT and IMPT plans resulted in a decrease in the predictive risk of necrosis for the pediatric plans studied in this work. Sensitivity analysis upheld the qualitative findings of the risk models used in this study, however more accurate models that take into account dose and volume are needed.« less

Prediction of Febrile Neutropenia after Chemotherapy Based on Pretreatment Risk Factors among Cancer Patients

PubMed Central

Aagaard, Theis; Roen, Ashley; Daugaard, Gedske; Brown, Peter; Sengeløv, Henrik; Mocroft, Amanda; Lundgren, Jens; Helleberg, Marie

2017-01-01

Abstract Background Febrile neutropenia (FN) is a common complication to chemotherapy associated with a high burden of morbidity and mortality. Reliable prediction of individual risk based on pretreatment risk factors allows for stratification of preventive interventions. We aimed to develop such a risk stratification model to predict FN in the 30 days after initiation of chemotherapy. Methods We included consecutive treatment-naïve patients with solid cancers and diffuse large B-cell lymphomas at Copenhagen University Hospital, 2010–2015. Data were obtained from the PERSIMUNE repository of electronic health records. FN was defined as neutrophils ≤0.5 × 10E9/L ​at the time of either a blood culture sample or death. Time from initiation of chemotherapy to FN was analyzed using Fine-Gray models with death as a competing event. Risk factors investigated were: age, sex, body surface area, haemoglobin, albumin, neutrophil-to-lymphocyte ratio, Charlson Comorbidity Index (CCI) and chemotherapy drugs. Parameter estimates were scaled and summed to create the risk score. The scores were grouped into four: low, intermediate, high and very high risk. Results Among 8,585 patients, 467 experienced FN, incidence rate/30 person-days 0.05 (95% CI, 0.05–0.06). Age (1 point if > 65 years), albumin (1 point if < 39 g/L), CCI (1 point if > 2) and chemotherapy (range -5 to 6 points/drug) predicted FN. Median score at inclusion was 2 points (range –5 to 9). The cumulative incidence and the incidence rates and hazard ratios of FN are shown in Figure 1 and Table 1, respectively. Conclusion We developed a risk score to predict FN the first month after initiation of chemotherapy. The score is easy to use and provides good differentiation of risk groups; the score needs independent validation before routine use. Disclosures All authors: No reported disclosures.

Prediction of biomechanical parameters of the proximal femur using statistical appearance models and support vector regression.

PubMed

Fritscher, Karl; Schuler, Benedikt; Link, Thomas; Eckstein, Felix; Suhm, Norbert; Hänni, Markus; Hengg, Clemens; Schubert, Rainer

2008-01-01

Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.

Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk.

PubMed

Jieyi Li; Arandjelovic, Ognjen

2017-07-01

Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories, or display cumulative long term prognostics, in an intuitive and easily interpretable manner.

Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

PubMed

Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

2016-04-01

Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases. Copyright © 2016 Elsevier Inc. All rights reserved.

The Prediction of Type 1 Diabetes by Multiple Autoantibody Levels and Their Incorporation Into an Autoantibody Risk Score in Relatives of Type 1 Diabetic Patients

PubMed Central

Sosenko, Jay M.; Skyler, Jay S.; Palmer, Jerry P.; Krischer, Jeffrey P.; Yu, Liping; Mahon, Jeffrey; Beam, Craig A.; Boulware, David C.; Rafkin, Lisa; Schatz, Desmond; Eisenbarth, George

2013-01-01

OBJECTIVE We assessed whether a risk score that incorporates levels of multiple islet autoantibodies could enhance the prediction of type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS TrialNet Natural History Study participants (n = 784) were tested for three autoantibodies (GADA, IA-2A, and mIAA) at their initial screening. Samples from those positive for at least one autoantibody were subsequently tested for ICA and ZnT8A. An autoantibody risk score (ABRS) was developed from a proportional hazards model that combined autoantibody levels from each autoantibody along with their designations of positivity and negativity. RESULTS The ABRS was strongly predictive of T1D (hazard ratio [with 95% CI] 2.72 [2.23–3.31], P < 0.001). Receiver operating characteristic curve areas (with 95% CI) for the ABRS revealed good predictability (0.84 [0.78–0.90] at 2 years, 0.81 [0.74–0.89] at 3 years, P < 0.001 for both). The composite of levels from the five autoantibodies was predictive of T1D before and after an adjustment for the positivity or negativity of autoantibodies (P < 0.001). The findings were almost identical when ICA was excluded from the risk score model. The combination of the ABRS and the previously validated Diabetes Prevention Trial–Type 1 Risk Score (DPTRS) predicted T1D more accurately (0.93 [0.88–0.98] at 2 years, 0.91 [0.83–0.99] at 3 years) than either the DPTRS or the ABRS alone (P ≤ 0.01 for all comparisons). CONCLUSIONS These findings show the importance of considering autoantibody levels in assessing the risk of T1D. Moreover, levels of multiple autoantibodies can be incorporated into an ABRS that accurately predicts T1D. PMID:23818528

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

PubMed

Kessler, R C; van Loo, H M; Wardenaar, K J; Bossarte, R M; Brenner, L A; Cai, T; Ebert, D D; Hwang, I; Li, J; de Jonge, P; Nierenberg, A A; Petukhova, M V; Rosellini, A J; Sampson, N A; Schoevers, R A; Wilcox, M A; Zaslavsky, A M

2016-10-01

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports

PubMed Central

Kessler, Ronald C.; van Loo, Hanna M.; Wardenaar, Klaas J.; Bossarte, Robert M.; Brenner, Lisa A.; Cai, Tianxi; Ebert, David Daniel; Hwang, Irving; Li, Junlong; de Jonge, Peter; Nierenberg, Andrew A.; Petukhova, Maria V.; Rosellini, Anthony J.; Sampson, Nancy A.; Schoevers, Robert A.; Wilcox, Marsha A.; Zaslavsky, Alan M.

2015-01-01

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models. PMID:26728563

A comparison between the clinical significance and growth mixture modelling early change methods at predicting negative outcomes.

PubMed

Flood, Nicola; Page, Andrew; Hooke, Geoff

2018-05-03

Routine outcome monitoring benefits treatment by identifying potential no change and deterioration. The present study compared two methods of identifying early change and their ability to predict negative outcomes on self-report symptom and wellbeing measures. 1467 voluntary day patients participated in a 10-day group Cognitive Behaviour Therapy (CBT) program and completed the symptom and wellbeing measures daily. Early change, as defined by (a) the clinical significance method and (b) longitudinal modelling, was compared on each measure. Early change, as defined by the simpler clinical significance method, was superior at predicting negative outcomes than longitudinal modelling. The longitudinal modelling method failed to detect a group of deteriorated patients, and agreement between the early change methods and the final unchanged outcome was higher for the clinical significance method. Therapists could use the clinical significance early change method during treatment to alert them of patients at risk for negative outcomes, which in turn could allow therapists to prevent those negative outcomes from occurring.

A biomarker-based risk score to predict death in patients with atrial fibrillation: the ABC (age, biomarkers, clinical history) death risk score

PubMed Central

Hijazi, Ziad; Oldgren, Jonas; Lindbäck, Johan; Alexander, John H; Connolly, Stuart J; Eikelboom, John W; Ezekowitz, Michael D; Held, Claes; Hylek, Elaine M; Lopes, Renato D; Yusuf, Salim; Granger, Christopher B; Siegbahn, Agneta; Wallentin, Lars

2018-01-01

Abstract Aims In atrial fibrillation (AF), mortality remains high despite effective anticoagulation. A model predicting the risk of death in these patients is currently not available. We developed and validated a risk score for death in anticoagulated patients with AF including both clinical information and biomarkers. Methods and results The new risk score was developed and internally validated in 14 611 patients with AF randomized to apixaban vs. warfarin for a median of 1.9 years. External validation was performed in 8548 patients with AF randomized to dabigatran vs. warfarin for 2.0 years. Biomarker samples were obtained at study entry. Variables significantly contributing to the prediction of all-cause mortality were assessed by Cox-regression. Each variable obtained a weight proportional to the model coefficients. There were 1047 all-cause deaths in the derivation and 594 in the validation cohort. The most important predictors of death were N-terminal pro B-type natriuretic peptide, troponin-T, growth differentiation factor-15, age, and heart failure, and these were included in the ABC (Age, Biomarkers, Clinical history)-death risk score. The score was well-calibrated and yielded higher c-indices than a model based on all clinical variables in both the derivation (0.74 vs. 0.68) and validation cohorts (0.74 vs. 0.67). The reduction in mortality with apixaban was most pronounced in patients with a high ABC-death score. Conclusion A new biomarker-based score for predicting risk of death in anticoagulated AF patients was developed, internally and externally validated, and well-calibrated in two large cohorts. The ABC-death risk score performed well and may contribute to overall risk assessment in AF. ClinicalTrials.gov identifier NCT00412984 and NCT00262600 PMID:29069359

Prediction of Ischemic Heart Disease and Stroke in Survivors of Childhood Cancer.

PubMed

Chow, Eric J; Chen, Yan; Hudson, Melissa M; Feijen, Elizabeth A M; Kremer, Leontien C; Border, William L; Green, Daniel M; Meacham, Lillian R; Mulrooney, Daniel A; Ness, Kirsten K; Oeffinger, Kevin C; Ronckers, Cécile M; Sklar, Charles A; Stovall, Marilyn; van der Pal, Helena J; van Dijk, Irma W E M; van Leeuwen, Flora E; Weathers, Rita E; Robison, Leslie L; Armstrong, Gregory T; Yasui, Yutaka

2018-01-01

Purpose We aimed to predict individual risk of ischemic heart disease and stroke in 5-year survivors of childhood cancer. Patients and Methods Participants in the Childhood Cancer Survivor Study (CCSS; n = 13,060) were observed through age 50 years for the development of ischemic heart disease and stroke. Siblings (n = 4,023) established the baseline population risk. Piecewise exponential models with backward selection estimated the relationships between potential predictors and each outcome. The St Jude Lifetime Cohort Study (n = 1,842) and the Emma Children's Hospital cohort (n = 1,362) were used to validate the CCSS models. Results Ischemic heart disease and stroke occurred in 265 and 295 CCSS participants, respectively. Risk scores based on a standard prediction model that included sex, chemotherapy, and radiotherapy (cranial, neck, and chest) exposures achieved an area under the curve and concordance statistic of 0.70 and 0.70 for ischemic heart disease and 0.63 and 0.66 for stroke, respectively. Validation cohort area under the curve and concordance statistics ranged from 0.66 to 0.67 for ischemic heart disease and 0.68 to 0.72 for stroke. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups. The cumulative incidences at age 50 years among CCSS low-risk groups were < 5%, compared with approximately 20% for high-risk groups ( P < .001); cumulative incidence was only 1% for siblings ( P < .001 v low-risk survivors). Conclusion Information available to clinicians soon after completion of childhood cancer therapy can predict individual risk for subsequent ischemic heart disease and stroke with reasonable accuracy and discrimination through age 50 years. These models provide a framework on which to base future screening strategies and interventions.

Evaluation of Non-Laboratory and Laboratory Prediction Models for Current and Future Diabetes Mellitus: A Cross-Sectional and Retrospective Cohort Study

PubMed Central

Hahn, Seokyung; Moon, Min Kyong; Park, Kyong Soo; Cho, Young Min

2016-01-01

Background Various diabetes risk scores composed of non-laboratory parameters have been developed, but only a few studies performed cross-validation of these scores and a comparison with laboratory parameters. We evaluated the performance of diabetes risk scores composed of non-laboratory parameters, including a recently published Korean risk score (KRS), and compared them with laboratory parameters. Methods The data of 26,675 individuals who visited the Seoul National University Hospital Healthcare System Gangnam Center for a health screening program were reviewed for cross-sectional validation. The data of 3,029 individuals with a mean of 6.2 years of follow-up were reviewed for longitudinal validation. The KRS and 16 other risk scores were evaluated and compared with a laboratory prediction model developed by logistic regression analysis. Results For the screening of undiagnosed diabetes, the KRS exhibited a sensitivity of 81%, a specificity of 58%, and an area under the receiver operating characteristic curve (AROC) of 0.754. Other scores showed AROCs that ranged from 0.697 to 0.782. For the prediction of future diabetes, the KRS exhibited a sensitivity of 74%, a specificity of 54%, and an AROC of 0.696. Other scores had AROCs ranging from 0.630 to 0.721. The laboratory prediction model composed of fasting plasma glucose and hemoglobin A1c levels showed a significantly higher AROC (0.838, P < 0.001) than the KRS. The addition of the KRS to the laboratory prediction model increased the AROC (0.849, P = 0.016) without a significant improvement in the risk classification (net reclassification index: 4.6%, P = 0.264). Conclusions The non-laboratory risk scores, including KRS, are useful to estimate the risk of undiagnosed diabetes but are inferior to the laboratory parameters for predicting future diabetes. PMID:27214034

Measuring Burden of Unhealthy Behaviours Using a Multivariable Predictive Approach: Life Expectancy Lost in Canada Attributable to Smoking, Alcohol, Physical Inactivity, and Diet

PubMed Central

Perez, Richard; Taljaard, Monica; Hennessy, Deirdre; Wilson, Kumanan; Tanuseputro, Peter; Bennett, Carol; Tuna, Meltem; Fisher, Stacey; Rosella, Laura C.

2016-01-01

Background Behaviours such as smoking, poor diet, physical inactivity, and unhealthy alcohol consumption are leading risk factors for death. We assessed the Canadian burden attributable to these behaviours by developing, validating, and applying a multivariable predictive model for risk of all-cause death. Methods A predictive algorithm for 5 y risk of death—the Mortality Population Risk Tool (MPoRT)—was developed and validated using the 2001 to 2008 Canadian Community Health Surveys. There were approximately 1 million person-years of follow-up and 9,900 deaths in the development and validation datasets. After validation, MPoRT was used to predict future mortality and estimate the burden of smoking, alcohol, physical inactivity, and poor diet in the presence of sociodemographic and other risk factors using the 2010 national survey (approximately 90,000 respondents). Canadian period life tables were generated using predicted risk of death from MPoRT. The burden of behavioural risk factors attributable to life expectancy was estimated using hazard ratios from the MPoRT risk model. Findings The MPoRT 5 y mortality risk algorithms were discriminating (C-statistic: males 0.874 [95% CI: 0.867–0.881]; females 0.875 [0.868–0.882]) and well calibrated in all 58 predefined subgroups. Discrimination was maintained or improved in the validation cohorts. For the 2010 Canadian population, unhealthy behaviour attributable life expectancy lost was 6.0 years for both men and women (for men 95% CI: 5.8 to 6.3 for women 5.8 to 6.2). The Canadian life expectancy associated with health behaviour recommendations was 17.9 years (95% CI: 17.7 to 18.1) greater for people with the most favourable risk profile compared to those with the least favourable risk profile (88.2 years versus 70.3 years). Smoking, by itself, was associated with 32% to 39% of the difference in life expectancy across social groups (by education achieved or neighbourhood deprivation). Conclusions Multivariable predictive algorithms such as MPoRT can be used to assess health burdens for sociodemographic groups or for small changes in population exposure to risks, thereby addressing some limitations of more commonly used measurement approaches. Unhealthy behaviours have a substantial collective burden on the life expectancy of the Canadian population. PMID:27529741

Validation of the Saskatoon Falls Prevention Consortium's Falls Screening and Referral Algorithm

PubMed Central

Lawson, Sara Nicole; Zaluski, Neal; Petrie, Amanda; Arnold, Cathy; Basran, Jenny

2013-01-01

ABSTRACT Purpose: To investigate the concurrent validity of the Saskatoon Falls Prevention Consortium's Falls Screening and Referral Algorithm (FSRA). Method: A total of 29 older adults (mean age 77.7 [SD 4.0] y) residing in an independent-living senior's complex who met inclusion criteria completed a demographic questionnaire and the components of the FSRA and Berg Balance Scale (BBS). The FSRA consists of the Elderly Fall Screening Test (EFST) and the Multi-factor Falls Questionnaire (MFQ); it is designed to categorize individuals into low, moderate, or high fall-risk categories to determine appropriate management pathways. A predictive model for probability of fall risk, based on previous research, was used to determine concurrent validity of the FSRI. Results: The FSRA placed 79% of participants into the low-risk category, whereas the predictive model found the probability of fall risk to range from 0.04 to 0.74, with a mean of 0.35 (SD 0.25). No statistically significant correlation was found between the FSRA and the predictive model for probability of fall risk (Spearman's ρ=0.35, p=0.06). Conclusion: The FSRA lacks concurrent validity relative to to a previously established model of fall risk and appears to over-categorize individuals into the low-risk group. Further research on the FSRA as an adequate tool to screen community-dwelling older adults for fall risk is recommended. PMID:24381379

Improving runoff risk estimates: Formulating runoff as a bivariate process using the SCS curve number method

NASA Astrophysics Data System (ADS)

Shaw, Stephen B.; Walter, M. Todd

2009-03-01

The Soil Conservation Service curve number (SCS-CN) method is widely used to predict storm runoff for hydraulic design purposes, such as sizing culverts and detention basins. As traditionally used, the probability of calculated runoff is equated to the probability of the causative rainfall event, an assumption that fails to account for the influence of variations in soil moisture on runoff generation. We propose a modification to the SCS-CN method that explicitly incorporates rainfall return periods and the frequency of different soil moisture states to quantify storm runoff risks. Soil moisture status is assumed to be correlated to stream base flow. Fundamentally, this approach treats runoff as the outcome of a bivariate process instead of dictating a 1:1 relationship between causative rainfall and resulting runoff volumes. Using data from the Fall Creek watershed in western New York and the headwaters of the French Broad River in the mountains of North Carolina, we show that our modified SCS-CN method improves frequency discharge predictions in medium-sized watersheds in the eastern United States in comparison to the traditional application of the method.

Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Jia; Gensheimer, Michael F.; Dong, Xinzhe

2016-08-01

Purpose: To develop an intratumor partitioning framework for identifying high-risk subregions from {sup 18}F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. Methods and Materials: In this institutional review board–approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET andmore » CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP). Results: Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05). Conclusion: We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.« less

[Forensic assessment of violence risk].

PubMed

Pujol Robinat, Amadeo; Mohíno Justes, Susana; Gómez-Durán, Esperanza L

2014-03-01

Over the last 20 years there have been steps forward in the field of scientific research on prediction and handling different violent behaviors. In this work we go over the classic concept of "criminal dangerousness" and the more current of "violence risk assessment". We analyze the evolution of such assessment from the practice of non-structured clinical expert opinion to current actuarial methods and structured clinical expert opinion. Next we approach the problem of assessing physical violence risk analyzing the HCR-20 (Assessing Risk for Violence) and we also review the classic and complex subject of the relation between mental disease and violence. One of the most problematic types of violence, difficult to assess and predict, is sexual violence. We study the different actuarial and sexual violence risk prediction instruments and in the end we advise an integral approach to the problem. We also go through partner violence risk assessment, describing the most frequently used scales, especially SARA (Spouse Assault Risk Assessment) and EPV-R. Finally we give practical advice on risk assessment, emphasizing the importance of having maximum information about the case, carrying out a clinical examination, psychopathologic exploration and the application of one of the described risk assessment scales. We'll have to express an opinion about the dangerousness/risk of future violence from the subject and some recommendations on the conduct to follow and the most advisable treatment. Copyright © 2014 Elsevier España, S.L. All rights reserved.

Do Executive Function Deficits Predict Later Substance Use Disorders among Adolescents and Young Adults?

ERIC Educational Resources Information Center

Wilens, Timothy E.; Martelon, MaryKate; Fried, Ronna; Petty, Carter; Bateman, Clancey; Biederman, Joseph

2011-01-01

Objective: There is increasing interest regarding the risk and overlap of executive function deficits (EFDs) in stable cigarette smoking and substance use disorders (SUD). Therefore, we examined whether earlier EFD was a risk factor for subsequent cigarette smoking and SUD and further explored the relationship between EFD and SUD. Method: We…

What Implementation Components Predict Positive Outcomes in a Parenting Program?

ERIC Educational Resources Information Center

Álvarez, Míriam; Rodrigo, María José; Byrne, Sonia

2018-01-01

Objectives: To examine the components affecting the quality of the implementation and their impact on the outcomes of the "Growing Up Happily in the Family" program targeted at parents with children aged 0-5. Method: At-risk and non-at-risk parents (N = 196) participated in 26 groups in local social services. Adherence, adaptations,…

Evaluation of health risks for contaminated aquifers.

PubMed Central

Piver, W T; Jacobs, T L; Medina, M A

1997-01-01

This review focuses on progress in the development of transport models for heterogeneous contaminated aquifers, the use of predicted contaminant concentrations in groundwater for risk assessment for heterogeneous human populations, and the evaluation of aquifer remediation technologies. Major limitations and areas for continuing research for all methods presented in this review are identified. Images Figure 2. PMID:9114282

Parental Perceptions of Neighborhood Processes, Stress, Personal Control, and Risk for Physical Child Abuse and Neglect

ERIC Educational Resources Information Center

Guterman, Neil B.; Lee, Shawna J.; Taylor, Catherine A.; Rathouz, Paul J.

2009-01-01

Objective: This study set out to examine whether mothers' individual perceptions of their neighborhood social processes predict their risk for physical child abuse and neglect directly and/or indirectly via pathways involving parents' reported stress and sense of personal control in the parenting role. Methods: In-home and phone interview data…

Home, automated office, and conventional office blood pressure as predictors of cardiovascular risk.

PubMed

Andreadis, Emmanuel A; Papademetriou, Vasilios; Geladari, Charalampia V; Kolyvas, George N; Angelopoulos, Epameinondas T; Aronis, Konstantinos N

2017-03-01

Automated office blood pressure (AOBP) has recently been shown to closely predict cardiovascular (CV) events in the elderly. Home blood pressure (HBP) has also been accepted as a valuable method in the prediction of CV disease. This study aimed to compare conventional office BP (OBP), HBP, and AOBP in order to evaluate their value in predicting CV events and deaths in hypertensives. We assessed 236 initially treatment naïve hypertensives, examined between 2009 and 2013. The end points were any CV and non-CV event including mortality, myocardial infarction, coronary heart disease, hospitalization for heart failure, severe arrhythmia, stroke, and intermittent claudication. We fitted proportional hazards models using the different modalities as predictors and evaluated their predictive performance using three metrics: time-dependent receiver operating characteristics curves, the Akaike's Information Criterion, and Harrell's C-index. After a mean follow-up of 7 years, 23 participants (39% women) had experienced ≥1 CV event. Conventional office systolic (hazard ratio [HR] per 1 mm Hg increase in BP, 1.028; 95% confidence interval [CI], 1.009-1.048), automated office systolic (HR per 1 mm Hg increase in BP, 1.031; 95% CI, 1.008-1.054), and home systolic (HR, 1.025; 95% CI, 1.003-1.047) were predictive of CV events. All systolic BP measurements were predictive after adjustment for other CV risk factors (P < .05). The predictive performance of the different modalities was similar. Conventional OBP was significantly higher than AOBP and average HBP. AOBP predicts equally well to OBP and HBP CV events. It appears to be comparable to HBP in the assessment of CV risk, and therefore, its introduction into guidelines and clinical practice as the reference method for assessing BP in the office seems reasonable after verification of these findings by randomized trials. Copyright © 2017 American Society of Hypertension. All rights reserved.

Glycemic Control Indices and Their Aggregation in the Prediction of Nocturnal Hypoglycemia From Intermittent Blood Glucose Measurements.

PubMed

Sampath, Sivananthan; Tkachenko, Pavlo; Renard, Eric; Pereverzev, Sergei V

2016-11-01

Despite the risk associated with nocturnal hypoglycemia (NH) there are only a few methods aiming at the prediction of such events based on intermittent blood glucose monitoring data. One of the first methods that potentially can be used for NH prediction is based on the low blood glucose index (LBGI) and suggested, for example, in Accu-Chek® Connect as a hypoglycemia risk indicator. On the other hand, nowadays there are other glucose control indices (GCI), which could be used for NH prediction in the same spirit as LBGI. In the present study we propose a general approach of combining NH predictors constructed from different GCI. The approach is based on a recently developed strategy for aggregating ranking algorithms in machine learning. NH predictors have been calibrated and tested on data extracted from clinical trials, performed in EU FP7-funded project DIAdvisor. Then, to show a portability of the method we have tested it on another dataset that was received from EU Horizon 2020-funded project AMMODIT. We exemplify the proposed approach by aggregating NH predictors that have been constructed based on 4 GCI associated with hypoglycemia. Even though these predictors have been preliminary optimized to exhibit better performance on the considered dataset, our aggregation approach allows a further performance improvement. On the dataset, where a portability of the proposed approach has been demonstrated, the aggregating predictor has exhibited the following performance: sensitivity 77%, specificity 83.4%, positive predictive value 80.2%, negative predictive value 80.6%, which is higher than conventionally considered as acceptable. The proposed approach shows potential to be used in telemedicine systems for NH prediction. © 2016 Diabetes Technology Society.

Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal.

PubMed

Mohebbi, Maryam; Ghassemian, Hassan

2011-08-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L(max )), average length of the diagonal lines (L(mean)), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches.

Job stress models for predicting burnout syndrome: a review.

PubMed

Chirico, Francesco

2016-01-01

In Europe, the Council Directive 89/391 for improvement of workers' safety and health has emphasized the importance of addressing all occupational risk factors, and hence also psychosocial and organizational risk factors. Nevertheless, the construct of "work-related stress" elaborated from EU-OSHA is not totally corresponding with the "psychosocial" risk, that is a broader category of risk, comprising various and different psychosocial risk factors. The term "burnout", without any binding definition, tries to integrate symptoms as well as cause of the burnout process. In Europe, the most important methods developed for the work related stress risk assessment are based on the Cox's transactional model of job stress. Nevertheless, there are more specific models for predicting burnout syndrome. This literature review provides an overview of job burnout, highlighting the most important models of job burnout, such as the Job Strain, the Effort/Reward Imbalance and the Job Demands-Resources models. The difference between these models and the Cox's model of job stress is explored.

Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers

PubMed Central

Street, Amy E.; Rosellini, Anthony J.; Ursano, Robert J.; Heeringa, Steven G.; Hill, Eric D.; Monahan, John; Naifeh, James A.; Petukhova, Maria V.; Reis, Ben Y.; Sampson, Nancy A.; Bliese, Paul D.; Stein, Murray B.; Zaslavsky, Alan M.; Kessler, Ronald C.

2016-01-01

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence, but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically-guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively-recorded (in the population) and self-reported (in a representative survey) victimization. Capture-recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the Receiver Operating Characteristic curve was .83−.88. 33.7-63.2% of victimizations occurred among soldiers in the highest-risk ventile (5%). This high concentration of risk suggests that the models could be useful in targeting preventive interventions, although final determination would require careful weighing of intervention costs, effectiveness, and competing risks. PMID:28154788

[Psychosocial risk factors at work as predictors of mobbing].

PubMed

Meseguer de Pedro, Mariano; Soler Sánchez, María I; García-Izquierdo, Mariano; Sáez Navarro, M C; Sánchez Meca, Julio

2007-05-01

This work analyses the way in which various psychosocial risk indicators may predict mobbing. A sample of 638 workers, 168 men and 470 women, from the fruit-and-vegetable sector was evaluated. An anonymous questionnaire was administered to all employees who were present on the evaluation days in the companies comprising the study. After analysing the data obtained with the mobbing questionnaire NAQ-RE (Sáez, García-Izquierdo, and Llor, 2003) and with the psychosocial risk factors evaluation method of the INSHT (Martín and Pérez, 1997), using canonical regression, we found that several psychosocial factors such as role definition, mental workload, interest in the workers, and supervision / participation predict two types of mobbing: personal mobbing and work-performance-related mobbing.

[Medium-term forecast of solar cosmic rays radiation risk during a manned Mars mission].

PubMed

Petrov, V M; Vlasov, A G

2006-01-01

Medium-term forecasting radiation hazard from solar cosmic rays will be vital in a manned Mars mission. Modern methods of space physics lack acceptable reliability in medium-term forecasting the SCR onset and parameters. The proposed estimation of average radiation risk from SCR during the manned Mars mission is made with the use of existing SCR fluence and spectrum models and correlation of solar particle event frequency with predicted Wolf number. Radiation risk is considered an additional death probability from acute radiation reactions (ergonomic component) or acute radial disease in flight. The algorithm for radiation risk calculation is described and resulted risk levels for various periods of the 23-th solar cycle are presented. Applicability of this method to advance forecasting and possible improvements are being investigated. Recommendations to the crew based on risk estimation are exemplified.

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

PubMed Central

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

Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case

PubMed Central

Li, Guowei; Thabane, Lehana; Delate, Thomas; Witt, Daniel M.; Levine, Mitchell A. H.; Cheng, Ji; Holbrook, Anne

2016-01-01

Objectives To construct and validate a prediction model for individual combined benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke, neither event, or both) in patients with atrial fibrillation (AF) with and without warfarin therapy. Methods Using the Kaiser Permanente Colorado databases, we included patients newly diagnosed with AF between January 1, 2005 and December 31, 2012 for model construction and validation. The primary outcome was a prediction model of composite of stroke or major bleeding using polytomous logistic regression (PLR) modelling. The secondary outcome was a prediction model of all-cause mortality using the Cox regression modelling. Results We included 9074 patients with 4537 and 4537 warfarin users and non-users, respectively. In the derivation cohort (n = 4632), there were 136 strokes (2.94%), 280 major bleedings (6.04%) and 1194 deaths (25.78%) occurred. In the prediction models, warfarin use was not significantly associated with risk of stroke, but increased the risk of major bleeding and decreased the risk of death. Both the PLR and Cox models were robust, internally and externally validated, and with acceptable model performances. Conclusions In this study, we introduce a new methodology for predicting individual combined benefit and harm outcomes associated with warfarin therapy for patients with AF. Should this approach be validated in other patient populations, it has potential advantages over existing risk stratification approaches as a patient-physician aid for shared decision-making PMID:27513986

Use of Influenza Risk Assessment Tool for Prepandemic Preparedness

PubMed Central

Trock, Susan C.

2018-01-01

In 2010, the Centers for Disease Control and Prevention began to develop an Influenza Risk Assessment Tool (IRAT) to methodically capture and assess information relating to influenza A viruses not currently circulating among humans. The IRAT uses a multiattribute, additive model to generate a summary risk score for each virus. Although the IRAT is not intended to predict the next pandemic influenza A virus, it has provided input into prepandemic preparedness decisions. PMID:29460739

AUTONOMY AND RELATEDNESS IN MOTHER-TEEN INTERACTIONS AS PREDICTORS OF INVOLVEMENT IN ADOLESCENT DATING AGGRESSION.

PubMed

Niolon, Phyllis Holditch; Kuperminc, Gabriel P; Allen, Joseph P

2015-04-01

This multi-method, longitudinal study examines the negotiation of autonomy and relatedness between teens and their mothers as etiologic predictors of perpetration and victimization of dating aggression two years later. Observations of 88 mid-adolescents and their mothers discussing a topic of disagreement were coded for each individual's demonstrations of autonomy and relatedness using a validated coding system. Adolescents self-reported on perpetration and victimization of physical and psychological dating aggression two years later. We hypothesized that mother's and adolescents' behaviors supporting autonomy and relatedness would longitudinally predict lower reporting of dating aggression, and that their behaviors inhibiting autonomy and relatedness would predict higher reporting of dating aggression. Hypotheses were not supported; main findings were characterized by interactions of sex and risk status with autonomy. Maternal behaviors supporting autonomy predicted higher reports of perpetration and victimization of physical dating aggression for girls, but not for boys. Adolescent behaviors supporting autonomy predicted higher reports of perpetration of physical dating aggression for high-risk adolescents, but not for low-risk adolescents. Results indicate that autonomy is a dynamic developmental process, operating differently as a function of social contexts in predicting dating aggression. Examination of these and other developmental processes within parent-child relationships is important in predicting dating aggression, but may depend on social context.

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

PubMed

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

2018-04-17

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

New developments in cerebral blood flow autoregulation analysis in preterm infants: a mechanistic approach.

PubMed

Riera, Joan; Cabañas, Fernando; Serrano, José Javier; Madero, Rosario; Pellicer, Adelina

2016-03-01

Impaired autoregulation capacity implies that changes in cerebral perfusion follow changes in blood pressure; however, no analytical method has explored such a signal causality relationship in infants. We sought to develop a method to assess cerebral autoregulation from a mechanistic point of view and explored the predictive capacity of the method to classify infants at risk for adverse outcomes. The partial directed coherence (PDC) method, which considers synchronicity and directionality of signal dependence across frequencies, was used to analyze the relationship between spontaneous changes in mean arterial pressure (MAP) and the cerebral tissue oxygenation index (TOI). PDCMAP>TOI indicated that changes in TOI were induced by MAP changes, and PDCTOI>MAP indicated the opposite. The PDCMAP>TOI and PDCTOI>MAP values differed. PDCMAP>TOI adjusted by gestational age predicted low superior vena cava flow (≤41 ml/kg per min), with an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.63-0.81; P < 0.001), whereas PDCTOI>MAP did not. The adjusted pPDCMAP>TOI (the average value per patient) predicted severe intracranial hemorrhage and mortality. PDCMAP>TOI allows for a noninvasive physiological interpretation of the pressure autoregulation process in neonates. PDCMAP>TOI is a good classifier for infants at risk of brain hypoperfusion and adverse outcomes.

A decision-analytic approach to predict state regulation of hydraulic fracturing.

PubMed

Linkov, Igor; Trump, Benjamin; Jin, David; Mazurczak, Marcin; Schreurs, Miranda

2014-01-01

The development of horizontal drilling and hydraulic fracturing methods has dramatically increased the potential for the extraction of previously unrecoverable natural gas. Nonetheless, the potential risks and hazards associated with such technologies are not without controversy and are compounded by frequently changing information and an uncertain landscape of international politics and laws. Where each nation has its own energy policies and laws, predicting how a state with natural gas reserves that require hydraulic fracturing will regulate the industry is of paramount importance for potential developers and extractors. We present a method for predicting hydraulic fracturing decisions using multiple-criteria decision analysis. The case study evaluates the decisions of five hypothetical countries with differing political, social, environmental, and economic priorities, choosing among four policy alternatives: open hydraulic fracturing, limited hydraulic fracturing, completely banned hydraulic fracturing, and a cap and trade program. The result is a model that identifies the preferred policy alternative for each archetypal country and demonstrates the sensitivity the decision to particular metrics. Armed with such information, observers can predict each country's likely decisions related to natural gas exploration as more data become available or political situations change. Decision analysis provides a method to manage uncertainty and address forecasting concerns where rich and objective data may be lacking. For the case of hydraulic fracturing, the various political pressures and extreme uncertainty regarding the technology's risks and benefits serve as a prime platform to demonstrate how decision analysis can be used to predict future behaviors.

Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers.

PubMed

Darvishi, Ebrahim; Khotanlou, Hassan; Khoubi, Jamshid; Giahi, Omid; Mahdavi, Neda

2017-09-01

This study aimed to provide an empirical model of predicting low back pain (LBP) by considering the occupational, personal, and psychological risk factor interactions in workers population employed in industrial units using an artificial neural networks approach. A total of 92 workers with LBP as the case group and 68 healthy workers as a control group were selected in various industrial units with similar occupational conditions. The demographic information and personal, occupational, and psychosocial factors of the participants were collected via interview, related questionnaires, consultation with occupational medicine, and also the Rapid Entire Body Assessment worksheet and National Aeronautics and Space Administration Task Load Index software. Then, 16 risk factors for LBP were used as input variables to develop the prediction model. Networks with various multilayered structures were developed using MATLAB. The developed neural networks with 1 hidden layer and 26 neurons had the least error of classification in both training and testing phases. The mean of classification accuracy of the developed neural networks for the testing and training phase data were about 88% and 96%, respectively. In addition, the mean of classification accuracy of both training and testing data was 92%, indicating much better results compared with other methods. It appears that the prediction model using the neural network approach is more accurate compared with other applied methods. Because occupational LBP is usually untreatable, the results of prediction may be suitable for developing preventive strategies and corrective interventions. Copyright © 2017. Published by Elsevier Inc.

Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling.

PubMed

Nakas, Christos T; Schütz, Narayan; Werners, Marcus; Leichtle, Alexander B

2016-01-01

Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures.

Proposed Nomogram Predicting the Individual Risk of Malignancy in the Patients With Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas.

PubMed

Jang, Jin-Young; Park, Taesung; Lee, Selyeong; Kim, Yongkang; Lee, Seung Yeoun; Kim, Sun-Whe; Kim, Song-Cheol; Song, Ki-Byung; Yamamoto, Masakazu; Hatori, Takashi; Hirono, Seiko; Satoi, Sohei; Fujii, Tsutomu; Hirano, Satoshi; Hashimoto, Yasushi; Shimizu, Yashuhiro; Choi, Dong Wook; Choi, Seong Ho; Heo, Jin Seok; Motoi, Fuyuhiko; Matsumoto, Ippei; Lee, Woo Jung; Kang, Chang Moo; Han, Ho-Seong; Yoon, Yoo-Seok; Sho, Masayuki; Nagano, Hiroaki; Honda, Goro; Kim, Sang Geol; Yu, Hee Chul; Chung, Jun Chul; Nagakawa, Yuichi; Seo, Hyung Il; Yamaue, Hiroki

2017-12-01

This study evaluated individual risks of malignancy and proposed a nomogram for predicting malignancy of branch duct type intraductal papillary mucinous neoplasms (BD-IPMNs) using the large database for IPMN. Although consensus guidelines list several malignancy predicting factors in patients with BD-IPMN, those variables have different predictability and individual quantitative prediction of malignancy risk is limited. Clinicopathological factors predictive of malignancy were retrospectively analyzed in 2525 patients with biopsy proven BD-IPMN at 22 tertiary hospitals in Korea and Japan. The patients with main duct dilatation >10 mm and inaccurate information were excluded. The study cohort consisted of 2258 patients. Malignant IPMNs were defined as those with high grade dysplasia and associated invasive carcinoma. Of 2258 patients, 986 (43.7%) had low, 443 (19.6%) had intermediate, 398 (17.6%) had high grade dysplasia, and 431 (19.1%) had invasive carcinoma. To construct and validate the nomogram, patients were randomly allocated into training and validation sets, with fixed ratios of benign and malignant lesions. Multiple logistic regression analysis resulted in five variables (cyst size, duct dilatation, mural nodule, serum CA19-9, and CEA) being selected to construct the nomogram. In the validation set, this nomogram showed excellent discrimination power through a 1000 times bootstrapped calibration test. A nomogram predicting malignancy in patients with BD-IPMN was constructed using a logistic regression model. This nomogram may be useful in identifying patients at risk of malignancy and for selecting optimal treatment methods. The nomogram is freely available at http://statgen.snu.ac.kr/software/nomogramIPMN.

Readmission prediction via deep contextual embedding of clinical concepts.

PubMed

Xiao, Cao; Ma, Tengfei; Dieng, Adji B; Blei, David M; Wang, Fei

2018-01-01

Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients. The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions. This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.

Age at exposure and attained age variations of cancer risk in the Japanese A-bomb and radiotherapy cohorts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schneider, Uwe, E-mail: uwe.schneider@uzh.ch; Walsh, Linda

Purpose: Phenomenological risk models for radiation-induced cancer are frequently applied to estimate the risk of radiation-induced cancers at radiotherapy doses. Such models often include the effect modification, of the main risk to radiation dose response, by age at exposure and attained age. The aim of this paper is to compare the patterns in risk effect modification by age, between models obtained from the Japanese atomic-bomb (A-bomb) survivor data and models for cancer risks previously reported for radiotherapy patients. Patterns in risk effect modification by age from the epidemiological studies of radiotherapy patients were also used to refine and extend themore » risk effect modification by age obtained from the A-bomb survivor data, so that more universal models can be presented here. Methods: Simple log-linear and power functions of age for the risk effect modification applied in models of the A-bomb survivor data are compared to risks from epidemiological studies of second cancers after radiotherapy. These functions of age were also refined and fitted to radiotherapy risks. The resulting age models provide a refined and extended functional dependence of risk with age at exposure and attained age especially beyond 40 and 65 yr, respectively, and provide a better representation than the currently available simple age functions. Results: It was found that the A-bomb models predict risk similarly to the outcomes of testicular cancer survivors. The survivors of Hodgkin’s disease show steeper variations of risk with both age at exposure and attained age. The extended models predict solid cancer risk increase as a function of age at exposure beyond 40 yr and the risk decrease as a function of attained age beyond 65 yr better than the simple models. Conclusions: The standard functions for risk effect modification by age, based on the A-bomb survivor data, predict second cancer risk in radiotherapy patients for ages at exposure prior to 40 yr and attained ages before 55 yr reasonably well. However, for larger ages, the refined and extended models can be applied to predict the risk as a function of age.« less

Fracture prediction and calibration of a Canadian FRAX® tool: a population-based report from CaMos

PubMed Central

Fraser, L.-A.; Langsetmo, L.; Berger, C.; Ioannidis, G.; Goltzman, D.; Adachi, J. D.; Papaioannou, A.; Josse, R.; Kovacs, C. S.; Olszynski, W. P.; Towheed, T.; Hanley, D. A.; Kaiser, S. M.; Prior, J.; Jamal, S.; Kreiger, N.; Brown, J. P.; Johansson, H.; Oden, A.; McCloskey, E.; Kanis, J. A.

2016-01-01

Summary A new Canadian WHO fracture risk assessment (FRAX®) tool to predict 10-year fracture probability was compared with observed 10-year fracture outcomes in a large Canadian population-based study (CaMos). The Canadian FRAX tool showed good calibration and discrimination for both hip and major osteoporotic fractures. Introduction The purpose of this study was to validate a new Canadian WHO fracture risk assessment (FRAX®) tool in a prospective, population-based cohort, the Canadian Multi-centre Osteoporosis Study (CaMos). Methods A FRAX tool calibrated to the Canadian population was developed by the WHO Collaborating Centre for Metabolic Bone Diseases using national hip fracture and mortality data. Ten-year FRAX probabilities with and without bone mineral density (BMD) were derived for CaMos women (N=4,778) and men (N=1,919) and compared with observed fracture outcomes to 10 years (Kaplan–Meier method). Cox proportional hazard models were used to investigate the contribution of individual FRAX variables. Results Mean overall 10-year FRAX probability with BMD for major osteoporotic fractures was not significantly different from the observed value in men [predicted 5.4% vs. observed 6.4% (95%CI 5.2–7.5%)] and only slightly lower in women [predicted 10.8% vs. observed 12.0% (95%CI 11.0–12.9%)]. FRAX was well calibrated for hip fracture assessment in women [predicted 2.7% vs. observed 2.7% (95%CI 2.2–3.2%)] but underestimated risk in men [predicted 1.3% vs. observed 2.4% (95%CI 1.7–3.1%)]. FRAX with BMD showed better fracture discrimination than FRAX without BMD or BMD alone. Age, body mass index, prior fragility fracture and femoral neck BMD were significant independent predictors of major osteoporotic fractures; sex, age, prior fragility fracture and femoral neck BMD were significant independent predictors of hip fractures. Conclusion The Canadian FRAX tool provides predictions consistent with observed fracture rates in Canadian women and men, thereby providing a valuable tool for Canadian clinicians assessing patients at risk of fracture. PMID:21161508

Estimating relative risks for common outcome using PROC NLP.

PubMed

Yu, Binbing; Wang, Zhuoqiao

2008-05-01

In cross-sectional or cohort studies with binary outcomes, it is biologically interpretable and of interest to estimate the relative risk or prevalence ratio, especially when the response rates are not rare. Several methods have been used to estimate the relative risk, among which the log-binomial models yield the maximum likelihood estimate (MLE) of the parameters. Because of restrictions on the parameter space, the log-binomial models often run into convergence problems. Some remedies, e.g., the Poisson and Cox regressions, have been proposed. However, these methods may give out-of-bound predicted response probabilities. In this paper, a new computation method using the SAS Nonlinear Programming (NLP) procedure is proposed to find the MLEs. The proposed NLP method was compared to the COPY method, a modified method to fit the log-binomial model. Issues in the implementation are discussed. For illustration, both methods were applied to data on the prevalence of microalbuminuria (micro-protein leakage into urine) for kidney disease patients from the Diabetes Control and Complications Trial. The sample SAS macro for calculating relative risk is provided in the appendix.

Histological changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in high-risk prostate cancer patients

PubMed Central

O’Brien, Catherine; True, Lawrence D.; Higano, Celestia S.; Rademacher, Brooks L. S.; Garzotto, Mark; Beer, Tomasz M.

2011-01-01

Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high risk prostate cancer. Little is known about the clinical significance of post-chemotherapy tumor histopathology. We assessed the prognostic and predictive value of histological features (intraductal carcinoma, vacuolated cell morphology, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with pre-prostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) at 2 and 5 years was 65% and 49%, respectively. In univariate analyses (using Kaplan-Meier method and log-rank tests) intraductal (p=0.001) and cribriform (p=0.014) histologies were associated with shorter RFS. In multivariate analyses, using Cox’s proportional hazards regression, baseline PSA (p=0.004), lymph node metastases (p<0.001), and cribriform histology (p=0.007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (p=0.007) predicted lymph node metastases. Intraductal and cribriform histologies apparently predict post-chemotherapy outcome. PMID:20231619

Examination of Substance Use, Risk Factors, and Protective Factors on Student Academic Test Score Performance

PubMed Central

Arthur, Michael W.; Brown, Eric C.; Briney, John S.; Hawkins, J. David; Abbott, Robert D.; Catalano, Richard F.; Becker, Linda; Langer, Michael; Mueller, Martin T.

2016-01-01

BACKGROUND School administrators and teachers face difficult decisions about how best to use school resources in order to meet academic achievement goals. Many are hesitant to adopt prevention curricula that are not focused directly on academic achievement. Yet, some have hypothesized that prevention curricula can remove barriers to learning and, thus, promote achievement. This study examined relationships between school levels of student substance use and risk and protective factors that predict adolescent problem behaviors and achievement test performance in Washington State. METHODS Hierarchical Generalized Linear Models were used to examine predictive associations between school-averaged levels of substance use and risk and protective factors and Washington State students’ likelihood of meeting achievement test standards on the Washington Assessment of Student Learning, statistically controlling for demographic and economic factors known to be associated with achievement. RESULTS Results indicate that levels of substance use and risk/protective factors predicted the academic test score performance of students. Many of these effects remained significant even after controlling for model covariates. CONCLUSIONS The findings suggest that implementing prevention programs that target empirically identified risk and protective factors have the potential to positively affect students’ academic achievement. PMID:26149305

A Strategy to Safely Live and Work in the Space Radiation Environment

NASA Technical Reports Server (NTRS)

Corbin, Barbara J.; Sulzman, Frank M.; Krenek, Sam

2006-01-01

The goal of the National Aeronautics and Space Agency and the Space Radiation Project is to ensure that astronauts can safely live and work in the space radiation environment. The space radiation environment poses both acute and chronic risks to crew health and safety, but unlike some other aspects of space travel, space radiation exposure has clinically relevant implications for the lifetime of the crew. The term safely means that risks are sufficiently understood such that acceptable limits on mission, post-mission and multi-mission consequences (for example, excess lifetime fatal cancer risk) can be defined. The Space Radiation Project strategy has several elements. The first element is to use a peer-reviewed research program to increase our mechanistic knowledge and genetic capabilities to develop tools for individual risk projection, thereby reducing our dependency on epidemiological data and population-based risk assessment. The second element is to use the NASA Space Radiation Laboratory to provide a ground-based facility to study the understanding of health effects/mechanisms of damage from space radiation exposure and the development and validation of biological models of risk, as well as methods for extrapolation to human risk. The third element is a risk modeling effort that integrates the results from research efforts into models of human risk to reduce uncertainties in predicting risk of carcinogenesis, central nervous system damage, degenerative tissue disease, and acute radiation effects. To understand the biological basis for risk, we must also understand the physical aspects of the crew environment. Thus the fourth element develops computer codes to predict radiation transport properties, evaluate integrated shielding technologies and provide design optimization recommendations for the design of human space systems. Understanding the risks and determining methods to mitigate the risks are keys to a successful radiation protection strategy.

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

PubMed Central

2011-01-01

Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. Conclusions We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. PMID:22023778

Iowa radon leukaemia study: a hierarchical population risk model for spatially correlated exposure measured with error.

PubMed

Smith, Brian J; Zhang, Lixun; Field, R William

2007-11-10

This paper presents a Bayesian model that allows for the joint prediction of county-average radon levels and estimation of the associated leukaemia risk. The methods are motivated by radon data from an epidemiologic study of residential radon in Iowa that include 2726 outdoor and indoor measurements. Prediction of county-average radon is based on a geostatistical model for the radon data which assumes an underlying continuous spatial process. In the radon model, we account for uncertainties due to incomplete spatial coverage, spatial variability, characteristic differences between homes, and detector measurement error. The predicted radon averages are, in turn, included as a covariate in Poisson models for incident cases of acute lymphocytic (ALL), acute myelogenous (AML), chronic lymphocytic (CLL), and chronic myelogenous (CML) leukaemias reported to the Iowa cancer registry from 1973 to 2002. Since radon and leukaemia risk are modelled simultaneously in our approach, the resulting risk estimates accurately reflect uncertainties in the predicted radon exposure covariate. Posterior mean (95 per cent Bayesian credible interval) estimates of the relative risk associated with a 1 pCi/L increase in radon for ALL, AML, CLL, and CML are 0.91 (0.78-1.03), 1.01 (0.92-1.12), 1.06 (0.96-1.16), and 1.12 (0.98-1.27), respectively. Copyright 2007 John Wiley & Sons, Ltd.

Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.

PubMed

Harrison, David A; Brady, Anthony R; Parry, Gareth J; Carpenter, James R; Rowan, Kathy

2006-05-01

To assess the performance of published risk prediction models in common use in adult critical care in the United Kingdom and to recalibrate these models in a large representative database of critical care admissions. Prospective cohort study. A total of 163 adult general critical care units in England, Wales, and Northern Ireland, during the period of December 1995 to August 2003. A total of 231,930 admissions, of which 141,106 met inclusion criteria and had sufficient data recorded for all risk prediction models. None. The published versions of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE II UK, APACHE III, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Models (MPM) II were evaluated for discrimination and calibration by means of a combination of appropriate statistical measures recommended by an expert steering committee. All models showed good discrimination (the c index varied from 0.803 to 0.832) but imperfect calibration. Recalibration of the models, which was performed by both the Cox method and re-estimating coefficients, led to improved discrimination and calibration, although all models still showed significant departures from perfect calibration. Risk prediction models developed in another country require validation and recalibration before being used to provide risk-adjusted outcomes within a new country setting. Periodic reassessment is beneficial to ensure calibration is maintained.

Nutritional Risk Screening 2002 as a Predictor of Postoperative Outcomes in Patients Undergoing Abdominal Surgery: A Systematic Review and Meta-Analysis of Prospective Cohort Studies

PubMed Central

Sun, Zhen; Kong, Xin-Juan; Jing, Xue; Deng, Run-Jun; Tian, Zi-Bin

2015-01-01

Background The nutritional risk screening (NRS 2002) has been applied increasingly in patients who underwent abdominal surgery for nutritional risk assessment. However, the usefulness of the NRS 2002 for predicting is controversial. This meta-analysis was to examine whether a preoperative evaluation of nutritional risk by NRS 2002 provided prediction of postoperative outcomes in patients undergoing abdominal surgery. Methods A systematic literature search for published papers was conducted using the following online databases: MEDLINE, EMBASE, the Cochrane library, EBSCO, CRD databases, Cinahl, PsycInfo and BIOSIS previews. The pooled odds ratio (OR) or weight mean difference (WMD) was calculated using a random-effect model or a fix-effect model. Results Eleven studies with a total of 3527 patients included in this study. Postoperative overall complications were more frequent in nutritional risk patients versus patients without nutritional risk (the pooled OR 3.13 [2.51, 3.90] p<0.00001). The pooled OR of mortality for the nutritional risk group and non-nutritional risk group was 3.61 [1.38, 9.47] (p = 0.009). Furthermore, the postoperative hospital stay was significant longer in the preoperative nutritional risk group than in the nutritional normal group (WMD 5.58 [4.21, 6.95] p<0.00001). Conclusions The present study has demonstrated that patients at preoperative nutritional risk have increased complication rates, high mortality and prolonged hospital stay after surgery. However, NRS 2002 needs to be validated in larger samples of patients undergoing abdominal surgery by better reference method. PMID:26172830

The ACS NSQIP Risk Calculator Is a Fair Predictor of Acute Periprosthetic Joint Infection.

PubMed

Wingert, Nathaniel C; Gotoff, James; Parrilla, Edgardo; Gotoff, Robert; Hou, Laura; Ghanem, Elie

2016-07-01

Periprosthetic joint infection (PJI) is a severe complication from the patient's perspective and an expensive one in a value-driven healthcare model. Risk stratification can help identify those patients who may have risk factors for complications that can be mitigated in advance of elective surgery. Although numerous surgical risk calculators have been created, their accuracy in predicting outcomes, specifically PJI, has not been tested. (1) How accurate is the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Surgical Site Infection Calculator in predicting 30-day postoperative infection? (2) How accurate is the calculator in predicting 90-day postoperative infection? We isolated 1536 patients who underwent 1620 primary THAs and TKAs at our institution during 2011 to 2013. Minimum followup was 90 days. The ACS NSQIP Surgical Risk Calculator was assessed in its ability to predict acute PJI within 30 and 90 days postoperatively. Patients who underwent a repeat surgical procedure within 90 days of the index arthroplasty and in whom at least one positive intraoperative culture was obtained at time of reoperation were considered to have PJI. A total of 19 cases of PJI were identified, including 11 at 30 days and an additional eight instances by 90 days postoperatively. Patient-specific risk probabilities for PJI based on demographics and comorbidities were recorded from the ACS NSQIP Surgical Risk Calculator website. The area under the curve (AUC) for receiver operating characteristic (ROC) curves was calculated to determine the predictability of the risk probability for PJI. The AUC is an effective method for quantifying the discriminatory capacity of a diagnostic test to correctly classify patients with and without infection in which it is defined as excellent (AUC 0.9-1), good (AUC 0.8-0.89), fair (AUC 0.7-0.79), poor (AUC 0.6-0.69), or fail/no discriminatory capacity (AUC 0.5-0.59). A p value of < 0.05 was considered to be statistically significant. The ACS NSQIP Surgical Risk Calculator showed only fair accuracy in predicting 30-day PJI (AUC: 74.3% [confidence interval {CI}, 59.6%-89.0%]. For 90-day PJI, the risk calculator was also only fair in accuracy (AUC: 71.3% [CI, 59.9%-82.6%]). Conclusions The ACS NSQIP Surgical Risk Calculator is a fair predictor of acute PJI at the 30- and 90-day intervals after primary THA and TKA. Practitioners should exercise caution in using this tool as a predictive aid for PJI, because it demonstrates only fair value in this application. Existing predictive tools for PJI could potentially be made more robust by incorporating preoperative risk factors and including operative and early postoperative variables. Level III, diagnostic study.

Product component genealogy modeling and field-failure prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Caleb; Hong, Yili; Meeker, William Q.

Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life-cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can bemore » achieved in predicting time to failure, thus yielding more accurate field-failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures.« less

Product component genealogy modeling and field-failure prediction

DOE PAGES

King, Caleb; Hong, Yili; Meeker, William Q.

2016-04-13

Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life-cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can bemore » achieved in predicting time to failure, thus yielding more accurate field-failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures.« less

Predicting preterm birth among participants of North Carolina’s Pregnancy Medical Home Program

PubMed Central

Tucker, Christine M.; Berrien, Kate; Menard, M. Kathryn; Herring, Amy H.; Daniels, Julie; Rowley, Diane L.; Halpern, Carolyn Tucker

2016-01-01

Objective To determine which combination of risk factors from Community Care of North Carolina’s (CCNC) Pregnancy Medical Home (PMH) risk screening form was most predictive of preterm birth (PTB) by parity and race/ethnicity. Methods This retrospective cohort included pregnant Medicaid patients screened by the PMH program before 24 weeks gestation who delivered a live birth in North Carolina between September 2011-September 2012 (N=15,428). Data came from CCNC’s Case Management Information System, Medicaid claims, and birth certificates. Logistic regression with backward stepwise elimination was used to arrive at the final models. To internally validate the predictive model, we used bootstrapping techniques. Results The prevalence of PTB was 11%. Multifetal gestation, a previous PTB, cervical insufficiency, diabetes, renal disease, and hypertension were the strongest risk factors with odds ratios ranging from 2.34 to 10.78. Non-Hispanic black race, underweight, smoking during pregnancy, asthma, other chronic conditions, nulliparity, and a history of a low birth weight infant or fetal death/second trimester loss were additional predictors in the final predictive model. About half of the risk factors prioritized by the PMH program remained in our final model (ROC=0.66). The odds of PTB associated with food insecurity and obesity differed by parity. The influence of unsafe or unstable housing and short interpregnancy interval on PTB differed by race/ethnicity. Conclusions Evaluation of the PMH risk screen provides insight to ensure women at highest risk are prioritized for care management. Using multiple data sources, salient risk factors for PTB were identified, allowing for better-targeted approaches for PTB prevention. PMID:26112751

The impact of birth weight on cardiovascular disease risk in the Women's Health Initiative

PubMed Central

Smith, CJ; Ryckman, KK; Barnabei, Vanessa M.; Howard, Barbara; Isasi, Carmen R.; Sarto, Gloria; Tom, Sarah E.; Van Horn, Linda; Wallace, Robert; Robinson, Jennifer G

2016-01-01

Background and Aims Cardiovascular disease (CVD) is among the leading causes of morbidity and mortality worldwide. Traditional risk factors predict 75-80% of an individual's risk of incident CVD. However, the role of early life experiences in future disease risk is gaining attention. The Barker hypothesis proposes fetal origins of adult disease, with consistent evidence demonstrating the deleterious consequences of birth weight outside the normal range. In this study, we investigate the role of birth weight in CVD risk prediction. Methods and Results The Women's Health Initiative (WHI) represents a large national cohort of post-menopausal women with 63 815 participants included in this analysis. Univariable proportional hazards regression analyses evaluated the association of 4 self-reported birth weight categories against 3 CVD outcome definitions, which included indicators of coronary heart disease, ischemic stroke, coronary revascularization, carotid artery disease and peripheral arterial disease. The role of birth weight was also evaluated for prediction of CVD events in the presence of traditional risk factors using 3 existing CVD risk prediction equations: one body mass index (BMI)-based and two laboratory-based models. Low birth weight (LBW) (< 6 lbs.) was significantly associated with all CVD outcome definitions in univariable analyses (HR=1.086, p=0.009). LBW was a significant covariate in the BMI-based model (HR=1.128, p<0.0001) but not in the lipid-based models. Conclusion LBW (<6 lbs.) is independently associated with CVD outcomes in the WHI cohort. This finding supports the role of the prenatal and postnatal environment in contributing to the development of adult chronic disease. PMID:26708645

Usefulness of the addition of beta-2-microglobulin, cystatin C and C-reactive protein to an established risk factors model to improve mortality risk prediction in patients undergoing coronary angiography.

PubMed

Nead, Kevin T; Zhou, Margaret J; Caceres, Roxanne Diaz; Sharp, Stephen J; Wehner, Mackenzie R; Olin, Jeffrey W; Cooke, John P; Leeper, Nicholas J

2013-03-15

Evidence-based therapies are available to reduce the risk for death from cardiovascular disease, yet many patients go untreated. Novel methods are needed to identify those at highest risk for cardiovascular death. In this study, the biomarkers β2-microglobulin, cystatin C, and C-reactive protein were measured at baseline in a cohort of participants who underwent coronary angiography. Adjusted Cox proportional-hazards models were used to determine whether the biomarkers predicted all-cause and cardiovascular mortality. Additionally, improvements in risk reclassification and discrimination were evaluated by calculating the net reclassification improvement, C-index, and integrated discrimination improvement with the addition of the biomarkers to a baseline model of risk factors for cardiovascular disease and death. During a median follow-up period of 5.6 years, there were 78 deaths among 470 participants. All biomarkers independently predicted future all-cause and cardiovascular mortality. A significant improvement in risk reclassification was observed for all-cause (net reclassification improvement 35.8%, p = 0.004) and cardiovascular (net reclassification improvement 61.9%, p = 0.008) mortality compared to the baseline risk factors model. Additionally, there was significantly increased risk discrimination with C-indexes of 0.777 (change in C-index 0.057, 95% confidence interval 0.016 to 0.097) and 0.826 (change in C-index 0.071, 95% confidence interval 0.010 to 0.133) for all-cause and cardiovascular mortality, respectively. Improvements in risk discrimination were further supported using the integrated discrimination improvement index. In conclusion, this study provides evidence that β2-microglobulin, cystatin C, and C-reactive protein predict mortality and improve risk reclassification and discrimination for a high-risk cohort of patients who undergo coronary angiography. Copyright © 2013 Elsevier Inc. All rights reserved.

Risk factors predict post-traumatic stress disorder differently in men and women

PubMed Central

Christiansen, Dorte M; Elklit, Ask

2008-01-01

Background About twice as many women as men develop post-traumatic stress disorder (PTSD), even though men as a group are exposed to more traumatic events. Exposure to different trauma types does not sufficiently explain why women are more vulnerable. Methods The present work examines the effect of age, previous trauma, negative affectivity (NA), anxiety, depression, persistent dissociation, and social support on PTSD separately in men and women. Subjects were exposed to either a series of explosions in a firework factory near a residential area or to a high school stabbing incident. Results Some gender differences were found in the predictive power of well known risk factors for PTSD. Anxiety predicted PTSD in men, but not in women, whereas the opposite was found for depression. Dissociation was a better predictor for PTSD in women than in men in the explosion sample but not in the stabbing sample. Initially, NA predicted PTSD better in women than men in the explosion sample, but when compared only to other significant risk factors, it significantly predicted PTSD for both men and women in both studies. Previous traumatic events and age did not significantly predict PTSD in either gender. Conclusion Gender differences in the predictive value of social support on PTSD appear to be very complex, and no clear conclusions can be made based on the two studies included in this article. PMID:19017412

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

NASA Astrophysics Data System (ADS)

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.

Support vector regression to predict porosity and permeability: Effect of sample size

NASA Astrophysics Data System (ADS)

Al-Anazi, A. F.; Gates, I. D.

2012-02-01

Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function type and loss functions used.

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys

PubMed Central

Ueda, Peter; Woodward, Mark; Lu, Yuan; Hajifathalian, Kaveh; Al-Wotayan, Rihab; Aguilar-Salinas, Carlos A; Ahmadvand, Alireza; Azizi, Fereidoun; Bentham, James; Cifkova, Renata; Di Cesare, Mariachiara; Eriksen, Louise; Farzadfar, Farshad; Ferguson, Trevor S; Ikeda, Nayu; Khalili, Davood; Khang, Young-Ho; Lanska, Vera; León-Muñoz, Luz; Magliano, Dianna J; Margozzini, Paula; Msyamboza, Kelias P; Mutungi, Gerald; Oh, Kyungwon; Oum, Sophal; Rodríguez-Artalejo, Fernando; Rojas-Martinez, Rosalba; Valdivia, Gonzalo; Wilks, Rainford; Shaw, Jonathan E; Stevens, Gretchen A; Tolstrup, Janne S; Zhou, Bin; Salomon, Joshua A; Ezzati, Majid; Danaei, Goodarz

2017-01-01

Summary Background Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40–74 years. Methods Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40–64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. Findings Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40–64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. Interpretation Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements. PMID:28126460

Neonatal Candidiasis: Epidemiology, Risk Factors, and Clinical Judgment

PubMed Central

Benjamin, Daniel K.; Stoll, Barbara J.; Gantz, Marie G.; Walsh, Michele C.; Sanchez, Pablo J.; Das, Abhik; Shankaran, Seetha; Higgins, Rosemary D.; Auten, Kathy J.; Miller, Nancy A.; Walsh, Thomas J.; Laptook, Abbot R.; Carlo, Waldemar A.; Kennedy, Kathleen A.; Finer, Neil N.; Duara, Shahnaz; Schibler, Kurt; Chapman, Rachel L.; Van Meurs, Krisa P.; Frantz, Ivan D.; Phelps, Dale L.; Poindexter, Brenda B.; Bell, Edward F.; O’Shea, T. Michael; Watterberg, Kristi L.; Goldberg, Ronald N.

2011-01-01

OBJECTIVE Invasive candidiasis is a leading cause of infection-related morbidity and mortality in extremely low-birth-weight (<1000 g) infants. We quantify risk factors predicting infection in high-risk premature infants and compare clinical judgment with a prediction model of invasive candidiasis. METHODS The study involved a prospective observational cohort of infants <1000 g birth weight at 19 centers of the NICHD Neonatal Research Network. At each sepsis evaluation, clinical information was recorded, cultures obtained, and clinicians prospectively recorded their estimate of the probability of invasive candidiasis. Two models were generated with invasive candidiasis as their outcome: 1) potentially modifiable risk factors and 2) a clinical model at time of blood culture to predict candidiasis. RESULTS Invasive candidiasis occurred in 137/1515 (9.0%) infants and was documented by positive culture from ≥ 1 of these sources: blood (n=96), cerebrospinal fluid (n=9), urine obtained by catheterization (n=52), or other sterile body fluid (n=10). Mortality was not different from infants who had positive blood culture compared to those with isolated positive urine culture. Incidence varied from 2–28% at the 13 centers enrolling ≥ 50 infants. Potentially modifiable risk factors (model 1) included central catheter, broad-spectrum antibiotics (e.g., third-generation cephalosporins), intravenous lipid emulsion, endotracheal tube, and antenatal antibiotics. The clinical prediction model (model 2) had an area under the receiver operating characteristic curve of 0.79, and was superior to clinician judgment (0.70) in predicting subsequent invasive candidiasis. Performance of clinical judgment did not vary significantly with level of training. CONCLUSION Prior antibiotics, presence of a central catheter, endotracheal tube, and center were strongly associated with invasive candidiasis. Modeling was more accurate in predicting invasive candidiasis than clinical judgment. PMID:20876174

Vision & Strategy: Predictive Ecotoxicology in the 21st Century

DTIC Science & Technology

2011-01-01

their relative abundance or modifications QSARs —Correlation of ecological or toxicological activity with chemical structure to understand or predict...data and collection methods. The dramatic increase in the amount of toxicological data we can collect and analyze is complemented by our improved...diverse disciplines such as biochemistry, ecology, molecular biology, toxicology , bioinformatics, and health and environmental risk assess- ment

Assessment of Specific Characteristics of Abnormal General Movements: Does It Enhance the Prediction of Cerebral Palsy?

ERIC Educational Resources Information Center

Hamer, Elisa G.; Bos, Arend F.; Hadders-Algra, Mijna

2011-01-01

Aim: Abnormal general movements at around 3 months corrected age indicate a high risk of cerebral palsy (CP). We aimed to determine whether specific movement characteristics can improve the predictive power of definitely abnormal general movements. Method: Video recordings of 46 infants with definitely abnormal general movements at 9 to 13 weeks…

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

PubMed

Nouretdinov, Ilia; Costafreda, Sergi G; Gammerman, Alexander; Chervonenkis, Alexey; Vovk, Vladimir; Vapnik, Vladimir; Fu, Cynthia H Y

2011-05-15

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. Copyright © 2010 Elsevier Inc. All rights reserved.

Neural responses to exclusion predict susceptibility to social influence

PubMed Central

Falk, Emily B.; Cascio, Christopher N.; O’Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J.; Bingham, C. Raymond; Shope, Jean T.; Ouimet, Marie Claude; Pradhan, Anuj K.; Simons-Morton, Bruce G.

2014-01-01

Purpose Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence, and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American teens, traffic-related crashes are leading causes of non-fatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents’ vulnerability to peer influence. Methods We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently-licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately one week after the neuroimaging session. Results Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside of the neuroimaging lab one week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. Conclusions These results speak to the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging lab. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. PMID:24759437

Arsenic in Drinking Water and Lung Cancer Mortality in the United States: An Analysis Based on US Counties and 30 Years of Observation (1950–1979)

PubMed Central

Afari-Dwamena, Nana Ama; Li, Ji; Chen, Rusan; Feinleib, Manning; Lamm, Steven H.

2016-01-01

Background. To examine whether the US EPA (2010) lung cancer risk estimate derived from the high arsenic exposures (10–934 µg/L) in southwest Taiwan accurately predicts the US experience from low arsenic exposures (3–59 µg/L). Methods. Analyses have been limited to US counties solely dependent on underground sources for their drinking water supply with median arsenic levels of ≥3 µg/L. Results. Cancer risks (slopes) were found to be indistinguishable from zero for males and females. The addition of arsenic level did not significantly increase the explanatory power of the models. Stratified, or categorical, analysis yielded relative risks that hover about 1.00. The unit risk estimates were nonpositive and not significantly different from zero, and the maximum (95% UCL) unit risk estimates for lung cancer were lower than those in US EPA (2010). Conclusions. These data do not demonstrate an increased risk of lung cancer associated with median drinking water arsenic levels in the range of 3–59 µg/L. The upper-bound estimates of the risks are lower than the risks predicted from the SW Taiwan data and do not support those predictions. These results are consistent with a recent metaregression that indicated no increased lung cancer risk for arsenic exposures below 100–150 µg/L. PMID:27382373

Common Atrial Fibrillation Risk Alleles at 4q25 Predict Recurrence after Catheter-based Atrial Fibrillation Ablation

PubMed Central

Shoemaker, M. Benjamin; Muhammad, Raafia; Parvez, Babar; White, Brenda W.; Streur, Megan; Song, Yanna; Stubblefield, Tanya; Kucera, Gayle; Blair, Marcia; Rytlewski, Jason; Parvathaneni, Sunthosh; Nagarakanti, Rangadham; Saavedra, Pablo; Ellis, Christopher; Whalen, S. Patrick; Roden, Dan M; Darbar, Dawood

2012-01-01

Background Common single nucleotide polymorphisms (SNPs) at chromosome 4q25 (rs2200733, rs10033464) are associated with both lone and typical AF. Risk alleles at 4q25 have recently been shown to predict recurrence of AF after ablation in a population of predominately lone AF, but lone AF represents only 5–30% of AF cases. Objective To test the hypothesis that 4q25 AF risk alleles can predict response to AF ablation in the majority of AF cases. Methods Patients enrolled in the Vanderbilt AF Registry underwent 378 catheter-based AF ablations (median age 60 years, 71% male, 89% typical AF) between 2004 and 2011. The primary endpoint was time to recurrence of any non-sinus atrial tachyarrhythmia (atrial tachycardia, atrial flutter, or AF; [AT/AF]). Results Two-hundred AT/AF recurrences (53%) were observed. In multivariable analysis, the rs2200733 risk allele predicted a 24% shorter recurrence-free time (survival time ratio 0.76 95% confidence interval [CI] 0.6–0.95, P=0.016) compared with wild-type. The heterozygous haplotype demonstrated a 21% shorter recurrence-free time (survival time ratio = 0.79, 95% CI 0.62–0.99) and the homozygous risk allele carriers a 39% shorter recurrence-free time (survival time ratio = 0.61, 95% CI 0.37–1.0) (P=0.037). Conclusion Risk alleles at the 4q25 loci predict impaired clinical response to AF ablation in a population of predominately typical AF patients. Our findings suggest the rs2200733 polymorphism may hold promise as an as an objectively measured patient characteristic that can used as a clinical tool for selection of patients for AF ablation. PMID:23178686

Treatment default amongst patients with tuberculosis in urban Morocco: predicting and explaining default and post-default sputum smear and drug susceptibility results.

PubMed

Cherkaoui, Imad; Sabouni, Radia; Ghali, Iraqi; Kizub, Darya; Billioux, Alexander C; Bennani, Kenza; Bourkadi, Jamal Eddine; Benmamoun, Abderrahmane; Lahlou, Ouafae; Aouad, Rajae El; Dooley, Kelly E

2014-01-01

Public tuberculosis (TB) clinics in urban Morocco. Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance. Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals' perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing. 91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one's treatment duration. Age >50 years, never smoking, and having friends who knew one's diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare. The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings.

Development and validation of an automated delirium risk assessment system (Auto-DelRAS) implemented in the electronic health record system.

PubMed

Moon, Kyoung-Ja; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

2018-01-01

A key component of the delirium management is prevention and early detection. To develop an automated delirium risk assessment system (Auto-DelRAS) that automatically alerts health care providers of an intensive care unit (ICU) patient's delirium risk based only on data collected in an electronic health record (EHR) system, and to evaluate the clinical validity of this system. Cohort and system development designs were used. Medical and surgical ICUs in two university hospitals in Seoul, Korea. A total of 3284 patients for the development of Auto-DelRAS, 325 for external validation, 694 for validation after clinical applications. The 4211 data items were extracted from the EHR system and delirium was measured using CAM-ICU (Confusion Assessment Method for Intensive Care Unit). The potential predictors were selected and a logistic regression model was established to create a delirium risk scoring algorithm to construct the Auto-DelRAS. The Auto-DelRAS was evaluated at three months and one year after its application to clinical practice to establish the predictive validity of the system. Eleven predictors were finally included in the logistic regression model. The results of the Auto-DelRAS risk assessment were shown as high/moderate/low risk on a Kardex screen. The predictive validity, analyzed after the clinical application of Auto-DelRAS after one year, showed a sensitivity of 0.88, specificity of 0.72, positive predictive value of 0.53, negative predictive value of 0.94, and a Youden index of 0.59. A relatively high level of predictive validity was maintained with the Auto-DelRAS system, even one year after it was applied to clinical practice. Copyright © 2017. Published by Elsevier Ltd.

External Validation of European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) for Risk Prioritization in an Iranian Population

PubMed Central

Atashi, Alireza; Amini, Shahram; Tashnizi, Mohammad Abbasi; Moeinipour, Ali Asghar; Aazami, Mathias Hossain; Tohidnezhad, Fariba; Ghasemi, Erfan; Eslami, Saeid

2018-01-01

Introduction The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery. Objective The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. Methods A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. Results Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P<0.01). Conclusion Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration of this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran. PMID:29617500

Applications of Principled Search Methods in Climate Influences and Mechanisms

NASA Technical Reports Server (NTRS)

Glymour, Clark

2005-01-01

Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used.

Low RMRratio as a Surrogate Marker for Energy Deficiency, the Choice of Predictive Equation Vital for Correctly Identifying Male and Female Ballet Dancers at Risk.

PubMed

Staal, Sarah; Sjödin, Anders; Fahrenholtz, Ida; Bonnesen, Karen; Melin, Anna Katarina

2018-06-22

Ballet dancers are reported to have an increased risk for energy deficiency with or without disordered eating behavior. A low ratio between measured ( m ) and predicted ( p ) resting metabolic rate (RMR ratio  < 0.90) is a recognized surrogate marker for energy deficiency. We aimed to evaluate the prevalence of suppressed RMR using different methods to calculate p RMR and to explore associations with additional markers of energy deficiency. Female (n = 20) and male (n = 20) professional ballet dancers, 19-35 years of age, were enrolled. m RMR was assessed by respiratory calorimetry (ventilated open hood). p RMR was determined using the Cunningham and Harris-Benedict equations, and different tissue compartments derived from whole-body dual-energy X-ray absorptiometry assessment. The protocol further included assessment of body composition and bone mineral density, blood pressure, disordered eating (Eating Disorder Inventory-3), and for females, the Low Energy Availability in Females Questionnaire. The prevalence of suppressed RMR was generally high but also clearly dependent on the method used to calculate p RMR, ranging from 25% to 80% in males and 35% to 100% in females. Five percent had low bone mineral density, whereas 10% had disordered eating and 25% had hypotension. Forty percent of females had elevated Low Energy Availability in Females Questionnaire score and 50% were underweight. Suppressed RMR was associated with elevated Low Energy Availability in Females Questionnaire score in females and with higher training volume in males. In conclusion, professional ballet dancers are at risk for energy deficiency. The number of identified dancers at risk varies greatly depending on the method used to predict RMR when using RMR ratio as a marker for energy deficiency.

Predictive factors for somatization in a trauma sample

PubMed Central

2009-01-01

Background Unexplained somatic symptoms are common among trauma survivors. The relationship between trauma and somatization appears to be mediated by posttraumatic stress disorder (PTSD). However, only few studies have focused on what other psychological risk factors may predispose a trauma victim towards developing somatoform symptoms. Methods The present paper examines the predictive value of PTSD severity, dissociation, negative affectivity, depression, anxiety, and feeling incompetent on somatization in a Danish sample of 169 adult men and women who were affected by a series of explosions in a firework factory settled in a residential area. Results Negative affectivity and feelings of incompetence significantly predicted somatization, explaining 42% of the variance. PTSD was significant until negative affectivity was controlled for. Conclusion Negative affectivity and feelings of incompetence significantly predicted somatization in the trauma sample whereas dissociation, depression, and anxiety were not associated with degree of somatization. PTSD as a risk factor was mediated by negative affectivity. PMID:19126224

Comparison between urine albumin-to-creatinine ratio and urine protein dipstick testing for prevalence and ability to predict the risk for chronic kidney disease in the general population (Iwate-KENCO study): a prospective community-based cohort study.

PubMed

Koeda, Yorihiko; Tanaka, Fumitaka; Segawa, Toshie; Ohta, Mutsuko; Ohsawa, Masaki; Tanno, Kozo; Makita, Shinji; Ishibashi, Yasuhiro; Itai, Kazuyoshi; Omama, Shin-Ichi; Onoda, Toshiyuki; Sakata, Kiyomi; Ogasawara, Kuniaki; Okayama, Akira; Nakamura, Motoyuki

2016-05-12

This study compared the combination of estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) vs. eGFR and urine protein reagent strip testing to determine chronic kidney disease (CKD) prevalence, and each method's ability to predict the risk for cardiovascular events in the general Japanese population. Baseline data including eGFR, UACR, and urine dipstick tests were obtained from the general population (n = 22 975). Dipstick test results (negative, trace, positive) were allocated to three levels of UACR (<30, 30-300, >300), respectively. In accordance with Kidney Disease Improving Global Outcomes CKD prognosis heat mapping, the cohort was classified into four risk grades (green: grade 1; yellow: grade 2; orange: grade 3, red: grade 4) based on baseline eGFR and UACR levels or dipstick tests. During the mean follow-up period of 5.6 years, 708 new onset cardiovascular events were recorded. For CKD identified by eGFR and dipstick testing (dipstick test ≥ trace and eGFR <60 mL/min/1.73 m(2)), the incidence of CKD was found to be 9 % in the general population. In comparison to non-CKD (grade 1), although cardiovascular risk was significantly higher in risk grades ≥3 (relative risk (RR) = 1.70; 95 % CI: 1.28-2.26), risk predictive ability was not significant in risk grade 2 (RR = 1.20; 95 % CI: 0.95-1.52). When CKD was defined by eGFR and UACR (UACR ≥30 mg/g Cr and eGFR <60 mL/min/1.73 m(2)), prevalence was found to be 29 %. Predictive ability in risk grade 2 (RR = 1.41; 95 % CI: 1.19-1.66) and risk grade ≥3 (RR = 1.76; 95 % CI: 1.37-2.28) were both significantly greater than for non-CKD. Reclassification analysis showed a significant improvement in risk predictive abilities when CKD risk grading was based on UACR rather than on dipstick testing in this population (p < 0.001). Although prevalence of CKD was higher when detected by UACR rather than urine dipstick testing, the predictive ability for cardiovascular events from UACR-based risk grading was superior to that of dipstick-based risk grading in the general population.

Independent external validation of nomograms for predicting risk of low-trauma fracture and hip fracture

PubMed Central

Langsetmo, Lisa; Nguyen, Tuan V.; Nguyen, Nguyen D.; Kovacs, Christopher S.; Prior, Jerilynn C.; Center, Jacqueline R.; Morin, Suzanne; Josse, Robert G.; Adachi, Jonathan D.; Hanley, David A.; Eisman, John A.

2011-01-01

Background A set of nomograms based on the Dubbo Osteoporosis Epidemiology Study predicts the five- and ten-year absolute risk of fracture using age, bone mineral density and history of falls and low-trauma fracture. We assessed the discrimination and calibration of these nomograms among participants in the Canadian Multicentre Osteoporosis Study. Methods We included participants aged 55–95 years for whom bone mineral density measurement data and at least one year of follow-up data were available. Self-reported incident fractures were identified by yearly postal questionnaire or interview (years 3, 5 and 10). We included low-trauma fractures before year 10, except those of the skull, face, hands, ankles and feet. We used a Cox proportional hazards model. Results Among 4152 women, there were 583 fractures, with a mean follow-up time of 8.6 years. Among 1606 men, there were 116 fractures, with a mean follow-up time of 8.3 years. Increasing age, lower bone mineral density, prior fracture and prior falls were associated with increased risk of fracture. For low-trauma fractures, the concordance between predicted risk and fracture events (Harrell C) was 0.69 among women and 0.70 among men. For hip fractures, the concordance was 0.80 among women and 0.85 among men. The observed fracture risk was similar to the predicted risk in all quintiles of risk except the highest quintile of women, where it was lower. The net reclassification index (19.2%, 95% confidence interval [CI] 6.3% to 32.2%), favours the Dubbo nomogram over the current Canadian guidelines for men. Interpretation The published nomograms provide good fracture-risk discrimination in a representative sample of the Canadian population. PMID:21173069

From Statistical Significance to Clinical Relevance: A Simple Algorithm to Integrate BNP and the Seattle Heart Failure Model for Risk Stratification in Heart Failure

PubMed Central

AbouEzzeddine, Omar F.; French, Benjamin; Mirzoyev, Sultan A.; Jaffe, Allan S; Levy, Wayne C.; Fang, James C.; Sweitzer, Nancy K.; Cappola, Thomas P.; Redfield, Margaret M.

2016-01-01

Background Heart failure (HF) guidelines recommend brain natriuretic peptide (BNP) and multivariable risk-scores such as the Seattle HF Model (SHFM) to predict risk in HF with reduced ejection fraction (HFrEF). A practical way to integrate information from these two prognostic tools is lacking. We sought to establish a SHFM+BNP risk-stratification algorithm. Methods The retrospective derivation cohort included consecutive patients with HFrEF at Mayo. One-year outcome (death, transplantation or ventricular assist device) was assessed. The SHFM+BNP algorithm was derived by stratifying patients within SHFM-predicted risk categories (≤2.5%, 2.6–≤10%, >10%) according to BNP above or below 700 pg/mL and comparing SHFM-predicted and observed event rates within each SHFM+BNP category. The algorithm was validated in a prospective, multicenter HFrEF registry (Penn HF Study). Results Derivation (n=441; one-year event rate 17%) and validation (n=1513; one-year event rate 12%) cohorts differed with the former being older and more likely ischemic with worse symptoms, lower EF, worse renal function, higher BNP and SHFM scores. In both cohorts, across the three SHFM-predicted risk strata, a BNP>700 pg/ml consistently identified patients with approximately three-fold the risk that the SHFM would have otherwise estimated regardless stage of HF, intensity and duration of HF-therapy, and comorbidities. Conversely, the SHFM was appropriately calibrated in patients with a BNP<700 pg/ml. Conclusion The simple SHFM+BNP algorithm displays stable performance across diverse HFrEF cohorts and may enhance risk stratification to enable appropriate decisions regarding HF therapeutic or palliative strategies. PMID:27021278

Detailed Assessments of Childhood AdversityEnhance Prediction of Central Obesity Independent of Gender, Race, Adult Psychosocial Risk and Health Behaviors

PubMed Central

Davis, Cynthia R.; Dearing, Eric; Usher, Nicole; Trifiletti, Sarah; Zaichenko, Lesya; Ollen, Elizabeth; Brinkoetter, Mary T.; Crowell-Doom, Cindy; Joung, Kyoung; Park, Kyung Hee; Mantzoros, Christos S.; Crowell, Judith A.

2017-01-01

Objective This study examined whether a novel indicator of overall childhood adversity, incorporating number of adversities, severity, and chronicity, predicted central obesity beyond contributions of “modifiable” risk factors including psychosocial characteristics and health behaviors in a diverse sample of midlife adults. The study also examined whether the overall adversity score (number of adversities X severity X chronicity) better predicted obesity compared to cumulative adversity (number of adversities), a more traditional assessment of childhood adversity. Materials/Methods 210 Black/African Americans and White/European Americans, mean age = 45.8; ±3.3 years, were studied cross-sectionally. Regression analysis examined overall childhood adversity as a direct, non-modifiable risk factor for central obesity (waist-hip ratio) and body mass index (BMI), with and without adjustment for established adult psychosocial risk factors (education, employment, social functioning) and heath behavior risk factors (smoking, drinking, diet, exercise). Results Overall childhood adversity was an independent significant predictor of central obesity, and the relations between psychosocial and health risk factors and central obesity were not significant when overall adversity was in the model. Overall adversity was not a statistically significant predictor of BMI. Conclusions Overall childhood adversity, incorporating severity and chronicity and cumulative scores, predicts central obesity beyond more contemporaneous risk factors often considered modifiable. This is consistent with early dysregulation of metabolic functioning. Findings can inform practitioners interested in the impact of childhood adversity and personalizing treatment approaches of obesity within high-risk populations. Prevention/intervention research is necessary to discover and address the underlying causes and impact of childhood adversity on metabolic functioning. PMID:24211017

Global Risk Score and Clinical SYNTAX Score as Predictors of Clinical Outcomes of Patients Undergoing Unprotected Left Main Percutaneous Catheter Intervention

PubMed Central

Cuenza, Lucky; Collado, Marianne P.; Ho Khe Sui, James

2017-01-01

Background Risk stratification is an important component of left main percutaneous catheter intervention (PCI) which has emerged as a feasible alternative to cardiac surgery. We sought to compare the clinical SYNTAX score and the global risk score in predicting outcomes of patients undergoing unprotected left main PCI in our institution. Methods Clinical, angiographic and procedural characteristics of 92 patients who underwent unprotected left main PCI (mean age 62 ± 12.1 years) were analyzed. Patients were risk stratified into tertiles of high, intermediate and low risk using the global risk score (GRS) and the clinical SYNTAX score (CSS) and were prospectively followed up at 1 year for the occurrence of major adverse cardiovascular events (MACEs), defined as a composite of all cause mortality, cardiac mortality, non-fatal myocardial infarction, stroke, coronary artery bypass, and target vessel revascularization. Results There were 26 (28.2%) who experienced MACEs, of which 10 (10.8%) patients died. Multivariable hazards analysis showed that the GRS (hazard ratio (HR) = 5.5, P = 0.001) and CSS (HR = 4.3, P = 0.001) were both independent predictors of MACEs. Kaplan-Meier analysis showed higher incidence of MACEs with the intermediate and higher risk categories compared to those classified as low risk. Receiver-operator characteristic analysis showed that the GRS has better discriminatory ability than the CSS in the prediction of 1 year MACEs (0.891 vs. 0.743, P = 0.007). Conclusion The GRS and CSS are predictive of outcomes after left main PCI. The GRS appears to have superior predictive and prognostic utility compared to the CSS. This study emphasizes the importance of combining both anatomic and clinical variables for optimum prognostication and management decisions in left main PCI. PMID:29317974

Can RSScan footscan(®) D3D™ software predict injury in a military population following plantar pressure assessment? A prospective cohort study.

PubMed

Franklyn-Miller, Andrew; Bilzon, James; Wilson, Cassie; McCrory, Paul

2014-03-01

Injury in initial military training is common with incidences from 25 to 65% of recruits sustaining musculoskeletal injury. Risk factors for injury include extrinsic factors such as rapid onset of high volume training, but intrinsic factors such as lower limb biomechanics and foot type. Prediction of injury would allow more effective training delivery, reduce manpower wastage and improve duty of care to individuals by addressing potential interventions. Plantar pressure interpretation of footfall has been shown to reflect biomechanical intrinsic abnormality although no quantifiable method of risk stratification exists. To identify if pressure plate assessment of walking gait is predictive of injury in a military population. 200 male subjects commencing Naval Officer training were assessed by plantar pressure plate recording, of foot contact pressures. A software interpretation, D3D™, stratified the interpretation to measure 4 specific areas of potential correction. Participants were graded as to high, medium and low risk of injury and subsequently followed up for injury through their basic training. Seventy two percent of all injuries were attributed to subjects in the high and medium risk of injury as defined by the risk categorization. 47% of all injuries were sustained in the high-risk group. Participants categorized in the high-risk group for injury were significantly more likely to sustain injury than in medium or low groups (p<0.001, OR 5.28 with 95% CI 2.88, 9.70). Plantar pressure assessment of risk for overuse lower limb injury can be predictive of sustaining an overuse injury in a controlled training environment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Pretest probability estimation in the evaluation of patients with possible deep vein thrombosis.

PubMed

Vinson, David R; Patel, Jason P; Irving, Cedric S

2011-07-01

An estimation of pretest probability is integral to the proper interpretation of a negative compression ultrasound in the diagnostic assessment of lower-extremity deep vein thrombosis. We sought to determine the rate, method, and predictors of pretest probability estimation in such patients. This cross-sectional study of outpatients was conducted in a suburban community hospital in 2006. Estimation of pretest probability was done by enzyme-linked immunosorbent assay d-dimer, Wells criteria, and unstructured clinical impression. Using logistic regression analysis, we measured predictors of documented risk assessment. A cohort analysis was undertaken to compare 3-month thromboembolic outcomes between risk groups. Among 524 cases, 289 (55.2%) underwent pretest probability estimation using the following methods: enzyme-linked immunosorbent assay d-dimer (228; 43.5%), clinical impression (106; 20.2%), and Wells criteria (24; 4.6%), with 69 (13.2%) patients undergoing a combination of at least two methods. Patient factors were not predictive of pretest probability estimation, but the specialty of the clinician was predictive; emergency physicians (P < .0001) and specialty clinicians (P = .001) were less likely than primary care clinicians to perform risk assessment. Thromboembolic events within 3 months were experienced by 0 of 52 patients in the explicitly low-risk group, 4 (1.8%) of 219 in the explicitly moderate- to high-risk group, and 1 (0.4%) of 226 in the group that did not undergo explicit risk assessment. Negative ultrasounds in the workup of deep vein thrombosis are commonly interpreted in isolation apart from pretest probability estimations. Risk assessments varied by physician specialties. Opportunities exist for improvement in the diagnostic evaluation of these patients. Copyright © 2011 Elsevier Inc. All rights reserved.

Cellular Proliferation by Multiplex Immunohistochemistry Identifies High-Risk Multiple Myeloma in Newly Diagnosed, Treatment-Naive Patients.

PubMed

Ely, Scott; Forsberg, Peter; Ouansafi, Ihsane; Rossi, Adriana; Modin, Alvin; Pearse, Roger; Pekle, Karen; Perry, Arthur; Coleman, Morton; Jayabalan, David; Di Liberto, Maurizio; Chen-Kiang, Selina; Niesvizky, Ruben; Mark, Tomer M

2017-12-01

Therapeutic options for multiple myeloma (MM) are growing, yet clinical outcomes remain heterogeneous. Cytogenetic analysis and disease staging are mainstays of risk stratification, but data suggest a complex interplay between numerous abnormalities. Myeloma cell proliferation is a metric shown to predict outcomes, but available methods are not feasible in clinical practice. Multiplex immunohistochemistry (mIHC), using multiple immunostains simultaneously, is universally available for clinical use. We tested mIHC as a method to calculate a plasma cell proliferation index (PCPI). By mIHC, marrow trephine core biopsy samples were costained for CD138, a plasma cell-specific marker, and Ki-67. Myeloma cells (CD138 + ) were counted as proliferating if coexpressing Ki-67. Retrospective analysis was performed on 151 newly diagnosed, treatment-naive patients divided into 2 groups on the basis of myeloma cell proliferation: low (PCPI ≤ 5%, n = 87), and high (PCPI > 5%, n = 64). Median overall survival (OS) was not reached versus 78.9 months (P = .0434) for the low versus high PCPI groups. Multivariate analysis showed that only high-risk cytogenetics (hazard ratio [HR] = 2.02; P = .023), International Staging System (ISS) stage > I (HR = 2.30; P = .014), and PCPI > 5% (HR = 1.70; P = .041) had independent effects on OS. Twenty-three (36%) of the 64 patients with low-risk disease (ISS stage 1, without high-risk cytogenetics) were uniquely reidentified as high risk by PCPI. PCPI is a practical method that predicts OS in newly diagnosed myeloma and facilitates broader use of MM cell proliferation for risk stratification. Copyright © 2017 Elsevier Inc. All rights reserved.

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

2018-03-01

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p < 0.05) and odds ratio was 4.60 with a 95% confidence interval of [3.16, 6.70]. Study demonstrated that this new LPP-based feature regeneration approach enabled to produce an optimal feature vector and yield improved performance in assisting to predict risk of women having breast cancer detected in the next subsequent mammography screening.

Predicting the onset and persistence of episodes of depression in primary health care. The predictD-Spain study: Methodology

PubMed Central

Bellón, Juan Ángel; Moreno-Küstner, Berta; Torres-González, Francisco; Montón-Franco, Carmen; GildeGómez-Barragán, María Josefa; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; de Dios Luna, Juan; Cervilla, Jorge A; Gutierrez, Blanca; Martínez-Cañavate, María Teresa; Oliván-Blázquez, Bárbara; Vázquez-Medrano, Ana; Sánchez-Artiaga, María Soledad; March, Sebastia; Motrico, Emma; Ruiz-García, Victor Manuel; Brangier-Wainberg, Paulette Renée; del Mar Muñoz-García, María; Nazareth, Irwin; King, Michael

2008-01-01

Background The effects of putative risk factors on the onset and/or persistence of depression remain unclear. We aim to develop comprehensive models to predict the onset and persistence of episodes of depression in primary care. Here we explain the general methodology of the predictD-Spain study and evaluate the reliability of the questionnaires used. Methods This is a prospective cohort study. A systematic random sample of general practice attendees aged 18 to 75 has been recruited in seven Spanish provinces. Depression is being measured with the CIDI at baseline, and at 6, 12, 24 and 36 months. A set of individual, environmental, genetic, professional and organizational risk factors are to be assessed at each follow-up point. In a separate reliability study, a proportional random sample of 401 participants completed the test-retest (251 researcher-administered and 150 self-administered) between October 2005 and February 2006. We have also checked 118,398 items for data entry from a random sample of 480 patients stratified by province. Results All items and questionnaires had good test-retest reliability for both methods of administration, except for the use of recreational drugs over the previous six months. Cronbach's alphas were good and their factorial analyses coherent for the three scales evaluated (social support from family and friends, dissatisfaction with paid work, and dissatisfaction with unpaid work). There were 191 (0.16%) data entry errors. Conclusion The items and questionnaires were reliable and data quality control was excellent. When we eventually obtain our risk index for the onset and persistence of depression, we will be able to determine the individual risk of each patient evaluated in primary health care. PMID:18657275

Predicting non-familial major physical violent crime perpetration in the US Army from administrative data.

PubMed

Rosellini, A J; Monahan, J; Street, A E; Heeringa, S G; Hill, E D; Petukhova, M; Reis, B Y; Sampson, N A; Bliese, P; Schoenbaum, M; Stein, M B; Ursano, R J; Kessler, R C

2016-01-01

Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers. A consolidated administrative database for all 975 057 soldiers in the US Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011-2013 sample. Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80-0.82 in 2004-2009 and 0.77 in the 2011-2013 validation sample. Of all administratively recorded crimes, 36.2-33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks.

Usefulness of a 50-meter round walking test for fall prediction in the elderly requiring long-term care

PubMed Central

Hachiya, Mizuki; Murata, Shin; Otao, Hiroshi; Ihara, Takehiko; Mizota, Katsuhiko; Asami, Toyoko

2015-01-01

[Purpose] This study aimed to verify the usefulness of a 50-m round walking test developed as an assessment method for walking ability in the elderly. [Subjects] The subjects were 166 elderly requiring long-term care individuals (mean age, 80.5 years). [Methods] In order to evaluate the factors that had affected falls in the subjects in the previous year, we performed the 50-m round walking test, functional reach test, one-leg standing test, and 5-m walking test and measured grip strength and quadriceps strength. [Results] The 50-m round walking test was selected as a variable indicating fall risk based on the results of multiple logistic regression analysis. The cutoff value of the 50-m round walking test for determining fall risk was 0.66 m/sec. The area under the receiver operating characteristic curve was 0.64. The sensitivity of the cutoff value was 65.7%, the specificity was 63.6%, the positive predictive value was 55.0%, the negative predictive value was 73.3%, and the accuracy was 64.5%. [Conclusion] These results suggest that the 50-m round walking test is a potentially useful parameter for the determination of fall risk in the elderly requiring long-term care. PMID:26834327

Usefulness of a 50-meter round walking test for fall prediction in the elderly requiring long-term care.

PubMed

Hachiya, Mizuki; Murata, Shin; Otao, Hiroshi; Ihara, Takehiko; Mizota, Katsuhiko; Asami, Toyoko

2015-12-01

[Purpose] This study aimed to verify the usefulness of a 50-m round walking test developed as an assessment method for walking ability in the elderly. [Subjects] The subjects were 166 elderly requiring long-term care individuals (mean age, 80.5 years). [Methods] In order to evaluate the factors that had affected falls in the subjects in the previous year, we performed the 50-m round walking test, functional reach test, one-leg standing test, and 5-m walking test and measured grip strength and quadriceps strength. [Results] The 50-m round walking test was selected as a variable indicating fall risk based on the results of multiple logistic regression analysis. The cutoff value of the 50-m round walking test for determining fall risk was 0.66 m/sec. The area under the receiver operating characteristic curve was 0.64. The sensitivity of the cutoff value was 65.7%, the specificity was 63.6%, the positive predictive value was 55.0%, the negative predictive value was 73.3%, and the accuracy was 64.5%. [Conclusion] These results suggest that the 50-m round walking test is a potentially useful parameter for the determination of fall risk in the elderly requiring long-term care.

Risk Prediction Models of Locoregional Failure After Radical Cystectomy for Urothelial Carcinoma: External Validation in a Cohort of Korean Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ku, Ja Hyeon; Kim, Myong; Jeong, Chang Wook

2014-08-01

Purpose: To evaluate the predictive accuracy and general applicability of the locoregional failure model in a different cohort of patients treated with radical cystectomy. Methods and Materials: A total of 398 patients were included in the analysis. Death and isolated distant metastasis were considered competing events, and patients without any events were censored at the time of last follow-up. The model included the 3 variables pT classification, the number of lymph nodes identified, and margin status, as follows: low risk (≤pT2), intermediate risk (≥pT3 with ≥10 nodes removed and negative margins), and high risk (≥pT3 with <10 nodes removed ormore » positive margins). Results: The bootstrap-corrected concordance index of the model 5 years after radical cystectomy was 66.2%. When the risk stratification was applied to the validation cohort, the 5-year locoregional failure estimates were 8.3%, 21.2%, and 46.3% for the low-risk, intermediate-risk, and high-risk groups, respectively. The risk of locoregional failure differed significantly between the low-risk and intermediate-risk groups (subhazard ratio [SHR], 2.63; 95% confidence interval [CI], 1.35-5.11; P<.001) and between the low-risk and high-risk groups (SHR, 4.28; 95% CI, 2.17-8.45; P<.001). Although decision curves were appropriately affected by the incidence of the competing risk, decisions about the value of the models are not likely to be affected because the model remains of value over a wide range of threshold probabilities. Conclusions: The model is not completely accurate, but it demonstrates a modest level of discrimination, adequate calibration, and meaningful net benefit gain for prediction of locoregional failure after radical cystectomy.« less

Sequential screening for psychosocial and behavioral risk during pregnancy in a population of urban African-Americans

PubMed Central

Kiely, Michele; Gantz, Marie G.; El-Khorazaty, M. Nabil; El-Mohandes, Ayman AE

2013-01-01

Objective Screening for psychosocial and behavioral risks, such as depression, intimate partner violence and smoking, during pregnancy is considered state-of-the-art in prenatal care. This prospective longitudinal analysis examines the added benefit of repeated screening over a one-time screen in identifying such risks during pregnancy. Design Data were collected as part of a randomized controlled trial to address intimate partner violence, depression, smoking and environmental tobacco smoke exposure in African-Americans women. Setting PNC sites in the District of Columbia serving mainly minority women Population 1044 African-American pregnant women in the District of Columbia Methods Mothers were classified by their initial response (acknowledgement of risks) and updated during pregnancy. Risks were considered new if they were not previously reported. Standard hypothesis tests and logistic regression were used to predict acknowledgment of any new risk(s) during pregnancy. Main Outcome Measures New risks; psychosocial variables to understand what factors might help identify acknowledgement of additional risk(s). Results Repeated screening identified more mothers acknowledging risk over time. Reported smoking increased by 11%, environmental tobacco smoke exposure by 19%, intimate partner violence by 9%, and depression by 20%. The psychosocial variables collected at the baseline that were entered into the logistic regression model included relationship status, education, Medicaid, illicit drug use, and alcohol use during pregnancy. Among these, only education less than high school was associated in acknowledgement of new risk in the bivariate analyses and significantly predicted identification of new risks (OR=1.39, 95%CI, 1.01-1.90). Conclusions It is difficult early on to predict who will acknowledge new risks over the course of pregnancy, thus all women should be screened repeatedly to allow identification and intervention during prenatal care. PMID:23906260

Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient.

PubMed

Karimi Moridani, Mohammad; Setarehdan, Seyed Kamaledin; Motie Nasrabadi, Ali; Hajinasrollah, Esmaeil

2016-01-01

Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patient's ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patient's death is very informative for diagnosing the patient's condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, J; Gensheimer, M; Dong, X

Purpose: To develop an intra-tumor partitioning framework for identifying high-risk subregions from 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and CT imaging, and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. Methods: In this institutional review board-approved retrospective study, we analyzed the pre-treatment FDG-PET and CT scans of 44 lung cancer patients treated with radiotherapy. A novel, intra-tumor partitioning method was developed based on a two-stage clustering process: first at patient-level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified bymore » merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP). Results: Three spatially distinct subregions were identified within each tumor, which were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI = 0.66–0.67. When restricting the analysis to patients with stage III disease (n = 32), the same subregion achieved an even higher CI = 0.75 (HR = 3.93, logrank p = 0.002) for predicting OS, and a CI = 0.76 (HR = 4.84, logrank p = 0.002) for predicting OFP. In comparison, conventional imaging markers including tumor volume, SUVmax and MTV50 were not predictive of OS or OFP, with CI mostly below 0.60 (p < 0.001). Conclusion: We propose a robust intra-tumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.« less

[Prediction of psychosis by stepwise multilevel assessment--the Basel FePsy (Early Recognition of Psychosis)-Project].

PubMed

Riecher-Rössler, A; Aston, J; Borgwardt, S; Bugra, H; Fuhr, P; Gschwandtner, U; Koutsouleris, N; Pflueger, M; Tamagni, C; Radü, E-W; Rapp, C; Smieskova, R; Studerus, E; Walter, A; Zimmermann, R

2013-05-01

We have conducted various studies in Basel with the aim of improving the methods for the early detection of psychosis (Früherkennung von Psychosen, FePsy). From 1.3.2000 to 29.2.2004 234 individuals were screened using the Basel Screening Instrument for Psychosis (BSIP). 106 patients were identified as at risk for psychosis; out of these 53 remained in follow-up for up to 7 years (mean 5.4 years). The assessments were done with a specifically developed instrument for history taking, various scales for the psychopathology, assessments of neuropsychology and fine motor functioning, clinical and quantitative EEG, MRI of the brain, laboratory etc. Based on the BSIP alone, a relatively reliable prediction was possible: 21 (39.6%) of the individuals identified as at risk developed psychosis within the follow-up time. Post-hoc prediction could be improved to 81% by weighting psychopathology and including neuropsychology. Including the other domains obviously allows further improvements of prediction. The risk for psychosis should be assessed in a stepwise procedure. In a first step, a clinically oriented screening should be conducted. If an at-risk status is found, further assessments in various domains should be done in a specialised centre. © Georg Thieme Verlag KG Stuttgart · New York.

IDENTIFYING AREAS WITH A HIGH RISK OF HUMAN INFECTION WITH THE AVIAN INFLUENZA A (H7N9) VIRUS IN EAST ASIA

PubMed Central

Fuller, Trevon; Havers, Fiona; Xu, Cuiling; Fang, Li-Qun; Cao, Wu-Chun; Shu, Yuelong; Widdowson, Marc-Alain; Smith, Thomas B.

2014-01-01

Summary Objectives The rapid emergence, spread, and disease severity of avian influenza A(H7N9) in China has prompted concerns about a possible pandemic and regional spread in the coming months. The objective of this study was to predict the risk of future human infections with H7N9 in China and neighboring countries by assessing the association between H7N9 cases at sentinel hospitals and putative agricultural, climatic, and demographic risk factors. Methods This cross-sectional study used the locations of H7N9 cases and negative cases from China’s influenza-like illness surveillance network. After identifying H7N9 risk factors with logistic regression, we used Geographic Information Systems (GIS) to construct predictive maps of H7N9 risk across Asia. Results Live bird market density was associated with human H7N9 infections reported in China from March-May 2013. Based on these cases, our model accurately predicted the virus’ spread into Guangxi autonomous region in February 2014. Outside China, we find there is a high risk that the virus will spread to northern Vietnam, due to the import of poultry from China. Conclusions Our risk map can focus efforts to improve surveillance in poultry and humans, which may facilitate early identification and treatment of human cases. PMID:24642206

Relations among Affect, Abstinence Motivation and Confidence, and Daily Smoking Lapse Risk

PubMed Central

Minami, Haruka; Yeh, Vivian M.; Bold, Krysten W.; Chapman, Gretchen B.; McCarthy, Danielle E.

2016-01-01

Aims This study tested the hypothesis that changes in momentary affect, abstinence motivation, and confidence would predict lapse risk over the next 12–24 hours using Ecological Momentary Assessment (EMA) data from smokers attempting to quit smoking. Method 103 adult, daily, treatment-seeking smokers recorded their momentary affect, motivation to quit, abstinence confidence, and smoking behaviors in near real time with multiple EMA reports per day using electronic diaries post-quit. Results Multilevel models indicated that initial levels of negative affect were associated with smoking, even after controlling for earlier smoking status, and that short-term increases in negative affect predicted lapses up to 12, but not 24, hours later. Positive affect had significant effects on subsequent abstinence confidence, but not motivation to quit. High levels of motivation appeared to reduce increases in lapse risk that occur over hours while momentary changes in confidence did not predict lapse risk over 12 hours. Conclusion Negative affect had short-lived effects on lapse risk, whereas higher levels of motivation protected against the risk of lapsing that accumulates over hours. An increase in positive affect was associated with greater confidence to quit, but such changes in confidence did not reduce short-term lapse risk, contrary to expectations. Relations observed among affect, cognitions, and lapse seem to depend critically on the timing of assessments. PMID:24955665

Evaluation of the Risk of Grade 3 Oral and Pharyngeal Dysphagia Using Atlas-Based Method and Multivariate Analyses of Individual Patient Dose Distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Otter, Sophie; Schick, Ulrike; Gulliford, Sarah

Purpose: The study aimed to apply the atlas of complication incidence (ACI) method to patients receiving radical treatment for head and neck squamous cell carcinomas (HNSCC), to generate constraints based on dose-volume histograms (DVHs), and to identify clinical and dosimetric parameters that predict the risk of grade 3 oral mucositis (g3OM) and pharyngeal dysphagia (g3PD). Methods and Materials: Oral and pharyngeal mucosal DVHs were generated for 253 patients who received radiation (RT) or chemoradiation (CRT). They were used to produce ACI for g3OM and g3PD. Multivariate analysis (MVA) of the effect of dosimetry, clinical, and patient-related variables was performed usingmore » logistic regression and bootstrapping. Receiver operating curve (ROC) analysis was also performed, and the Youden index was used to find volume constraints that discriminated between volumes that predicted for toxicity. Results: We derived statistically significant dose-volume constraints for g3OM over the range v28 to v70. Only 3 statistically significant constraints were derived for g3PD v67, v68, and v69. On MVA, mean dose to the oral mucosa predicted for g3OM and concomitant chemotherapy and mean dose to the inferior constrictor (IC) predicted for g3PD. Conclusions: We have used the ACI method to evaluate incidences of g3OM and g3PD and ROC analysis to generate constraints to predict g3OM and g3PD derived from entire individual patient DVHs. On MVA, the strongest predictors were radiation dose (for g3OM) and concomitant chemotherapy (for g3PD).« less