Predicting and validating protein interactions using network structure.

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Pao-Yang Chen

2008-07-01

Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

Validity of one-repetition maximum predictive equations in men with spinal cord injury.

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Ribeiro Neto, F; Guanais, P; Dornelas, E; Coutinho, A C B; Costa, R R G

2017-10-01

Cross-sectional study. The study aimed (a) to test the cross-validation of current one-repetition maximum (1RM) predictive equations in men with spinal cord injury (SCI); (b) to compare the current 1RM predictive equations to a newly developed equation based on the 4- to 12-repetition maximum test (4-12RM). SARAH Rehabilitation Hospital Network, Brasilia, Brazil. Forty-five men aged 28.0 years with SCI between C6 and L2 causing complete motor impairment were enrolled in the study. Volunteers were tested, in a random order, in 1RM test or 4-12RM with 2-3 interval days. Multiple regression analysis was used to generate an equation for predicting 1RM. There were no significant differences between 1RM test and the current predictive equations. ICC values were significant and were classified as excellent for all current predictive equations. The predictive equation of Lombardi presented the best Bland-Altman results (0.5 kg and 12.8 kg for mean difference and interval range around the differences, respectively). The two created equation models for 1RM demonstrated the same and a high adjusted R 2 (0.971, Ppredictive equations are accurate to assess individuals with SCI at the bench press exercise. However, the predictive equation of Lombardi presented the best associated cross-validity results. A specific 1RM prediction equation was also elaborated for individuals with SCI. The created equation should be tested in order to verify whether it presents better accuracy than the current ones.

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

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Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

2018-02-01

To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

Predictive validity of the Slovene Matura

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Valentin Bucik

2001-09-01

Full Text Available Passing Matura is the last step of the secondary school graduation, but it is also the entrance ticket for the university. Besides, the summary score of Matura exam takes part in the selection process for the particular university studies in case of 'numerus clausus'. In discussing either aim of Matura important dilemmas arise, namely, is the Matura examination sufficiently exact and rightful procedure to, firstly, use its results for settling starting studying conditions and, secondly, to select validly, reliably and sensibly the best candidates for university studies. There are some questions concerning predictive validity of Matura that should be answered, e.g. (i does Matura as an enrollment procedure add to the qualitaty of the study; (ii is it a better selection tool than entrance examinations formerly used in different faculties in the case of 'numerus clausus'; and (iii is it reasonable to expect high predictive validity of Matura results for success at the university at all. Recent results show that in the last few years the dropout-rate is lower than before, the pass-rate between the first and the second year is higher and the average duration of study per student is shorter. It is clear, however, that it is not possible to simply predict the study success from the Matura results. There are too many factors influencing the success in the university studies. In most examined study programs the correlation between Matura results and study success is positive but moderate, therefore it can not be said categorically that only candidates accepted according to the Matura results are (or will be the best students. Yet it has been shown that Matura is a standardized procedure, comparable across different candidates entering university, and that – when compared entrance examinations – it is more objective, reliable, and hen ce more valid and fair a procedure. In addition, comparable procedures of university recruiting and selection can be

Validation of Occupants’ Behaviour Models for Indoor Quality Parameter and Energy Consumption Prediction

DEFF Research Database (Denmark)

Fabi, Valentina; Sugliano, Martina; Andersen, Rune Korsholm

2015-01-01

Occupants’ behaviour related to building control system plays a significant role to achieve thermal comfort and air quality in naturally-ventilated buildings. Generally, the published models of occupant's behavior are not validated, meaning that the predictive power has not yet been tested. For t...

Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose

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You Wang

2016-07-01

Full Text Available In the application of electronic noses (E-noses, probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt’s method, Softmax regression and Naive Bayes. Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt’s method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples.

The Predictive Validity of Projective Measures.

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Suinn, Richard M.; Oskamp, Stuart

Written for use by clinical practitioners as well as psychological researchers, this book surveys recent literature (1950-1965) on projective test validity by reviewing and critically evaluating studies which shed light on what may reliably be predicted from projective test results. Two major instruments are covered: the Rorschach and the Thematic…

Disentangling the Predictive Validity of High School Grades for Academic Success in University

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Vulperhorst, Jonne; Lutz, Christel; de Kleijn, Renske; van Tartwijk, Jan

2018-01-01

To refine selective admission models, we investigate which measure of prior achievement has the best predictive validity for academic success in university. We compare the predictive validity of three core high school subjects to the predictive validity of high school grade point average (GPA) for academic achievement in a liberal arts university…

Predictive validity of the comprehensive basic science examination mean score for assessment of medical students' performance

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Firouz Behboudi

2002-04-01

Full Text Available Background Medical education curriculum improvements can be achieved bye valuating students performance. Medical students have to pass two undergraduate comprehensive examinations, basic science and preinternship, in Iran. Purpose To measure validity of the students' mean score in comprehensive basic science exam (CBSE for predicting their performance in later curriculum phases. Methods This descriptive cross-sectional study was conducted on 95 (38 women and 55 men Guilan medical university students. Their admission to the university was 81% by regional quota and 12% by shaheed and other organizations' share. They first enrolled in 1994 and were able to pass CBS£ at first try. Data on gender, regional quota, and average grades of CBS£, PC, and CPIE were collected by a questionnaire. The calculations were done by SPSS package. Results The correlation coefficient between CBS£ and CPIE mean scores (0.65 was higher than correlation coefficient between CBS£ and PC mean scores (0.49. The predictive validity of CBS£ average grade was significant for students' performance in CPIE; however, the predictive validity of CBSE mean scores for students I pe1jormance in PC was lower. Conclusion he students' mean score in CBSE can be a good denominator for their further admission. We recommend further research to assess the predictive validity for each one of the basic courses. Keywords predictive validity, comprehensive basic exam

CFD Validation Studies for Hypersonic Flow Prediction

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Gnoffo, Peter A.

2001-01-01

A series of experiments to measure pressure and heating for code validation involving hypersonic, laminar, separated flows was conducted at the Calspan-University at Buffalo Research Center (CUBRC) in the Large Energy National Shock (LENS) tunnel. The experimental data serves as a focus for a code validation session but are not available to the authors until the conclusion of this session. The first set of experiments considered here involve Mach 9.5 and Mach 11.3 N2 flow over a hollow cylinder-flare with 30 degree flare angle at several Reynolds numbers sustaining laminar, separated flow. Truncated and extended flare configurations are considered. The second set of experiments, at similar conditions, involves flow over a sharp, double cone with fore-cone angle of 25 degrees and aft-cone angle of 55 degrees. Both sets of experiments involve 30 degree compressions. Location of the separation point in the numerical simulation is extremely sensitive to the level of grid refinement in the numerical predictions. The numerical simulations also show a significant influence of Reynolds number on extent of separation. Flow unsteadiness was easily introduced into the double cone simulations using aggressive relaxation parameters that normally promote convergence.

When Significant Others Suffer: German Validation of the Burden Assessment Scale (BAS.

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Christina Hunger

Full Text Available There is a need of an economical, reliable, and valid instrument in the German-speaking countries to measure the burden of relatives who care for mentally ill persons. We translated the Burden Assessment Scale (BAS and conducted a study investigating factor structure, psychometric quality and predictive validity. We used confirmative factor analyses (CFA, maximum-likelihood method to examine the dimensionality of the German BAS in a sample of 215 relatives (72% women; M = 32 years, SD = 14, range: 18 to 77; 39% employed of mentally ill persons (50% (ex-partner or (best friend; M = 32 years, SD = 13, range 8 to 64; main complaints were depression and/or anxiety. Cronbach's α determined the internal consistency. We examined predictive validity using regression analyses including the BAS and validated scales of social systems functioning (Experience In Social Systems Questionnaire, EXIS.pers, EXIS.org and psychopathology (Brief Symptom Inventory, BSI. Variables that might have influenced the dependent variables (e.g. age, gender, education, employment and civil status were controlled by their introduction in the first step, and the BAS in the second step of the regression analyses. A model with four correlated factors (Disrupted Activities, Personal Distress, Time Perspective, Guilt showed the best fit. With respect to the number of items included, the internal consistency was very good. The modified German BAS predicted relatives' social systems functioning and psychopathology. The economical design makes the 19-item BAS promising for practice-oriented research, and for studies under time constraints. Strength, limitations and future directions are discussed.

Brief implicit association test: Validity and utility in prediction of voting behavior

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Pavlović Maša D.

2013-01-01

Full Text Available We employed the Brief Implicit Association Test (a recently developed short version of IAT to measure implicit political attitudes toward four political parties running for Serbian parliament. To test its criterion validity, we measured voting intention and actual voting behavior. In addition, we introduced political involvement as a potential moderator of the BIAT’s predictive and incremental validity. The BIAT demonstrated good internal and predictive validity, but lacked incremental validity over self-report measures. Predictive power of the BIAT was moderated by political involvement - the BIAT scores were stronger predictors of voting intention and behavior among voters highly involved in politics. [Projekat Ministarstva nauke Republike Srbije, br. 179018

External validation of approaches to prediction of falls during hospital rehabilitation stays and development of a new simpler tool

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Angela Vratsistas-Curto

2017-12-01

Full Text Available Objectives: To test the external validity of 4 approaches to fall prediction in a rehabilitation setting (Predict_FIRST, Ontario Modified STRATIFY (OMS, physiotherapists’ judgement of fall risk (PT_Risk, and falls in the past year (Past_Falls, and to develop and test the validity of a simpler tool for fall prediction in rehabilitation (Predict_CM2. Participants: A total of 300 consecutively-admitted rehabilitation inpatients. Methods: Prospective inception cohort study. Falls during the rehabilitation stay were monitored. Potential predictors were extracted from medical records. Results: Forty-one patients (14% fell during their rehabilitation stay. The external validity, area under the receiver operating characteristic curve (AUC, for predicting future fallers was: 0.71 (95% confidence interval (95% CI: 0.61–0.81 for OMS (Total_Score; 0.66 (95% CI: 0.57–0.74 for Predict_FIRST; 0.65 (95% CI 0.57–0.73 for PT_Risk; and 0.52 for Past_Falls (95% CI: 0.46–0.60. A simple 3-item tool (Predict_CM2 was developed from the most predictive individual items (impaired mobility/transfer ability, impaired cognition, and male sex. The accuracy of Predict_CM2 was 0.73 (95% CI: 0.66–0.81, comparable to OMS (Total_Score (p = 0.52, significantly better than Predict_FIRST (p = 0.04, and Past_Falls (p < 0.001, and approaching significantly better than PT_Risk (p = 0.09. Conclusion: Predict_CM2 is a simpler screening tool with similar accuracy for predicting fallers in rehabilitation to OMS (Total_Score and better accuracy than Predict_FIRST or Past_Falls. External validation of Predict_CM2 is required.

External validation of the Cairns Prediction Model (CPM) to predict conversion from laparoscopic to open cholecystectomy.

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Hu, Alan Shiun Yew; Donohue, Peter O'; Gunnarsson, Ronny K; de Costa, Alan

2018-03-14

Valid and user-friendly prediction models for conversion to open cholecystectomy allow for proper planning prior to surgery. The Cairns Prediction Model (CPM) has been in use clinically in the original study site for the past three years, but has not been tested at other sites. A retrospective, single-centred study collected ultrasonic measurements and clinical variables alongside with conversion status from consecutive patients who underwent laparoscopic cholecystectomy from 2013 to 2016 in The Townsville Hospital, North Queensland, Australia. An area under the curve (AUC) was calculated to externally validate of the CPM. Conversion was necessary in 43 (4.2%) out of 1035 patients. External validation showed an area under the curve of 0.87 (95% CI 0.82-0.93, p = 1.1 × 10 -14 ). In comparison with most previously published models, which have an AUC of approximately 0.80 or less, the CPM has the highest AUC of all published prediction models both for internal and external validation. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

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

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

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Predictive validation of an influenza spread model.

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Ayaz Hyder

Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

Predictive Validation of an Influenza Spread Model

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Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

Systematic validation of predicted microRNAs for cyclin D1

International Nuclear Information System (INIS)

Jiang, Qiong; Feng, Ming-Guang; Mo, Yin-Yuan

2009-01-01

MicroRNAs are the endogenous small non-coding RNA molecules capable of silencing protein coding genes at the posttranscriptional level. Based on computer-aided predictions, a single microRNA could have over a hundred of targets. On the other hand, a single protein-coding gene could be targeted by many potential microRNAs. However, only a relatively small number of these predicted microRNA/mRNA interactions are experimentally validated, and no systematic validation has been carried out using a reporter system. In this study, we used luciferease reporter assays to validate microRNAs that can silence cyclin D1 (CCND1) because CCND1 is a well known proto-oncogene implicated in a variety of types of cancers. We chose miRanda (http://www.microRNA.org) as a primary prediction method. We then cloned 51 of 58 predicted microRNA precursors into pCDH-CMV-MCS-EF1-copGFP and tested for their effect on the luciferase reporter carrying the 3'-untranslated region (UTR) of CCND1 gene. Real-time PCR revealed the 45 of 51 cloned microRNA precursors expressed a relatively high level of the exogenous microRNAs which were used in our validation experiments. By an arbitrary cutoff of 35% reduction, we identified 7 microRNAs that were able to suppress Luc-CCND1-UTR activity. Among them, 4 of them were previously validated targets and the rest 3 microRNAs were validated to be positive in this study. Of interest, we found that miR-503 not only suppressed the luciferase activity, but also suppressed the endogenous CCND1 both at protein and mRNA levels. Furthermore, we showed that miR-503 was able to reduce S phase cell populations and caused cell growth inhibition, suggesting that miR-503 may be a putative tumor suppressor. This study provides a more comprehensive picture of microRNA/CCND1 interactions and it further demonstrates the importance of experimental target validation

Validated predictive modelling of the environmental resistome.

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Amos, Gregory C A; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

2015-06-01

Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

Basic Modelling principles and Validation of Software for Prediction of Collision Damage

DEFF Research Database (Denmark)

Simonsen, Bo Cerup

2000-01-01

This report describes basic modelling principles, the theoretical background and validation examples for the collision damage prediction module in the ISESO stand-alone software.......This report describes basic modelling principles, the theoretical background and validation examples for the collision damage prediction module in the ISESO stand-alone software....

Validating health impact assessment: Prediction is difficult (especially about the future)

International Nuclear Information System (INIS)

Petticrew, Mark; Cummins, Steven; Sparks, Leigh; Findlay, Anne

2007-01-01

Health impact assessment (HIA) has been recommended as a means of estimating how policies, programmes and projects may impact on public health and on health inequalities. This paper considers the difference between predicting health impacts and measuring those impacts. It draws upon a case study of the building of a new hypermarket in a deprived area of Glasgow, which offered an opportunity to reflect on the issue of the predictive validity of HIA, and to consider the difference between potential and actual impacts. We found that the actual impacts of the new hypermarket on diet differed from that which would have been predicted based on previous studies. Furthermore, they challenge current received wisdom about the impact of food retail outlets in poorer areas. These results are relevant to the validity of HIA as a process and emphasise the importance of further research on the predictive validity of HIA, which should help improve its value to decision-makers

A prediction score for significant coronary artery disease in Chinese patients ≥50 years old referred for rheumatic valvular heart disease surgery.

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Xu, Zhenjun; Pan, Jun; Chen, Tao; Zhou, Qing; Wang, Qiang; Cao, Hailong; Fan, Fudong; Luo, Xuan; Ge, Min; Wang, Dongjin

2018-04-01

Our goal was to establish a prediction score and protocol for the preoperative prediction of significant coronary artery disease (CAD) in patients with rheumatic valvular heart disease. Using multivariate logistic regression analysis, we validated the model based on 490 patients without a history of myocardial infarction and who underwent preoperative screening coronary angiography. Significant CAD was defined as ≥50% narrowing of the diameter of the lumen of the left main coronary artery or ≥70% narrowing of the diameter of the lumen of the left anterior descending coronary artery, left circumflex artery or right coronary artery. Significant CAD was present in 9.8% of patients. Age, smoking, diabetes mellitus, diastolic blood pressure, low-density lipoprotein cholesterol and ischaemia evident on an electrocardiogram were independently associated with significant CAD and were entered into the multivariate model. According to the logistic regression predictive risk score, preoperative coronary angiography is recommended in (i) postmenopausal women between 50 and 59 years of age with ≥9.1% logistic regression predictive risk score; (ii) postmenopausal women who are ≥60 years old with a logistic regression predictive risk score ≥6.6% and (iii) men ≥50 years old whose logistic regression predictive risk score was ≥2.8%. Based on this predictive model, 246 (50.2%) preoperative coronary angiograms could be safely avoided. The negative predictive value of the model was 98.8% (246 of 249). This model was accurate for the preoperative prediction of significant CAD in patients with rheumatic valvular heart disease. This model must be validated in larger cohorts and various populations.

Perioperative Respiratory Adverse Events in Pediatric Ambulatory Anesthesia: Development and Validation of a Risk Prediction Tool.

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Subramanyam, Rajeev; Yeramaneni, Samrat; Hossain, Mohamed Monir; Anneken, Amy M; Varughese, Anna M

2016-05-01

Perioperative respiratory adverse events (PRAEs) are the most common cause of serious adverse events in children receiving anesthesia. Our primary aim of this study was to develop and validate a risk prediction tool for the occurrence of PRAE from the onset of anesthesia induction until discharge from the postanesthesia care unit in children younger than 18 years undergoing elective ambulatory anesthesia for surgery and radiology. The incidence of PRAE was studied. We analyzed data from 19,059 patients from our department's quality improvement database. The predictor variables were age, sex, ASA physical status, morbid obesity, preexisting pulmonary disorder, preexisting neurologic disorder, and location of ambulatory anesthesia (surgery or radiology). Composite PRAE was defined as the presence of any 1 of the following events: intraoperative bronchospasm, intraoperative laryngospasm, postoperative apnea, postoperative laryngospasm, postoperative bronchospasm, or postoperative prolonged oxygen requirement. Development and validation of the risk prediction tool for PRAE were performed using a split sampling technique to split the database into 2 independent cohorts based on the year when the patient received ambulatory anesthesia for surgery and radiology using logistic regression. A risk score was developed based on the regression coefficients from the validation tool. The performance of the risk prediction tool was assessed by using tests of discrimination and calibration. The overall incidence of composite PRAE was 2.8%. The derivation cohort included 8904 patients, and the validation cohort included 10,155 patients. The risk of PRAE was 3.9% in the development cohort and 1.8% in the validation cohort. Age ≤ 3 years (versus >3 years), ASA physical status II or III (versus ASA physical status I), morbid obesity, preexisting pulmonary disorder, and surgery (versus radiology) significantly predicted the occurrence of PRAE in a multivariable logistic regression

Validation of water sorption-based clay prediction models for calcareous soils

DEFF Research Database (Denmark)

Arthur, Emmanuel; Razzaghi, Fatemeh; Moosavi, Ali

2017-01-01

on prediction accuracy. The soils had clay content ranging from 9 to 61% and CaCO3 from 24 to 97%. The three water sorption models considered showed a reasonably fair prediction of the clay content from water sorption at 28% relative humidity (RMSE and ME values ranging from 10.6 to 12.1 and −8.1 to −4......Soil particle size distribution (PSD), particularly the active clay fraction, mediates soil engineering, agronomic and environmental functions. The tedious and costly nature of traditional methods of determining PSD prompted the development of water sorption-based models for determining the clay...... fraction. The applicability of such models to semi-arid soils with significant amounts of calcium carbonate and/or gypsum is unknown. The objective of this study was to validate three water sorption-based clay prediction models for 30 calcareous soils from Iran and identify the effect of CaCO3...

On the incremental validity of irrational beliefs to predict subjective well-being while controlling for personality factors.

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Spörrle, Matthias; Strobel, Maria; Tumasjan, Andranik

2010-11-01

This research examines the incremental validity of irrational thinking as conceptualized by Albert Ellis to predict diverse aspects of subjective well-being while controlling for the influence of personality factors. Rational-emotive behavior therapy (REBT) argues that irrational beliefs result in maladaptive emotions leading to reduced well-being. Although there is some early scientific evidence for this relation, it has never been investigated whether this connection would still persist when statistically controlling for the Big Five personality factors, which were consistently found to be important determinants of well-being. Regression analyses revealed significant incremental validity of irrationality over personality factors when predicting life satisfaction, but not when predicting subjective happiness. Results are discussed with respect to conceptual differences between these two aspects of subjective well-being.

A critical review of predictive models for the onset of significant void in forced-convection subcooled boiling

International Nuclear Information System (INIS)

Dorra, H.; Lee, S.C.; Bankoff, S.G.

1993-06-01

This predictive models for the onset of significant void (OSV) in forced-convection subcooled boiling are reviewed and compared with extensive data. Three analytical models and seven empirical correlations are considered in this review. These models and correlations are put onto a common basis and are compared, again on a common basis, with a variety of data. The evaluation of their range of validity and applicability under various operating conditions are discussed. The results show that the correlations of Saha-Zuber seems to be the best model to predict OSV in vertical subcooled boiling flow

Chemotherapy effectiveness and mortality prediction in surgically treated osteosarcoma dogs: A validation study.

Science.gov (United States)

Schmidt, A F; Nielen, M; Withrow, S J; Selmic, L E; Burton, J H; Klungel, O H; Groenwold, R H H; Kirpensteijn, J

2016-03-01

Canine osteosarcoma is the most common bone cancer, and an important cause of mortality and morbidity, in large purebred dogs. Previously we constructed two multivariable models to predict a dog's 5-month or 1-year mortality risk after surgical treatment for osteosarcoma. According to the 5-month model, dogs with a relatively low risk of 5-month mortality benefited most from additional chemotherapy treatment. In the present study, we externally validated these results using an independent cohort study of 794 dogs. External performance of our prediction models showed some disagreement between observed and predicted risk, mean difference: -0.11 (95% confidence interval [95% CI]-0.29; 0.08) for 5-month risk and 0.25 (95%CI 0.10; 0.40) for 1-year mortality risk. After updating the intercept, agreement improved: -0.0004 (95%CI-0.16; 0.16) and -0.002 (95%CI-0.15; 0.15). The chemotherapy by predicted mortality risk interaction (P-value=0.01) showed that the chemotherapy compared to no chemotherapy effectiveness was modified by 5-month mortality risk: dogs with a relatively lower risk of mortality benefited most from additional chemotherapy. Chemotherapy effectiveness on 1-year mortality was not significantly modified by predicted risk (P-value=0.28). In conclusion, this external validation study confirmed that our multivariable risk prediction models can predict a patient's mortality risk and that dogs with a relatively lower risk of 5-month mortality seem to benefit most from chemotherapy. Copyright © 2016 Elsevier B.V. All rights reserved.

Validity of the Optometry Admission Test in Predicting Performance in Schools and Colleges of Optometry.

Science.gov (United States)

Kramer, Gene A.; Johnston, JoElle

1997-01-01

A study examined the relationship between Optometry Admission Test scores and pre-optometry or undergraduate grade point average (GPA) with first and second year performance in optometry schools. The test's predictive validity was limited but significant, and comparable to those reported for other admission tests. In addition, the scores…

External validation of multivariable prediction models: a systematic review of methodological conduct and reporting

Science.gov (United States)

2014-01-01

Background Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models. Methods We conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures. Results 11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models. Conclusions The vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling

Predictive Validity of National Basketball Association Draft Combine on Future Performance.

Science.gov (United States)

Teramoto, Masaru; Cross, Chad L; Rieger, Randall H; Maak, Travis G; Willick, Stuart E

2018-02-01

Teramoto, M, Cross, CL, Rieger, RH, Maak, TG, and Willick, SE. Predictive validity of national basketball association draft combine on future performance. J Strength Cond Res 32(2): 396-408, 2018-The National Basketball Association (NBA) Draft Combine is an annual event where prospective players are evaluated in terms of their athletic abilities and basketball skills. Data collected at the Combine should help NBA teams select right the players for the upcoming NBA draft; however, its value for predicting future performance of players has not been examined. This study investigated predictive validity of the NBA Draft Combine on future performance of basketball players. We performed a principal component analysis (PCA) on the 2010-2015 Combine data to reduce correlated variables (N = 234), a correlation analysis on the Combine data and future on-court performance to examine relationships (maximum pairwise N = 217), and a robust principal component regression (PCR) analysis to predict first-year and 3-year on-court performance from the Combine measures (N = 148 and 127, respectively). Three components were identified within the Combine data through PCA (= Combine subscales): length-size, power-quickness, and upper-body strength. As per the correlation analysis, the individual Combine items for anthropometrics, including height without shoes, standing reach, weight, wingspan, and hand length, as well as the Combine subscale of length-size, had positive, medium-to-large-sized correlations (r = 0.313-0.545) with defensive performance quantified by Defensive Box Plus/Minus. The robust PCR analysis showed that the Combine subscale of length-size was a predictor most significantly associated with future on-court performance (p ≤ 0.05), including Win Shares, Box Plus/Minus, and Value Over Replacement Player, followed by upper-body strength. In conclusion, the NBA Draft Combine has value for predicting future performance of players.

Reliability and Validity of the Load-Velocity Relationship to Predict the 1RM Back Squat.

Science.gov (United States)

Banyard, Harry G; Nosaka, Kazunori; Haff, G Gregory

2017-07-01

Banyard, HG, Nosaka, K, and Haff, GG. Reliability and validity of the load-velocity relationship to predict the 1RM back squat. J Strength Cond Res 31(7): 1897-1904, 2017-This study investigated the reliability and validity of the load-velocity relationship to predict the free-weight back squat one repetition maximum (1RM). Seventeen strength-trained males performed three 1RM assessments on 3 separate days. All repetitions were performed to full depth with maximal concentric effort. Predicted 1RMs were calculated by entering the mean concentric velocity of the 1RM (V1RM) into an individualized linear regression equation, which was derived from the load-velocity relationship of 3 (20, 40, 60% of 1RM), 4 (20, 40, 60, 80% of 1RM), or 5 (20, 40, 60, 80, 90% of 1RM) incremental warm-up sets. The actual 1RM (140.3 ± 27.2 kg) was very stable between 3 trials (ICC = 0.99; SEM = 2.9 kg; CV = 2.1%; ES = 0.11). Predicted 1RM from 5 warm-up sets up to and including 90% of 1RM was the most reliable (ICC = 0.92; SEM = 8.6 kg; CV = 5.7%; ES = -0.02) and valid (r = 0.93; SEE = 10.6 kg; CV = 7.4%; ES = 0.71) of the predicted 1RM methods. However, all predicted 1RMs were significantly different (p ≤ 0.05; ES = 0.71-1.04) from the actual 1RM. Individual variation for the actual 1RM was small between trials ranging from -5.6 to 4.8% compared with the most accurate predictive method up to 90% of 1RM, which was more variable (-5.5 to 27.8%). Importantly, the V1RM (0.24 ± 0.06 m·s) was unreliable between trials (ICC = 0.42; SEM = 0.05 m·s; CV = 22.5%; ES = 0.14). The load-velocity relationship for the full depth free-weight back squat showed moderate reliability and validity but could not accurately predict 1RM, which was stable between trials. Thus, the load-velocity relationship 1RM prediction method used in this study cannot accurately modify sessional training loads because of large V1RM variability.

Recurrent epistaxis: predicting risk of 30-day readmission, derivation and validation of RHINO-ooze score.

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Addison, A; Paul, C; Kuo, R; Lamyman, A; Martinez-Devesa, P; Hettige, R

2017-06-01

To derive and validate a predictive scoring tool (RHINO-ooze score) with good sensitivity and specificity in identifying patients with epistaxis at high risk of 30 day readmission and to enable risk stratification for possible definitive intervention. Using medical databases, we searched for factors influencing recurrent epistaxis. The information ascertained together with our analysis of retrospective data on patients admitted with epistaxis between October 2013 and September 2014, was used as the derivation cohort to develop the predictive scoring model (RHINO-ooze score). The tool was validated by performing statistical analysis on the validation cohort of patients admitted with epistaxis between October 2014 and October 2015. Multiple linear regressions with backwards elimination was used to derive the predictive model. The area under the curve (AUC), sensitivity and specificity were calculated. 834 admissions were encountered within the study period. Using the derivative cohort (n= 302) the RHINO-ooze score with a maximum score of 8 from five variables (Recent admission, Haemorrhage point unidentified, Increasing age over 70, posterior Nasal packing, Oral anticoagulant) was developed. The RHINO-ooze score had a chi-square value of 99.72 with a significance level of smaller than 0.0001 and hence an overall good model fit. Comparison between the derivative and validation groups revealed similar rates of 30-day readmission between the cohorts. The sensitivity and specificity of predicting 30-day readmission in high risk patients with recurrent epistaxis (RHINO-ooze score equal/larger than 6) was 81% and 84%, respectively. The RHINO-ooze scoring tool demonstrates good specificity and sensitivity in predicting the risk of 30 day readmission in patients with epistaxis and can be used as an adjunct to clinical decision making with regards to timing of operative intervention in order to reduce readmission rates.

Measurement of predictive validity in violence risk assessment studies: a second-order systematic review.

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Singh, Jay P; Desmarais, Sarah L; Van Dorn, Richard A

2013-01-01

The objective of the present review was to examine how predictive validity is analyzed and reported in studies of instruments used to assess violence risk. We reviewed 47 predictive validity studies published between 1990 and 2011 of 25 instruments that were included in two recent systematic reviews. Although all studies reported receiver operating characteristic curve analyses and the area under the curve (AUC) performance indicator, this methodology was defined inconsistently and findings often were misinterpreted. In addition, there was between-study variation in benchmarks used to determine whether AUCs were small, moderate, or large in magnitude. Though virtually all of the included instruments were designed to produce categorical estimates of risk - through the use of either actuarial risk bins or structured professional judgments - only a minority of studies calculated performance indicators for these categorical estimates. In addition to AUCs, other performance indicators, such as correlation coefficients, were reported in 60% of studies, but were infrequently defined or interpreted. An investigation of sources of heterogeneity did not reveal significant variation in reporting practices as a function of risk assessment approach (actuarial vs. structured professional judgment), study authorship, geographic location, type of journal (general vs. specialized audience), sample size, or year of publication. Findings suggest a need for standardization of predictive validity reporting to improve comparison across studies and instruments. Copyright © 2013 John Wiley & Sons, Ltd.

On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.

Science.gov (United States)

Roy, Kunal; Mitra, Indrani

2011-07-01

Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

In-Hospital Risk Prediction for Post-stroke Depression. Development and Validation of the Post-stroke Depression Prediction Scale

NARCIS (Netherlands)

Thóra Hafsteinsdóttir; Roelof G.A. Ettema; Diederick Grobbee; Prof. Dr. Marieke J. Schuurmans; Janneke van Man-van Ginkel; Eline Lindeman

2013-01-01

Background and Purpose—The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early

Validation and Inter-comparison Against Observations of GODAE Ocean View Ocean Prediction Systems

Science.gov (United States)

Xu, J.; Davidson, F. J. M.; Smith, G. C.; Lu, Y.; Hernandez, F.; Regnier, C.; Drevillon, M.; Ryan, A.; Martin, M.; Spindler, T. D.; Brassington, G. B.; Oke, P. R.

2016-02-01

For weather forecasts, validation of forecast performance is done at the end user level as well as by the meteorological forecast centers. In the development of Ocean Prediction Capacity, the same level of care for ocean forecast performance and validation is needed. Herein we present results from a validation against observations of 6 Global Ocean Forecast Systems under the GODAE OceanView International Collaboration Network. These systems include the Global Ocean Ice Forecast System (GIOPS) developed by the Government of Canada, two systems PSY3 and PSY4 from the French Mercator-Ocean Ocean Forecasting Group, the FOAM system from UK met office, HYCOM-RTOFS from NOAA/NCEP/NWA of USA, and the Australian Bluelink-OceanMAPS system from the CSIRO, the Australian Meteorological Bureau and the Australian Navy.The observation data used in the comparison are sea surface temperature, sub-surface temperature, sub-surface salinity, sea level anomaly, and sea ice total concentration data. Results of the inter-comparison demonstrate forecast performance limits, strengths and weaknesses of each of the six systems. This work establishes validation protocols and routines by which all new prediction systems developed under the CONCEPTS Collaborative Network will be benchmarked prior to approval for operations. This includes anticipated delivery of CONCEPTS regional prediction systems over the next two years including a pan Canadian 1/12th degree resolution ice ocean prediction system and limited area 1/36th degree resolution prediction systems. The validation approach of comparing forecasts to observations at the time and location of the observation is called Class 4 metrics. It has been adopted by major international ocean prediction centers, and will be recommended to JCOMM-WMO as routine validation approach for operational oceanography worldwide.

Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.

Science.gov (United States)

Mathioudakis, Nestoras Nicolas; Everett, Estelle; Routh, Shuvodra; Pronovost, Peter J; Yeh, Hsin-Chieh; Golden, Sherita Hill; Saria, Suchi

2018-01-01

To develop and validate a multivariable prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults. We collected pharmacologic, demographic, laboratory, and diagnostic data from 128 657 inpatient days in which at least 1 unit of subcutaneous insulin was administered in the absence of intravenous insulin, total parenteral nutrition, or insulin pump use (index days). These data were used to develop multivariable prediction models for biochemical and clinically significant hypoglycemia (blood glucose (BG) of ≤70 mg/dL and model development and validation, respectively. Using predictors of age, weight, admitting service, insulin doses, mean BG, nadir BG, BG coefficient of variation (CV BG ), diet status, type 1 diabetes, type 2 diabetes, acute kidney injury, chronic kidney disease (CKD), liver disease, and digestive disease, our model achieved a c-statistic of 0.77 (95% CI 0.75 to 0.78), positive likelihood ratio (+LR) of 3.5 (95% CI 3.4 to 3.6) and negative likelihood ratio (-LR) of 0.32 (95% CI 0.30 to 0.35) for prediction of biochemical hypoglycemia. Using predictors of sex, weight, insulin doses, mean BG, nadir BG, CV BG , diet status, type 1 diabetes, type 2 diabetes, CKD stage, and steroid use, our model achieved a c-statistic of 0.80 (95% CI 0.78 to 0.82), +LR of 3.8 (95% CI 3.7 to 4.0) and -LR of 0.2 (95% CI 0.2 to 0.3) for prediction of clinically significant hypoglycemia. Hospitalized patients at risk of insulin-associated hypoglycemia can be identified using validated prediction models, which may support the development of real-time preventive interventions.

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes.

Science.gov (United States)

Zhao, Lue Ping; Carlsson, Annelie; Larsson, Helena Elding; Forsander, Gun; Ivarsson, Sten A; Kockum, Ingrid; Ludvigsson, Johnny; Marcus, Claude; Persson, Martina; Samuelsson, Ulf; Örtqvist, Eva; Pyo, Chul-Woo; Bolouri, Hamid; Zhao, Michael; Nelson, Wyatt C; Geraghty, Daniel E; Lernmark, Åke

2017-11-01

It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10 -92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations. Copyright © 2017 John Wiley & Sons, Ltd.

Predictive Simulation of Material Failure Using Peridynamics -- Advanced Constitutive Modeling, Verification and Validation

Science.gov (United States)

2016-03-31

AFRL-AFOSR-VA-TR-2016-0309 Predictive simulation of material failure using peridynamics- advanced constitutive modeling, verification , and validation... Self -explanatory. 8. PERFORMING ORGANIZATION REPORT NUMBER. Enter all unique alphanumeric report numbers assigned by the performing organization, e.g...for public release. Predictive simulation of material failure using peridynamics-advanced constitutive modeling, verification , and validation John T

Developing and Validating a Predictive Model for Stroke Progression

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L.E. Craig

2011-12-01

Full Text Available Background: Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Methods: Two patient cohorts were used for this study – the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863 was used to develop the model. Variables that were statistically significant (p 0.1 in turn. The second cohort (n = 216 was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Results: Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72–0.73] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50–0.92]. Conclusion: The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the

Developing and validating a predictive model for stroke progression.

Science.gov (United States)

Craig, L E; Wu, O; Gilmour, H; Barber, M; Langhorne, P

2011-01-01

Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Two patient cohorts were used for this study - the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p p > 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72-0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50-0.92)]. The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and calibration of the predictive model appear

Developing and Validating a Predictive Model for Stroke Progression

Science.gov (United States)

Craig, L.E.; Wu, O.; Gilmour, H.; Barber, M.; Langhorne, P.

2011-01-01

Background Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Methods Two patient cohorts were used for this study – the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Results Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72–0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50–0.92)]. Conclusion The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

Energy Technology Data Exchange (ETDEWEB)

Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov [Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993–0002 (United States); Cross, Kevin P. [Leadscope, Inc., 1393 Dublin Road, Columbus, OH, 43215–1084 (United States)

2012-05-01

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

International Nuclear Information System (INIS)

Valerio, Luis G.; Cross, Kevin P.

2012-01-01

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.

Slip Validation and Prediction for Mars Exploration Rovers

Directory of Open Access Journals (Sweden)

Jeng Yen

2008-04-01

Full Text Available This paper presents a novel technique to validate and predict the rover slips on Martian surface for NASA’s Mars Exploration Rover mission (MER. Different from the traditional approach, the proposed method uses the actual velocity profile of the wheels and the digital elevation map (DEM from the stereo images of the terrain to formulate the equations of motion. The six wheel speed from the empirical encoder data comprises the vehicle's velocity, and the rover motion can be estimated using mixed differential and algebraic equations. Applying the discretization operator to these equations, the full kinematics state of the rover is then resolved by the configuration kinematics solution in the Rover Sequencing and Visualization Program (RSVP. This method, with the proper wheel slip and sliding factors, produces accurate simulation of the Mars Exploration rovers, which have been validated with the earth-testing vehicle. This computational technique has been deployed to the operation of the MER rovers in the extended mission period. Particularly, it yields high quality prediction of the rover motion on high slope areas. The simulated path of the rovers has been validated using the telemetry from the onboard Visual Odometry (VisOdom. Preliminary results indicate that the proposed simulation is very effective in planning the path of the rovers on the high-slope areas.

Testing the Predictive Validity and Construct of Pathological Video Game Use

Science.gov (United States)

Groves, Christopher L.; Gentile, Douglas; Tapscott, Ryan L.; Lynch, Paul J.

2015-01-01

Three studies assessed the construct of pathological video game use and tested its predictive validity. Replicating previous research, Study 1 produced evidence of convergent validity in 8th and 9th graders (N = 607) classified as pathological gamers. Study 2 replicated and extended the findings of Study 1 with college undergraduates (N = 504). Predictive validity was established in Study 3 by measuring cue reactivity to video games in college undergraduates (N = 254), such that pathological gamers were more emotionally reactive to and provided higher subjective appraisals of video games than non-pathological gamers and non-gamers. The three studies converged to show that pathological video game use seems similar to other addictions in its patterns of correlations with other constructs. Conceptual and definitional aspects of Internet Gaming Disorder are discussed. PMID:26694472

Testing the Predictive Validity and Construct of Pathological Video Game Use

Directory of Open Access Journals (Sweden)

Christopher L. Groves

2015-12-01

Full Text Available Three studies assessed the construct of pathological video game use and tested its predictive validity. Replicating previous research, Study 1 produced evidence of convergent validity in 8th and 9th graders (N = 607 classified as pathological gamers. Study 2 replicated and extended the findings of Study 1 with college undergraduates (N = 504. Predictive validity was established in Study 3 by measuring cue reactivity to video games in college undergraduates (N = 254, such that pathological gamers were more emotionally reactive to and provided higher subjective appraisals of video games than non-pathological gamers and non-gamers. The three studies converged to show that pathological video game use seems similar to other addictions in its patterns of correlations with other constructs. Conceptual and definitional aspects of Internet Gaming Disorder are discussed.

Cross-Validation of Aerobic Capacity Prediction Models in Adolescents.

Science.gov (United States)

Burns, Ryan Donald; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Saint-Maurice, Pedro F; Welk, Greg J; Mahar, Matthew T

2015-08-01

Cardiorespiratory endurance is a component of health-related fitness. FITNESSGRAM recommends the Progressive Aerobic Cardiovascular Endurance Run (PACER) or One mile Run/Walk (1MRW) to assess cardiorespiratory endurance by estimating VO2 Peak. No research has cross-validated prediction models from both PACER and 1MRW, including the New PACER Model and PACER-Mile Equivalent (PACER-MEQ) using current standards. The purpose of this study was to cross-validate prediction models from PACER and 1MRW against measured VO2 Peak in adolescents. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years (Mean = 14.7 ± 1.3 years; 32 girls, 52 boys) who completed the PACER and 1MRW in addition to a laboratory maximal treadmill test to measure VO2 Peak. Multiple correlations among various models with measured VO2 Peak were considered moderately strong (R = .74-0.78), and prediction error (RMSE) ranged from 5.95 ml·kg⁻¹,min⁻¹ to 8.27 ml·kg⁻¹.min⁻¹. Criterion-referenced agreement into FITNESSGRAM's Healthy Fitness Zones was considered fair-to-good among models (Kappa = 0.31-0.62; Agreement = 75.5-89.9%; F = 0.08-0.65). In conclusion, prediction models demonstrated moderately strong linear relationships with measured VO2 Peak, fair prediction error, and fair-to-good criterion referenced agreement with measured VO2 Peak into FITNESSGRAM's Healthy Fitness Zones.

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

Science.gov (United States)

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

2016-01-01

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

External Validation of a Prediction Model for Successful External Cephalic Version

NARCIS (Netherlands)

de Hundt, Marcella; Vlemmix, Floortje; Kok, Marjolein; van der Steeg, Jan W.; Bais, Joke M.; Mol, Ben W.; van der Post, Joris A.

2012-01-01

We sought external validation of a prediction model for the probability of a successful external cephalic version (ECV). We evaluated the performance of the prediction model with calibration and discrimination. For clinical practice, we developed a score chart to calculate the probability of a

Predicting umbilical artery pH during labour: Development and validation of a nomogram using fetal heart rate patterns.

Science.gov (United States)

Ramanah, Rajeev; Omar, Sikiyah; Guillien, Alicia; Pugin, Aurore; Martin, Alain; Riethmuller, Didier; Mottet, Nicolas

2018-06-01

Nomograms are statistical models that combine variables to obtain the most accurate and reliable prediction for a particular risk. Fetal heart rate (FHR) interpretation alone has been found to be poorly predictive for fetal acidosis while other clinical risk factors exist. The aim of this study was to create and validate a nomogram based on FHR patterns and relevant clinical parameters to provide a non-invasive individualized prediction of umbilical artery pH during labour. A retrospective observational study was conducted on 4071 patients in labour presenting singleton pregnancies at >34 gestational weeks and delivering vaginally. Clinical characteristics, FHR patterns and umbilical cord gas of 1913 patients were used to construct a nomogram predicting an umbilical artery (Ua) pH <7.18 (10th centile of the study population) after an univariate and multivariate stepwise logistic regression analysis. External validation was obtained from an independent cohort of 2158 patients. Area under the receiver operating characteristics (ROC) curve, sensitivity, specificity, positive and negative predictive values of the nomogram were determined. Upon multivariate analysis, parity (p < 0.01), induction of labour (p = 0.01), a prior uterine scar (p = 0.02), maternal fever (p = 0.02) and the type of FHR (p < 0.01) were significantly associated with an Ua pH <7.18 (p < 0.05). Apgar score at 1, 5 and 10 min were significantly lower in the group with an Ua pH <7.18 (p < 0.01). The nomogram constructed had a Concordance Index of 0.75 (area under the curve) with a sensitivity of 57%, a specificity of 91%, a negative predictive value of 5% and a positive predictive value of 99%. Calibration found no difference between the predicted probabilities and the observed rate of Ua pH <7.18 (p = 0.63). The validation set had a Concordance Index of 0.72 and calibration with a p < 0.77. We successfully developed and validated a nomogram to predict Ua pH by

Validating spatiotemporal predictions of an important pest of small grains.

Science.gov (United States)

Merrill, Scott C; Holtzer, Thomas O; Peairs, Frank B; Lester, Philip J

2015-01-01

Arthropod pests are typically managed using tactics applied uniformly to the whole field. Precision pest management applies tactics under the assumption that within-field pest pressure differences exist. This approach allows for more precise and judicious use of scouting resources and management tactics. For example, a portion of a field delineated as attractive to pests may be selected to receive extra monitoring attention. Likely because of the high variability in pest dynamics, little attention has been given to developing precision pest prediction models. Here, multimodel synthesis was used to develop a spatiotemporal model predicting the density of a key pest of wheat, the Russian wheat aphid, Diuraphis noxia (Kurdjumov). Spatially implicit and spatially explicit models were synthesized to generate spatiotemporal pest pressure predictions. Cross-validation and field validation were used to confirm model efficacy. A strong within-field signal depicting aphid density was confirmed with low prediction errors. Results show that the within-field model predictions will provide higher-quality information than would be provided by traditional field scouting. With improvements to the broad-scale model component, the model synthesis approach and resulting tool could improve pest management strategy and provide a template for the development of spatially explicit pest pressure models. © 2014 Society of Chemical Industry.

Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial.

Science.gov (United States)

Collis, Joe; Connor, Anthony J; Paczkowski, Marcin; Kannan, Pavitra; Pitt-Francis, Joe; Byrne, Helen M; Hubbard, Matthew E

2017-04-01

In this work, we present a pedagogical tumour growth example, in which we apply calibration and validation techniques to an uncertain, Gompertzian model of tumour spheroid growth. The key contribution of this article is the discussion and application of these methods (that are not commonly employed in the field of cancer modelling) in the context of a simple model, whose deterministic analogue is widely known within the community. In the course of the example, we calibrate the model against experimental data that are subject to measurement errors, and then validate the resulting uncertain model predictions. We then analyse the sensitivity of the model predictions to the underlying measurement model. Finally, we propose an elementary learning approach for tuning a threshold parameter in the validation procedure in order to maximize predictive accuracy of our validated model.

Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

Science.gov (United States)

Daetwyler, Hans D.; Calus, Mario P. L.; Pong-Wong, Ricardo; de los Campos, Gustavo; Hickey, John M.

2013-01-01

The genomic prediction of phenotypes and breeding values in animals and plants has developed rapidly into its own research field. Results of genomic prediction studies are often difficult to compare because data simulation varies, real or simulated data are not fully described, and not all relevant results are reported. In addition, some new methods have been compared only in limited genetic architectures, leading to potentially misleading conclusions. In this article we review simulation procedures, discuss validation and reporting of results, and apply benchmark procedures for a variety of genomic prediction methods in simulated and real example data. Plant and animal breeding programs are being transformed by the use of genomic data, which are becoming widely available and cost-effective to predict genetic merit. A large number of genomic prediction studies have been published using both simulated and real data. The relative novelty of this area of research has made the development of scientific conventions difficult with regard to description of the real data, simulation of genomes, validation and reporting of results, and forward in time methods. In this review article we discuss the generation of simulated genotype and phenotype data, using approaches such as the coalescent and forward in time simulation. We outline ways to validate simulated data and genomic prediction results, including cross-validation. The accuracy and bias of genomic prediction are highlighted as performance indicators that should be reported. We suggest that a measure of relatedness between the reference and validation individuals be reported, as its impact on the accuracy of genomic prediction is substantial. A large number of methods were compared in example simulated and real (pine and wheat) data sets, all of which are publicly available. In our limited simulations, most methods performed similarly in traits with a large number of quantitative trait loci (QTL), whereas in traits

Predicting the ungauged basin : Model validation and realism assessment

NARCIS (Netherlands)

Van Emmerik, T.H.M.; Mulder, G.; Eilander, D.; Piet, M.; Savenije, H.H.G.

2015-01-01

The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of

Predicting the ungauged basin: model validation and realism assessment

NARCIS (Netherlands)

van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert

2015-01-01

The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

Science.gov (United States)

Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

2017-10-01

Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (PLearning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that

Validating Inertial Confinement Fusion (ICF) predictive capability using perturbed capsules

Science.gov (United States)

Schmitt, Mark; Magelssen, Glenn; Tregillis, Ian; Hsu, Scott; Bradley, Paul; Dodd, Evan; Cobble, James; Flippo, Kirk; Offerman, Dustin; Obrey, Kimberly; Wang, Yi-Ming; Watt, Robert; Wilke, Mark; Wysocki, Frederick; Batha, Steven

2009-11-01

Achieving ignition on NIF is a monumental step on the path toward utilizing fusion as a controlled energy source. Obtaining robust ignition requires accurate ICF models to predict the degradation of ignition caused by heterogeneities in capsule construction and irradiation. LANL has embarked on a project to induce controlled defects in capsules to validate our ability to predict their effects on fusion burn. These efforts include the validation of feature-driven hydrodynamics and mix in a convergent geometry. This capability is needed to determine the performance of capsules imploded under less-than-optimum conditions on future IFE facilities. LANL's recently initiated Defect Implosion Experiments (DIME) conducted at Rochester's Omega facility are providing input for these efforts. Recent simulation and experimental results will be shown.

Predictive Validity of Curriculum-Based Measures for English Learners at Varying English Proficiency Levels

Science.gov (United States)

Kim, Jennifer Sun; Vanderwood, Michael L.; Lee, Catherine Y.

2016-01-01

This study examined the predictive validity of curriculum-based measures in reading for Spanish-speaking English learners (ELs) at various levels of English proficiency. Third-grade Spanish-speaking EL students were screened during the fall using DIBELS Oral Reading Fluency (DORF) and Daze. Predictive validity was examined in relation to spring…

Predictive validity of the Biomedical Admissions Test: an evaluation and case study.

Science.gov (United States)

McManus, I C; Ferguson, Eamonn; Wakeford, Richard; Powis, David; James, David

2011-01-01

There has been an increase in the use of pre-admission selection tests for medicine. Such tests need to show good psychometric properties. Here, we use a paper by Emery and Bell [2009. The predictive validity of the Biomedical Admissions Test for pre-clinical examination performance. Med Educ 43:557-564] as a case study to evaluate and comment on the reporting of psychometric data in the field of medical student selection (and the comments apply to many papers in the field). We highlight pitfalls when reliability data are not presented, how simple zero-order associations can lead to inaccurate conclusions about the predictive validity of a test, and how biases need to be explored and reported. We show with BMAT that it is the knowledge part of the test which does all the predictive work. We show that without evidence of incremental validity it is difficult to assess the value of any selection tests for medicine.

Validation of models that predict Cesarean section after induction of labor

NARCIS (Netherlands)

Verhoeven, C. J. M.; Oudenaarden, A.; Hermus, M. A. A.; Porath, M. M.; Oei, S. G.; Mol, B. W. J.

2009-01-01

Objective Models for the prediction of Cesarean delivery after induction of labor can be used to improve clinical decision-making. The objective of this study was to validate two existing models, published by Peregrine et al. and Rane et al., for the prediction of Cesarean section after induction of

Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

Science.gov (United States)

Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

2016-11-01

Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

A robust approach to QMU, validation, and conservative prediction.

Energy Technology Data Exchange (ETDEWEB)

Segalman, Daniel Joseph; Paez, Thomas Lee; Bauman, Lara E

2013-01-01

A systematic approach to defining margin in a manner that incorporates statistical information and accommodates data uncertainty, but does not require assumptions about specific forms of the tails of distributions is developed. This approach extends to calculations underlying validation assessment and quantitatively conservative predictions.

A new model using routinely available clinical parameters to predict significant liver fibrosis in chronic hepatitis B.

Directory of Open Access Journals (Sweden)

Wai-Kay Seto

Full Text Available OBJECTIVE: We developed a predictive model for significant fibrosis in chronic hepatitis B (CHB based on routinely available clinical parameters. METHODS: 237 treatment-naïve CHB patients [58.4% hepatitis B e antigen (HBeAg-positive] who had undergone liver biopsy were randomly divided into two cohorts: training group (n = 108 and validation group (n = 129. Liver histology was assessed for fibrosis. All common demographics, viral serology, viral load and liver biochemistry were analyzed. RESULTS: Based on 12 available clinical parameters (age, sex, HBeAg status, HBV DNA, platelet, albumin, bilirubin, ALT, AST, ALP, GGT and AFP, a model to predict significant liver fibrosis (Ishak fibrosis score ≥3 was derived using the five best parameters (age, ALP, AST, AFP and platelet. Using the formula log(index+1 = 0.025+0.0031(age+0.1483 log(ALP+0.004 log(AST+0.0908 log(AFP+1-0.028 log(platelet, the PAPAS (Platelet/Age/Phosphatase/AFP/AST index predicts significant fibrosis with an area under the receiving operating characteristics (AUROC curve of 0.776 [0.797 for patients with ALT <2×upper limit of normal (ULN] The negative predictive value to exclude significant fibrosis was 88.4%. This predictive power is superior to other non-invasive models using common parameters, including the AST/platelet/GGT/AFP (APGA index, AST/platelet ratio index (APRI, and the FIB-4 index (AUROC of 0.757, 0.708 and 0.723 respectively. Using the PAPAS index, 67.5% of liver biopsies for patients being considered for treatment with ALT <2×ULN could be avoided. CONCLUSION: The PAPAS index can predict and exclude significant fibrosis, and may reduce the need for liver biopsy in CHB patients.

Preventing patient absenteeism: validation of a predictive overbooking model.

Science.gov (United States)

Reid, Mark W; Cohen, Samuel; Wang, Hank; Kaung, Aung; Patel, Anish; Tashjian, Vartan; Williams, Demetrius L; Martinez, Bibiana; Spiegel, Brennan M R

2015-12-01

To develop a model that identifies patients at high risk for missing scheduled appointments ("no-shows" and cancellations) and to project the impact of predictive overbooking in a gastrointestinal endoscopy clinic-an exemplar resource-intensive environment with a high no-show rate. We retrospectively developed an algorithm that uses electronic health record (EHR) data to identify patients who do not show up to their appointments. Next, we prospectively validated the algorithm at a Veterans Administration healthcare network clinic. We constructed a multivariable logistic regression model that assigned a no-show risk score optimized by receiver operating characteristic curve analysis. Based on these scores, we created a calendar of projected open slots to offer to patients and compared the daily performance of predictive overbooking with fixed overbooking and typical "1 patient, 1 slot" scheduling. Data from 1392 patients identified several predictors of no-show, including previous absenteeism, comorbid disease burden, and current diagnoses of mood and substance use disorders. The model correctly classified most patients during the development (area under the curve [AUC] = 0.80) and validation phases (AUC = 0.75). Prospective testing in 1197 patients found that predictive overbooking averaged 0.51 unused appointments per day versus 6.18 for typical booking (difference = -5.67; 95% CI, -6.48 to -4.87; P < .0001). Predictive overbooking could have increased service utilization from 62% to 97% of capacity, with only rare clinic overflows. Information from EHRs can accurately predict whether patients will no-show. This method can be used to overbook appointments, thereby maximizing service utilization while staying within clinic capacity.

Incremental Validity of Personality Measures in Predicting Underwater Performance and Adaptation.

Science.gov (United States)

Colodro, Joaquín; Garcés-de-Los-Fayos, Enrique J; López-García, Juan J; Colodro-Conde, Lucía

2015-03-17

Intelligence and personality traits are currently considered effective predictors of human behavior and job performance. However, there are few studies about their relevance in the underwater environment. Data from a sample of military personnel performing scuba diving courses were analyzed with regression techniques, testing the contribution of individual differences and ascertaining the incremental validity of the personality in an environment with extreme psychophysical demands. The results confirmed the incremental validity of personality traits (ΔR 2 = .20, f 2 = .25) over the predictive contribution of general mental ability (ΔR 2 = .07, f 2 = .08) in divers' performance. Moreover, personality (R(L)2 = .34) also showed a higher validity to predict underwater adaptation than general mental ability ( R(L)2 = .09). The ROC curve indicated 86% of the maximum possible discrimination power for the prediction of underwater adaptation, AUC = .86, p personality traits as predictors of an effective response to the changing circumstances of military scuba diving. They also may improve the understanding of the behavioral effects and psychophysiological complications of diving and can also provide guidance for psychological intervention and prevention of risk in this extreme environment.

The predictive validity of ideal partner preferences: a review and meta-analysis.

Science.gov (United States)

Eastwick, Paul W; Luchies, Laura B; Finkel, Eli J; Hunt, Lucy L

2014-05-01

A central element of interdependence theory is that people have standards against which they compare their current outcomes, and one ubiquitous standard in the mating domain is the preference for particular attributes in a partner (ideal partner preferences). This article reviews research on the predictive validity of ideal partner preferences and presents a new integrative model that highlights when and why ideals succeed or fail to predict relational outcomes. Section 1 examines predictive validity by reviewing research on sex differences in the preference for physical attractiveness and earning prospects. Men and women reliably differ in the extent to which these qualities affect their romantic evaluations of hypothetical targets. Yet a new meta-analysis spanning the attraction and relationships literatures (k = 97) revealed that physical attractiveness predicted romantic evaluations with a moderate-to-strong effect size (r = ∼.40) for both sexes, and earning prospects predicted romantic evaluations with a small effect size (r = ∼.10) for both sexes. Sex differences in the correlations were small (r difference = .03) and uniformly nonsignificant. Section 2 reviews research on individual differences in ideal partner preferences, drawing from several theoretical traditions to explain why ideals predict relational evaluations at different relationship stages. Furthermore, this literature also identifies alternative measures of ideal partner preferences that have stronger predictive validity in certain theoretically sensible contexts. Finally, a discussion highlights a new framework for conceptualizing the appeal of traits, the difference between live and hypothetical interactions, and the productive interplay between mating research and broader psychological theories.

An approach to model validation and model-based prediction -- polyurethane foam case study.

Energy Technology Data Exchange (ETDEWEB)

Dowding, Kevin J.; Rutherford, Brian Milne

2003-07-01

Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical

Validity of a Manual Soft Tissue Profile Prediction Method Following Mandibular Setback Osteotomy

OpenAIRE

Kolokitha, Olga-Elpis

2007-01-01

Objectives The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. Methods To test the validity of the manu...

Validation of a multi-objective, predictive urban traffic model

NARCIS (Netherlands)

Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.

2013-01-01

This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a

Review and evaluation of performance measures for survival prediction models in external validation settings

Directory of Open Access Journals (Sweden)

M. Shafiqur Rahman

2017-04-01

Full Text Available Abstract Background When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice. Methods An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software. Results Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell’s concordance measure which tended to increase as censoring increased. Conclusions We recommend that Uno’s concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller’s measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston’s D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive

The predictive validity of safety climate.

Science.gov (United States)

Johnson, Stephen E

2007-01-01

Safety professionals have increasingly turned their attention to social science for insight into the causation of industrial accidents. One social construct, safety climate, has been examined by several researchers [Cooper, M. D., & Phillips, R. A. (2004). Exploratory analysis of the safety climate and safety behavior relationship. Journal of Safety Research, 35(5), 497-512; Gillen, M., Baltz, D., Gassel, M., Kirsch, L., & Vacarro, D. (2002). Perceived safety climate, job Demands, and coworker support among union and nonunion injured construction workers. Journal of Safety Research, 33(1), 33-51; Neal, A., & Griffin, M. A. (2002). Safety climate and safety behaviour. Australian Journal of Management, 27, 66-76; Zohar, D. (2000). A group-level model of safety climate: Testing the effect of group climate on microaccidents in manufacturing jobs. Journal of Applied Psychology, 85(4), 587-596; Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group-level climates. Journal of Applied Psychology, 90(4), 616-628] who have documented its importance as a factor explaining the variation of safety-related outcomes (e.g., behavior, accidents). Researchers have developed instruments for measuring safety climate and have established some degree of psychometric reliability and validity. The problem, however, is that predictive validity has not been firmly established, which reduces the credibility of safety climate as a meaningful social construct. The research described in this article addresses this problem and provides additional support for safety climate as a viable construct and as a predictive indicator of safety-related outcomes. This study used 292 employees at three locations of a heavy manufacturing organization to complete the 16 item Zohar Safety Climate Questionnaire (ZSCQ) [Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group

Gene prediction validation and functional analysis of redundant pathways

DEFF Research Database (Denmark)

Sønderkær, Mads

2011-01-01

have employed a large mRNA-seq data set to improve and validate ab initio predicted gene models. This direct experimental evidence also provides reliable determinations of UTR regions and polyadenylation sites, which are not easily predicted in plants. Furthermore, once an annotated genome sequence...... is available, gene expression by mRNA-Seq enables acquisition of a more complete overview of gene isoform usage in complex enzymatic pathways enabling the identification of key genes. Metabolism in potatoes This information is useful e.g. for crop improvement based on manipulation of agronomically important...

A Reliable and Valid Survey to Predict a Patient’s Gagging Intensity

Directory of Open Access Journals (Sweden)

Casey M. Hearing

2014-07-01

Full Text Available Objectives: The aim of this study was to devise a reliable and valid survey to predict the intensity of someone’s gag reflex. Material and Methods: A 10-question Predictive Gagging Survey was created, refined, and tested on 59 undergraduate participants. The questions focused on risk factors and experiences that would indicate the presence and strength of someone’s gag reflex. Reliability was assessed by administering the survey to a group of 17 participants twice, with 3 weeks separating the two administrations. Finally, the survey was given to 25 dental patients. In these cases, patients completed an informed consent form, filled out the survey, and then had a maxillary impression taken while their gagging response was quantified from 1 to 5 on the Fiske and Dickinson Gagging Intensity Index. Results: There was a moderate positive correlation between the Predictive Gagging Survey and Fiske and Dickinson’s Gagging Severity Index, r = +0.64, demonstrating the survey’s validity. Furthermore, the test-retest reliability was r = +0.96, demonstrating the survey’s reliability. Conclusions: The Predictive Gagging Survey is a 10-question survey about gag-related experiences and behaviours. We established that it is a reliable and valid method to assess the strength of someone’s gag reflex.

Validations and improvements of airfoil trailing-edge noise prediction models using detailed experimental data

DEFF Research Database (Denmark)

Kamruzzaman, M.; Lutz, Th.; Würz, W.

2012-01-01

This paper describes an extensive assessment and a step by step validation of different turbulent boundary-layer trailing-edge noise prediction schemes developed within the European Union funded wind energy project UpWind. To validate prediction models, measurements of turbulent boundary-layer pr...... with measurements in the frequency region higher than 1 kHz, whereas they over-predict the sound pressure level in the low-frequency region. Copyright © 2011 John Wiley & Sons, Ltd.......-layer properties such as two-point turbulent velocity correlations, the spectra of the associated wall pressure fluctuations and the emitted trailing-edge far-field noise were performed in the laminar wind tunnel of the Institute of Aerodynamics and Gas Dynamics, University of Stuttgart. The measurements were...... carried out for a NACA 643-418 airfoil, at Re  =  2.5 ×106, angle of attack of −6° to 6°. Numerical results of different prediction schemes are extensively validated and discussed elaborately. The investigations on the TNO-Blake noise prediction model show that the numerical wall pressure fluctuation...

Multivariable prediction model for suspected giant cell arteritis: development and validation

Directory of Open Access Journals (Sweden)

Ing EB

2017-11-01

, erythrocyte sedimentation rate (ESR, C-reactive protein (CRP, and platelet level. Multiple imputation was performed for missing data. Logistic regression was used to compare our models with the non-histologic American College of Rheumatology (ACR GCA classification criteria. Internal validation was performed with 10-fold cross validation and bootstrap techniques. External validation was performed by geographic site.Results: There were 530 complete TABx records: 397 were negative and 133 positive for GCA. Age, jaw claudication, VL, platelets, and log CRP were statistically significant predictors of positive TABx, whereas ESR, gender, headache, and temporal artery abnormality were not. The parsimonious model had a cross-validated bootstrap area under the receiver operating characteristic curve (AUROC of 0.810 (95% CI =0.766–0.854, geographic external validation AUROC’s in the range of 0.75–0.85, calibration pH–L of 0.812, sensitivity of 43.6%, and specificity of 95.2%, which outperformed the ACR criteria.Conclusion: Our prediction rule with calculator and nomogram aids in the triage of patients with suspected GCA and may decrease the need for TABx in select low-score at-risk subjects. However, misclassification remains a concern.Keywords: temporal artery biopsy, diagnosis, prediction rule, nomogram, giant cell arteritis, validation

Investigating Postgraduate College Admission Interviews: Generalizability Theory Reliability and Incremental Predictive Validity

Science.gov (United States)

Arce-Ferrer, Alvaro J.; Castillo, Irene Borges

2007-01-01

The use of face-to-face interviews is controversial for college admissions decisions in light of the lack of availability of validity and reliability evidence for most college admission processes. This study investigated reliability and incremental predictive validity of a face-to-face postgraduate college admission interview with a sample of…

The reliability, validity, sensitivity, specificity and predictive values of the Chinese version of the Rowland Universal Dementia Assessment Scale.

Science.gov (United States)

Chen, Chia-Wei; Chu, Hsin; Tsai, Chia-Fen; Yang, Hui-Ling; Tsai, Jui-Chen; Chung, Min-Huey; Liao, Yuan-Mei; Chi, Mei-Ju; Chou, Kuei-Ru

2015-11-01

The purpose of this study was to translate the Rowland Universal Dementia Assessment Scale into Chinese and to evaluate the psychometric properties (reliability and validity) and the diagnostic properties (sensitivity, specificity and predictive values) of the Chinese version of the Rowland Universal Dementia Assessment Scale. The accurate detection of early dementia requires screening tools with favourable cross-cultural linguistic and appropriate sensitivity, specificity, and predictive values, particularly for Chinese-speaking populations. This was a cross-sectional, descriptive study. Overall, 130 participants suspected to have cognitive impairment were enrolled in the study. A test-retest for determining reliability was scheduled four weeks after the initial test. Content validity was determined by five experts, whereas construct validity was established by using contrasted group technique. The participants' clinical diagnoses were used as the standard in calculating the sensitivity, specificity, positive predictive value and negative predictive value. The study revealed that the Chinese version of the Rowland Universal Dementia Assessment Scale exhibited a test-retest reliability of 0.90, an internal consistency reliability of 0.71, an inter-rater reliability (kappa value) of 0.88 and a content validity index of 0.97. Both the patients and healthy contrast group exhibited significant differences in their cognitive ability. The optimal cut-off points for the Chinese version of the Rowland Universal Dementia Assessment Scale in the test for mild cognitive impairment and dementia were 24 and 22, respectively; moreover, for these two conditions, the sensitivities of the scale were 0.79 and 0.76, the specificities were 0.91 and 0.81, the areas under the curve were 0.85 and 0.78, the positive predictive values were 0.99 and 0.83 and the negative predictive values were 0.96 and 0.91 respectively. The Chinese version of the Rowland Universal Dementia Assessment Scale

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

Science.gov (United States)

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

2011-01-01

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

Development and validation of a multivariate prediction model for patients with acute pancreatitis in Intensive Care Medicine.

Science.gov (United States)

Zubia-Olaskoaga, Felix; Maraví-Poma, Enrique; Urreta-Barallobre, Iratxe; Ramírez-Puerta, María-Rosario; Mourelo-Fariña, Mónica; Marcos-Neira, María-Pilar; García-García, Miguel Ángel

2018-03-01

Development and validation of a multivariate prediction model for patients with acute pancreatitis (AP) admitted in Intensive Care Units (ICU). A prospective multicenter observational study, in 1 year period, in 46 international ICUs (EPAMI study). adults admitted to an ICU with AP and at least one organ failure. Development of a multivariate prediction model, using the worst data of the stay in ICU, based in multivariate analysis, simple imputation in a development cohort. The model was validated in another cohort. 374 patients were included (mortality of 28.9%). Variables with statistical significance in multivariate analysis were age, no alcoholic and no biliary etiology, development of shock, development of respiratory failure, need of continuous renal replacement therapy, and intra-abdominal pressure. The model created with these variables presented an AUC of ROC curve of 0.90 (CI 95% 0.81-0.94) in the validation cohort. We developed a multivariable prediction model, and AP cases could be classified as low mortality risk (between 2 and 9.5 points, mortality of 1.35%), moderate mortality risk (between 10 and 12.5 points, 28.92% of mortality), and high mortality risk (13 points of more, mortality of 88.37%). Our model presented better AUC of ROC curve than APACHE II (0.91 vs 0.80) and SOFA in the first 24 h (0.91 vs 0.79). We developed and validated a multivariate prediction model, which can be applied in any moment of the stay in ICU, with better discriminatory power than APACHE II and SOFA in the first 24 h. Copyright © 2018 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Validity of impedance-based predictions of total body water as measured by 2H dilution in African HIV/AIDS outpatients

International Nuclear Information System (INIS)

Diouf, Adama; Idohou Dossou, Nicole; Wade, Salimata; Gartner, Agnes; Sanon, Dominique Alexis; Bluck, Les; Wright, Antony

2009-01-01

Measurements of body composition are crucial in identifying HIV-infected patients at risk of malnutrition. No information is available on the validity of indirect body composition methods in African HIV-infected outpatients. Our aim was to test the validity of fifteen published equations, developed in whites, African-Americans and/or Africans who were or not HIV-infected, for predicting total body water (TBW) from bioelectrical impedance analysis (BIA) in HIV-infected patients. The second aim was to develop specific predictive equations. Thirty-four HIV-infected patients without antiretroviral treatment and oedema at the beginning of the study (age 39 (SD 7) years, BMI 18.7 (SD 3.7) kg/m2, TBW 30.4 (SD 7.2 kg) were measured at inclusion then 3 and 6 months later. In the resulting eighty-eight measurements, we compared TBW values predicted from BIA to those measured by 2H dilution. Range of bias values was 0.1-4.3, and errors showed acceptable values (2.2-3.4 kg) for fourteen equations and a high value (10.4) for one equation. Two equations developed in non-HIV-infected subjects showed non-significant bias and could be used in African HIV-infected patients. In the other cases, poor agreement indicated a lack of validity. Specific equations developed from our sample showed a higher precision of TBW prediction when using resistance at 1000kHz (1.7kg) than at 50kHz (2.3kg), this latter precision being similar to that of the valid published equations (2.3 and 2.8kg). The valid published or developed predictive equations should be cross-validated in large independent samples of African HIV-infected patients. (Authors)

Predictive validity of a three-dimensional model of performance anxiety in the context of tae-kwon-do.

Science.gov (United States)

Cheng, Wen-Nuan Kara; Hardy, Lew; Woodman, Tim

2011-02-01

We tested the predictive validity of the recently validated three-dimensional model of performance anxiety (Chang, Hardy, & Markland, 2009) with elite tae-kwon-do competitors (N = 99). This conceptual framework emphasized the adaptive potential of anxiety by including a regulatory dimension (reflected by perceived control) along with the intensity-oriented dimensions of cognitive and physiological anxiety. Anxiety was assessed 30 min before a competitive contest using the Three-Factor Anxiety Inventory. Competitors rated their performance on a tae-kwon-do-specific performance scale within 30 min after completion of their contest. Moderated hierarchical regression analyses revealed initial support for the predictive validity of the three-dimensional performance anxiety model. The regulatory dimension of anxiety (perceived control) revealed significant main and interactive effects on performance. This dimension appeared to be adaptive, as performance was better under high than low perceived control, and best vs. worst performance was associated with highest vs. lowest perceived control, respectively. Results are discussed in terms of the importance of the regulatory dimension of anxiety.

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

International Nuclear Information System (INIS)

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

2015-01-01

Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. From the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination. AUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. (orig.)

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

Energy Technology Data Exchange (ETDEWEB)

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

2015-10-15

Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. From the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination. AUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. (orig.)

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

NARCIS (Netherlands)

Onland, Wes; Debray, Thomas P.; Laughon, Matthew M.; Miedema, Martijn; Cools, Filip; Askie, Lisa M.; Asselin, Jeanette M.; Calvert, Sandra A.; Courtney, Sherry E.; Dani, Carlo; Durand, David J.; Marlow, Neil; Peacock, Janet L.; Pillow, J. Jane; Soll, Roger F.; Thome, Ulrich H.; Truffert, Patrick; Schreiber, Michael D.; van Reempts, Patrick; Vendettuoli, Valentina; Vento, Giovanni; van Kaam, Anton H.; Moons, Karel G.; Offringa, Martin

2013-01-01

Bronchopulmonary dysplasia (BPD) is a common complication of preterm birth. Very different models using clinical parameters at an early postnatal age to predict BPD have been developed with little extensive quantitative validation. The objective of this study is to review and validate clinical

External validation of a biomarker and clinical prediction model for hospital mortality in acute respiratory distress syndrome.

Science.gov (United States)

Zhao, Zhiguo; Wickersham, Nancy; Kangelaris, Kirsten N; May, Addison K; Bernard, Gordon R; Matthay, Michael A; Calfee, Carolyn S; Koyama, Tatsuki; Ware, Lorraine B

2017-08-01

Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort. The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied. The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70-0.79) in FACTT, compared to 0.72 (95% CI 0.67-0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70-0.76) when FACTT and VALID were combined. We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion.

Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model.

Science.gov (United States)

Schroeder, Emily B; Xu, Stan; Goodrich, Glenn K; Nichols, Gregory A; O'Connor, Patrick J; Steiner, John F

2017-07-01

To develop and externally validate a prediction model for the 6-month risk of a severe hypoglycemic event among individuals with pharmacologically treated diabetes. The development cohort consisted of 31,674 Kaiser Permanente Colorado members with pharmacologically treated diabetes (2007-2015). The validation cohorts consisted of 38,764 Kaiser Permanente Northwest members and 12,035 HealthPartners members. Variables were chosen that would be available in electronic health records. We developed 16-variable and 6-variable models, using a Cox counting model process that allows for the inclusion of multiple 6-month observation periods per person. Across the three cohorts, there were 850,992 6-month observation periods, and 10,448 periods with at least one severe hypoglycemic event. The six-variable model contained age, diabetes type, HgbA1c, eGFR, history of a hypoglycemic event in the prior year, and insulin use. Both prediction models performed well, with good calibration and c-statistics of 0.84 and 0.81 for the 16-variable and 6-variable models, respectively. In the external validation cohorts, the c-statistics were 0.80-0.84. We developed and validated two prediction models for predicting the 6-month risk of hypoglycemia. The 16-variable model had slightly better performance than the 6-variable model, but in some practice settings, use of the simpler model may be preferred. Copyright © 2017 Elsevier Inc. All rights reserved.

Validity of childhood adiposity classification in predicting adolescent overweight and obesity.

Science.gov (United States)

Huerta, Michael; Zarka, Salman; Bibi, Haim; Haviv, Jacob; Scharf, Shimon; Gdalevich, Michael

2010-05-03

Identification of children at risk for adolescent overweight can assist in targeting interventions. Uncertainty remains regarding the validity of current body mass index (BMI) reference values in predicting future risk on a population basis. This study aimed to assess the validity of current childhood adiposity classifications in predicting adolescent overweight and obesity among Israeli youth. Historical cohort study. School-based childhood health studies and adolescent physical examinations. A total of 3 163 subjects surveyed first at age 8-15 and again at age 17-19. Age, sex, height, weight and BMI. Sensitivity, specificity, positive and negative predictive values, and relative risk of childhood adiposity classification. Childhood overweight and obesity showed low sensitivity and high specificity for predicting adolescent overweight and obesity. Positive predictive values were low and varied by age and sex, but negative predictive values were consistently high in both sexes and all ages (range 0.85-0.99). After adjusting for age and sex, both childhood overweight and obesity substantially increased the risk of adolescent overweight (relative risk [RR] 7.03 and 7.20, respectively) and adolescent obesity (RR 24.34 and 28.41, respectively). Childhood overweight and obesity are strong risk factors for adolescent overweight and obesity among Israeli youth. Normal weight children were at very low risk for adolescent overweight. These findings suggest that population-based health promotion aimed at maintaining normal weight among children should be given preference over risk-guided approaches targeting weight reduction among obese children.

Development and validation of a predictive model for excessive postpartum blood loss: A retrospective, cohort study.

Science.gov (United States)

Rubio-Álvarez, Ana; Molina-Alarcón, Milagros; Arias-Arias, Ángel; Hernández-Martínez, Antonio

2018-03-01

postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. Despite the use of uterotonics agents as preventive measure, it remains a challenge to identify those women who are at increased risk of postpartum bleeding. to develop and to validate a predictive model to assess the risk of excessive bleeding in women with vaginal birth. retrospective cohorts study. "Mancha-Centro Hospital" (Spain). the elaboration of the predictive model was based on a derivation cohort consisting of 2336 women between 2009 and 2011. For validation purposes, a prospective cohort of 953 women between 2013 and 2014 were employed. Women with antenatal fetal demise, multiple pregnancies and gestations under 35 weeks were excluded METHODS: we used a multivariate analysis with binary logistic regression, Ridge Regression and areas under the Receiver Operating Characteristic curves to determine the predictive ability of the proposed model. there was 197 (8.43%) women with excessive bleeding in the derivation cohort and 63 (6.61%) women in the validation cohort. Predictive factors in the final model were: maternal age, primiparity, duration of the first and second stages of labour, neonatal birth weight and antepartum haemoglobin levels. Accordingly, the predictive ability of this model in the derivation cohort was 0.90 (95% CI: 0.85-0.93), while it remained 0.83 (95% CI: 0.74-0.92) in the validation cohort. this predictive model is proved to have an excellent predictive ability in the derivation cohort, and its validation in a latter population equally shows a good ability for prediction. This model can be employed to identify women with a higher risk of postpartum haemorrhage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive Validity And Usefulness Of Visual Scanning Task In Hiv ...

African Journals Online (AJOL)

The visual scanning task is a useful screening tool for brain damage in HIV/AIDS by inference from impairment of visual information processing and disturbances in perceptual mental strategies. There is progressive neuro-cognitive decline as the disease worsens. Keywords: brain, cognition, HIV/AIDS, predictive validity, ...

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

Science.gov (United States)

Ritchie, Carrie; Hendrikz, Joan; Jull, Gwendolen; Elliott, James; Sterling, Michele

2015-04-01

Retrospective secondary analysis of data. To investigate the external validity of the whiplash clinical prediction rule (CPR). We recently derived a whiplash CPR to consolidate previously established prognostic factors for poor recovery from a whiplash injury and predicted 2 recovery pathways. Prognostic factors for full recovery were being less than 35 years of age and having an initial Neck Disability Index (NDI) score of 32% or less. Prognostic factors for ongoing moderate/severe pain and disability were being 35 years of age or older, having an initial NDI score of 40% or more, and the presence of hyperarousal symptoms. Validation is required to confirm the reproducibility and accuracy of this CPR. Clinician feedback on the usefulness of the CPR is also important to gauge acceptability. A secondary analysis of data from 101 individuals with acute whiplash-associated disorder who had previously participated in either a randomized controlled clinical trial or prospective cohort study was performed using accuracy statistics. Full recovery was defined as NDI score at 6 months of 10% or less, and ongoing moderate/severe pain and disability were defined as an NDI score at 6 months of 30% or greater. In addition, a small sample of physical therapists completed an anonymous survey on the clinical acceptability and usability of the tool. Results The positive predictive value of ongoing moderate/severe pain and disability was 90.9% in the validation cohort, and the positive predictive value of full recovery was 80.0%. Surveyed physical therapists reported that the whiplash CPR was simple, understandable, would be easy to use, and was an acceptable prognostic tool. External validation of the whiplash CPR confirmed the reproducibility and accuracy of this dual-pathway tool for individuals with acute whiplash-associated disorder. Further research is needed to assess prospective validation, the impact of inclusion on practice, and to examine the efficacy of linking treatment

Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

NARCIS (Netherlands)

Maaren, M.C. van; Steenbeek, C.D. van; Pharoah, P.D.; Witteveen, A.; Sonke, G.S.; Strobbe, L.J.A.; Poortmans, P.; Siesling, S.

2017-01-01

BACKGROUND: PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. METHODS: All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected

Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

NARCIS (Netherlands)

van Maaren, M. C.; van Steenbeek, C. D.; Pharoah, P. D.P.; Witteveen, A.; Sonke, Gabe S.; Strobbe, L.J.A.; Poortmans, P.M.P.; Siesling, S.

2017-01-01

Background PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. Methods All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected from

The Predictive Validity of the ABFM's In-Training Examination.

Science.gov (United States)

O'Neill, Thomas R; Li, Zijia; Peabody, Michael R; Lybarger, Melanie; Royal, Kenneth; Puffer, James C

2015-05-01

Our objective was to examine the predictive validity of the American Board of Family Medicine's (ABFM) In-Training Examination (ITE) with regard to predicting outcomes on the ABFM certification examination. This study used a repeated measures design across three levels of medical training (PGY1--PGY2, PGY2--PGY3, and PGY3--initial certification) with three different cohorts (2010--2011, 2011--2012, and 2012--2013) to examine: (1) how well the residents' ITE scores correlated with their test scores in the following year, (2) what the typical score increase was across training years, and (3) what was the sensitivity, specificity, positive predictive value, and negative predictive value of the PGY3 scores with regard to predicting future results on the MC-FP Examination. ITE scores generally correlate at about .7 with the following year's ITE or with the following year's certification examination. The mean growth from PGY1 to PGY2 was 52 points, from PGY2 to PGY3 was 34 points, and from PGY3 to initial certification was 27 points. The sensitivity, specificity, positive predictive value, and negative predictive value were .91, .47, .96, and .27, respectively. The ITE is a useful predictor of future ITE and initial certification examination performance.

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

LENUS (Irish Health Repository)

Na, Xi

2015-04-23

Prediction of severe clinical outcomes in Clostridium difficile infection (CDI) is important to inform management decisions for optimum patient care. Currently, treatment recommendations for CDI vary based on disease severity but validated methods to predict severe disease are lacking. The aim of the study was to derive and validate a clinical prediction tool for severe outcomes in CDI.

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

Science.gov (United States)

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

2018-01-01

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

Parent- and Self-Reported Dimensions of Oppositionality in Youth: Construct Validity, Concurrent Validity, and the Prediction of Criminal Outcomes in Adulthood

Science.gov (United States)

Aebi, Marcel; Plattner, Belinda; Metzke, Christa Winkler; Bessler, Cornelia; Steinhausen, Hans-Christoph

2013-01-01

Background: Different dimensions of oppositional defiant disorder (ODD) have been found as valid predictors of further mental health problems and antisocial behaviors in youth. The present study aimed at testing the construct, concurrent, and predictive validity of ODD dimensions derived from parent- and self-report measures. Method: Confirmatory…

The predictive and discriminant validity of the zone of proximal development.

Science.gov (United States)

Meijer, J; Elshout, J J

2001-03-01

Dynamic measurement procedures are supposed to uncover the zone of proximal development and to increase predictive validity in comparison to conventional, static measurement procedures. Two alternative explanations for the discrepancies between static and dynamic measurements were investigated. The first focuses on Vygotsky's learning potential theory, the second considers the role of anxiety tendency during test taking. If test anxious tendencies are mitigated by dynamic testing procedures, in particular the availability of assistance, the concept of the zone of proximal development may be superfluous in explaining the differences between the outcomes of static and dynamic measurement. Participants were students from secondary education in the Netherlands. They were tested repeatedly in grade three as well as in grade four. Participants were between 14 and 17 years old; their average age was 15.4 years with a standard deviation of .52. Two types of mathematics tests were used in a longitudinal experiment. The first type of test consisted of open-ended items, which participants had to solve completely on their own. With the second type of test, assistance was available to participants during the test. The latter so-called learning test was conceived of as a dynamic testing procedure. Furthermore, a test anxiety questionnaire was administered repeatedly. Structural equation modelling was used to analyse the data. Apart from emotionality and worry, lack of self-confidence appears to be an important constituent of test anxiety. The learning test appears to contribute to the predictive validity of conventional tests and thus a part of Vygotsky's claims were substantiated. Moreover, the mere inclusion of a test anxiety factor into an explanatory model for the gathered data is not sufficient. Apart from test anxiety and mathematical ability it is necessary to assume a factor which may be construed as mathematics learning potential. The results indicate that the observed

Screening for frailty in community-dwelling elderly subjects: Predictive validity of the modified SEGA instrument.

Science.gov (United States)

Oubaya, N; Dramé, M; Novella, J-L; Quignard, E; Cunin, C; Jolly, D; Mahmoudi, R

2017-11-01

To study the capacity of the SEGAm instrument to predict loss of independence among elderly community-dwelling subjects. The study was performed in four French departments (Ardennes, Marne, Meurthe-et-Moselle, Meuse). Subjects aged 65 years or more, living at home, who could read and understand French, with a degree of autonomy corresponding to groups 5 or 6 in the AGGIR autonomy evaluation scale were included. Assessment included demographic characteristics, comprehensive geriatric assessment, and the SEGAm instrument at baseline. Subjects had follow-up visits at home at 6 and 12 months. During follow-up, vital status and level of independence were recorded. Logistic regression was used to study predictive validity of the SEGAm instrument. Among the 116 subjects with complete follow-up, 84 (72.4%) were classed as not very frail at baseline, 23 (19.8%) as frail, and 9 (7.8%) as very frail; 63 (54.3%) suffered loss of at least one ADL or IADL at 12 months. By multivariable analysis, frailty status at baseline was significantly associated with loss of independence during the 12 months of follow-up (OR=4.52, 95% CI=1.40-14.68; p=0.01). We previously validated the SEGAm instrument in terms of feasibility, acceptability, internal structure validity, reliability, and discriminant validity. This instrument appears to be a suitable tool for screening frailty among community-dwelling elderly subjects, and could be used as a basis to plan early targeted interventions for subjects at risk of adverse outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

Science.gov (United States)

Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

2017-05-22

PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

Bilirubin nomogram for prediction of significant hyperbilirubinemia in north Indian neonates.

Science.gov (United States)

Pathak, Umesh; Chawla, Deepak; Kaur, Saranjit; Jain, Suksham

2013-04-01

(i) To construct hour-specific serum total bilirubin (STB) nomogram in neonates born at =35 weeks of gestation; (ii)To evaluate efficacy of pre-discharge bilirubin measurement in predicting hyperbilirubinemia needing treatment. Diagnostic test performance in a prospective cohort study. Teaching hospital in Northern India. Healthy neonates with gestation =35 weeks or birth weight =2000 g. Serum total bilirubin was measured in all enrolled neonates at 24 ± 6, 72-96 and 96-144 h of postnatal age and when indicated clinically. Neonates were followed up during hospital stay and after discharge till completion of 7th postnatal day. Key outcome was significant hyperbilirubinemia (SHB) defined as need of phototherapy based on modified American Academy of Pediatrics (AAP) guidelines. In neonates born at 38 or more weeks of gestation middle line and in neonates born at 37 or less completed weeks of gestation, lower line of phototherapy thresholds were used to initiate phototherapy. For construction of nomogram, STB values were clubbed in six-hour epochs (age ± 3 hours) for postnatal age up to 48 h and twelve-hour epochs (age ± 6 hours) for age beyond 48 h. Predictive ability of the nomogram was assessed by calculating sensitivity, specificity, positive predictive value, negative predictive value and likelihood ratio, by plotting receiver-operating characteristics (ROC) curve and calculating c-statistic. 997 neonates (birth weight: 2627 ± 536 g, gestation: 37.8 ± 1.5 weeks) were enrolled, of which 931 completed followup. Among enrolled neonates 344 (34.5%) were low birth weight. Rate of exclusive breastfeeding during hospital stay was more than 80%. Bilirubin nomogram was constructed using 40th, 75th and 95th percentile values of hour-specific bilirubin. Pre-discharge STB of =95th percentile was assigned to be in high-risk zone, between 75th and 94th centile in upper-intermediate risk zone, between 40th and 74th centile in lower-intermediate risk zone and below 40th

Experimental validation of the twins prediction program for rolling noise. Pt.2: results

NARCIS (Netherlands)

Thompson, D.J.; Fodiman, P.; Mahé, H.

1996-01-01

Two extensive measurement campaigns have been carried out to validate the TWINS prediction program for rolling noise, as described in part 1 of this paper. This second part presents the experimental results of vibration and noise during train pass-bys and compares them with predictions from the

Predicting DMS-IV cluster B personality disorder criteria from MMPI-2 and Rorschach data: a test of incremental validity.

Science.gov (United States)

Blais, M A; Hilsenroth, M J; Castlebury, F; Fowler, J C; Baity, M R

2001-02-01

Despite their frequent conjoint clinical use, the incremental validity of Rorschach (Rorschach, 1921/1942) and MMPI (Hathaway & McKinley, 1943) data has not been adequately established, nor has any study to date explored the incremental validity of these tests for predicting Diagnostic and Statistical Manual of Mental Disorders (4th ed. [DSM-IV]; American Psychiatric Association, 1994) personality disorders (PDs). In a reanalysis of existing data, we used select Rorschach variables and the MMPI PD scales to predict DSM-IV antisocial, borderline, histrionic, and narcissistic PD criteria in a sample of treatment-seeking outpatients. The correlational findings revealed alimited relation between Rorschach and MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) variables, with only 5 of 30 correlations reaching significance (p psychological characteristics of the DSM-IV Cluster B PDs.

What is required for the validation of in vitro assays for predicting contaminant relative bioavailability? Considerations and criteria

International Nuclear Information System (INIS)

Juhasz, Albert L.; Basta, Nicholas T.; Smith, Euan

2013-01-01

A number of studies have shown the potential of in vitro assays to predict contaminant in vivo relative bioavailability in order to refine human health exposure assessment. Although the term ‘validated’ has been used to describe the goodness of fit between in vivo and in vitro observations, its misuse has arisen from semantic considerations in addition to the lack of defined criteria for establishing performance validation. While several internal validation methods may be utilised, performance validation should preferably focus on assessing the agreement of model predictions with a set of data which are independent of those used to construct the model. In order to achieve robust validated predictive models, a number of parameters (e.g. size of data set, source of independent soils, contaminant concentration range, animal model, relative bioavailability endpoint) need to be considered in addition to defined criteria for establishing performance validation which are currently lacking. -- Defined criteria for establishing in vivo–in vitro performance validation are required in order to ensure robust, defensible predictive models for human health exposure assessment

Clinical validation of an epigenetic assay to predict negative histopathological results in repeat prostate biopsies.

Science.gov (United States)

Partin, Alan W; Van Neste, Leander; Klein, Eric A; Marks, Leonard S; Gee, Jason R; Troyer, Dean A; Rieger-Christ, Kimberly; Jones, J Stephen; Magi-Galluzzi, Cristina; Mangold, Leslie A; Trock, Bruce J; Lance, Raymond S; Bigley, Joseph W; Van Criekinge, Wim; Epstein, Jonathan I

2014-10-01

The DOCUMENT multicenter trial in the United States validated the performance of an epigenetic test as an independent predictor of prostate cancer risk to guide decision making for repeat biopsy. Confirming an increased negative predictive value could help avoid unnecessary repeat biopsies. We evaluated the archived, cancer negative prostate biopsy core tissue samples of 350 subjects from a total of 5 urological centers in the United States. All subjects underwent repeat biopsy within 24 months with a negative (controls) or positive (cases) histopathological result. Centralized blinded pathology evaluation of the 2 biopsy series was performed in all available subjects from each site. Biopsies were epigenetically profiled for GSTP1, APC and RASSF1 relative to the ACTB reference gene using quantitative methylation specific polymerase chain reaction. Predetermined analytical marker cutoffs were used to determine assay performance. Multivariate logistic regression was used to evaluate all risk factors. The epigenetic assay resulted in a negative predictive value of 88% (95% CI 85-91). In multivariate models correcting for age, prostate specific antigen, digital rectal examination, first biopsy histopathological characteristics and race the test proved to be the most significant independent predictor of patient outcome (OR 2.69, 95% CI 1.60-4.51). The DOCUMENT study validated that the epigenetic assay was a significant, independent predictor of prostate cancer detection in a repeat biopsy collected an average of 13 months after an initial negative result. Due to its 88% negative predictive value adding this epigenetic assay to other known risk factors may help decrease unnecessary repeat prostate biopsies. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

The predictive and external validity of the STarT Back Tool in Danish primary care.

Science.gov (United States)

Morsø, Lars; Kent, Peter; Albert, Hanne B; Hill, Jonathan C; Kongsted, Alice; Manniche, Claus

2013-08-01

The STarT Back Tool (SBT) was recently translated into Danish and its concurrent validity described. This study tested the predictive validity of the Danish SBT. Danish primary care patients (n = 344) were compared to a UK cohort. SBT subgroup validity for predicting high activity limitation at 3 months' follow-up was assessed using descriptive proportions, relative risks, AUC and odds ratios. The SBT had a statistically similar predictive ability in Danish primary care as in UK primary care. Unadjusted relative risks for poor clinical outcome on activity limitation in the Danish cohort were 2.4 (1.7-3.4) for the medium-risk subgroup and 2.8 (1.8-3.8) for the high-risk subgroup versus 3.1 (2.5-3.9) and 4.5 (3.6-5.6) for the UK cohort. Adjusting for confounders appeared to explain the lower predictive ability of the Danish high-risk group. The Danish SBT distinguished between low- and medium-risk subgroups with a similar predictive ability of the UK SBT. That distinction is useful information for informing patients about their expected prognosis and may help guiding clinicians' choice of treatment. However, cross-cultural differences in the SBT psychosocial subscale may reduce the predictive ability of the high-risk subgroup in Danish primary care.

Validation of an Acoustic Impedance Prediction Model for Skewed Resonators

Science.gov (United States)

Howerton, Brian M.; Parrott, Tony L.

2009-01-01

An impedance prediction model was validated experimentally to determine the composite impedance of a series of high-aspect ratio slot resonators incorporating channel skew and sharp bends. Such structures are useful for packaging acoustic liners into constrained spaces for turbofan noise control applications. A formulation of the Zwikker-Kosten Transmission Line (ZKTL) model, incorporating the Richards correction for rectangular channels, is used to calculate the composite normalized impedance of a series of six multi-slot resonator arrays with constant channel length. Experimentally, acoustic data was acquired in the NASA Langley Normal Incidence Tube over the frequency range of 500 to 3500 Hz at 120 and 140 dB OASPL. Normalized impedance was reduced using the Two-Microphone Method for the various combinations of channel skew and sharp 90o and 180o bends. Results show that the presence of skew and/or sharp bends does not significantly alter the impedance of a slot resonator as compared to a straight resonator of the same total channel length. ZKTL predicts the impedance of such resonators very well over the frequency range of interest. The model can be used to design arrays of slot resonators that can be packaged into complex geometries heretofore unsuitable for effective acoustic treatment.

Development and validation of a predictive equation for lean body mass in children and adolescents.

Science.gov (United States)

Foster, Bethany J; Platt, Robert W; Zemel, Babette S

2012-05-01

Lean body mass (LBM) is not easy to measure directly in the field or clinical setting. Equations to predict LBM from simple anthropometric measures, which account for the differing contributions of fat and lean to body weight at different ages and levels of adiposity, would be useful to both human biologists and clinicians. To develop and validate equations to predict LBM in children and adolescents across the entire range of the adiposity spectrum. Dual energy X-ray absorptiometry was used to measure LBM in 836 healthy children (437 females) and linear regression was used to develop sex-specific equations to estimate LBM from height, weight, age, body mass index (BMI) for age z-score and population ancestry. Equations were validated using bootstrapping methods and in a local independent sample of 332 children and in national data collected by NHANES. The mean difference between measured and predicted LBM was - 0.12% (95% limits of agreement - 11.3% to 8.5%) for males and - 0.14% ( - 11.9% to 10.9%) for females. Equations performed equally well across the entire adiposity spectrum, as estimated by BMI z-score. Validation indicated no over-fitting. LBM was predicted within 5% of measured LBM in the validation sample. The equations estimate LBM accurately from simple anthropometric measures.

DSM-5 antisocial personality disorder: predictive validity in a prison sample.

Science.gov (United States)

Edens, John F; Kelley, Shannon E; Lilienfeld, Scott O; Skeem, Jennifer L; Douglas, Kevin S

2015-04-01

Symptoms of antisocial personality disorder (ASPD), particularly remorselessness, are frequently introduced in legal settings as a risk factor for future violence in prison, despite a paucity of research on the predictive validity of this disorder. We examined whether an ASPD diagnosis or symptom-criteria counts could prospectively predict any form of institutional misconduct, as well as aggressive and violent infractions among newly admitted prisoners. Adult male (n = 298) and female (n = 55) offenders were recruited from 4 prison systems across the United States. At the time of study enrollment, diagnostic information was collected using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997) supplemented by a detailed review of official records. Disciplinary records were obtained from inmates' respective prisons covering a 1-year period following study enrollment and misconduct was categorized hierarchically as any (general), aggressive (verbal/physical), or violent (physical). Dichotomous ASPD diagnoses and adult symptom-criteria counts did not significantly predict institutional misconduct across our 3 outcome variables, with effect sizes being close to 0 in magnitude. The symptom of remorselessness in particular showed no relation to future misconduct in prison. Childhood symptom counts of conduct disorder demonstrated modest predictive utility. Our results offer essentially no support for the claim that ASPD diagnoses can predict institutional misconduct in prison, regardless of the number of adult symptoms present. In forensic contexts, testimony that an ASPD diagnosis identifies defendants who will pose a serious threat while incarcerated in prison presently lacks any substantial scientific foundation. (c) 2015 APA, all rights reserved).

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

Science.gov (United States)

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

2018-05-01

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

The Reliability and Predictive Validity of the Stalking Risk Profile.

Science.gov (United States)

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

2018-03-01

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

Development and validation of an ICD-10-based disability predictive index for patients admitted to hospitals with trauma.

Science.gov (United States)

Wada, Tomoki; Yasunaga, Hideo; Yamana, Hayato; Matsui, Hiroki; Fushimi, Kiyohide; Morimura, Naoto

2018-03-01

There was no established disability predictive measurement for patients with trauma that could be used in administrative claims databases. The aim of the present study was to develop and validate a diagnosis-based disability predictive index for severe physical disability at discharge using the International Classification of Diseases, 10th revision (ICD-10) coding. This retrospective observational study used the Diagnosis Procedure Combination database in Japan. Patients who were admitted to hospitals with trauma and discharged alive from 01 April 2010 to 31 March 2015 were included. Pediatric patients under 15 years old were excluded. Data for patients admitted to hospitals from 01 April 2010 to 31 March 2013 was used for development of a disability predictive index (derivation cohort), while data for patients admitted to hospitals from 01 April 2013 to 31 March 2015 was used for the internal validation (validation cohort). The outcome of interest was severe physical disability defined as the Barthel Index score of predictive index for each patient was defined as the sum of the scores. The predictive performance of the index was validated using the receiver operating characteristic curve analysis in the validation cohort. The derivation cohort included 1,475,158 patients, while the validation cohort included 939,659 patients. Of the 939,659 patients, 235,382 (25.0%) were discharged with severe physical disability. The c-statistics of the disability predictive index was 0.795 (95% confidence interval [CI] 0.794-0.795), while that of a model using the disability predictive index and patient baseline characteristics was 0.856 (95% CI 0.855-0.857). Severe physical disability at discharge may be well predicted with patient age, sex, CCI score, and the diagnosis-based disability predictive index in patients admitted to hospitals with trauma. Copyright © 2018 Elsevier Ltd. All rights reserved.

The predictive validity of selection for entry into postgraduate training in general practice: evidence from three longitudinal studies.

Science.gov (United States)

Patterson, Fiona; Lievens, Filip; Kerrin, Máire; Munro, Neil; Irish, Bill

2013-11-01

The selection methodology for UK general practice is designed to accommodate several thousand applicants per year and targets six core attributes identified in a multi-method job-analysis study To evaluate the predictive validity of selection methods for entry into postgraduate training, comprising a clinical problem-solving test, a situational judgement test, and a selection centre. A three-part longitudinal predictive validity study of selection into training for UK general practice. In sample 1, participants were junior doctors applying for training in general practice (n = 6824). In sample 2, participants were GP registrars 1 year into training (n = 196). In sample 3, participants were GP registrars sitting the licensing examination after 3 years, at the end of training (n = 2292). The outcome measures include: assessor ratings of performance in a selection centre comprising job simulation exercises (sample 1); supervisor ratings of trainee job performance 1 year into training (sample 2); and licensing examination results, including an applied knowledge examination and a 12-station clinical skills objective structured clinical examination (OSCE; sample 3). Performance ratings at selection predicted subsequent supervisor ratings of job performance 1 year later. Selection results also significantly predicted performance on both the clinical skills OSCE and applied knowledge examination for licensing at the end of training. In combination, these longitudinal findings provide good evidence of the predictive validity of the selection methods, and are the first reported for entry into postgraduate training. Results show that the best predictor of work performance and training outcomes is a combination of a clinical problem-solving test, a situational judgement test, and a selection centre. Implications for selection methods for all postgraduate specialties are considered.

A Validation of Subchannel Based CHF Prediction Model for Rod Bundles

International Nuclear Information System (INIS)

Hwang, Dae-Hyun; Kim, Seong-Jin

2015-01-01

A large number of CHF data base were procured from various sources which included square and non-square lattice test bundles. CHF prediction accuracy was evaluated for various models including CHF lookup table method, empirical correlations, and phenomenological DNB models. The parametric effect of the mass velocity and unheated wall has been investigated from the experimental result, and incorporated into the development of local parameter CHF correlation applicable to APWR conditions. According to the CHF design criterion, the CHF should not occur at the hottest rod in the reactor core during normal operation and anticipated operational occurrences with at least a 95% probability at a 95% confidence level. This is accomplished by assuring that the minimum DNBR (Departure from Nucleate Boiling Ratio) in the reactor core is greater than the limit DNBR which accounts for the accuracy of CHF prediction model. The limit DNBR can be determined from the inverse of the lower tolerance limit of M/P that is evaluated from the measured-to-predicted CHF ratios for the relevant CHF data base. It is important to evaluate an adequacy of the CHF prediction model for application to the actual reactor core conditions. Validation of CHF prediction model provides the degree of accuracy inferred from the comparison of solution and data. To achieve a required accuracy for the CHF prediction model, it may be necessary to calibrate the model parameters by employing the validation results. If the accuracy of the model is acceptable, then it is applied to the real complex system with the inferred accuracy of the model. In a conventional approach, the accuracy of CHF prediction model was evaluated from the M/P statistics for relevant CHF data base, which was evaluated by comparing the nominal values of the predicted and measured CHFs. The experimental uncertainty for the CHF data was not considered in this approach to determine the limit DNBR. When a subchannel based CHF prediction model

Test-Retest Reliability and Predictive Validity of the Implicit Association Test in Children

Science.gov (United States)

Rae, James R.; Olson, Kristina R.

2018-01-01

The Implicit Association Test (IAT) is increasingly used in developmental research despite minimal evidence of whether children's IAT scores are reliable across time or predictive of behavior. When test-retest reliability and predictive validity have been assessed, the results have been mixed, and because these studies have differed on many…

Developing and validating a model to predict the success of an IHCS implementation: the Readiness for Implementation Model

Science.gov (United States)

Gustafson, David H; Hawkins, Robert P; Brennan, Patricia F; Dinauer, Susan; Johnson, Pauley R; Siegler, Tracy

2010-01-01

Objective To develop and validate the Readiness for Implementation Model (RIM). This model predicts a healthcare organization's potential for success in implementing an interactive health communication system (IHCS). The model consists of seven weighted factors, with each factor containing five to seven elements. Design Two decision-analytic approaches, self-explicated and conjoint analysis, were used to measure the weights of the RIM with a sample of 410 experts. The RIM model with weights was then validated in a prospective study of 25 IHCS implementation cases. Measurements Orthogonal main effects design was used to develop 700 conjoint-analysis profiles, which varied on seven factors. Each of the 410 experts rated the importance and desirability of the factors and their levels, as well as a set of 10 different profiles. For the prospective 25-case validation, three time-repeated measures of the RIM scores were collected for comparison with the implementation outcomes. Results Two of the seven factors, ‘organizational motivation’ and ‘meeting user needs,’ were found to be most important in predicting implementation readiness. No statistically significant difference was found in the predictive validity of the two approaches (self-explicated and conjoint analysis). The RIM was a better predictor for the 1-year implementation outcome than the half-year outcome. Limitations The expert sample, the order of the survey tasks, the additive model, and basing the RIM cut-off score on experience are possible limitations of the study. Conclusion The RIM needs to be empirically evaluated in institutions adopting IHCS and sustaining the system in the long term. PMID:20962135

Validating computational predictions of night-time ventilation in Stanford's Y2E2 building

Science.gov (United States)

Chen, Chen; Lamberti, Giacomo; Gorle, Catherine

2017-11-01

Natural ventilation can significantly reduce building energy consumption, but robust design is a challenging task. We previously presented predictions of natural ventilation performance in Stanford's Y2E2 building using two models with different levels of fidelity, embedded in an uncertainty quantification framework to identify the dominant uncertain parameters and predict quantified confidence intervals. The results showed a slightly high cooling rate for the volume-averaged temperature, and the initial thermal mass temperature and window discharge coefficients were found to have an important influence on the results. To further investigate the potential role of these parameters on the observed discrepancies, the current study is performing additional measurements in the Y2E2 building. Wall temperatures are recorded throughout the nightflush using thermocouples; flow rates through windows are measured using hotwires; and spatial variability in the air temperature is explored. The measured wall temperatures are found the be within the range of our model assumptions, and the measured velocities agree reasonably well with our CFD predications. Considerable local variations in the indoor air temperature have been recorded, largely explaining the discrepancies in our earlier validation study. Future work will therefore focus on a local validation of the CFD results with the measurements. Center for Integrated Facility Engineering (CIFE).

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Science.gov (United States)

Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin

2015-01-01

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. PMID:26590280

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).

Science.gov (United States)

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.

Prediction of prostate cancer in unscreened men: external validation of a risk calculator.

Science.gov (United States)

van Vugt, Heidi A; Roobol, Monique J; Kranse, Ries; Määttänen, Liisa; Finne, Patrik; Hugosson, Jonas; Bangma, Chris H; Schröder, Fritz H; Steyerberg, Ewout W

2011-04-01

Prediction models need external validation to assess their value beyond the setting where the model was derived from. To assess the external validity of the European Randomized study of Screening for Prostate Cancer (ERSPC) risk calculator (www.prostatecancer-riskcalculator.com) for the probability of having a positive prostate biopsy (P(posb)). The ERSPC risk calculator was based on data of the initial screening round of the ERSPC section Rotterdam and validated in 1825 and 531 men biopsied at the initial screening round in the Finnish and Swedish sections of the ERSPC respectively. P(posb) was calculated using serum prostate specific antigen (PSA), outcome of digital rectal examination (DRE), transrectal ultrasound and ultrasound assessed prostate volume. The external validity was assessed for the presence of cancer at biopsy by calibration (agreement between observed and predicted outcomes), discrimination (separation of those with and without cancer), and decision curves (for clinical usefulness). Prostate cancer was detected in 469 men (26%) of the Finnish cohort and in 124 men (23%) of the Swedish cohort. Systematic miscalibration was present in both cohorts (mean predicted probability 34% versus 26% observed, and 29% versus 23% observed, both pscreened men, but overestimated the risk of a positive biopsy. Further research is necessary to assess the performance and applicability of the ERSPC risk calculator when a clinical setting is considered rather than a screening setting. Copyright © 2010 Elsevier Ltd. All rights reserved.

Development and validation of multivariable predictive model for thromboembolic events in lymphoma patients.

Science.gov (United States)

Antic, Darko; Milic, Natasa; Nikolovski, Srdjan; Todorovic, Milena; Bila, Jelena; Djurdjevic, Predrag; Andjelic, Bosko; Djurasinovic, Vladislava; Sretenovic, Aleksandra; Vukovic, Vojin; Jelicic, Jelena; Hayman, Suzanne; Mihaljevic, Biljana

2016-10-01

Lymphoma patients are at increased risk of thromboembolic events but thromboprophylaxis in these patients is largely underused. We sought to develop and validate a simple model, based on individual clinical and laboratory patient characteristics that would designate lymphoma patients at risk for thromboembolic event. The study population included 1,820 lymphoma patients who were treated in the Lymphoma Departments at the Clinics of Hematology, Clinical Center of Serbia and Clinical Center Kragujevac. The model was developed using data from a derivation cohort (n = 1,236), and further assessed in the validation cohort (n = 584). Sixty-five patients (5.3%) in the derivation cohort and 34 (5.8%) patients in the validation cohort developed thromboembolic events. The variables independently associated with risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m(2) , reduced mobility, extranodal localization, development of neutropenia and hemoglobin level 3). For patients classified at risk (intermediate and high-risk scores), the model produced negative predictive value of 98.5%, positive predictive value of 25.1%, sensitivity of 75.4%, and specificity of 87.5%. A high-risk score had positive predictive value of 65.2%. The diagnostic performance measures retained similar values in the validation cohort. Developed prognostic Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis in cancer patients. Am. J. Hematol. 91:1014-1019, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Validation of Skeletal Muscle cis-Regulatory Module Predictions Reveals Nucleotide Composition Bias in Functional Enhancers

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Kwon, Andrew T.; Chou, Alice Yi; Arenillas, David J.; Wasserman, Wyeth W.

2011-01-01

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions. PMID:22144875

Validation of skeletal muscle cis-regulatory module predictions reveals nucleotide composition bias in functional enhancers.

Directory of Open Access Journals (Sweden)

Andrew T Kwon

2011-12-01

Full Text Available We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions.

Broadband Fan Noise Prediction System for Turbofan Engines. Volume 3; Validation and Test Cases

Science.gov (United States)

Morin, Bruce L.

2010-01-01

Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the third volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User s Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by validation studies that were done on three fan rigs. It concludes with recommended improvements and additional studies for BFaNS.

Discrete fracture modelling for the Stripa tracer validation experiment predictions

International Nuclear Information System (INIS)

Dershowitz, W.; Wallmann, P.

1992-02-01

Groundwater flow and transport through three-dimensional networks of discrete fractures was modeled to predict the recovery of tracer from tracer injection experiments conducted during phase 3 of the Stripa site characterization and validation protect. Predictions were made on the basis of an updated version of the site scale discrete fracture conceptual model used for flow predictions and preliminary transport modelling. In this model, individual fractures were treated as stochastic features described by probability distributions of geometric and hydrologic properties. Fractures were divided into three populations: Fractures in fracture zones near the drift, non-fracture zone fractures within 31 m of the drift, and fractures in fracture zones over 31 meters from the drift axis. Fractures outside fracture zones are not modelled beyond 31 meters from the drift axis. Transport predictions were produced using the FracMan discrete fracture modelling package for each of five tracer experiments. Output was produced in the seven formats specified by the Stripa task force on fracture flow modelling. (au)

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

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

2017-04-01

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

Validation of three noninvasive laboratory variables to predict significant fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia

International Nuclear Information System (INIS)

Ado, Ayman A.; Al-Swat, Khalid; Azzam, N.; Al-Faleh, Faleh; Ahmed, S.

2007-01-01

We tested the clinical utility of the platelet count, aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio, and the AST to platelet ratio index (APRI) score in predicting the presence or absence of advanced fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia. Liver biopsy procedures performed on chronic hepatitis C patients in our gastroenterology unit at King Khalid University Hospital were traced form records between 1998 to 2003. The hospital computer database was then accessed and detailed laboratory parameters obtained. By plotting receiver operating characteristic curves (ROC), three selected models (platelet count, AST/ALT ratio and the APRI score) were compared in terms of the best variable to predict significant fibrosis. Two hundred and forty-six patients with hepatitis C were included in this analysis. Overall, 26% of patients had advanced fibrosis. When comparing the three above mentioned prediction models, APRI score was the one associated with the highest area under the curve (AUC) = 0.812 (95%Cl, 0.756-0.868) on the ROC curves, compared to the platelet count and AST/ALT ratio, which yielded an AUC of 0.783 (0.711-0.855) and 0.716 (0.642-0.789), respectively. The APRI score seemed to be the best predictive variable for the presence or absence of advanced fibrosis in Saudi hepatitis C patients. (author)

Job Embeddedness Demonstrates Incremental Validity When Predicting Turnover Intentions for Australian University Employees

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Heritage, Brody; Gilbert, Jessica M.; Roberts, Lynne D.

2016-01-01

Job embeddedness is a construct that describes the manner in which employees can be enmeshed in their jobs, reducing their turnover intentions. Recent questions regarding the properties of quantitative job embeddedness measures, and their predictive utility, have been raised. Our study compared two competing reflective measures of job embeddedness, examining their convergent, criterion, and incremental validity, as a means of addressing these questions. Cross-sectional quantitative data from 246 Australian university employees (146 academic; 100 professional) was gathered. Our findings indicated that the two compared measures of job embeddedness were convergent when total scale scores were examined. Additionally, job embeddedness was capable of demonstrating criterion and incremental validity, predicting unique variance in turnover intention. However, this finding was not readily apparent with one of the compared job embeddedness measures, which demonstrated comparatively weaker evidence of validity. We discuss the theoretical and applied implications of these findings, noting that job embeddedness has a complementary place among established determinants of turnover intention. PMID:27199817

Validity of a manual soft tissue profile prediction method following mandibular setback osteotomy.

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Kolokitha, Olga-Elpis

2007-10-01

The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication.

The Predictive Validity of CBM Writing Indices for Eighth-Grade Students

Science.gov (United States)

Amato, Janelle M.; Watkins, Marley W.

2011-01-01

Curriculum-based measurement (CBM) is an alternative to traditional assessment techniques. Technical work has begun to identify CBM writing indices that are psychometrically sound for monitoring older students' writing proficiency. This study examined the predictive validity of CBM writing indices in a sample of 447 eighth-grade students.…

Computational prediction and experimental validation of Ciona intestinalis microRNA genes

Directory of Open Access Journals (Sweden)

Pasquinelli Amy E

2007-11-01

Full Text Available Abstract Background This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development. Results In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling. Conclusion The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.

Can preventable adverse events be predicted among hospitalized older patients? The development and validation of a predictive model.

NARCIS (Netherlands)

Steeg, L. van de; Langelaan, M.; Wagner, C.

2014-01-01

Objective: To develop and validate a predictive model for preventable adverse events (AEs) in hospitalized older patients, using clinically important risk factors that are readily available on admission. Design: Data from two retrospective patient record review studies on AEs were used. Risk factors

Predictive Validity of a Student Self-Report Screener of Behavioral and Emotional Risk in an Urban High School

Science.gov (United States)

Dowdy, Erin; Harrell-Williams, Leigh; Dever, Bridget V.; Furlong, Michael J.; Moore, Stephanie; Raines, Tara; Kamphaus, Randy W.

2016-01-01

Increasingly, schools are implementing school-based screening for risk of behavioral and emotional problems; hence, foundational evidence supporting the predictive validity of screening instruments is important to assess. This study examined the predictive validity of the Behavior Assessment System for Children-2 Behavioral and Emotional Screening…

Geographic and temporal validity of prediction models: Different approaches were useful to examine model performance

NARCIS (Netherlands)

P.C. Austin (Peter); D. van Klaveren (David); Y. Vergouwe (Yvonne); D. Nieboer (Daan); D.S. Lee (Douglas); E.W. Steyerberg (Ewout)

2016-01-01

textabstractObjective: Validation of clinical prediction models traditionally refers to the assessment of model performance in new patients. We studied different approaches to geographic and temporal validation in the setting of multicenter data from two time periods. Study Design and Setting: We

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields.

Science.gov (United States)

Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin; Strawn, Laura K

2016-02-01

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

An assessment of the validity of inelastic design analysis methods by comparisons of predictions with test results

International Nuclear Information System (INIS)

Corum, J.M.; Clinard, J.A.; Sartory, W.K.

1976-01-01

The use of computer programs that employ relatively complex constitutive theories and analysis procedures to perform inelastic design calculations on fast reactor system components introduces questions of validation and acceptance of the analysis results. We may ask ourselves, ''How valid are the answers.'' These questions, in turn, involve the concepts of verification of computer programs as well as qualification of the computer programs and of the underlying constitutive theories and analysis procedures. This paper addresses the latter - the qualification of the analysis methods for inelastic design calculations. Some of the work underway in the United States to provide the necessary information to evaluate inelastic analysis methods and computer programs is described, and typical comparisons of analysis predictions with inelastic structural test results are presented. It is emphasized throughout that rather than asking ourselves how valid, or correct, are the analytical predictions, we might more properly question whether or not the combination of the predictions and the associated high-temperature design criteria leads to an acceptable level of structural integrity. It is believed that in this context the analysis predictions are generally valid, even though exact correlations between predictions and actual behavior are not obtained and cannot be expected. Final judgment, however, must be reserved for the design analyst in each specific case. (author)

Predictive validity of driving-simulator assessments following traumatic brain injury: a preliminary study.

Science.gov (United States)

Lew, Henry L; Poole, John H; Lee, Eun Ha; Jaffe, David L; Huang, Hsiu-Chen; Brodd, Edward

2005-03-01

To evaluate whether driving simulator and road test evaluations can predict long-term driving performance, we conducted a prospective study on 11 patients with moderate to severe traumatic brain injury. Sixteen healthy subjects were also tested to provide normative values on the simulator at baseline. At their initial evaluation (time-1), subjects' driving skills were measured during a 30-minute simulator trial using an automated 12-measure Simulator Performance Index (SPI), while a trained observer also rated their performance using a Driving Performance Inventory (DPI). In addition, patients were evaluated on the road by a certified driving evaluator. Ten months later (time-2), family members observed patients driving for at least 3 hours over 4 weeks and rated their driving performance using the DPI. At time-1, patients were significantly impaired on automated SPI measures of driving skill, including: speed and steering control, accidents, and vigilance to a divided-attention task. These simulator indices significantly predicted the following aspects of observed driving performance at time-2: handling of automobile controls, regulation of vehicle speed and direction, higher-order judgment and self-control, as well as a trend-level association with car accidents. Automated measures of simulator skill (SPI) were more sensitive and accurate than observational measures of simulator skill (DPI) in predicting actual driving performance. To our surprise, the road test results at time-1 showed no significant relation to driving performance at time-2. Simulator-based assessment of patients with brain injuries can provide ecologically valid measures that, in some cases, may be more sensitive than a traditional road test as predictors of long-term driving performance in the community.

Genome-Wide Association Studies and Comparison of Models and Cross-Validation Strategies for Genomic Prediction of Quality Traits in Advanced Winter Wheat Breeding Lines

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Peter S. Kristensen

2018-02-01

Full Text Available The aim of the this study was to identify SNP markers associated with five important wheat quality traits (grain protein content, Zeleny sedimentation, test weight, thousand-kernel weight, and falling number, and to investigate the predictive abilities of GBLUP and Bayesian Power Lasso models for genomic prediction of these traits. In total, 635 winter wheat lines from two breeding cycles in the Danish plant breeding company Nordic Seed A/S were phenotyped for the quality traits and genotyped for 10,802 SNPs. GWAS were performed using single marker regression and Bayesian Power Lasso models. SNPs with large effects on Zeleny sedimentation were found on chromosome 1B, 1D, and 5D. However, GWAS failed to identify single SNPs with significant effects on the other traits, indicating that these traits were controlled by many QTL with small effects. The predictive abilities of the models for genomic prediction were studied using different cross-validation strategies. Leave-One-Out cross-validations resulted in correlations between observed phenotypes corrected for fixed effects and genomic estimated breeding values of 0.50 for grain protein content, 0.66 for thousand-kernel weight, 0.70 for falling number, 0.71 for test weight, and 0.79 for Zeleny sedimentation. Alternative cross-validations showed that the genetic relationship between lines in training and validation sets had a bigger impact on predictive abilities than the number of lines included in the training set. Using Bayesian Power Lasso instead of GBLUP models, gave similar or slightly higher predictive abilities. Genomic prediction based on all SNPs was more effective than prediction based on few associated SNPs.

Predicting Hemorrhagic Transformation of Acute Ischemic Stroke: Prospective Validation of the HeRS Score.

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Marsh, Elisabeth B; Llinas, Rafael H; Schneider, Andrea L C; Hillis, Argye E; Lawrence, Erin; Dziedzic, Peter; Gottesman, Rebecca F

2016-01-01

Hemorrhagic transformation (HT) increases the morbidity and mortality of ischemic stroke. Anticoagulation is often indicated in patients with atrial fibrillation, low ejection fraction, or mechanical valves who are hospitalized with acute stroke, but increases the risk of HT. Risk quantification would be useful. Prior studies have investigated risk of systemic hemorrhage in anticoagulated patients, but none looked specifically at HT. In our previously published work, age, infarct volume, and estimated glomerular filtration rate (eGFR) significantly predicted HT. We created the hemorrhage risk stratification (HeRS) score based on regression coefficients in multivariable modeling and now determine its validity in a prospectively followed inpatient cohort.A total of 241 consecutive patients presenting to 2 academic stroke centers with acute ischemic stroke and an indication for anticoagulation over a 2.75-year period were included. Neuroimaging was evaluated for infarct volume and HT. Hemorrhages were classified as symptomatic versus asymptomatic, and by severity. HeRS scores were calculated for each patient and compared to actual hemorrhage status using receiver operating curve analysis.Area under the curve (AUC) comparing predicted odds of hemorrhage (HeRS score) to actual hemorrhage status was 0.701. Serum glucose (P hemorrhages were more likely to be symptomatic and more severe.The HeRS score is a valid predictor of HT in patients with ischemic stroke and indication for anticoagulation.

Predictive validity of the personal qualities assessment for selection of medical students in Scotland.

Science.gov (United States)

Dowell, Jon; Lumsden, Mary Ann; Powis, David; Munro, Don; Bore, Miles; Makubate, Boikanyo; Kumwenda, Ben

2011-01-01

The Personal Qualities Assessment (PQA) was developed to enhance medical student selection by measuring a range of non-cognitive attributes in the applicants to medical school. Applicants to the five Scottish medical schools were invited to pilot the test in 2001 and 2002. To evaluate the predictive validity of PQA for selecting medical students. A longitudinal cohort study was conducted in which PQA scores were compared with senior year medical school performance. Consent to access performance markers was obtained from 626 students (61.6% of 1017 entrants in 2002-2003). Linkable Foundation Year (4th) rankings were available for 411 (66%) students and objective structured clinical examination (OSCE) rankings for 335 (54%) of those consenting. Both samples were representative of the original cohort. No significant correlations were detected between separate elements of the PQA assessment and student performance. However, using the algorithm advocated by Powis et al. those defined as 'non-extreme' (libertarian-communitarian moral orientation scales were ranked higher in OSCEs (average of 7.5% or 25 out of 335, p = 0.049). This study was limited by high attrition and basic outcome markers which are insensitive to relevant non-cognitive characteristics. However, it is the largest currently available study of predictive validity for the PQA assessment. There was one finding of significance: that those students who were identified by PQA as 'not extreme' on the two personal characteristics scales performed better in an OSCE measure of professionalism. Futures studies are required since psychometric testing for both cognitive and non-cognitive attributes are increasingly used in admission process and these should include more and better measures of professionalism against which to correlate non-cognitive traits.

A Cross-Validation Study of Police Recruit Performance as Predicted by the IPI and MMPI.

Science.gov (United States)

Shusman, Elizabeth J.; And Others

Validation and cross-validation studies were conducted using the Minnesota Multiphasic Personality Inventory (MMPI) and Inwald Personality Inventory (IPI) to predict job performance for 698 urban male police officers who completed a six-month training academy. Job performance criteria evaluated included absence, lateness, derelictions, negative…

Predictive Validity and Accuracy of Oral Reading Fluency for English Learners

Science.gov (United States)

Vanderwood, Michael L.; Tung, Catherine Y.; Checca, C. Jason

2014-01-01

The predictive validity and accuracy of an oral reading fluency (ORF) measure for a statewide assessment in English language arts was examined for second-grade native English speakers (NESs) and English learners (ELs) with varying levels of English proficiency. In addition to comparing ELs with native English speakers, the impact of English…

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

Directory of Open Access Journals (Sweden)

Dimitrov BD

2015-04-01

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

Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma

Energy Technology Data Exchange (ETDEWEB)

Zhao, Hui [Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu (China); Department of Hepatopancreatobiliary Surgery, Nanjing Medical University Affiliated Wuxi Second People' s Hospital, Wuxi, Jiangsu (China); Hua, Ye [Department of Neurology, Nanjing Medical University Affiliated Wuxi Second People’s Hospital, Wuxi, Jiangsu (China); Dai, Tu [Department of Hepatopancreatobiliary Surgery, Nanjing Medical University Affiliated Wuxi Second People' s Hospital, Wuxi, Jiangsu (China); He, Jian; Tang, Min [Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu (China); Fu, Xu; Mao, Liang [Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu (China); Jin, Huihan, E-mail: 45687061@qq.com [Department of Hepatopancreatobiliary Surgery, Nanjing Medical University Affiliated Wuxi Second People' s Hospital, Wuxi, Jiangsu (China); Qiu, Yudong, E-mail: yudongqiu510@163.com [Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu (China)

2017-03-15

Highlights: • This study aimed to establish a novel predictive scoring model of MVI in HCC patients. • Preoperative imaging features on CECT, such as intratumoral arteries, non-nodule type and absence of radiological tumor capsule were independent predictors for MVI. • The predictive scoring model is of great value in prediction of MVI regardless of tumor size. - Abstract: Purpose: Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion. Methods: A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n = 206) and validation cohort (n = 103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts. Results: Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5 cm and >5 cm in AUROC (P = 0.910). Conclusions: The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI

Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma

International Nuclear Information System (INIS)

Zhao, Hui; Hua, Ye; Dai, Tu; He, Jian; Tang, Min; Fu, Xu; Mao, Liang; Jin, Huihan; Qiu, Yudong

2017-01-01

Highlights: • This study aimed to establish a novel predictive scoring model of MVI in HCC patients. • Preoperative imaging features on CECT, such as intratumoral arteries, non-nodule type and absence of radiological tumor capsule were independent predictors for MVI. • The predictive scoring model is of great value in prediction of MVI regardless of tumor size. - Abstract: Purpose: Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion. Methods: A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n = 206) and validation cohort (n = 103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts. Results: Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5 cm and >5 cm in AUROC (P = 0.910). Conclusions: The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI

The predictive validity of a situational judgement test, a clinical problem solving test and the core medical training selection methods for performance in specialty training .

Science.gov (United States)

Patterson, Fiona; Lopes, Safiatu; Harding, Stephen; Vaux, Emma; Berkin, Liz; Black, David

2017-02-01

The aim of this study was to follow up a sample of physicians who began core medical training (CMT) in 2009. This paper examines the long-term validity of CMT and GP selection methods in predicting performance in the Membership of Royal College of Physicians (MRCP(UK)) examinations. We performed a longitudinal study, examining the extent to which the GP and CMT selection methods (T1) predict performance in the MRCP(UK) examinations (T2). A total of 2,569 applicants from 2008-09 who completed CMT and GP selection methods were included in the study. Looking at MRCP(UK) part 1, part 2 written and PACES scores, both CMT and GP selection methods show evidence of predictive validity for the outcome variables, and hierarchical regressions show the GP methods add significant value to the CMT selection process. CMT selection methods predict performance in important outcomes and have good evidence of validity; the GP methods may have an additional role alongside the CMT selection methods. © Royal College of Physicians 2017. All rights reserved.

Validation of a Predictive Model for Survival in Metastatic Cancer Patients Attending an Outpatient Palliative Radiotherapy Clinic

International Nuclear Information System (INIS)

Chow, Edward; Abdolell, Mohamed; Panzarella, Tony; Harris, Kristin; Bezjak, Andrea; Warde, Padraig; Tannock, Ian

2009-01-01

Purpose: To validate a predictive model for survival of patients attending a palliative radiotherapy clinic. Methods and Materials: We described previously a model that had good predictive value for survival of patients referred during 1999 (1). The six prognostic factors (primary cancer site, site of metastases, Karnofsky performance score, and the fatigue, appetite and shortness-of-breath items from the Edmonton Symptom Assessment Scale) identified in this training set were extracted from the prospective database for the year 2000. We generated a partial score whereby each prognostic factor was assigned a value proportional to its prognostic weight. The sum of the partial scores for each patient was used to construct a survival prediction score (SPS). Patients were also grouped according to the number of these risk factors (NRF) that they possessed. The probability of survival at 3, 6, and 12 months was generated. The models were evaluated for their ability to predict survival in this validation set with appropriate statistical tests. Results: The median survival and survival probabilities of the training and validation sets were similar when separated into three groups using both SPS and NRF methods. There was no statistical difference in the performance of the SPS and NRF methods in survival prediction. Conclusion: Both the SPS and NRF models for predicting survival in patients referred for palliative radiotherapy have been validated. The NRF model is preferred because it is simpler and avoids the need to remember the weightings among the prognostic factors

External validation of the NUn score for predicting anastomotic leakage after oesophageal resection.

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Paireder, Matthias; Jomrich, Gerd; Asari, Reza; Kristo, Ivan; Gleiss, Andreas; Preusser, Matthias; Schoppmann, Sebastian F

2017-08-29

Early detection of anastomotic leakage (AL) after oesophageal resection for malignancy is crucial. This retrospective study validates a risk score, predicting AL, which includes C-reactive protein, albumin and white cell count in patients undergoing oesophageal resection between 2003 and 2014. For validation of the NUn score a receiver operating characteristic (ROC) curve is estimated. Area under the ROC curve (AUC) is reported with 95% confidence interval (CI). Among 258 patients (79.5% male) 32 patients showed signs of anastomotic leakage (12.4%). NUn score in our data has a median of 9.3 (range 6.2-17.6). The odds ratio for AL was 1.31 (CI 1.03-1.67; p = 0.028). AUC for AL was 0.59 (CI 0.47-0.72). Using the original cutoff value of 10, the sensitivity was 45.2% an the specificity was 73.8%. This results in a positive predictive value of 19.4% and a negative predictive value of 90.6%. The proportion of variation in AL occurrence, which is explained by the NUn score, was 2.5% (PEV = 0.025). This study provides evidence for an external validation of a simple risk score for AL after oesophageal resection. In this cohort, the NUn score is not useful due to its poor discrimination.

Development and validation of a prediction model for loss of physical function in elderly hemodialysis patients.

Science.gov (United States)

Fukuma, Shingo; Shimizu, Sayaka; Shintani, Ayumi; Kamitani, Tsukasa; Akizawa, Tadao; Fukuhara, Shunichi

2017-09-05

Among aging hemodialysis patients, loss of physical function has become a major issue. We developed and validated a model of predicting loss of physical function among elderly hemodialysis patients. We conducted a cohort study involving maintenance hemodialysis patients  ≥65 years of age from the Dialysis Outcomes and Practice Pattern Study in Japan. The derivation cohort included 593 early phase (1996-2004) patients and the temporal validation cohort included 447 late-phase (2005-12) patients. The main outcome was the incidence of loss of physical function, defined as the 12-item Short Form Health Survey physical function score decreasing to 0 within a year. Using backward stepwise logistic regression by Akaike's Information Criteria, six predictors (age, gender, dementia, mental health, moderate activity and ascending stairs) were selected for the final model. Points were assigned based on the regression coefficients and the total score was calculated by summing the points for each predictor. In total, 65 (11.0%) and 53 (11.9%) hemodialysis patients lost their physical function within 1 year in the derivation and validation cohorts, respectively. This model has good predictive performance quantified by both discrimination and calibration. The proportion of the loss of physical function increased sequentially through low-, middle-, and high-score categories based on the model (2.5%, 11.7% and 22.3% in the validation cohort, respectively). The loss of physical function was strongly associated with 1-year mortality [adjusted odds ratio 2.48 (95% confidence interval 1.26-4.91)]. We developed and validated a risk prediction model with good predictive performance for loss of physical function in elderly hemodialysis patients. Our simple prediction model may help physicians and patients make more informed decisions for healthy longevity. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA.

Predictive Validity of the Columbia-Suicide Severity Rating Scale for Short-Term Suicidal Behavior: A Danish Study of Adolescents at a High Risk of Suicide.

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Conway, Paul Maurice; Erlangsen, Annette; Teasdale, Thomas William; Jakobsen, Ida Skytte; Larsen, Kim Juul

2017-07-03

Using the Columbia-Suicide Severity Rating Scale (C-SSRS), we examined the predictive and incremental predictive validity of past-month suicidal behavior and ideation for short-term suicidal behavior among adolescents at high risk of suicide. The study was conducted in 2014 on a sample of 85 adolescents (90.6% females) who participated at follow-up (85.9%) out of the 99 (49.7%) baseline respondents. All adolescents were recruited from a specialized suicide-prevention clinic in Denmark. Through multivariate logistic regression analyses, we examined whether baseline suicidal behavior predicted subsequent suicidal behavior (actual attempts and suicidal behavior of any type, including preparatory acts, aborted, interrupted and actual attempts; mean follow-up of 80.8 days, SD = 52.4). Furthermore, we examined whether suicidal ideation severity and intensity incrementally predicted suicidal behavior at follow-up over and above suicidal behavior at baseline. Actual suicide attempts at baseline strongly predicted suicide attempts at follow-up. Baseline suicidal ideation severity and intensity did not significantly predict future actual attempts over and above baseline attempts. The suicidal ideation intensity items deterrents and duration were significant predictors of subsequent actual attempts after adjustment for baseline suicide attempts and suicidal behavior of any type, respectively. Suicidal ideation severity and intensity, and the intensity items frequency, duration and deterrents, all significantly predicted any type of suicidal behavior at follow-up, also after adjusting for baseline suicidal behavior. The present study points to an incremental predictive validity of the C-SSRS suicidal ideation scales for short-term suicidal behavior of any type among high-risk adolescents.

An integrated computational validation approach for potential novel miRNA prediction

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Pooja Viswam

2017-12-01

Full Text Available MicroRNAs (miRNAs are short, non-coding RNAs between 17bp-24bp length that regulate gene expression by targeting mRNA molecules. The regulatory functions of miRNAs are known to be majorly associated with disease phenotypes such as cancer, cell signaling, cell division, growth and other metabolisms. Novel miRNAs are defined as sequences which does not have any similarity with the existing known sequences and void of any experimental evidences. In recent decades, the advent of next-generation sequencing allows us to capture the small RNA molecules form the cells and developing methods to estimate their expression levels. Several computational algorithms are available to predict the novel miRNAs from the deep sequencing data. In this work, we integrated three novel miRNA prediction programs miRDeep, miRanalyzer and miRPRo to compare and validate their prediction efficiency. The dicer cleavage sites, alignment density, seed conservation, minimum free energy, AU-GC percentage, secondary loop scores, false discovery rates and confidence scores will be considered for comparison and evaluation. Efficiency to identify isomiRs and base pair mismatches in a strand specific manner will also be considered for the computational validation. Further, the criteria and parameters for the identification of the best possible novel miRNA with minimal false positive rates were deduced.

Validation of the ASSERT subchannel code for prediction of CHF in standard and non-standard CANDU bundle geometries

International Nuclear Information System (INIS)

Kiteley, J.C.; Carver, M.B.; Zhou, Q.N.

1993-01-01

The ASSERT code has been developed to address the three-dimensional computation of flow and phase distribution and fuel element surface temperatures within the horizontal subchannels of CANDU PHWR fuel channels, and to provide a detailed prediction of critical heat flux distribution throughout the bundle. The ASSERT subchannel code has been validated extensively against a wide repertoire of experiments; its combination of three-dimensional prediction of local flow conditions with a comprehensive method of predicting critical heat flux (CHF) at these local conditions makes it a unique tool for predicting CHF for situations outside the existing experimental data base. In particular, ASSERT is the only tool available to systematically investigate CHF under conditions of local geometric variations, such as pressure tube creep and fuel element strain. This paper discusses the numerical methodology used in ASSERT, the constitutive relationships incorporated, and the CHF assessment methodology. The evolutionary validation plan is discussed, and early validation exercises are summarized. The paper concentrates, however, on more recent validation exercises in standard and non-standard geometries. 28 refs., 12 figs

Validation of the ASSERT subchannel code: Prediction of critical heat flux in standard and nonstandard CANDU bundle geometries

International Nuclear Information System (INIS)

Carver, M.B.; Kiteley, J.C.; Zhou, R.Q.N.; Junop, S.V.; Rowe, D.S.

1995-01-01

The ASSERT code has been developed to address the three-dimensional computation of flow and phase distribution and fuel element surface temperatures within the horizontal subchannels of Canada uranium deuterium (CANDU) pressurized heavy water reactor fuel channels and to provide a detailed prediction of critical heat flux (CHF) distribution throughout the bundle. The ASSERT subchannel code has been validated extensively against a wide repertoire of experiments; its combination of three-dimensional prediction of local flow conditions with a comprehensive method of predicting CHF at these local conditions makes it a unique tool for predicting CHF for situations outside the existing experimental database. In particular, ASSERT is an appropriate tool to systematically investigate CHF under conditions of local geometric variations, such as pressure tube creep and fuel element strain. The numerical methodology used in ASSERT, the constitutive relationships incorporated, and the CHF assessment methodology are discussed. The evolutionary validation plan is also discussed and early validation exercises are summarized. More recent validation exercises in standard and nonstandard geometries are emphasized

Six factors of adult dyslexia assesed by cognitive tests and self-report questions: Very high predictive validity

NARCIS (Netherlands)

Tamboer, P.; Vorst, H.C.M.; de Jong, P.F.

2017-01-01

The Multiple Diagnostic Digital Dyslexia Test for Adults (MDDDT-A) consists of 12 newly developed tests and self-report questions in the Dutch language. Predictive validity and construct validity were investigated and compared with validity of a standard test battery of dyslexia (STB) in a sample of

In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain.

Science.gov (United States)

Abidin, Shahidee Zainal; Leong, Jia-Wen; Mahmoudi, Marzieh; Nordin, Norshariza; Abdullah, Syahril; Cheah, Pike-See; Ling, King-Hwa

2017-08-01

MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.

Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF in fish

Directory of Open Access Journals (Sweden)

Milan Chiara

2010-07-01

Full Text Available Abstract Background Bioconcentration factor (BCF describes the behaviour of a chemical in terms of its likelihood of concentrating in organisms in the environment. It is a fundamental property in recent regulations, such as the European Community Regulation on chemicals and their safe use or the Globally Harmonized System for classification, labelling and packaging. These new regulations consider the possibility of reducing or waiving animal tests using alternative methods, such as in silico methods. This study assessed and validated the CAESAR predictive model for BCF in fish. Results To validate the model, new experimental data were collected and used to create an external set, as a second validation set (a first validation exercise had been done just after model development. The performance of the model was compared with BCFBAF v3.00. For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model. R2 and Q2 were good and accuracy in classification higher than 90%. Applying an offset of 0.5 to the compounds predicted with BCF close to the thresholds, the number of false negatives (the most dangerous errors dropped considerably (less than 0.6% of chemicals. Conclusions The CAESAR model for BCF is useful for regulatory purposes because it is robust, reliable and predictive. It is also fully transparent and documented and has a well-defined applicability domain, as required by REACH. The model is freely available on the CAESAR web site and easy to use. The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

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

Science.gov (United States)

Emery, Joanne L; Bell, John F

2009-06-01

Some medical courses in the UK have many more applicants than places and almost all applicants have the highest possible previous and predicted examination grades. The BioMedical Admissions Test (BMAT) was designed to assist in the student selection process specifically for a number of 'traditional' medical courses with clear pre-clinical and clinical phases and a strong focus on science teaching in the early years. It is intended to supplement the information provided by examination results, interviews and personal statements. This paper reports on the predictive validity of the BMAT and its predecessor, the Medical and Veterinary Admissions Test. Results from the earliest 4 years of the test (2000-2003) were matched to the pre-clinical examination results of those accepted onto the medical course at the University of Cambridge. Correlation and logistic regression analyses were performed for each cohort. Section 2 of the test ('Scientific Knowledge') correlated more strongly with examination marks than did Section 1 ('Aptitude and Skills'). It also had a stronger relationship with the probability of achieving the highest examination class. The BMAT and its predecessor demonstrate predictive validity for the pre-clinical years of the medical course at the University of Cambridge. The test identifies important differences in skills and knowledge between candidates, not shown by their previous attainment, which predict their examination performance. It is thus a valid source of additional admissions information for medical courses with a strong scientific emphasis when previous attainment is very high.

Development and validation of a digital work simulation to predict workplace deviance

NARCIS (Netherlands)

Dubbelt, L.; Oostrom, J.K.; drs. Hiemstra, A.M.F.; Modderman, J.P.L.

2015-01-01

”This paper describes a new and innovative measure that is developed to predict workplace deviance through the measurement of Machiavellianism and Compliant Behavior. Two field studies were conducted to study the validity of the digital work simulation. In Study 1, (N = 113) support was found for

Global cue inconsistency diminishes learning of cue validity

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Tony Wang

2016-11-01

Full Text Available We present a novel two-stage probabilistic learning task that examines the participants’ ability to learn and utilize valid cues across several levels of probabilistic feedback. In the first stage, participants sample from one of three cues that gives predictive information about the outcome of the second stage. Participants are rewarded for correct prediction of the outcome in stage two. Only one of the three cues gives valid predictive information and thus participants can maximise their reward by learning to sample from the valid cue. The validity of this predictive information, however, is reinforced across several levels of probabilistic feedback. A second manipulation involved changing the consistency of the predictive information in stage one and the outcome in stage two. The results show that participants, with higher probabilistic feedback, learned to utilise the valid cue. In inconsistent task conditions, however, participants were significantly less successful in utilising higher validity cues. We interpret this result as implying that learning in probabilistic categorization is based on developing a representation of the task that allows for goal-directed action.

Prediction and validation of hemodialysis duration in acute methanol poisoning.

Science.gov (United States)

Lachance, Philippe; Mac-Way, Fabrice; Desmeules, Simon; De Serres, Sacha A; Julien, Anne-Sophie; Douville, Pierre; Ghannoum, Marc; Agharazii, Mohsen

2015-11-01

The duration of hemodialysis (HD) in methanol poisoning (MP) is dependent on the methanol concentration, the operational parameters used during HD, and the presence and severity of metabolic acidosis. However, methanol assays are not easily available, potentially leading to undue extension or premature termination of treatment. Here we provide a prediction model for the duration of high-efficiency HD in MP. In a retrospective cohort study, we identified 71 episodes of MP in 55 individuals who were treated with alcohol dehydrogenase inhibition and HD. Four patients had residual visual abnormality at discharge and only one patient died. In 46 unique episodes of MP with high-efficiency HD the mean methanol elimination half-life (T1/2) during HD was 108 min in women, significantly different from the 129 min in men. In a training set of 28 patients with MP, using the 90th percentile of gender-specific elimination T1/2 (147 min in men and 141 min in women) and a target methanol concentration of 4 mmol/l allowed all cases to reach a safe methanol of under 6 mmol/l. The prediction model was confirmed in a validation set of 18 patients with MP. High-efficiency HD time in hours can be estimated using 3.390 × (Ln (MCi/4)) for women and 3.534 × (Ln (MCi/4)) for men, where MCi is the initial methanol concentration in mmol/l, provided that metabolic acidosis is corrected.

External validation of models predicting the individual risk of metachronous peritoneal carcinomatosis from colon and rectal cancer.

Science.gov (United States)

Segelman, J; Akre, O; Gustafsson, U O; Bottai, M; Martling, A

2016-04-01

To externally validate previously published predictive models of the risk of developing metachronous peritoneal carcinomatosis (PC) after resection of nonmetastatic colon or rectal cancer and to update the predictive model for colon cancer by adding new prognostic predictors. Data from all patients with Stage I-III colorectal cancer identified from a population-based database in Stockholm between 2008 and 2010 were used. We assessed the concordance between the predicted and observed probabilities of PC and utilized proportional-hazard regression to update the predictive model for colon cancer. When applied to the new validation dataset (n = 2011), the colon and rectal cancer risk-score models predicted metachronous PC with a concordance index of 79% and 67%, respectively. After adding the subclasses of pT3 and pT4 stage and mucinous tumour to the colon cancer model, the concordance index increased to 82%. In validation of external and recent cohorts, the predictive accuracy was strong in colon cancer and moderate in rectal cancer patients. The model can be used to identify high-risk patients for planned second-look laparoscopy/laparotomy for possible subsequent cytoreductive surgery and hyperthermic intraperitoneal chemotherapy. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

Validation of the assert subchannel code: Prediction of CHF in standard and non-standard Candu bundle geometries

International Nuclear Information System (INIS)

Carver, M.B.; Kiteley, J.C.; Zhou, R.Q.N.; Junop, S.V.; Rowe, D.S.

1993-01-01

The ASSERT code has been developed to address the three-dimensional computation of flow and phase distribution and fuel element surface temperatures within the horizontal subchannels of CANDU PHWR fuel channels, and to provide a detailed prediction of critical heat flux (CHF) distribution throughout the bundle. The ASSERT subchannel code has been validated extensively against a wide repertoire of experiments; its combination of three-dimensional prediction of local flow conditions with a comprehensive method of prediting CHF at these local conditions, makes it a unique tool for predicting CHF for situations outside the existing experimental data base. In particular, ASSERT is an appropriate tool to systematically investigate CHF under conditions of local geometric variations, such as pressure tube creep and fuel element strain. This paper discusses the numerical methodology used in ASSERT, the constitutive relationships incorporated, and the CHF assessment methodology. The evolutionary validation plan is discussed, and early validation exercises are summarized. The paper concentrates, however, on more recent validation exercises in standard and non-standard geometries

Method of administration of PROMIS scales did not significantly impact score level, reliability, or validity

DEFF Research Database (Denmark)

Bjorner, Jakob B; Rose, Matthias; Gandek, Barbara

2014-01-01

OBJECTIVES: To test the impact of the method of administration (MOA) on score level, reliability, and validity of scales developed in the Patient Reported Outcomes Measurement Information System (PROMIS). STUDY DESIGN AND SETTING: Two nonoverlapping parallel forms each containing eight items from......, no significant mode differences were found and all confidence intervals were within the prespecified minimal important difference of 0.2 standard deviation. Parallel-forms reliabilities were very high (ICC = 0.85-0.93). Only one across-mode ICC was significantly lower than the same-mode ICC. Tests of validity...... questionnaire (PQ), personal digital assistant (PDA), or personal computer (PC) and a second form by PC, in the same administration. Method equivalence was evaluated through analyses of difference scores, intraclass correlations (ICCs), and convergent/discriminant validity. RESULTS: In difference score analyses...

Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

Science.gov (United States)

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G M; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M

2014-01-01

In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

Directory of Open Access Journals (Sweden)

Hui Miao

Full Text Available BACKGROUND: In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. MATERIALS AND METHODS: We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic. RESULTS: We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53 to 0.63 (95% CI, 0.60-0.66. CONCLUSION: The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

Exploring the Predictive Validity of the Susceptibility to Smoking Construct for Tobacco Cigarettes, Alternative Tobacco Products, and E-Cigarettes.

Science.gov (United States)

Cole, Adam G; Kennedy, Ryan David; Chaurasia, Ashok; Leatherdale, Scott T

2017-12-06

Within tobacco prevention programming, it is useful to identify youth that are at risk for experimenting with various tobacco products and e-cigarettes. The susceptibility to smoking construct is a simple method to identify never-smoking students that are less committed to remaining smoke-free. However, the predictive validity of this construct has not been tested within the Canadian context or for the use of other tobacco products and e-cigarettes. This study used a large, longitudinal sample of secondary school students that reported never using tobacco cigarettes and non-current use of alternative tobacco products or e-cigarettes at baseline in Ontario, Canada. The sensitivity, specificity, and positive and negative predictive values of the susceptibility construct for predicting tobacco cigarette, e-cigarette, cigarillo or little cigar, cigar, hookah, and smokeless tobacco use one and two years after baseline measurement were calculated. At baseline, 29.4% of the sample was susceptible to future tobacco product or e-cigarette use. The sensitivity of the construct ranged from 43.2% (smokeless tobacco) to 59.5% (tobacco cigarettes), the specificity ranged from 70.9% (smokeless tobacco) to 75.9% (tobacco cigarettes), and the positive predictive value ranged from 2.6% (smokeless tobacco) to 32.2% (tobacco cigarettes). Similar values were calculated for each measure of the susceptibility construct. A significant number of youth that did not currently use tobacco products or e-cigarettes at baseline reported using tobacco products and e-cigarettes over a two-year follow-up period. The predictive validity of the susceptibility construct was high and the construct can be used to predict other tobacco product and e-cigarette use among youth. This study presents the predictive validity of the susceptibility construct for the use of tobacco cigarettes among secondary school students in Ontario, Canada. It also presents a novel use of the susceptibility construct for

Validity and validation of expert (Q)SAR systems.

Science.gov (United States)

Hulzebos, E; Sijm, D; Traas, T; Posthumus, R; Maslankiewicz, L

2005-08-01

At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure-activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three 'valid' classes results in predictivity of > or = 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by 'if-then' reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.

Status self-validation of a multifunctional sensor using a multivariate relevance vector machine and predictive filters

International Nuclear Information System (INIS)

Shen, Zhengguang; Wang, Qi

2013-01-01

A novel strategy by using a multivariable relevance vector machine coupled with predictive filters for status self-validation of a multifunctional sensor is proposed. The working principle and online updating algorithm of predictive filters are emphasized for multiple fault detection, isolation and recovery (FDIR), and the incorrect sensor measurements are validated online. The multivariable relevance vector machine is then employed for the signal reconstruction of the multifunctional sensor to generate the final validated measurement values (VMV) of multiple measured components, in which its advantages of sparse models and multivariable simultaneous outputs are fully used. With all likely uncertainty sources of the multifunctional self-validating sensor taken into account, the uncertainty propagation model is deduced in detail to evaluate the online validated uncertainty (VU) under a fault-free situation while a qualitative uncertainty component is appended to indicate the accuracy changes of VMV under different types of fault. A real experimental system of a multifunctional self-validating sensor is designed to verify the performance of the proposed strategy. From the real-time capacity and fault recovery accuracy of FDIR, and runtime of signal reconstruction under small samples, a performance comparison among different methods is made. Results demonstrate that the proposed scheme provides a better solution to the status self-validation of a multifunctional self-validating sensor under both normal and abnormal situations. (paper)

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

DEFF Research Database (Denmark)

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

2015-01-01

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

External validation of the MRI-DRAGON score: early prediction of stroke outcome after intravenous thrombolysis.

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Turc, Guillaume; Aguettaz, Pierre; Ponchelle-Dequatre, Nelly; Hénon, Hilde; Naggara, Olivier; Leclerc, Xavier; Cordonnier, Charlotte; Leys, Didier; Mas, Jean-Louis; Oppenheim, Catherine

2014-01-01

The aim of our study was to validate in an independent cohort the MRI-DRAGON score, an adaptation of the (CT-) DRAGON score to predict 3-month outcome in acute ischemic stroke patients undergoing MRI before intravenous thrombolysis (IV-tPA). We reviewed consecutive (2009-2013) anterior circulation stroke patients treated within 4.5 hours by IV-tPA in the Lille stroke unit (France), where MRI is the first-line pretherapeutic work-up. We assessed the discrimination and calibration of the MRI-DRAGON score to predict poor 3-month outcome, defined as modified Rankin Score >2, using c-statistic and the Hosmer-Lemeshow test, respectively. We included 230 patients (mean ±SD age 70.4±16.0 years, median [IQR] baseline NIHSS 8 [5]-[14]; poor outcome in 78(34%) patients). The c-statistic was 0.81 (95%CI 0.75-0.87), and the Hosmer-Lemeshow test was not significant (p = 0.54). The MRI-DRAGON score showed good prognostic performance in the external validation cohort. It could therefore be used to inform the patient's relatives about long-term prognosis and help to identify poor responders to IV-tPA alone, who may be candidates for additional therapeutic strategies, if they are otherwise eligible for such procedures based on the institutional criteria.

External validation of the MRI-DRAGON score: early prediction of stroke outcome after intravenous thrombolysis.

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Guillaume Turc

Full Text Available The aim of our study was to validate in an independent cohort the MRI-DRAGON score, an adaptation of the (CT- DRAGON score to predict 3-month outcome in acute ischemic stroke patients undergoing MRI before intravenous thrombolysis (IV-tPA.We reviewed consecutive (2009-2013 anterior circulation stroke patients treated within 4.5 hours by IV-tPA in the Lille stroke unit (France, where MRI is the first-line pretherapeutic work-up. We assessed the discrimination and calibration of the MRI-DRAGON score to predict poor 3-month outcome, defined as modified Rankin Score >2, using c-statistic and the Hosmer-Lemeshow test, respectively.We included 230 patients (mean ±SD age 70.4±16.0 years, median [IQR] baseline NIHSS 8 [5]-[14]; poor outcome in 78(34% patients. The c-statistic was 0.81 (95%CI 0.75-0.87, and the Hosmer-Lemeshow test was not significant (p = 0.54.The MRI-DRAGON score showed good prognostic performance in the external validation cohort. It could therefore be used to inform the patient's relatives about long-term prognosis and help to identify poor responders to IV-tPA alone, who may be candidates for additional therapeutic strategies, if they are otherwise eligible for such procedures based on the institutional criteria.

Validation and prediction of traditional Chinese physical operation on spinal disease using multiple deformation models.

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Pan, Lei; Yang, Xubo; Gu, Lixu; Lu, Wenlong; Fang, Min

2011-03-01

Traditional Chinese medical massage is a physical manipulation that achieves satisfactory results on spinal diseases, according to its advocates. However, the method relies on an expert's experience. Accurate analysis and simulation of massage are essential for validation of traditional Chinese physical treatment. The objective of this study is to provide analysis and simulation that can reproducibly verify and predict treatment efficacy. An improved physical multi-deformation model for simulating human cervical spine is proposed. First, the human spine, which includes muscle, vertebrae and inter- vertebral disks, are segmented and reconstructed from clinical CT and MR images. Homogeneous landmark registration is employed to align the spine models before and after the massage manipulation. Central line mass spring and contact FEM deformation models are used to individually evaluate spinal anatomy variations. The response of the human spine during the massage process is simulated based on specific clinical cases. Ten sets of patient data, including muscle-force relationships, displacement of vertebrae, strain and stress distribution on inter-vertebral disks were collected, including the pre-operation, post-operation and the 3-month follow-up. The simulation results demonstrate that traditional Chinese massage could significantly affect and treat most mild spinal disease. A new method that simulates a traditional Chinese medical massage operation on the human spine may be a useful tool to scientifically validate and predict treatment efficacy.

Predictive Validity of Delay Discounting Behavior in Adolescence: A Longitudinal Twin Study

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Isen, Joshua D.; Sparks, Jordan C.; Iacono, William G.

2014-01-01

A standard assumption in the delay discounting literature is that individuals who exhibit steeper discounting of hypothetical rewards also experience greater difficulty deferring gratification to real-world rewards. There is ample cross-sectional evidence that delay discounting paradigms reflect a variety of maladaptive psychosocial outcomes, including substance use pathology. We sought to determine whether a computerized assessment of hypothetical delay discounting (HDD) taps into behavioral impulsivity in a community sample of adolescent twins (N = 675). Using a longitudinal design, we hypothesized that greater HDD at age 14–15 predicts real-world impulsive choices and risk for substance use disorders in late adolescence. We also examined the genetic and environmental structure of HDD performance. Individual differences in HDD behavior showed moderate heritability, and were prospectively associated with real-world temporal discounting at age 17–18. Contrary to expectations, HDD was not consistently related to substance use or trait impulsivity. Although a significant association between HDD behavior and past substance use emerged in males, this effect was mediated by cognitive ability. In both sexes, HDD failed to predict a comprehensive index of substance use problems and behavioral disinhibition in late adolescence. In sum, we present some of the first evidence that HDD performance is heritable and predictive of real-world temporal discounting of rewards. Nevertheless, HDD might not serve as a valid marker of substance use disorder risk in younger adolescents, particularly females. PMID:24999868

Attempted development and cross-validation of predictive models of individual-level and organizational-level turnover of nuclear power operators

International Nuclear Information System (INIS)

Vasa-Sideris, S.J.

1989-01-01

Nuclear power accounts for 209% of the electric power generated in the U.S. by 107 nuclear plants which employ over 8,700 operators. Operator turnover is significant to utilities from the economic point of view since it costs almost three hundred thousand dollars to train and qualify one operator, and because turnover affects plant operability and therefore plant safety. The study purpose was to develop and cross-validate individual-level and organizational-level models of turnover of nuclear power plant operators. Data were obtained by questionnaires and from published data for 1983 and 1984 on a number of individual, organizational, and environmental predictors. Plants had been in operation for two or more years. Questionnaires were returned by 29 out of 50 plants on over 1600 operators. The objectives were to examine the reliability of the turnover criterion, to determine the classification accuracy of the multivariate predictive models and of categories of predictors (individual, organizational, and environmental) and to determine if a homology existed between the individual-level and organizational-level models. The method was to examine the shrinkage that occurred between foldback design (in which the predictive models were reapplied to the data used to develop them) and cross-validation. Results did not support the hypothesis objectives. Turnover data were accurate but not stable between the two years. No significant differences were detected between the low and high turnover groups at the organization or individual level in cross-validation. Lack of stability in the criterion, restriction of range, and small sample size at the organizational level were serious limitations of this study. The results did support the methods. Considerable shrinkage occurred between foldback and cross-validation of the models

The Predictive Validity of using Admissions Testing and Multiple Mini-interviews in Undergraduate University Admissions

DEFF Research Database (Denmark)

Makransky, Guido; Havmose, Philip S.; Vang, Maria Louison

2017-01-01

The aim of this study was to evaluate the predictive validity of a two-step admissions procedure that included a cognitive ability test followed by multiple mini-interviews (MMI) used to assess non-cognitive skills compared to a grade-based admissions relative to subsequent drop-out rates...... and academic achievement after one and two years of study. The participants consisted of the entire population of 422 psychology students who were admitted to the University of Southern Denmark between 2010 and 2013. The results showed significantly lower drop-out rates after the first year of study, and non......-significant lower drop-out rates after the second year of study for the admission procedure that included the assessment of non-cognitive skills though the MMI. Furthermore, this admission procedure resulted in a significant lower risk of failing the final exam after the first and second year of study, compared...

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

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

1996-01-01

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

Validation and uncertainty analysis of a pre-treatment 2D dose prediction model

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Baeza, Jose A.; Wolfs, Cecile J. A.; Nijsten, Sebastiaan M. J. J. G.; Verhaegen, Frank

2018-02-01

Independent verification of complex treatment delivery with megavolt photon beam radiotherapy (RT) has been effectively used to detect and prevent errors. This work presents the validation and uncertainty analysis of a model that predicts 2D portal dose images (PDIs) without a patient or phantom in the beam. The prediction model is based on an exponential point dose model with separable primary and secondary photon fluence components. The model includes a scatter kernel, off-axis ratio map, transmission values and penumbra kernels for beam-delimiting components. These parameters were derived through a model fitting procedure supplied with point dose and dose profile measurements of radiation fields. The model was validated against a treatment planning system (TPS; Eclipse) and radiochromic film measurements for complex clinical scenarios, including volumetric modulated arc therapy (VMAT). Confidence limits on fitted model parameters were calculated based on simulated measurements. A sensitivity analysis was performed to evaluate the effect of the parameter uncertainties on the model output. For the maximum uncertainty, the maximum deviating measurement sets were propagated through the fitting procedure and the model. The overall uncertainty was assessed using all simulated measurements. The validation of the prediction model against the TPS and the film showed a good agreement, with on average 90.8% and 90.5% of pixels passing a (2%,2 mm) global gamma analysis respectively, with a low dose threshold of 10%. The maximum and overall uncertainty of the model is dependent on the type of clinical plan used as input. The results can be used to study the robustness of the model. A model for predicting accurate 2D pre-treatment PDIs in complex RT scenarios can be used clinically and its uncertainties can be taken into account.

Short-Term Predictive Validity of Cluster Analytic and Dimensional Classification of Child Behavioral Adjustment in School

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Kim, Sangwon; Kamphaus, Randy W.; Baker, Jean A.

2006-01-01

A constructive debate over the classification of child psychopathology can be stimulated by investigating the validity of different classification approaches. We examined and compared the short-term predictive validity of cluster analytic and dimensional classifications of child behavioral adjustment in school using the Behavior Assessment System…

Development and validation of clinical prediction models for mortality, functional outcome and cognitive impairment after stroke: a study protocol.

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Fahey, Marion; Rudd, Anthony; Béjot, Yannick; Wolfe, Charles; Douiri, Abdel

2017-08-18

Stroke is a leading cause of adult disability and death worldwide. The neurological impairments associated with stroke prevent patients from performing basic daily activities and have enormous impact on families and caregivers. Practical and accurate tools to assist in predicting outcome after stroke at patient level can provide significant aid for patient management. Furthermore, prediction models of this kind can be useful for clinical research, health economics, policymaking and clinical decision support. 2869 patients with first-ever stroke from South London Stroke Register (SLSR) (1995-2004) will be included in the development cohort. We will use information captured after baseline to construct multilevel models and a Cox proportional hazard model to predict cognitive impairment, functional outcome and mortality up to 5 years after stroke. Repeated random subsampling validation (Monte Carlo cross-validation) will be evaluated in model development. Data from participants recruited to the stroke register (2005-2014) will be used for temporal validation of the models. Data from participants recruited to the Dijon Stroke Register (1985-2015) will be used for external validation. Discrimination, calibration and clinical utility of the models will be presented. Patients, or for patients who cannot consent their relatives, gave written informed consent to participate in stroke-related studies within the SLSR. The SLSR design was approved by the ethics committees of Guy's and St Thomas' NHS Foundation Trust, Kings College Hospital, Queens Square and Westminster Hospitals (London). The Dijon Stroke Registry was approved by the Comité National des Registres and the InVS and has authorisation of the Commission Nationale de l'Informatique et des Libertés. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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

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Ireland CJ

2018-03-01

Full Text Available Colin J Ireland,1 Andrea L Gordon,2 Sarah K Thompson,3 David I Watson,4 David C Whiteman,5 Richard L Reed,6 Adrian Esterman1,7 1School of Nursing and Midwifery, Division of Health Sciences, University of South Australia, Adelaide, SA, Australia; 2School of Pharmacy and Medical Science, Division of Health Sciences, University of South Australia, Adelaide, SA, Australia; 3Discipline of Surgery, University of Adelaide, Adelaide, SA, Australia; 4Department of Surgery, Flinders University, Bedford Park, SA, Australia; 5Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia; 6Discipline of General Practice, Flinders University, Bedford Park, SA, Australia; 7Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia Background: Esophageal adenocarcinoma is a disease that has a high mortality rate, the only known precursor being Barrett’s esophagus (BE. While screening for BE is not cost-effective at the population level, targeted screening might be beneficial. We have developed a risk prediction model to identify people with BE, and here we present the external validation of this model. Materials and methods: A cohort study was undertaken to validate a risk prediction model for BE. Individuals with endoscopy and histopathology proven BE completed a questionnaire containing variables previously identified as risk factors for this condition. Their responses were combined with data from a population sample for analysis. Risk scores were derived for each participant. Overall performance of the risk prediction model in terms of calibration and discrimination was assessed. Results: Scores from 95 individuals with BE and 636 individuals from the general population were analyzed. The Brier score was 0.118, suggesting reasonable overall performance. The area under the receiver operating characteristic was 0.83 (95% CI 0.78–0.87. The Hosmer–Lemeshow statistic was p=0

French validation and adaptation of the Grobman nomogram for prediction of vaginal birth after cesarean delivery.

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Haumonte, J-B; Raylet, M; Christophe, M; Mauviel, F; Bertrand, A; Desbriere, R; d'Ercole, C

2018-03-01

To validate Grobman nomogram for predicting vaginal birth after cesarean delivery (VBAC) in a French population and adapt it. Multicenter retrospective study of maternal and obstetric factors associated with VBAC between May 2012 and May 2013 in 6 maternity units. External validation and adaptation of the prenatal and intrapartum Grobman nomograms for vaginal birth prediction after cesarean delivery in a French cohort. The study included 523 women with previous cesarean deliveries; 70% underwent a trial of labor for a subsequent delivery (n=367) with a success rate of 65% (n=240). In the univariate analysis, 5 factors were associated with successful VBAC: previous vaginal delivery before the cesarean (P6 (P=0.03). A potentially recurrent indication (defined as arrest of dilation or descent as the indication for the previous cesarean) (P=0.039), a hypertensive disorder during pregnancy (P=0.05), and labor induction (P=0.017) were each associated with failed VBAC. External validation of the prenatal and intrapartum Grobman nomograms showed an area under the ROC curve of 69% (95% CI: 0.638, 0.736) and 65% (95% CI: 0.599, 0.700) respectively. Adaptation of the nomogram to the French cohort resulted in the inclusion of the following factors: maternal age, body mass index at last prenatal visit, hypertensive disorder, gestational age at delivery, recurring indication, cervical dilatation, and induction of labor. Its area under the curve to predict successful VBAC was 78% (95% CI: 0.738, 0.825). The nomogram to predict VBAC developed by Grobman et al. is validated in the French population. Adaptation to the French population, by excluding ethnicity, appeared to improve its performance. Impact of the nomogram use on the caesarean section rate has to be validated in a randomized control trial. Copyright © 2017. Published by Elsevier Masson SAS.

Advanced validation of CFD-FDTD combined method using highly applicable solver for reentry blackout prediction

International Nuclear Information System (INIS)

Takahashi, Yusuke

2016-01-01

An analysis model of plasma flow and electromagnetic waves around a reentry vehicle for radio frequency blackout prediction during aerodynamic heating was developed in this study. The model was validated based on experimental results from the radio attenuation measurement program. The plasma flow properties, such as electron number density, in the shock layer and wake region were obtained using a newly developed unstructured grid solver that incorporated real gas effect models and could treat thermochemically non-equilibrium flow. To predict the electromagnetic waves in plasma, a frequency-dependent finite-difference time-domain method was used. Moreover, the complicated behaviour of electromagnetic waves in the plasma layer during atmospheric reentry was clarified at several altitudes. The prediction performance of the combined model was evaluated with profiles and peak values of the electron number density in the plasma layer. In addition, to validate the models, the signal losses measured during communication with the reentry vehicle were directly compared with the predicted results. Based on the study, it was suggested that the present analysis model accurately predicts the radio frequency blackout and plasma attenuation of electromagnetic waves in plasma in communication. (paper)

On the short circuit resilience of organic solar cells: prediction and validation.

Science.gov (United States)

Oostra, A Jolt; Smits, Edsger C P; de Leeuw, Dago M; Blom, Paul W M; Michels, Jasper J

2015-09-07

The operational characteristics of organic solar cells manufactured with large area processing methods suffers from the occurrence of short-circuits due to defects in the photoactive thin film stack. In this work we study the effect of a shunt resistance on an organic solar cell and demonstrate that device performance is not affected negatively as long as the shunt resistance is higher than approximately 1000 Ohm. By studying charge transport across PSS-lithium fluoride/aluminum (LiF/Al) shunting junctions we show that this prerequisite is already met by applying a sufficiently thick (>1.5 nm) LiF layer. We demonstrate that this remarkable shunt-resilience stems from the formation of a significant charge transport barrier at the PSS-LiF/Al interface. We validate our predictions by fabricating devices with deliberately severed photoactive layers and find an excellent agreement between the calculated and experimental current-voltage characteristics.

Experimentally validated structural vibration frequencies’ prediction from frictional temperature signatures using numerical simulation: A case of laced cantilever beam-like structures

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Stephen M Talai

2016-12-01

Full Text Available This article pertains to the prediction of structural vibration frequencies from frictional temperature evolution through numerical simulation. To achieve this, a finite element analysis was carried on AISI 304 steel cantilever beam-like structures coupled with a lacing wire using the commercial software ABAQUS/CAE. The coupled temperature–displacement transient analysis simulated the frictional thermal generation. Furthermore, an experimental analysis was carried out with infrared cameras capturing the interfacial thermal images while the beams were subjected to forced excitation, thus validating the finite element analysis results. The analysed vibration frequencies using a MATLAB fast Fourier transform algorithm confirmed the validity of its prediction from the frictional temperature time domain waveform. This finding has a great significance to the mechanical and aerospace engineering communities for the effective structural health monitoring of dynamic structures online using infrared thermography, thus reducing the downtime and maintenance cost, leading to increased efficiency.

Evaluating the predictive accuracy and the clinical benefit of a nomogram aimed to predict survival in node-positive prostate cancer patients: External validation on a multi-institutional database.

Science.gov (United States)

Bianchi, Lorenzo; Schiavina, Riccardo; Borghesi, Marco; Bianchi, Federico Mineo; Briganti, Alberto; Carini, Marco; Terrone, Carlo; Mottrie, Alex; Gacci, Mauro; Gontero, Paolo; Imbimbo, Ciro; Marchioro, Giansilvio; Milanese, Giulio; Mirone, Vincenzo; Montorsi, Francesco; Morgia, Giuseppe; Novara, Giacomo; Porreca, Angelo; Volpe, Alessandro; Brunocilla, Eugenio

2018-04-06

To assess the predictive accuracy and the clinical value of a recent nomogram predicting cancer-specific mortality-free survival after surgery in pN1 prostate cancer patients through an external validation. We evaluated 518 prostate cancer patients treated with radical prostatectomy and pelvic lymph node dissection with evidence of nodal metastases at final pathology, at 10 tertiary centers. External validation was carried out using regression coefficients of the previously published nomogram. The performance characteristics of the model were assessed by quantifying predictive accuracy, according to the area under the curve in the receiver operating characteristic curve and model calibration. Furthermore, we systematically analyzed the specificity, sensitivity, positive predictive value and negative predictive value for each nomogram-derived probability cut-off. Finally, we implemented decision curve analysis, in order to quantify the nomogram's clinical value in routine practice. External validation showed inferior predictive accuracy as referred to in the internal validation (65.8% vs 83.3%, respectively). The discrimination (area under the curve) of the multivariable model was 66.7% (95% CI 60.1-73.0%) by testing with receiver operating characteristic curve analysis. The calibration plot showed an overestimation throughout the range of predicted cancer-specific mortality-free survival rates probabilities. However, in decision curve analysis, the nomogram's use showed a net benefit when compared with the scenarios of treating all patients or none. In an external setting, the nomogram showed inferior predictive accuracy and suboptimal calibration characteristics as compared to that reported in the original population. However, decision curve analysis showed a clinical net benefit, suggesting a clinical implication to correctly manage pN1 prostate cancer patients after surgery. © 2018 The Japanese Urological Association.

Validation of a prediction model that allows direct comparison of the Oxford Knee Score and American Knee Society clinical rating system.

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Maempel, J F; Clement, N D; Brenkel, I J; Walmsley, P J

2015-04-01

This study demonstrates a significant correlation between the American Knee Society (AKS) Clinical Rating System and the Oxford Knee Score (OKS) and provides a validated prediction tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed five years after TKR and completed AKS assessments and an OKS questionnaire. Multivariate regression analysis demonstrated significant correlations between OKS and the AKS knee and function scores but a stronger correlation (r = 0.68, p Society of Bone & Joint Surgery.

Reliability, Validity, and Significance of Assessment of Sense of Contribution in the Workplace

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Jiro Takaki

2014-01-01

Full Text Available The purpose of this study was to assess the validity and reliability of the Sense of Contribution Scale (SCS, a newly developed, 7-item questionnaire used to measure sense of contribution in the workplace. Workers at 272 organizations answered questionnaires that included the SCS. Because of non-participation or missing data, the number of subjects included in the analyses for internal consistency and validity varied from 1,675 to 2,462 (response rates 54.6%–80.2%. Fifty-four workers were included in the analysis of test–retest reliability (response rate, 77.1%. The SCS showed high internal consistency (Cronbach’s α coefficients in men and women were 0.85 and 0.86, respectively and test–retest reliability (intraclass correlation coefficient = 0.91. Significant (p < 0.001, positive, moderate correlations were found between the SCS score and scores for organization-based self-esteem and work engagement in both genders, which support the SCS’s convergent and discriminant validity. The criterion validity of the SCS was supported by the finding that in both genders, the SCS scores were significantly (p < 0.05 and inversely associated with psychological distress and sleep disturbance in crude and in multivariable analyses that adjusted for demographics, organization-based self-esteem, work engagement, effort–reward ratio, workplace bullying, and procedural and interactional justice. The SCS is a psychometrically satisfactory measure of sense of contribution in the workplace. The SCS provides a new and useful instrument to measure sense of contribution, which is independently associated with mental health in workers, for studies in organizational science, occupational health psychology and occupational medicine.

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

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Meyfroidt Geert

2011-10-01

Full Text Available Abstract Background The intensive care unit (ICU length of stay (LOS of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP, a machine learning technique. Methods Non-interventional study. Predictive modeling, separate development (n = 461 and validation (n = 499 cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task, and to predict the day of ICU discharge as a discrete variable (regression task. GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF ((actual-predicted/actual and calculating root mean squared relative errors (RMSRE. Results Median (P25-P75 ICU length of stay was 3 (2-5 days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%, which was significantly better than the EuroSCORE (p Conclusions A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a

Mortality after Spontaneous Subarachnoid Hemorrhage: Causality and Validation of a Prediction Model.

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Abulhasan, Yasser B; Alabdulraheem, Najayeb; Simoneau, Gabrielle; Angle, Mark R; Teitelbaum, Jeanne

2018-04-01

To evaluate primary causes of death after spontaneous subarachnoid hemorrhage (SAH) and externally validate the HAIR score, a prognostication tool, in a single academic institution. We reviewed all patients with SAH admitted to our neuro-intensive care unit between 2010 and 2016. Univariate and multivariate logistic regressions were performed to identify predictors of in-hospital mortality. The HAIR score predictors were Hunt and Hess grade at treatment decision, age, intraventricular hemorrhage, and rebleeding within 24 hours. Validation of the HAIR score was characterized with the receiver operating curve, the area under the curve, and a calibration plot. Among 434 patients with SAH, in-hospital mortality was 14.1%. Of the 61 mortalities, 54 (88.5%) had a neurologic cause of death or withdrawal of care and 7 (11.5%) had cardiac death. Median time from SAH to death was 6 days. The main causes of death were effect of the initial hemorrhage (26.2%), rebleeding (23%) and refractory cerebral edema (19.7%). Factors significantly associated with in-hospital mortality in the multivariate analysis were age, Hunt and Hess grade, and intracerebral hemorrhage. Maximum lumen size was also a significant risk factor after aneurysmal SAH. The HAIR score had a satisfactory discriminative ability, with an area under the curve of 0.89. The in-hospital mortality is lower than in previous reports, attesting to the continuing improvement of our institutional SAH care. The major causes are the same as in previous reports. Despite a different therapeutic protocol, the HAIR score showed good discrimination and could be a useful tool for predicting mortality. Copyright © 2018 Elsevier Inc. All rights reserved.

The predictive validity of the Drinking-Related Cognitions Scale in alcohol-dependent patients under abstinence-oriented treatment

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Sawayama Toru

2012-05-01

Full Text Available Abstract Background Cognitive factors associated with drinking behavior such as positive alcohol expectancies, self-efficacy, perception of impaired control over drinking and perception of drinking problems are considered to have a significant influence on treatment effects and outcome in alcohol-dependent patients. However, the development of a rating scale on lack of perception or denial of drinking problems and impaired control over drinking has not been substantial, even though these are important factors in patients under abstinence-oriented treatment as well as participants in self-help groups such as Alcoholics Anonymous (AA. The Drinking-Related Cognitions Scale (DRCS is a new self-reported rating scale developed to briefly measure cognitive factors associated with drinking behavior in alcohol-dependent patients under abstinence-oriented treatment, including positive alcohol expectancies, abstinence self-efficacy, perception of impaired control over drinking, and perception of drinking problems. Here, we conducted a prospective cohort study to explore the predictive validity of DRCS. Methods Participants in this study were 175 middle-aged and elderly Japanese male patients who met the DSM-IV Diagnostic Criteria for Alcohol Dependence. DRCS scores were recorded before and after the inpatient abstinence-oriented treatment program, and treatment outcome was evaluated one year after discharge. Results Of the 175 participants, 30 were not available for follow-up; thus the number of subjects for analysis in this study was 145. When the total DRCS score and subscale scores were compared before and after inpatient treatment, a significant increase was seen for both scores. Both the total DRCS score and each subscale score were significantly related to total abstinence, percentage of abstinent days, and the first drinking occasion during the one-year post-treatment period. Therefore, good treatment outcome was significantly predicted by low

The Validity of Conscientiousness Is Overestimated in the Prediction of Job Performance.

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Kepes, Sven; McDaniel, Michael A

2015-01-01

Sensitivity analyses refer to investigations of the degree to which the results of a meta-analysis remain stable when conditions of the data or the analysis change. To the extent that results remain stable, one can refer to them as robust. Sensitivity analyses are rarely conducted in the organizational science literature. Despite conscientiousness being a valued predictor in employment selection, sensitivity analyses have not been conducted with respect to meta-analytic estimates of the correlation (i.e., validity) between conscientiousness and job performance. To address this deficiency, we reanalyzed the largest collection of conscientiousness validity data in the personnel selection literature and conducted a variety of sensitivity analyses. Publication bias analyses demonstrated that the validity of conscientiousness is moderately overestimated (by around 30%; a correlation difference of about .06). The misestimation of the validity appears to be due primarily to suppression of small effects sizes in the journal literature. These inflated validity estimates result in an overestimate of the dollar utility of personnel selection by millions of dollars and should be of considerable concern for organizations. The fields of management and applied psychology seldom conduct sensitivity analyses. Through the use of sensitivity analyses, this paper documents that the existing literature overestimates the validity of conscientiousness in the prediction of job performance. Our data show that effect sizes from journal articles are largely responsible for this overestimation.

Acute Kidney Injury in Trauma Patients Admitted to Critical Care: Development and Validation of a Diagnostic Prediction Model.

Science.gov (United States)

Haines, Ryan W; Lin, Shih-Pin; Hewson, Russell; Kirwan, Christopher J; Torrance, Hew D; O'Dwyer, Michael J; West, Anita; Brohi, Karim; Pearse, Rupert M; Zolfaghari, Parjam; Prowle, John R

2018-02-26

Acute Kidney Injury (AKI) complicating major trauma is associated with increased mortality and morbidity. Traumatic AKI has specific risk factors and predictable time-course facilitating diagnostic modelling. In a single centre, retrospective observational study we developed risk prediction models for AKI after trauma based on data around intensive care admission. Models predicting AKI were developed using data from 830 patients, using data reduction followed by logistic regression, and were independently validated in a further 564 patients. AKI occurred in 163/830 (19.6%) with 42 (5.1%) receiving renal replacement therapy (RRT). First serum creatinine and phosphate, units of blood transfused in first 24 h, age and Charlson score discriminated need for RRT and AKI early after trauma. For RRT c-statistics were good to excellent: development: 0.92 (0.88-0.96), validation: 0.91 (0.86-0.97). Modelling AKI stage 2-3, c-statistics were also good, development: 0.81 (0.75-0.88) and validation: 0.83 (0.74-0.92). The model predicting AKI stage 1-3 performed moderately, development: c-statistic 0.77 (0.72-0.81), validation: 0.70 (0.64-0.77). Despite good discrimination of need for RRT, positive predictive values (PPV) at the optimal cut-off were only 23.0% (13.7-42.7) in development. However, PPV for the alternative endpoint of RRT and/or death improved to 41.2% (34.8-48.1) highlighting death as a clinically relevant endpoint to RRT.

Predicting surgical site infection after spine surgery: a validated model using a prospective surgical registry.

Science.gov (United States)

Lee, Michael J; Cizik, Amy M; Hamilton, Deven; Chapman, Jens R

2014-09-01

The impact of surgical site infection (SSI) is substantial. Although previous study has determined relative risk and odds ratio (OR) values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of SSI, rather than relative risk or OR values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. The purpose of this study was to create and validate a predictive model for the risk of SSI after spine surgery. This study performs a multivariate analysis of SSI after spine surgery using a large prospective surgical registry. Using the results of this analysis, this study will then create and validate a predictive model for SSI after spine surgery. The patient sample is from a high-quality surgical registry from our two institutions with prospectively collected, detailed demographic, comorbidity, and complication data. An SSI that required return to the operating room for surgical debridement. Using a prospectively collected surgical registry of more than 1,532 patients with extensive demographic, comorbidity, surgical, and complication details recorded for 2 years after the surgery, we identified several risk factors for SSI after multivariate analysis. Using the beta coefficients from those regression analyses, we created a model to predict the occurrence of SSI after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created a predictive model based on our beta coefficients from our multivariate analysis. The final predictive model for SSI had a receiver-operator curve characteristic of 0.72, considered to be a fair measure. The final model has been uploaded for use on SpineSage.com. We present a validated model for predicting SSI after spine surgery. The value in this model is that it gives

Testing earthquake prediction algorithms: Statistically significant advance prediction of the largest earthquakes in the Circum-Pacific, 1992-1997

Science.gov (United States)

Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.

1999-01-01

Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier

A prediction algorithm for first onset of major depression in the general population: development and validation.

Science.gov (United States)

Wang, JianLi; Sareen, Jitender; Patten, Scott; Bolton, James; Schmitz, Norbert; Birney, Arden

2014-05-01

Prediction algorithms are useful for making clinical decisions and for population health planning. However, such prediction algorithms for first onset of major depression do not exist. The objective of this study was to develop and validate a prediction algorithm for first onset of major depression in the general population. Longitudinal study design with approximate 3-year follow-up. The study was based on data from a nationally representative sample of the US general population. A total of 28 059 individuals who participated in Waves 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions and who had not had major depression at Wave 1 were included. The prediction algorithm was developed using logistic regression modelling in 21 813 participants from three census regions. The algorithm was validated in participants from the 4th census region (n=6246). Major depression occurred since Wave 1 of the National Epidemiologic Survey on Alcohol and Related Conditions, assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule-diagnostic and statistical manual for mental disorders IV. A prediction algorithm containing 17 unique risk factors was developed. The algorithm had good discriminative power (C statistics=0.7538, 95% CI 0.7378 to 0.7699) and excellent calibration (F-adjusted test=1.00, p=0.448) with the weighted data. In the validation sample, the algorithm had a C statistic of 0.7259 and excellent calibration (Hosmer-Lemeshow χ(2)=3.41, p=0.906). The developed prediction algorithm has good discrimination and calibration capacity. It can be used by clinicians, mental health policy-makers and service planners and the general public to predict future risk of having major depression. The application of the algorithm may lead to increased personalisation of treatment, better clinical decisions and more optimal mental health service planning.

External Validation Study of First Trimester Obstetric Prediction Models (Expect Study I): Research Protocol and Population Characteristics.

Science.gov (United States)

Meertens, Linda Jacqueline Elisabeth; Scheepers, Hubertina Cj; De Vries, Raymond G; Dirksen, Carmen D; Korstjens, Irene; Mulder, Antonius Lm; Nieuwenhuijze, Marianne J; Nijhuis, Jan G; Spaanderman, Marc Ea; Smits, Luc Jm

2017-10-26

A number of first-trimester prediction models addressing important obstetric outcomes have been published. However, most models have not been externally validated. External validation is essential before implementing a prediction model in clinical practice. The objective of this paper is to describe the design of a study to externally validate existing first trimester obstetric prediction models, based upon maternal characteristics and standard measurements (eg, blood pressure), for the risk of pre-eclampsia (PE), gestational diabetes mellitus (GDM), spontaneous preterm birth (PTB), small-for-gestational-age (SGA) infants, and large-for-gestational-age (LGA) infants among Dutch pregnant women (Expect Study I). The results of a pilot study on the feasibility and acceptability of the recruitment process and the comprehensibility of the Pregnancy Questionnaire 1 are also reported. A multicenter prospective cohort study was performed in The Netherlands between July 1, 2013 and December 31, 2015. First trimester obstetric prediction models were systematically selected from the literature. Predictor variables were measured by the Web-based Pregnancy Questionnaire 1 and pregnancy outcomes were established using the Postpartum Questionnaire 1 and medical records. Information about maternal health-related quality of life, costs, and satisfaction with Dutch obstetric care was collected from a subsample of women. A pilot study was carried out before the official start of inclusion. External validity of the models will be evaluated by assessing discrimination and calibration. Based on the pilot study, minor improvements were made to the recruitment process and online Pregnancy Questionnaire 1. The validation cohort consists of 2614 women. Data analysis of the external validation study is in progress. This study will offer insight into the generalizability of existing, non-invasive first trimester prediction models for various obstetric outcomes in a Dutch obstetric population

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

Directory of Open Access Journals (Sweden)

Hendrik Treutler

2016-10-01

Full Text Available Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92 % of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0.

External validation of a PCA-3-based nomogram for predicting prostate cancer and high-grade cancer on initial prostate biopsy.

Science.gov (United States)

Greene, Daniel J; Elshafei, Ahmed; Nyame, Yaw A; Kara, Onder; Malkoc, Ercan; Gao, Tianming; Jones, J Stephen

2016-08-01

The aim of this study was to externally validate a previously developed PCA3-based nomogram for the prediction of prostate cancer (PCa) and high-grade (intermediate and/or high-grade) prostate cancer (HGPCa) at the time of initial prostate biopsy. A retrospective review was performed on a cohort of 336 men from a large urban academic medical center. All men had serum PSA PCa, PSA at diagnosis, PCA3, total prostate volume (TPV), and abnormal finding on digital rectal exam (DRE). These variables were used to test the accuracy (concordance index) and calibration of a previously published PCA3 nomogram. Biopsy confirms PCa and HGPCa in 51.0% and 30.4% of validation patients, respectively. This differed from the original cohort in that it had significantly more PCa and HGPCA (51% vs. 44%, P = 0.019; and 30.4% vs. 19.1%, P PCa detection the concordance index was 75% and 77% for overall PCa and HGPCa, respectively. Calibration for overall PCa was good. This represents the first external validation of a PCA3-based prostate cancer predictive nomogram in a North American population. Prostate 76:1019-1023, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

Science.gov (United States)

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

2018-03-01

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

Validity of impedance-based equations for the prediction of total body water as measured by deuterium dilution in African women

International Nuclear Information System (INIS)

Dioum, Aissatou S.; Cisse, Aita; Wade, Salimata; Gartner, Agnes; Delpeuch, Francis; Maire, Bernard; Schutz, Yves

2005-01-01

Background: Little information is available on the validity of simple and indirect body-composition methods in non-Western populations. Equations for predicting body composition are population- specific, and body composition differs between blacks and whites. Objective:Wetestedthehypothesisthatthevalidityofequationsfor predicting total body water (TBW) from bioelectrical impedance analysis measurements is likely to depend on the racial background of the group from which the equations were derived. Design: The hypothesis was tested by comparing, in 36 African women, TBW values measured by deuterium dilution with those predicted by 23 equations developed in white, African American, or African subjects. These cross-validations in our African sample were also compared, whenever possible, with results from other studies in black subjects. Results: Errors in predicting TBW showed acceptable values (1.3- 1.9 kg) in all cases, whereas a large range of bias (0.2-6.1 kg) was observed independently of the ethnic origin of the sample from which the equations were derived. Three equations (2 from whites and 1 from blacks) showed nonsignificant bias and could be used in Africans. In all other cases, we observed either an overestimation or under estimation of TBW with variable bias values, regardless of racial background, yielding no clear trend for validity as a function of ethnic origin. Conclusions: The findings of this cross-validation study emphasize the need for further fundamental research to explore the causes of the poor validity of TBW prediction equations across populations rather than the need to develop new prediction equations for use in Africa. (Authors)

Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

Science.gov (United States)

Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

2011-01-01

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part II, External Validation and Usability Testing of a Smartphone App

Directory of Open Access Journals (Sweden)

Min Soo Choo

2017-04-01

Full Text Available Purpose We aimed to externally validate the prediction model we developed for having bladder outlet obstruction (BOO and requiring prostatic surgery using 2 independent data sets from tertiary referral centers, and also aimed to validate a mobile app for using this model through usability testing. Methods Formulas and nomograms predicting whether a subject has BOO and needs prostatic surgery were validated with an external validation cohort from Seoul National University Bundang Hospital and Seoul Metropolitan Government-Seoul National University Boramae Medical Center between January 2004 and April 2015. A smartphone-based app was developed, and 8 young urologists were enrolled for usability testing to identify any human factor issues of the app. Results A total of 642 patients were included in the external validation cohort. No significant differences were found in the baseline characteristics of major parameters between the original (n=1,179 and the external validation cohort, except for the maximal flow rate. Predictions of requiring prostatic surgery in the validation cohort showed a sensitivity of 80.6%, a specificity of 73.2%, a positive predictive value of 49.7%, and a negative predictive value of 92.0%, and area under receiver operating curve of 0.84. The calibration plot indicated that the predictions have good correspondence. The decision curve showed also a high net benefit. Similar evaluation results using the external validation cohort were seen in the predictions of having BOO. Overall results of the usability test demonstrated that the app was user-friendly with no major human factor issues. Conclusions External validation of these newly developed a prediction model demonstrated a moderate level of discrimination, adequate calibration, and high net benefit gains for predicting both having BOO and requiring prostatic surgery. Also a smartphone app implementing the prediction model was user-friendly with no major human factor issue.

Building and validation of a prognostic model for predicting extracorporeal circuit clotting in patients with continuous renal replacement therapy.

Science.gov (United States)

Fu, Xia; Liang, Xinling; Song, Li; Huang, Huigen; Wang, Jing; Chen, Yuanhan; Zhang, Li; Quan, Zilin; Shi, Wei

2014-04-01

To develop a predictive model for circuit clotting in patients with continuous renal replacement therapy (CRRT). A total of 425 cases were selected. 302 cases were used to develop a predictive model of extracorporeal circuit life span during CRRT without citrate anticoagulation in 24 h, and 123 cases were used to validate the model. The prediction formula was developed using multivariate Cox proportional-hazards regression analysis, from which a risk score was assigned. The mean survival time of the circuit was 15.0 ± 1.3 h, and the rate of circuit clotting was 66.6 % during 24 h of CRRT. Five significant variables were assigned a predicting score according to the regression coefficient: insufficient blood flow, no anticoagulation, hematocrit ≥0.37, lactic acid of arterial blood gas analysis ≤3 mmol/L and APTT R (2) = 0.232; P = 0.301). A risk score that includes the five above-mentioned variables can be used to predict the likelihood of extracorporeal circuit clotting in patients undergoing CRRT.

Tone Noise Predictions for a Spacecraft Cabin Ventilation Fan Ingesting Distorted Inflow and the Challenges of Validation

Science.gov (United States)

Koch, L. Danielle; Shook, Tony D.; Astler, Douglas T.; Bittinger, Samantha A.

2012-01-01

A fan tone noise prediction code has been developed at NASA Glenn Research Center that is capable of estimating duct mode sound power levels for a fan ingesting distorted inflow. This code was used to predict the circumferential and radial mode sound power levels in the inlet and exhaust duct of an axial spacecraft cabin ventilation fan. Noise predictions at fan design rotational speed were generated. Three fan inflow conditions were studied: an undistorted inflow, a circumferentially symmetric inflow distortion pattern (cylindrical rods inserted radially into the flowpath at 15deg, 135deg, and 255deg), and a circumferentially asymmetric inflow distortion pattern (rods located at 15deg, 52deg and 173deg). Noise predictions indicate that tones are produced for the distorted inflow cases that are not present when the fan operates with an undistorted inflow. Experimental data are needed to validate these acoustic predictions, as well as the aerodynamic performance predictions. Given the aerodynamic design of the spacecraft cabin ventilation fan, a mechanical and electrical conceptual design study was conducted. Design features of a fan suitable for obtaining detailed acoustic and aerodynamic measurements needed to validate predictions are discussed.

Validation of forcefields in predicting the physical and thermophysical properties of emeraldine base polyaniline

NARCIS (Netherlands)

Chen, X.P.; Yuan, C.A.; Wong, C.K.Y.; Koh, S.W.; Zhang, G.Q.

2011-01-01

We report a molecular modelling study to validate the forcefields [condensed-phase optimised molecular potentials for atomistic simulation studies (COMPASS) and polymer-consistent forcefield (PCFF)] in predicting the physical and thermophysical properties of polymers. This work comprises of two key

European Randomized Study of Screening for Prostate Cancer Risk Calculator: External Validation, Variability, and Clinical Significance.

Science.gov (United States)

Gómez-Gómez, Enrique; Carrasco-Valiente, Julia; Blanca-Pedregosa, Ana; Barco-Sánchez, Beatriz; Fernandez-Rueda, Jose Luis; Molina-Abril, Helena; Valero-Rosa, Jose; Font-Ugalde, Pilar; Requena-Tapia, Maria José

2017-04-01

To externally validate the European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator (RC) and to evaluate its variability between 2 consecutive prostate-specific antigen (PSA) values. We prospectively catalogued 1021 consecutive patients before prostate biopsy for suspicion of prostate cancer (PCa). The risk of PCa and significant PCa (Gleason score ≥7) from 749 patients was calculated according to ERSPC-RC (digital rectal examination-based version 3 of 4) for 2 consecutive PSA tests per patient. The calculators' predictions were analyzed using calibration plots and the area under the receiver operating characteristic curve (area under the curve). Cohen kappa coefficient was used to compare the ability and variability. Of 749 patients, PCa was detected in 251 (33.5%) and significant PCa was detected in 133 (17.8%). Calibration plots showed an acceptable parallelism and similar discrimination ability for both PSA levels with an area under the curve of 0.69 for PCa and 0.74 for significant PCa. The ERSPC showed 226 (30.2%) unnecessary biopsies with the loss of 10 significant PCa. The variability of the RC was 16% for PCa and 20% for significant PCa, and a higher variability was associated with a reduced risk of significant PCa. We can conclude that the performance of the ERSPC-RC in the present cohort shows a high similitude between the 2 PSA levels; however, the RC variability value is associated with a decreased risk of significant PCa. The use of the ERSPC in our cohort detects a high number of unnecessary biopsies. Thus, the incorporation of ERSPC-RC could help the clinical decision to carry out a prostate biopsy. Copyright © 2016 Elsevier Inc. All rights reserved.

The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: Introduction and forensic developmental validation.

Science.gov (United States)

Chaitanya, Lakshmi; Breslin, Krystal; Zuñiga, Sofia; Wirken, Laura; Pośpiech, Ewelina; Kukla-Bartoszek, Magdalena; Sijen, Titia; Knijff, Peter de; Liu, Fan; Branicki, Wojciech; Kayser, Manfred; Walsh, Susan

2018-07-01

Forensic DNA Phenotyping (FDP), i.e. the prediction of human externally visible traits from DNA, has become a fast growing subfield within forensic genetics due to the intelligence information it can provide from DNA traces. FDP outcomes can help focus police investigations in search of unknown perpetrators, who are generally unidentifiable with standard DNA profiling. Therefore, we previously developed and forensically validated the IrisPlex DNA test system for eye colour prediction and the HIrisPlex system for combined eye and hair colour prediction from DNA traces. Here we introduce and forensically validate the HIrisPlex-S DNA test system (S for skin) for the simultaneous prediction of eye, hair, and skin colour from trace DNA. This FDP system consists of two SNaPshot-based multiplex assays targeting a total of 41 SNPs via a novel multiplex assay for 17 skin colour predictive SNPs and the previous HIrisPlex assay for 24 eye and hair colour predictive SNPs, 19 of which also contribute to skin colour prediction. The HIrisPlex-S system further comprises three statistical prediction models, the previously developed IrisPlex model for eye colour prediction based on 6 SNPs, the previous HIrisPlex model for hair colour prediction based on 22 SNPs, and the recently introduced HIrisPlex-S model for skin colour prediction based on 36 SNPs. In the forensic developmental validation testing, the novel 17-plex assay performed in full agreement with the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines, as previously shown for the 24-plex assay. Sensitivity testing of the 17-plex assay revealed complete SNP profiles from as little as 63 pg of input DNA, equalling the previously demonstrated sensitivity threshold of the 24-plex HIrisPlex assay. Testing of simulated forensic casework samples such as blood, semen, saliva stains, of inhibited DNA samples, of low quantity touch (trace) DNA samples, and of artificially degraded DNA samples as well as

Prediction of flow in mix-proof valve by use of CFD - Validation by LDA

DEFF Research Database (Denmark)

Jensen, Bo Boye Busk; Friis, Alan

2004-01-01

was done on a spherical shaped mix-proof valve (MPV). Flow were predicted by Computational Fluid Dynamics (CFD) and validated by data obtained from experiments using laser sheet visualization and laser Doppler anemometry. Correction of the measured velocities and probe location was required as refraction......-wall region is shown. Fully 3D flow patterns were identified and valuable information was obtained for further investigations concerning prediction of cleanability in the MPV based on knowledge of the hydrodynamics herein....

Predictive Validity of the Body Adiposity Index in Overweight and Obese Adults Using Dual-Energy X-ray Absorptiometry

Science.gov (United States)

Ramírez-Vélez, Robinson; Correa-Bautista, Jorge Enrique; González-Ruíz, Katherine; Vivas, Andrés; García-Hermoso, Antonio; Triana-Reina, Hector Reynaldo

2016-01-01

The body adiposity index (BAI) is a recent anthropometric measure proven to be valid in predicting body fat percentage (BF%) in some populations. However, the results have been inconsistent across populations. This study was designed to verify the validity of BAI in predicting BF% in a sample of overweight/obese adults, using dual-energy X-ray absorptiometry (DEXA) as the reference method. A cross-sectional study was conducted in 48 participants (54% women, mean age 41.0 ± 7.3 years old). DEXA was used as the “gold standard” to determine BF%. Pearson’s correlation coefficient was used to evaluate the association between BAI and BF%, as assessed by DEXA. A paired sample t-test was used to test differences in mean BF% obtained with BAI and DEXA methods. To evaluate the concordance between BF% as measured by DEXA and as estimated by BAI, we used Lin’s concordance correlation coefficient and Bland–Altman agreement analysis. The correlation between BF% obtained by DEXA and that estimated by BAI was r = 0.844, p < 0.001. Paired t-test showed a significant mean difference in BF% between methods (BAI = 33.3 ± 6.2 vs. DEXA 39.0 ± 6.1; p < 0.001). The bias of the BAI was −6.0 ± 3.0 BF% (95% CI = −12.0 to 1.0), indicating that the BAI method significantly underestimated the BF% compared to the reference method. Lin’s concordance correlation coefficient was considered stronger (ρc = 0.923, 95% CI = 0.862 to 0.957). In obese adults, BAI presented low agreement with BF% measured by DEXA; therefore, BAI is not recommended for BF% prediction in this overweight/obese sample studied. PMID:27916871

Exploring the significance of human mobility patterns in social link prediction

KAUST Repository

Alharbi, Basma Mohammed

2014-01-01

Link prediction is a fundamental task in social networks. Recently, emphasis has been placed on forecasting new social ties using user mobility patterns, e.g., investigating physical and semantic co-locations for new proximity measure. This paper explores the effect of in-depth mobility patterns. Specifically, we study individuals\\' movement behavior, and quantify mobility on the basis of trip frequency, travel purpose and transportation mode. Our hybrid link prediction model is composed of two modules. The first module extracts mobility patterns, including travel purpose and mode, from raw trajectory data. The second module employs the extracted patterns for link prediction. We evaluate our method on two real data sets, GeoLife [15] and Reality Mining [5]. Experimental results show that our hybrid model significantly improves the accuracy of social link prediction, when comparing to primary topology-based solutions. Copyright 2014 ACM.

Predictive validity of examinations at the Secondary Education Certificate (SEC) level

OpenAIRE

Farrugia, Josette; Ventura, Frank

2007-01-01

This paper presents the predictive validity of results obtained by 16-year-old Maltese students in the May 2004 Secondary Education Certificate (SEC) examinations in Biology, Chemistry, Physics, Mathematics, Computing, English and Maltese for the Advanced level examinations in these subjects taken by the same students two years later. The study checks whether the SEC level is a good foundation for the higher level, the likelihood of obtaining a high grade at A-level from particular SEC result...

A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models.

Science.gov (United States)

Patching, Helena M M; Hudson, Laurence M; Cooke, Warrick; Garcia, Andres J; Hay, Simon I; Roberts, Mark; Moyes, Catherine L

2015-12-01

Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%-79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is

Predictive validity of proposed remission criteria in first-episode schizophrenic patients responding to antipsychotics

NARCIS (Netherlands)

Wunderink, Lex; Nienhuis, Fokko J.; Sytema, Sjoerd; Wiersma, Durk

The objective of this study was to examine the predictive validity of the remission criteria proposed by Andreasen et all in first-episode patients responding to antipsychotics. Antipsychotic responsive patients with first-episode schizophrenia showing symptom remission (n = 60) were compared with

Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model

Energy Technology Data Exchange (ETDEWEB)

Louie, Alexander V., E-mail: Dr.alexlouie@gmail.com [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, Massachusetts (United States); Haasbeek, Cornelis J.A. [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Mokhles, Sahar [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Rodrigues, George B. [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Stephans, Kevin L. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Lagerwaard, Frank J. [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Palma, David A. [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Videtic, Gregory M.M. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Warner, Andrew [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Takkenberg, Johanna J.M. [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Reddy, Chandana A. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Maat, Alex P.W.M. [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Woody, Neil M. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Slotman, Ben J.; Senan, Suresh [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands)

2015-09-01

Purpose: A prognostic model for 5-year overall survival (OS), consisting of recursive partitioning analysis (RPA) and a nomogram, was developed for patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR). Methods and Materials: A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogram for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. Both the RPA and the nomogram were externally validated in independent surgical (n=193) and SABR (n=543) datasets. Results: RPA identified 2 distinct risk classes based on tumor diameter, age, World Health Organization performance status (PS) and Charlson comorbidity index. This RPA had moderate discrimination in SABR datasets (c-index range: 0.52-0.60) but was of limited value in the surgical validation cohort. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internal validation (r{sup 2}=0.97) and external SABR (r{sup 2}=0.79) and surgical cohorts (r{sup 2}=0.91). Conclusions: The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities.

Predicting medical complications after spine surgery: a validated model using a prospective surgical registry.

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Lee, Michael J; Cizik, Amy M; Hamilton, Deven; Chapman, Jens R

2014-02-01

The possibility and likelihood of a postoperative medical complication after spine surgery undoubtedly play a major role in the decision making of the surgeon and patient alike. Although prior study has determined relative risk and odds ratio values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of medical complication, rather than relative risk or odds ratio values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. The purpose of this study was to create and validate a predictive model for the risk of medical complication during and after spine surgery. Statistical analysis using a prospective surgical spine registry that recorded extensive demographic, surgical, and complication data. Outcomes examined are medical complications that were specifically defined a priori. This analysis is a continuation of statistical analysis of our previously published report. Using a prospectively collected surgical registry of more than 1,476 patients with extensive demographic, comorbidity, surgical, and complication detail recorded for 2 years after surgery, we previously identified several risk factor for medical complications. Using the beta coefficients from those log binomial regression analyses, we created a model to predict the occurrence of medical complication after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created two predictive models: one predicting the occurrence of any medical complication and the other predicting the occurrence of a major medical complication. The final predictive model for any medical complications had a receiver operator curve characteristic of 0.76, considered to be a fair measure. The final predictive model for any major medical complications had

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

Science.gov (United States)

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

Predicting the ungauged basin: Model validation and realism assessment

Directory of Open Access Journals (Sweden)

Tim evan Emmerik

2015-10-01

Full Text Available The hydrological decade on Predictions in Ungauged Basins (PUB led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of model outcome has not been discussed to a great extent. With this paper we aim to contribute to the discussion on how one can determine the value and validity of a hydrological model developed for an ungauged basin. As in many cases no local, or even regional, data are available, alternative methods should be applied. Using a PUB case study in a genuinely ungauged basin in southern Cambodia, we give several examples of how one can use different types of soft data to improve model design, calibrate and validate the model, and assess the realism of the model output. A rainfall-runoff model was coupled to an irrigation reservoir, allowing the use of additional and unconventional data. The model was mainly forced with remote sensing data, and local knowledge was used to constrain the parameters. Model realism assessment was done using data from surveys. This resulted in a successful reconstruction of the reservoir dynamics, and revealed the different hydrological characteristics of the two topographical classes. This paper does not present a generic approach that can be transferred to other ungauged catchments, but it aims to show how clever model design and alternative data acquisition can result in a valuable hydrological model for an ungauged catchment.

The Smoking Consequences Questionnaire: Factor structure and predictive validity among Spanish-speaking Latino smokers in the United States.

Science.gov (United States)

Vidrine, Jennifer Irvin; Vidrine, Damon J; Costello, Tracy J; Mazas, Carlos; Cofta-Woerpel, Ludmila; Mejia, Luz Maria; Wetter, David W

2009-11-01

Much of the existing research on smoking outcome expectancies has been guided by the Smoking Consequences Questionnaire (SCQ ). Although the original version of the SCQ has been modified over time for use in different populations, none of the existing versions have been evaluated for use among Spanish-speaking Latino smokers in the United States. The present study evaluated the factor structure and predictive validity of the 3 previously validated versions of the SCQ--the original, the SCQ-Adult, and the SCQ-Spanish, which was developed with Spanish-speaking smokers in Spain--among Spanish-speaking Latino smokers in Texas. The SCQ-Spanish represented the least complex solution. Each of the SCQ-Spanish scales had good internal consistency, and the predictive validity of the SCQ-Spanish was partially supported. Nearly all the SCQ-Spanish scales predicted withdrawal severity even after controlling for demographics and dependence. Boredom Reduction predicted smoking relapse across the 5- and 12-week follow-up assessments in a multivariate model that also controlled for demographics and dependence. Our results support use of the SCQ-Spanish with Spanish-speaking Latino smokers in the United States.

Validation of CRIB II for prediction of mortality in premature babies.

Science.gov (United States)

Rastogi, Pallav Kumar; Sreenivas, V; Kumar, Nirmal

2010-02-01

Validation of Clinical Risk Index for Babies (CRIB II) score in predicting the neonatal mortality in preterm neonates < or = 32 weeks gestational age. Prospective cohort study. Tertiary care neonatal unit. 86 consecutively born preterm neonates with gestational age < or = 32 weeks. The five variables related to CRIB II were recorded within the first hour of admission for data analysis. The receiver operating characteristics (ROC) curve was used to check the accuracy of the mortality prediction. HL Goodness of fit test was used to see the discrepancy between observed and expected outcomes. A total of 86 neonates (males 59.6% mean birthweight: 1228 +/- 398 grams; mean gestational age: 28.3 +/- 2.4 weeks) were enrolled in the study, of which 17 (19.8%) left hospital against medical advice (LAMA) before reaching the study end point. Among 69 neonates completing the study, 24 (34.8%) had adverse outcome during hospital stay and 45 (65.2%) had favorable outcome. CRIB II correctly predicted adverse outcome in 90.3% (Hosmer Lemeshow goodness of fit test P=0.6). Area under curve (AUC) for CRIB II was 0.9032. In intention to treat analysis with LAMA cases included as survivors, the mortality prediction was 87%. If these were included as having died then mortality prediction was 83.1%. The CRIB II score was found to be a good predictive instrument for mortality in preterm infants < or = 32 weeks gestation.

An Automated Defect Prediction Framework using Genetic Algorithms: A Validation of Empirical Studies

Directory of Open Access Journals (Sweden)

Juan Murillo-Morera

2016-05-01

Full Text Available Today, it is common for software projects to collect measurement data through development processes. With these data, defect prediction software can try to estimate the defect proneness of a software module, with the objective of assisting and guiding software practitioners. With timely and accurate defect predictions, practitioners can focus their limited testing resources on higher risk areas. This paper reports the results of three empirical studies that uses an automated genetic defect prediction framework. This framework generates and compares different learning schemes (preprocessing + attribute selection + learning algorithms and selects the best one using a genetic algorithm, with the objective to estimate the defect proneness of a software module. The first empirical study is a performance comparison of our framework with the most important framework of the literature. The second empirical study is a performance and runtime comparison between our framework and an exhaustive framework. The third empirical study is a sensitivity analysis. The last empirical study, is our main contribution in this paper. Performance of the software development defect prediction models (using AUC, Area Under the Curve was validated using NASA-MDP and PROMISE data sets. Seventeen data sets from NASA-MDP (13 and PROMISE (4 projects were analyzed running a NxM-fold cross-validation. A genetic algorithm was used to select the components of the learning schemes automatically, and to assess and report the results. Our results reported similar performance between frameworks. Our framework reported better runtime than exhaustive framework. Finally, we reported the best configuration according to sensitivity analysis.

Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions.

Science.gov (United States)

Noirhomme, Quentin; Lesenfants, Damien; Gomez, Francisco; Soddu, Andrea; Schrouff, Jessica; Garraux, Gaëtan; Luxen, André; Phillips, Christophe; Laureys, Steven

2014-01-01

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with cross-validation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the cross-validation was further illustrated on real-data from a brain-computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation.

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

Science.gov (United States)

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

2011-01-01

Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

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

Directory of Open Access Journals (Sweden)

Michael King

Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Validation of model predictions of pore-scale fluid distributions during two-phase flow

Science.gov (United States)

Bultreys, Tom; Lin, Qingyang; Gao, Ying; Raeini, Ali Q.; AlRatrout, Ahmed; Bijeljic, Branko; Blunt, Martin J.

2018-05-01

Pore-scale two-phase flow modeling is an important technology to study a rock's relative permeability behavior. To investigate if these models are predictive, the calculated pore-scale fluid distributions which determine the relative permeability need to be validated. In this work, we introduce a methodology to quantitatively compare models to experimental fluid distributions in flow experiments visualized with microcomputed tomography. First, we analyzed five repeated drainage-imbibition experiments on a single sample. In these experiments, the exact fluid distributions were not fully repeatable on a pore-by-pore basis, while the global properties of the fluid distribution were. Then two fractional flow experiments were used to validate a quasistatic pore network model. The model correctly predicted the fluid present in more than 75% of pores and throats in drainage and imbibition. To quantify what this means for the relevant global properties of the fluid distribution, we compare the main flow paths and the connectivity across the different pore sizes in the modeled and experimental fluid distributions. These essential topology characteristics matched well for drainage simulations, but not for imbibition. This suggests that the pore-filling rules in the network model we used need to be improved to make reliable predictions of imbibition. The presented analysis illustrates the potential of our methodology to systematically and robustly test two-phase flow models to aid in model development and calibration.

Validation and Refinement of Prediction Models to Estimate Exercise Capacity in Cancer Survivors Using the Steep Ramp Test

NARCIS (Netherlands)

Stuiver, Martijn M.; Kampshoff, Caroline S.; Persoon, Saskia; Groen, Wim; van Mechelen, Willem; Chinapaw, Mai J. M.; Brug, Johannes; Nollet, Frans; Kersten, Marie-José; Schep, Goof; Buffart, Laurien M.

2017-01-01

Objective: To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo2(peak)) and peak power output (W-peak).&

Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables.

Science.gov (United States)

Roelen, Corné; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bültmann, Ute; Bjørner, Jakob

2018-01-01

The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for

A model of prediction and cross-validation of fat-free mass in men with motor complete spinal cord injury.

Science.gov (United States)

Gorgey, Ashraf S; Dolbow, David R; Gater, David R

2012-07-01

To establish and validate prediction equations by using body weight to predict legs, trunk, and whole-body fat-free mass (FFM) in men with chronic complete spinal cord injury (SCI). Cross-sectional design. Research setting in a large medical center. Individuals with SCI (N=63) divided into prediction (n=42) and cross-validation (n=21) groups. Not applicable. Whole-body FFM and regional FFM were determined by using dual-energy x-ray absorptiometry. Body weight was measured by using a wheelchair weighing scale after subtracting the weight of the chair. Body weight predicted legs FFM (legs FFM=.09×body weight+6.1; R(2)=.25, standard error of the estimate [SEE]=3.1kg, PFFM (trunk FFM=.21×body weight+8.6; R(2)=.56, SEE=3.6kg, PFFM (whole-body FFM=.288×body weight+26.3; R(2)=.53, SEE=5.3kg, PFFM(predicted) (FFM predicted from the derived equations) shared 86% of the variance in whole-body FFM(measured) (FFM measured using dual-energy x-ray absorptiometry scan) (R(2)=.86, SEE=1.8kg, PFFM(measured), and 66% of legs FFM(measured). The trunk FFM(predicted) shared 69% of the variance in trunk FFM(measured) (R(2)=.69, SEE=2.7kg, PFFM(predicted) shared 67% of the variance in legs FFM(measured) (R(2)=.67, SEE=2.8kg, PFFM did not differ between the prediction and validation groups. Body weight can be used to predict whole-body FFM and regional FFM. The predicted whole-body FFM improved the prediction of trunk FFM and legs FFM. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Predictive Validity of the Columbia-Suicide Severity Rating Scale for Short-Term Suicidal Behavior

DEFF Research Database (Denmark)

Conway, Paul Maurice; Erlangsen, Annette; Teasdale, Thomas William

2017-01-01

adolescents (90.6% females) who participated at follow-up (85.9%) out of the 99 (49.7%) baseline respondents. All adolescents were recruited from a specialized suicide-prevention clinic in Denmark. Through multivariate logistic regression analyses, we examined whether baseline suicidal behavior predicted......Using the Columbia-Suicide Severity Rating Scale (C-SSRS), we examined the predictive and incremental predictive validity of past-month suicidal behavior and ideation for short-term suicidal behavior among adolescents at high risk of suicide. The study was conducted in 2014 on a sample of 85...... subsequent suicidal behavior (actual attempts and suicidal behavior of any type, including preparatory acts, aborted, interrupted and actual attempts; mean follow-up of 80.8 days, SD = 52.4). Furthermore, we examined whether suicidal ideation severity and intensity incrementally predicted suicidal behavior...

Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions

Directory of Open Access Journals (Sweden)

Quentin Noirhomme

2014-01-01

Full Text Available Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with cross-validation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the cross-validation was further illustrated on real-data from a brain–computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation.

Validity of predictive equations for basal metabolic rate in Japanese adults.

Science.gov (United States)

Miyake, Rieko; Tanaka, Shigeho; Ohkawara, Kazunori; Ishikawa-Takata, Kazuko; Hikihara, Yuki; Taguri, Emiko; Kayashita, Jun; Tabata, Izumi

2011-01-01

Many predictive equations for basal metabolic rate (BMR) based on anthropometric measurements, age, and sex have been developed, mainly for healthy Caucasians. However, it has been reported that many of these equations, used widely, overestimate BMR not only for Asians, but also for Caucasians. The present study examined the accuracy of several predictive equations for BMR in Japanese subjects. In 365 healthy Japanese male and female subjects, aged 18 to 79 y, BMR was measured in the post-absorptive state using a mask and Douglas bag. Six predictive equations were examined. Total error was used as an index of the accuracy of each equation's prediction. Predicted BMR values by Dietary Reference Intakes for Japanese (Japan-DRI), Adjusted Dietary Reference Intakes for Japanese (Adjusted-DRI), and Ganpule equations were not significantly different from the measured BMR in either sex. On the other hand, Harris-Benedict, Schofield, and Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University equations were significantly higher than the measured BMR in both sexes. The prediction error by Japan-DRI, Adjusted-DRI, and Harris-Benedict equations was significantly correlated with body weight in both sexes. Total error using the Ganpule equation was low in both males and females (125 and 99 kcal/d, respectively). In addition, total error using the Adjusted-DRI equation was low in females (95 kcal/d). Thus, the Ganpule equation was the most accurate in predicting BMR in our healthy Japanese subjects, because the difference between the predicted and measured BMR was relatively small, and body weight had no effect on the prediction error.

External validation of the ability of the DRAGON score to predict outcome after thrombolysis treatment.

Science.gov (United States)

Ovesen, C; Christensen, A; Nielsen, J K; Christensen, H

2013-11-01

Easy-to-perform and valid assessment scales for the effect of thrombolysis are essential in hyperacute stroke settings. Because of this we performed an external validation of the DRAGON scale proposed by Strbian et al. in a Danish cohort. All patients treated with intravenous recombinant plasminogen activator between 2009 and 2011 were included. Upon admission all patients underwent physical and neurological examination using the National Institutes of Health Stroke Scale along with non-contrast CT scans and CT angiography. Patients were followed up through the Outpatient Clinic and their modified Rankin Scale (mRS) was assessed after 3 months. Three hundred and three patients were included in the analysis. The DRAGON scale proved to have a good discriminative ability for predicting highly unfavourable outcome (mRS 5-6) (area under the curve-receiver operating characteristic [AUC-ROC]: 0.89; 95% confidence interval [CI] 0.81-0.96; pDRAGON scale provided good discriminative capability (AUC-ROC: 0.89; 95% CI 0.78-1.0; p=0.003) for highly unfavourable outcome. We confirmed the validity of the DRAGON scale in predicting outcome after thrombolysis treatment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Development, external validation and clinical usefulness of a practical prediction model for radiation-induced dysphagia in lung cancer patients

International Nuclear Information System (INIS)

Dehing-Oberije, Cary; De Ruysscher, Dirk; Petit, Steven; Van Meerbeeck, Jan; Vandecasteele, Katrien; De Neve, Wilfried; Dingemans, Anne Marie C.; El Naqa, Issam; Deasy, Joseph; Bradley, Jeff; Huang, Ellen; Lambin, Philippe

2010-01-01

Introduction: Acute dysphagia is a distressing dose-limiting toxicity occurring frequently during concurrent chemo-radiation or high-dose radiotherapy for lung cancer. It can lead to treatment interruptions and thus jeopardize survival. Although a number of predictive factors have been identified, it is still not clear how these could offer assistance for treatment decision making in daily clinical practice. Therefore, we have developed and validated a nomogram to predict this side-effect. In addition, clinical usefulness was assessed by comparing model predictions to physicians' predictions. Materials and methods: Clinical data from 469 inoperable lung cancer patients, treated with curative intent, were collected prospectively. A prediction model for acute radiation-induced dysphagia was developed. Model performance was evaluated by the c-statistic and assessed using bootstrapping as well as two external datasets. In addition, a prospective study was conducted comparing model to physicians' predictions in 138 patients. Results: The final multivariate model consisted of age, gender, WHO performance status, mean esophageal dose (MED), maximum esophageal dose (MAXED) and overall treatment time (OTT). The c-statistic, assessed by bootstrapping, was 0.77. External validation yielded an AUC of 0.94 on the Ghent data and 0.77 on the Washington University St. Louis data for dysphagia ≥ grade 3. Comparing model predictions to the physicians' predictions resulted in an AUC of 0.75 versus 0.53, respectively. Conclusions: The proposed model performed well was successfully validated and demonstrated the ability to predict acute severe dysphagia remarkably better than the physicians. Therefore, this model could be used in clinical practice to identify patients at high or low risk.

Development and validation of a prediction model for tube feeding dependence after curative (chemo- radiation in head and neck cancer.

Directory of Open Access Journals (Sweden)

Kim Wopken

Full Text Available BACKGROUND: Curative radiotherapy or chemoradiation for head and neck cancer (HNC may result in severe acute and late side effects, including tube feeding dependence. The purpose of this prospective cohort study was to develop a prediction model for tube feeding dependence 6 months (TUBEM6 after curative (chemo- radiotherapy in HNC patients. PATIENTS AND METHODS: Tube feeding dependence was scored prospectively. To develop the multivariable model, a group LASSO analysis was carried out, with TUBEM6 as the primary endpoint (n = 427. The model was then validated in a test cohort (n = 183. The training cohort was divided into three groups based on the risk of TUBEM6 to test whether the model could be extrapolated to later time points (12, 18 and 24 months. RESULTS: Most important predictors for TUBEM6 were weight loss prior to treatment, advanced T-stage, positive N-stage, bilateral neck irradiation, accelerated radiotherapy and chemoradiation. Model performance was good, with an Area under the Curve of 0.86 in the training cohort and 0.82 in the test cohort. The TUBEM6-based risk groups were significantly associated with tube feeding dependence at later time points (p<0.001. CONCLUSION: We established an externally validated predictive model for tube feeding dependence after curative radiotherapy or chemoradiation, which can be used to predict TUBEM6.

Predicting Performance in Higher Education Using Proximal Predictors

Science.gov (United States)

Niessen, A. Susan M.; Meijer, Rob R.; Tendeiro, Jorge N.

2016-01-01

We studied the validity of two methods for predicting academic performance and student-program fit that were proximal to important study criteria. Applicants to an undergraduate psychology program participated in a selection procedure containing a trial-studying test based on a work sample approach, and specific skills tests in English and math. Test scores were used to predict academic achievement and progress after the first year, achievement in specific course types, enrollment, and dropout after the first year. All tests showed positive significant correlations with the criteria. The trial-studying test was consistently the best predictor in the admission procedure. We found no significant differences between the predictive validity of the trial-studying test and prior educational performance, and substantial shared explained variance between the two predictors. Only applicants with lower trial-studying scores were significantly less likely to enroll in the program. In conclusion, the trial-studying test yielded predictive validities similar to that of prior educational performance and possibly enabled self-selection. In admissions aimed at student-program fit, or in admissions in which past educational performance is difficult to use, a trial-studying test is a good instrument to predict academic performance. PMID:27073859

Development and validation of a preoperative prediction model for colorectal cancer T-staging based on MDCT images and clinical information.

Science.gov (United States)

Sa, Sha; Li, Jing; Li, Xiaodong; Li, Yongrui; Liu, Xiaoming; Wang, Defeng; Zhang, Huimao; Fu, Yu

2017-08-15

This study aimed to establish and evaluate the efficacy of a prediction model for colorectal cancer T-staging. T-staging was positively correlated with the level of carcinoembryonic antigen (CEA), expression of carbohydrate antigen 19-9 (CA19-9), wall deformity, blurred outer edges, fat infiltration, infiltration into the surrounding tissue, tumor size and wall thickness. Age, location, enhancement rate and enhancement homogeneity were negatively correlated with T-staging. The predictive results of the model were consistent with the pathological gold standard, and the kappa value was 0.805. The total accuracy of staging improved from 51.04% to 86.98% with the proposed model. The clinical, imaging and pathological data of 611 patients with colorectal cancer (419 patients in the training group and 192 patients in the validation group) were collected. A spearman correlation analysis was used to validate the relationship among these factors and pathological T-staging. A prediction model was trained with the random forest algorithm. T staging of the patients in the validation group was predicted by both prediction model and traditional method. The consistency, accuracy, sensitivity, specificity and area under the curve (AUC) were used to compare the efficacy of the two methods. The newly established comprehensive model can improve the predictive efficiency of preoperative colorectal cancer T-staging.

Site characterization and validation. Stage 2 - Preliminary predictions

International Nuclear Information System (INIS)

Olsson, O.; Black, J.H.; Gale, J.E.; Holmes, D.C.

1989-05-01

The Site Characterization and Validation (SCV) project is designed to assess how well we can characterize a volume of rock prior to using it as a repository. The programme of work focuses on the validation of the techniques used in site characterization. The SCV project contains 5 stages of work arranged in two 'cycles' of data-gathering, prediction, and validation. The first stage of work has included drilling of 6 boreholes (N2, N3, N4, W1, W2 and V3) and measurements of geology, fracture characteristics, stess, single borehole geophysical logging, radar, seismics and hydrogeology. The rock at the SCV site is granite with small lithological variations. Based essentially on radar and seismic results 5 'fracture zones' have been identified, named GA, GB, GC, GH and GI. They all extend acroos the entire SCV site. They aer basically in in two groups (GA, GB, GC and GH, GI). The first group are aligned N40 degree E with a dip of 35 degree to the south. The second group are aligned approximately N10 degree W dipping 60 degree E. From the stochastic analysis of the joint data it was possible to identify three main fracture orientation clusters. The orientation of two of these clusters agree roughly with orientation of the main features. Cluster B has roughly the same orientation as GH and GI, while features GA, GB and GC have an orientation similar to the more loosely defined cluster C. The orientation of the third cluster (A) is northwest with a dip to northeast. It is found that 94% of all measured hydraulic transmissivity is accounted for by 4% of the tested rock, not all of this 'concentrated' transmissivity is with the major features defined by geophysics. When the hydraulic connections across the site are examied they show that there are several welldefined zones which permit rapid transmission of hydraulic signals. These are essentially from the northeast to the southwest. (66 figs., 21 tabs., 33 refs.)

Development and Validation of Predictive Models of Cardiac Mortality and Transplantation in Resynchronization Therapy

Directory of Open Access Journals (Sweden)

Eduardo Arrais Rocha

2015-01-01

Full Text Available Abstract Background: 30-40% of cardiac resynchronization therapy cases do not achieve favorable outcomes. Objective: This study aimed to develop predictive models for the combined endpoint of cardiac death and transplantation (Tx at different stages of cardiac resynchronization therapy (CRT. Methods: Prospective observational study of 116 patients aged 64.8 ± 11.1 years, 68.1% of whom had functional class (FC III and 31.9% had ambulatory class IV. Clinical, electrocardiographic and echocardiographic variables were assessed by using Cox regression and Kaplan-Meier curves. Results: The cardiac mortality/Tx rate was 16.3% during the follow-up period of 34.0 ± 17.9 months. Prior to implantation, right ventricular dysfunction (RVD, ejection fraction < 25% and use of high doses of diuretics (HDD increased the risk of cardiac death and Tx by 3.9-, 4.8-, and 5.9-fold, respectively. In the first year after CRT, RVD, HDD and hospitalization due to congestive heart failure increased the risk of death at hazard ratios of 3.5, 5.3, and 12.5, respectively. In the second year after CRT, RVD and FC III/IV were significant risk factors of mortality in the multivariate Cox model. The accuracy rates of the models were 84.6% at preimplantation, 93% in the first year after CRT, and 90.5% in the second year after CRT. The models were validated by bootstrapping. Conclusion: We developed predictive models of cardiac death and Tx at different stages of CRT based on the analysis of simple and easily obtainable clinical and echocardiographic variables. The models showed good accuracy and adjustment, were validated internally, and are useful in the selection, monitoring and counseling of patients indicated for CRT.

Validation and clinical significance of the Childhood Myositis Assessment Scale for assessment of muscle function in the juvenile idiopathic inflammatory myopathies.

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Huber, Adam M; Feldman, Brian M; Rennebohm, Robert M; Hicks, Jeanne E; Lindsley, Carol B; Perez, Maria D; Zemel, Lawrence S; Wallace, Carol A; Ballinger, Susan H; Passo, Murray H; Reed, Ann M; Summers, Ronald M; White, Patience H; Katona, Ildy M; Miller, Frederick W; Lachenbruch, Peter A; Rider, Lisa G

2004-05-01

To examine the measurement characteristics of the Childhood Myositis Assessment Scale (CMAS) in children with juvenile idiopathic inflammatory myopathy (juvenile IIM), and to obtain preliminary data on the clinical significance of CMAS scores. One hundred eight children with juvenile IIM were evaluated on 2 occasions, 7-9 months apart, using various measures of physical function, strength, and disease activity. Interrater reliability, construct validity, and responsiveness of the CMAS were examined. The minimum clinically important difference (MID) and CMAS scores corresponding to various degrees of physical disability were estimated. The intraclass correlation coefficient for 26 patients assessed by 2 examiners was 0.89, indicating very good interrater reliability. The CMAS score correlated highly with the Childhood Health Assessment Questionnaire (C-HAQ) score and with findings on manual muscle testing (MMT) (r(s) = -0.73 and 0.73, respectively) and moderately with physician-assessed global disease activity and skin activity, parent-assessed global disease severity, and muscle magnetic resonance imaging (r(s) = -0.44 to -0.61), thereby demonstrating good construct validity. The standardized response mean was 0.81 (95% confidence interval 0.53, 1.09) in patients with at least 0.8 cm improvement on a 10-cm visual analog scale for physician-assessed global disease activity, indicating strong responsiveness. In bivariate regression models predicting physician-assessed global disease activity, MMT remained significant in models containing the CMAS (P = 0.03) while the C-HAQ did not (P = 0.4). Estimates of the MID ranged from 1.5 to 3.0 points on a 0-52-point scale. CMAS scores corresponding to no, mild, mild-to-moderate, and moderate physical disability, respectively, were 48, 45, 39, and 30. The CMAS exhibits good reliability, construct validity, and responsiveness, and is therefore a valid instrument for the assessment of physical function, muscle strength, and

Post-bronchoscopy pneumonia in patients suffering from lung cancer: Development and validation of a risk prediction score.

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Takiguchi, Hiroto; Hayama, Naoki; Oguma, Tsuyoshi; Harada, Kazuki; Sato, Masako; Horio, Yukihiro; Tanaka, Jun; Tomomatsu, Hiromi; Tomomatsu, Katsuyoshi; Takihara, Takahisa; Niimi, Kyoko; Nakagawa, Tomoki; Masuda, Ryota; Aoki, Takuya; Urano, Tetsuya; Iwazaki, Masayuki; Asano, Koichiro

2017-05-01

The incidence, risk factors, and consequences of pneumonia after flexible bronchoscopy in patients with lung cancer have not been studied in detail. We retrospectively analyzed the data from 237 patients with lung cancer who underwent diagnostic bronchoscopy between April 2012 and July 2013 (derivation sample) and 241 patients diagnosed between August 2013 and July 2014 (validation sample) in a tertiary referral hospital in Japan. A score predictive of post-bronchoscopy pneumonia was developed in the derivation sample and tested in the validation sample. Pneumonia developed after bronchoscopy in 6.3% and 4.1% of patients in the derivation and validation samples, respectively. Patients who developed post-bronchoscopy pneumonia needed to change or cancel their planned cancer therapy more frequently than those without pneumonia (56% vs. 6%, ppneumonia, which we added to develop our predictive score. The incidence of pneumonia associated with scores=0, 1, and ≥2 was 0, 3.7, and 13.4% respectively in the derivation sample (p=0.003), and 0, 2.9, and 9.7% respectively in the validation sample (p=0.016). The incidence of post-bronchoscopy pneumonia in patients with lung cancer was not rare and associated with adverse effects on the clinical course. A simple 3-point predictive score identified patients with lung cancer at high risk of post-bronchoscopy pneumonia prior to the procedure. Copyright © 2017 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

Validation of lactate clearance at 6 h for mortality prediction in critically ill children

OpenAIRE

Rajeev Kumar; Nirmal Kumar

2016-01-01

Background and Aims: To validate the lactate clearance (LC) at 6 h for mortality prediction in Pediatric Intensive Care Unit (PICU)-admitted patients and its comparison with a pediatric index of mortality 2 (PIM 2) score. Design: A prospective, observational study in a tertiary care center. Materials and Methods: Children

OR25: Validity of predictive equations for resting energy expenditure for overweight older adults with and without diabetes

NARCIS (Netherlands)

Verreijen, A. M.; Garrido, V.; Engberink, M.F.; Memelink, R. G.; Visser, M.; Weijs, P. J.

2017-01-01

Rationale: Predictive equations for resting energy expenditure (REE) are used in the treatment of overweight and obesity, but the validity of these equations in overweight older adults is unknown. This study evaluates which predictive REE equation is the best alternative to indirect calorimetry in

External validation of the ability of the DRAGON score to predict outcome after thrombolysis treatment

DEFF Research Database (Denmark)

Ovesen, Christian Aavang; Christensen, Anders; Nielsen, J K

2013-01-01

Easy-to-perform and valid assessment scales for the effect of thrombolysis are essential in hyperacute stroke settings. Because of this we performed an external validation of the DRAGON scale proposed by Strbian et al. in a Danish cohort. All patients treated with intravenous recombinant plasmino......Easy-to-perform and valid assessment scales for the effect of thrombolysis are essential in hyperacute stroke settings. Because of this we performed an external validation of the DRAGON scale proposed by Strbian et al. in a Danish cohort. All patients treated with intravenous recombinant...... and their modified Rankin Scale (mRS) was assessed after 3 months. Three hundred and three patients were included in the analysis. The DRAGON scale proved to have a good discriminative ability for predicting highly unfavourable outcome (mRS 5-6) (area under the curve-receiver operating characteristic [AUC-ROC]: 0...

Significance of SYT8 For the Detection, Prediction, and Treatment of Peritoneal Metastasis From Gastric Cancer.

Science.gov (United States)

Kanda, Mitsuro; Shimizu, Dai; Tanaka, Haruyoshi; Tanaka, Chie; Kobayashi, Daisuke; Hayashi, Masamichi; Iwata, Naoki; Niwa, Yukiko; Yamada, Suguru; Fujii, Tsutomu; Sugimoto, Hiroyuki; Murotani, Kenta; Fujiwara, Michitaka; Kodera, Yasuhiro

2018-03-01

To develop novel diagnostic and therapeutic targets specific for peritoneal metastasis of gastric cancer (GC). Advanced GC frequently recurs because of undetected micrometastases even after curative resection. Peritoneal metastasis has been the most frequent recurrent pattern after gastrectomy and is incurable. We conducted a recurrence pattern-specific transcriptome analysis in an independent cohort of 16 patients with stage III GC who underwent curative gastrectomy and adjuvant S-1 for screening candidate molecules specific for peritoneal metastasis of GC. Next, another 340 patients were allocated to discovery and validation sets (1:2) to evaluate the diagnostic and predictive value of the candidate molecule. The results of quantitative reverse-transcription PCR and immunohistochemical analysis were correlated with clinical characteristics and survival. The effects of siRNA-mediated knockdown on phenotype and fluorouracil sensitivity of GC cells were evaluated in vitro, and the therapeutic effects of siRNAs were evaluated using a mouse xenograft model. Synaptotagmin VIII (SYT8) was identified as a candidate biomarker specific to peritoneal metastasis. In the discovery set, the optimal cut-off of SYT8 expression was established as 0.005. Expression levels of SYT8 mRNA in GC tissues were elevated in the validation set comprising patients with peritoneal recurrence or metastasis. SYT8 levels above the cut-off value were significantly and specifically associated with peritoneal metastasis, and served as an independent prognostic marker for peritoneal recurrence-free survival of patients with stage II/III GC. The survival difference between patients with SYT8 levels above and below the cut-off was associated with patients who received adjuvant chemotherapy. Inhibition of SYT8 expression by GC cells correlated with decreased invasion, migration, and fluorouracil resistance. Intraperitoneal administration of SYT8-siRNA inhibited the growth of peritoneal nodules and

Prediction of dissolved reactive phosphorus losses from small agricultural catchments: calibration and validation of a parsimonious model

Directory of Open Access Journals (Sweden)

C. Hahn

2013-10-01

Full Text Available Eutrophication of surface waters due to diffuse phosphorus (P losses continues to be a severe water quality problem worldwide, causing the loss of ecosystem functions of the respective water bodies. Phosphorus in runoff often originates from a small fraction of a catchment only. Targeting mitigation measures to these critical source areas (CSAs is expected to be most efficient and cost-effective, but requires suitable tools. Here we investigated the capability of the parsimonious Rainfall-Runoff-Phosphorus (RRP model to identify CSAs in grassland-dominated catchments based on readily available soil and topographic data. After simultaneous calibration on runoff data from four small hilly catchments on the Swiss Plateau, the model was validated on a different catchment in the same region without further calibration. The RRP model adequately simulated the discharge and dissolved reactive P (DRP export from the validation catchment. Sensitivity analysis showed that the model predictions were robust with respect to the classification of soils into "poorly drained" and "well drained", based on the available soil map. Comparing spatial hydrological model predictions with field data from the validation catchment provided further evidence that the assumptions underlying the model are valid and that the model adequately accounts for the dominant P export processes in the target region. Thus, the parsimonious RRP model is a valuable tool that can be used to determine CSAs. Despite the considerable predictive uncertainty regarding the spatial extent of CSAs, the RRP can provide guidance for the implementation of mitigation measures. The model helps to identify those parts of a catchment where high DRP losses are expected or can be excluded with high confidence. Legacy P was predicted to be the dominant source for DRP losses and thus, in combination with hydrologic active areas, a high risk for water quality.

Factors associated with therapeutic inertia in hypertension: validation of a predictive model.

Science.gov (United States)

Redón, Josep; Coca, Antonio; Lázaro, Pablo; Aguilar, Ma Dolores; Cabañas, Mercedes; Gil, Natividad; Sánchez-Zamorano, Miguel Angel; Aranda, Pedro

2010-08-01

To study factors associated with therapeutic inertia in treating hypertension and to develop a predictive model to estimate the probability of therapeutic inertia in a given medical consultation, based on variables related to the consultation, patient, physician, clinical characteristics, and level of care. National, multicentre, observational, cross-sectional study in primary care and specialist (hospital) physicians who each completed a questionnaire on therapeutic inertia, provided professional data and collected clinical data on four patients. Therapeutic inertia was defined as a consultation in which treatment change was indicated (i.e., SBP >or= 140 or DBP >or= 90 mmHg in all patients; SBP >or= 130 or DBP >or= 80 in patients with diabetes or stroke), but did not occur. A predictive model was constructed and validated according to the factors associated with therapeutic inertia. Data were collected on 2595 patients and 13,792 visits. Therapeutic inertia occurred in 7546 (75%) of the 10,041 consultations in which treatment change was indicated. Factors associated with therapeutic inertia were primary care setting, male sex, older age, SPB and/or DBP values close to normal, treatment with more than one antihypertensive drug, treatment with an ARB II, and more than six visits/year. Physician characteristics did not weigh heavily in the association. The predictive model was valid internally and externally, with acceptable calibration, discrimination and reproducibility, and explained one-third of the variability in therapeutic inertia. Although therapeutic inertia is frequent in the management of hypertension, the factors explaining it are not completely clear. Whereas some aspects of the consultations were associated with therapeutic inertia, physician characteristics were not a decisive factor.

The Real World Significance of Performance Prediction

Science.gov (United States)

Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu

2012-01-01

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…

Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

Science.gov (United States)

James, Matthew T; Pannu, Neesh; Hemmelgarn, Brenda R; Austin, Peter C; Tan, Zhi; McArthur, Eric; Manns, Braden J; Tonelli, Marcello; Wald, Ron; Quinn, Robert R; Ravani, Pietro; Garg, Amit X

2017-11-14

Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Demographic, laboratory, and comorbidity variables measured prior to discharge. Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher

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.

Science.gov (United States)

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

Predicting the success of IVF: external validation of the van Loendersloot's model.

Science.gov (United States)

Sarais, Veronica; Reschini, Marco; Busnelli, Andrea; Biancardi, Rossella; Paffoni, Alessio; Somigliana, Edgardo

2016-06-01

Is the predictive model for IVF success proposed by van Loendersloot et al. valid in a different geographical and cultural context? The model discriminates well but was less accurate than in the original context where it was developed. Several independent groups have developed models that combine different variables with the aim of estimating the chance of pregnancy with IVF but only four of them have been externally validated. One of these four, the van Loendersloot's model, deserves particular attention and further investigation for at least three reasons; (i) the reported area under the receiver operating characteristics curve (c-statistics) in the temporal validation setting was the highest reported to date (0.68), (ii) the perspective of the model is clinically wise since it includes variables obtained from previous failed cycles, if any, so it can be applied to any women entering an IVF cycle, (iii) the model lacks external validation in a geographically different center. Retrospective cohort study of women undergoing oocyte retrieval for IVF between January 2013 and December 2013 at the infertility unit of the Fondazione Ca' Granda, Ospedale Maggiore Policlinico of Milan, Italy. Only the first oocyte retrieval cycle performed during the study period was included in the study. Women with previous IVF cycles were excluded if the last one before the study cycle was in another center. The main outcome was the cumulative live birth rate per oocytes retrieval. Seven hundred seventy-two women were selected. Variables included in the van Loendersloot's model and the relative weights (beta) were used. The variable resulting from this combination (Y) was transformed into a probability. The discriminatory capacity was assessed using the c-statistics. Calibration was made using a logistic regression that included Y as the unique variable and live birth as the outcome. Data are presented using both the original and the calibrated models. Performance was evaluated

Western Validation of a Novel Gastric Cancer Prognosis Prediction Model in US Gastric Cancer Patients.

Science.gov (United States)

Woo, Yanghee; Goldner, Bryan; Son, Taeil; Song, Kijun; Noh, Sung Hoon; Fong, Yuman; Hyung, Woo Jin

2018-03-01

A novel prediction model for accurate determination of 5-year overall survival of gastric cancer patients was developed by an international collaborative group (G6+). This prediction model was created using a single institution's database of 11,851 Korean patients and included readily available and clinically relevant factors. Already validated using external East Asian cohorts, its applicability in the American population was yet to be determined. Using the Surveillance, Epidemiology, and End Results (SEER) dataset, 2014 release, all patients diagnosed with gastric adenocarcinoma who underwent surgical resection between 2002 and 2012, were selected. Characteristics for analysis included: age, sex, depth of tumor invasion, number of positive lymph nodes, total lymph nodes retrieved, presence of distant metastasis, extent of resection, and histology. Concordance index (C-statistic) was assessed using the novel prediction model and compared with the prognostic index, the seventh edition of the TNM staging system. Of the 26,019 gastric cancer patients identified from the SEER database, 15,483 had complete datasets. Validation of the novel prediction tool revealed a C-statistic of 0.762 (95% CI 0.754 to 0.769) compared with the seventh TNM staging model, C-statistic 0.683 (95% CI 0.677 to 0.689), (p prediction model for gastric cancer in the American patient population. Its superior prediction of the 5-year survival of gastric cancer patients in a large Western cohort strongly supports its global applicability. Importantly, this model allows for accurate prognosis for an increasing number of gastric cancer patients worldwide, including those who received inadequate lymphadenectomy or underwent a noncurative resection. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

The macrophage activation marker sCD163 combined with markers of the Enhanced Liver Fibrosis (ELF) score predicts clinically significant portal hypertension in patients with cirrhosis

DEFF Research Database (Denmark)

Sandahl, T D; McGrail, R; Møller, H J

2016-01-01

BACKGROUND: Noninvasive identification of significant portal hypertension in patients with cirrhosis is needed in hepatology practice. AIM: To investigate whether the combination of sCD163 as a hepatic inflammation marker and the fibrosis markers of the Enhanced Liver Fibrosis score (ELF) can...... predict portal hypertension in patients with cirrhosis. METHODS: We measured sCD163 and the ELF components (hyaluronic acid, tissue inhibitor of metalloproteinase-1 and procollagen-III aminopeptide) in two separate cohorts of cirrhosis patients that underwent hepatic vein catheterisation. To test...... the predictive accuracy we developed a CD163-fibrosis portal hypertension score in an estimation cohort (n = 80) and validated the score in an independent cohort (n = 80). A HVPG ≥10 mmHg was considered clinically significant. RESULTS: Both sCD163 and the ELF components increased in a stepwise manner...

Predicting Relapse among Young Adults: Psychometric Validation of the Advanced Warning of Relapse (AWARE) Scale

Science.gov (United States)

Kelly, John F.; Hoeppner, Bettina B.; Urbanoski, Karen A.; Slaymaker, Valerie

2011-01-01

Objective Failure to maintain abstinence despite incurring severe harm is perhaps the key defining feature of addiction. Relapse prevention strategies have been developed to attenuate this propensity to relapse, but predicting who will, and who will not, relapse has stymied attempts to more efficiently tailor treatments according to relapse risk profile. Here we examine the psychometric properties of a promising relapse risk measure - the Advance WArning of RElapse scale (AWARE) scale (Miller and Harris, 2000) in an understudied but clinically important sample of young adults. Method Inpatient youth (N=303; Age 18-24; 26% female) completed the AWARE scale and the Brief Symptom Inventory-18 (BSI) at the end of residential treatment, and at 1-, 3-, and 6-months following discharge. Internal and convergent validity was tested for each of these four timepoints using confirmatory factor analysis and correlations (with BSI scores). Predictive validity was tested for relapse 1, 3, and 6 months following discharge, as was incremental utility, where AWARE scores were used as predictors of any substance use while controlling for treatment entry substance use severity and having spent time in a controlled environment following treatment. Results Confirmatory factor analysis revealed a single, internally consistent, 25-item factor that demonstrated convergent validity and predicted subsequent relapse alone and when controlling for other important relapse risk predictors. Conclusions The AWARE scale may be a useful and efficient clinical tool for assessing short-term relapse risk among young people and, thus, could serve to enhance the effectiveness of relapse prevention efforts. PMID:21700396

Predicting relapse among young adults: psychometric validation of the Advanced WArning of RElapse (AWARE) scale.

Science.gov (United States)

Kelly, John F; Hoeppner, Bettina B; Urbanoski, Karen A; Slaymaker, Valerie

2011-10-01

Failure to maintain abstinence despite incurring severe harm is perhaps the key defining feature of addiction. Relapse prevention strategies have been developed to attenuate this propensity to relapse, but predicting who will, and who will not, relapse has stymied attempts to more efficiently tailor treatments according to relapse risk profile. Here we examine the psychometric properties of a promising relapse risk measure-the Advance WArning of RElapse (AWARE) scale (Miller & Harris, 2000) in an understudied but clinically important sample of young adults. Inpatient youth (N=303; Ages 18-24; 26% female) completed the AWARE scale and the Brief Symptom Inventory-18 (BSI) at the end of residential treatment, and at 1-, 3-, and 6-months following discharge. Internal and convergent validity was tested for each of these four timepoints using confirmatory factor analysis and correlations (with BSI scores). Predictive validity was tested for relapse 1, 3, and 6 months following discharge, as was incremental utility, where AWARE scores were used as predictors of any substance use while controlling for treatment entry substance use severity and having spent time in a controlled environment following treatment. Confirmatory factor analysis revealed a single, internally consistent, 25-item factor that demonstrated convergent validity and predicted subsequent relapse alone and when controlling for other important relapse risk predictors. The AWARE scale may be a useful and efficient clinical tool for assessing short-term relapse risk among young people and, thus, could serve to enhance the effectiveness of relapse prevention efforts. Copyright © 2011 Elsevier Ltd. All rights reserved.

Validating prediction scales of type 2 diabetes mellitus in Spain: the SPREDIA-2 population-based prospective cohort study protocol

Science.gov (United States)

Salinero-Fort, Miguel Ángel; de Burgos-Lunar, Carmen; Mostaza Prieto, José; Lahoz Rallo, Carlos; Abánades-Herranz, Juan Carlos; Gómez-Campelo, Paloma; Laguna Cuesta, Fernando; Estirado De Cabo, Eva; García Iglesias, Francisca; González Alegre, Teresa; Fernández Puntero, Belén; Montesano Sánchez, Luis; Vicent López, David; Cornejo Del Río, Víctor; Fernández García, Pedro J; Sabín Rodríguez, Concesa; López López, Silvia; Patrón Barandío, Pedro

2015-01-01

Introduction The incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide. When diagnosed, many patients already have organ damage or advance subclinical atherosclerosis. An early diagnosis could allow the implementation of lifestyle changes and treatment options aimed at delaying the progression of the disease and to avoid cardiovascular complications. Different scores for identifying undiagnosed diabetes have been reported, however, their performance in populations of southern Europe has not been sufficiently evaluated. The main objectives of our study are: to evaluate the screening performance and cut-off points of the main scores that identify the risk of undiagnosed T2DM and prediabetes in a Spanish population, and to develop and validate our own predictive models of undiagnosed T2DM (screening model), and future T2DM (prediction risk model) after 5-year follow-up. As a secondary objective, we will evaluate the atherosclerotic burden of the population with undiagnosed T2DM. Methods and analysis Population-based prospective cohort study with baseline screening, to evaluate the performance of the FINDRISC, DANISH, DESIR, ARIC and QDScore, against the gold standard tests: Fasting plasma glucose, oral glucose tolerance and/or HbA1c. The sample size will include 1352 participants between the ages of 45 and 74 years. Analysis: sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratio positive, likelihood ratio negative and receiver operating characteristic curves and area under curve. Binary logistic regression for the first 700 individuals (derivation) and last 652 (validation) will be performed. All analyses will be calculated with their 95% CI; statistical significance will be p<0.05. Ethics and dissemination The study protocol has been approved by the Research Ethics Committee of the Carlos III Hospital (Madrid). The score performance and predictive model will be presented in medical conferences, workshops

Anatomical Cystocele Recurrence: Development and Internal Validation of a Prediction Model.

Science.gov (United States)

Vergeldt, Tineke F M; van Kuijk, Sander M J; Notten, Kim J B; Kluivers, Kirsten B; Weemhoff, Mirjam

2016-02-01

To develop a prediction model that estimates the risk of anatomical cystocele recurrence after surgery. The databases of two multicenter prospective cohort studies were combined, and we performed a retrospective secondary analysis of these data. Women undergoing an anterior colporrhaphy without mesh materials and without previous pelvic organ prolapse (POP) surgery filled in a questionnaire, underwent translabial three-dimensional ultrasonography, and underwent staging of POP preoperatively and postoperatively. We developed a prediction model using multivariable logistic regression and internally validated it using standard bootstrapping techniques. The performance of the prediction model was assessed by computing indices of overall performance, discriminative ability, calibration, and its clinical utility by computing test characteristics. Of 287 included women, 149 (51.9%) had anatomical cystocele recurrence. Factors included in the prediction model were assisted delivery, preoperative cystocele stage, number of compartments involved, major levator ani muscle defects, and levator hiatal area during Valsalva. Potential predictors that were excluded after backward elimination because of high P values were age, body mass index, number of vaginal deliveries, and family history of POP. The shrinkage factor resulting from the bootstrap procedure was 0.91. After correction for optimism, Nagelkerke's R and the Brier score were 0.15 and 0.22, respectively. This indicates satisfactory model fit. The area under the receiver operating characteristic curve of the prediction model was 71.6% (95% confidence interval 65.7-77.5). After correction for optimism, the area under the receiver operating characteristic curve was 69.7%. This prediction model, including history of assisted delivery, preoperative stage, number of compartments, levator defects, and levator hiatus, estimates the risk of anatomical cystocele recurrence.

The Prognostic Validity of the Timed Up and Go Test With a Dual Task for Predicting the Risk of Falls in the Elderly

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Martin Hofheinz

2016-03-01

Full Text Available Objective: The aim is to examine the prognostic validity of the Timed Up and Go Test with a cognitive and a manual dual task for predicting the risk of falls. Method: A follow-up study was performed. The data were recorded for 120 volunteers in an outpatient physiotherapy center, with a 12-month follow-up. The sample included 120 elderly men and women aged 60 to 87 years ( M age = 72.2 years living at home. The main measurements were as follows: The Timed Up and Go Test (TUG, the TUG with a cognitive dual task (TUGcog, and the TUG with a manual dual task (TUGman and falls. Results: In the 12-month follow-up, 37 persons (30.8% had a locomotive fall. The receiver operating characteristic (ROC curve shows significant results for the TUGcog. The area under the curve is 0.65 ( p = .008, with a 95% confidence interval (CI = [0.55, 0.76]. For the TUGman, the area under the curve is 0.57 with a 95% CI = [0.45, 0.68], which is not significant ( p = .256. For the TUG, the area under the curve is 0.58, which is not significant ( p = .256, 95% CI = [0.47, 0.69]. Conclusion: The TUGcog is a valid prognostic assessment to predict falls in community-dwelling elderly people.

A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria

NARCIS (Netherlands)

Kappe, F.R.; Boekholt, L.; den Rooyen, C.; van der Flier, H.

2009-01-01

Multiple and specific learning criteria were used to examine the predictive validity of the Learning Style Questionnaire (LSQ). Ninety-nine students in a college of higher learning in The Netherlands participated in a naturally occurring field study. The students were categorized into one of four

Validated thermodynamic prediction of AlP and eutectic (Si) solidification sequence in Al-Si cast alloys

International Nuclear Information System (INIS)

Liang, S M; Schmid-Fetzer, R

2016-01-01

The eutectic microstructure in hypoeutectic Al-Si cast alloys is strongly influenced by AlP particles which are potent nuclei for the eutectic (Si) phase. The solidification sequence of AlP and (Si) phases is, thus, crucial for the nucleation of eutectic silicon with marked impact on its morphology. This study presents this interdependence between Si- and P-compositions, relevant for Al-Si cast alloys, on the solidification sequence of AlP and (Si). These data are predicted from a series of thermodynamic calculations. The predictions are based on a self-consistent thermodynamic description of the Al-Si-P ternary alloy system developed recently. They are validated by independent experimental studies on microstructure and undercooling in hypoeutectic Al-Si alloys. A constrained Scheil solidification simulation technique is applied to predict the undercooling under clean heterogeneous nucleation conditions, validated by dedicated experimental observations on entrained droplets. These specific undercooling values may be very large and their quantitative dependence on Si and P content of the Al alloy is presented. (paper)

Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases.

Science.gov (United States)

Hwang, Eui Jin; Goo, Jin Mo; Kim, Jihye; Park, Sang Joon; Ahn, Soyeon; Park, Chang Min; Shin, Yeong-Gil

2017-08-01

To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth. For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE). SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004) CONCLUSION: Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold. • The 95 % percentile APE of a particular nodule can be predicted. • Estimated 95 % percentile APE can be utilized as an individualized threshold. • More sensitive diagnosis of metastasis can be made with an individualized threshold. • Tailored nodule management can be provided during nodule growth follow-up.

Validation of Heat-Flux Predictions on the Outer Air Seal of a Transonic Turbine Blade (Preprint)

National Research Council Canada - National Science Library

Clark, John P; Polanka, Marc D; Meininger, Matthew; Praisner, Thomas J

2006-01-01

.... So, a set of predictions of the heat flux on the Blade Outer Air Seal (BOAS) of a transonic turbine is here validated with time-resolved measurements obtained in a single-stage high pressure turbine rig...

Incremental Validity of the WJ III COG: Limited Predictive Effects beyond the GIA-E

Science.gov (United States)

McGill, Ryan J.; Busse, R. T.

2015-01-01

This study is an examination of the incremental validity of Cattell-Horn-Carroll (CHC) broad clusters from the Woodcock-Johnson III Tests of Cognitive Abilities (WJ III COG) for predicting scores on the Woodcock-Johnson III Tests of Achievement (WJ III ACH). The participants were children and adolescents, ages 6-18 (n = 4,722), drawn from the WJ…

Validation of the Chinese SAD PERSONS Scale to predict repeated self-harm in emergency attendees in Taiwan.

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Wu, Chia-Yi; Huang, Hui-Chun; Wu, Shu-I; Sun, Fang-Ju; Huang, Chiu-Ron; Liu, Shen-Ing

2014-02-17

Past and repeated self-harm are long-term risks to completed suicide. A brief rating scale to assess repetition risk of self-harm is important for high-risk identification and early interventions in suicide prevention. The study aimed to examine the validity of the Chinese SAD PERSONS Scale (CSPS) and to evaluate its feasibility in clinical settings. One hundred and forty-seven patients with self-harm were recruited from the Emergency Department and assessed at baseline and the sixth month. The controls, 284 people without self-harm from the Family Medicine Department in the same hospital were recruited and assessed concurrently. The psychometric properties of the CSPS were examined using baseline and follow-up measurements that assessed a variety of suicide risk factors. Clinical feasibility and applicability of the CSPS were further evaluated by a group of general nurses who used case vignette approach in CSPS risk assessment in clinical settings. An open-ended question inquiring their opinions of scale adaptation to hospital inpatient assessment for suicide risks were also analyzed using content analysis. The CSPS was significantly correlated with other scales measuring depression, hopelessness and suicide ideation. A cut-off point of the scale was at 4/5 in predicting 6-month self-harm repetition with the sensitivity and specificity being 65.4% and 58.1%, respectively. Based on the areas under the Receiver Operating Characteristic curves, the predictive validity of the scale showed a better performance than the other scales. Fifty-four nurses, evaluating the scale using case vignette found it a useful tool to raise the awareness of suicide risk and a considerable tool to be adopted into nursing care. The Chinese SAD PERSONS Scale is a brief instrument with acceptable psychometric properties for self-harm prediction. However, cautions should be paid to level of therapeutic relationships during assessment, staff workload and adequate training for wider clinical

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

NARCIS (Netherlands)

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

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

A six-factor model of brand personality and its predictive validity

Directory of Open Access Journals (Sweden)

Živanović Marko

2017-01-01

Full Text Available The study examines applicability and usefulness of HEXACO-based model in the description of brand personality. Following contemporary theoretical developments in human personality research, Study 1 explored the latent personality structure of 120 brands using descriptors of six personality traits as defined in HEXACO model: Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness. The results of exploratory factor analyses have supported HEXACO personality six-factor structure to a large extent. In Study 2 we addressed the question of predictive validity of HEXACO-based brand personality. Brand personality traits, but predominantly Honesty-Humility, accounted for substantial amount of variance in prediction of important aspects of consumer-brand relationship: attitude toward brand, perceived quality of a brand, and brand loyalty. The implications of applying HEXACO-based brand personality in marketing research are discussed. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 179018 and Grant no. 175012

Assessing the reliability, predictive and construct validity of historical, clinical and risk management-20 (HCR-20) in Mexican psychiatric inpatients.

Science.gov (United States)

Sada, Andrea; Robles-García, Rebeca; Martínez-López, Nicolás; Hernández-Ramírez, Rafael; Tovilla-Zarate, Carlos-Alfonso; López-Munguía, Fernando; Suárez-Alvarez, Enrique; Ayala, Xochitl; Fresán, Ana

2016-08-01

Assessing dangerousness to gauge the likelihood of future violent behaviour has become an integral part of clinical mental health practice in forensic and non-forensic psychiatric settings, one of the most effective instruments for this being the Historical, Clinical and Risk Management-20 (HCR-20). To examine the HCR-20 factor structure in Mexican psychiatric inpatients and to obtain its predictive validity and reliability for use in this population. In total, 225 patients diagnosed with psychotic, affective or personality disorders were included. The HCR-20 was applied at hospital admission and violent behaviours were assessed during psychiatric hospitalization using the Overt Aggression Scale (OAS). Construct validity, predictive validity and internal consistency were determined. Violent behaviour remains more severe in patients classified in the high-risk group during hospitalization. Fifteen items displayed adequate communalities in the original designated domains of the HCR-20 and internal consistency of the instruments was high. The HCR-20 is a suitable instrument for predicting violence risk in Mexican psychiatric inpatients.

Validation of a multi-marker model for the prediction of incident type 2 diabetes mellitus

DEFF Research Database (Denmark)

Lyssenko, Valeriya; Jørgensen, Torben; Gerwien, Robert W

2012-01-01

Purpose: To assess performance of a biomarker-based score that predicts the five-year risk of diabetes (Diabetes Risk Score, DRS) in an independent cohort that included 15-year follow-up. Method: DRS was developed on the Inter99 cohort, and validated on the Botnia cohort. Performance...... was benchmarked against other risk-assessment tools comparing calibration, time to event analysis, and net reclassification. Results: The area under the receiver-operating characteristic curve (AUC) was 0.84 for the Inter99 cohort and 0.78 for the Botnia cohort. In the Botnia cohort, DRS provided better...... discrimination than fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance, oral glucose tolerance test or risk scores derived from Framingham or San Antonio Study cohorts. Overall reclassification with DRS was significantly better than using FPG and glucose tolerance status (p

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

Directory of Open Access Journals (Sweden)

Sandrine Leroy

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

Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage : The SAHIT multinational cohort study

NARCIS (Netherlands)

Jaja, Blessing N R; Saposnik, Gustavo; Lingsma, Hester F.; Macdonald, Erin; Thorpe, Kevin E.; Mamdani, Muhammed; Steyerberg, Ewout W.; Molyneux, Andrew; Manoel, Airton Leonardo De Oliveira; Schatlo, Bawarjan; Hanggi, Daniel; Hasan, David M.; Wong, George K C; Etminan, Nima; Fukuda, Hitoshi; Torner, James C.; Schaller, Karl L.; Suarez, Jose I.; Stienen, Martin N.; Vergouwen, Mervyn D.I.; Rinkel, Gabriel J.E.; Spears, Julian; Cusimano, Michael D.; Todd, Michael; Le Roux, Peter; Kirkpatrick, Peter J.; Pickard, John; Van Den Bergh, Walter M.; Murray, Gordon D; Johnston, S. Claiborne; Yamagata, Sen; Mayer, Stephan A.; Schweizer, Tom A.; Macdonald, R. Loch

2018-01-01

Objective To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH). Design Cohort study with logistic regression analysis to combine predictors and treatment modality. Setting Subarachnoid

Implicit and explicit preferences for physical attractiveness in a romantic partner: a double dissociation in predictive validity.

Science.gov (United States)

Eastwick, Paul W; Eagly, Alice H; Finkel, Eli J; Johnson, Sarah E

2011-11-01

Five studies develop and examine the predictive validity of an implicit measure of the preference for physical attractiveness in a romantic partner. Three hypotheses were generally supported. First, 2 variants of the go/no-go association task revealed that participants, on average, demonstrate an implicit preference (i.e., a positive spontaneous affective reaction) for physical attractiveness in a romantic partner. Second, these implicit measures were not redundant with a traditional explicit measure: The correlation between these constructs was .00 on average, and the implicit measures revealed no reliable sex differences, unlike the explicit measure. Third, explicit and implicit measures exhibited a double dissociation in predictive validity. Specifically, explicit preferences predicted the extent to which attractiveness was associated with participants' romantic interest in opposite-sex photographs but not their romantic interest in real-life opposite-sex speed-daters or confederates. Implicit preferences showed the opposite pattern. This research extends prior work on implicit processes in romantic relationships and offers the first demonstration that any measure of a preference for a particular characteristic in a romantic partner (an implicit measure of physical attractiveness, in this case) predicts individuals' evaluation of live potential romantic partners.

Predictive Validity of Early Literacy Measures for Korean English Language Learners in the United States

Science.gov (United States)

Han, Jeanie Nam; Vanderwood, Michael L.; Lee, Catherine Y.

2015-01-01

This study examined the predictive validity of early literacy measures with first-grade Korean English language learners (ELLs) in the United States at varying levels of English proficiency. Participants were screened using Dynamic Indicators of Basic Early Literacy Skills (DIBELS) Phoneme Segmentation Fluency (PSF), DIBELS Nonsense Word Fluency…

External validation of the endometriosis fertility index (EFI) staging system for predicting non-ART pregnancy after endometriosis surgery.

Science.gov (United States)

Tomassetti, C; Geysenbergh, B; Meuleman, C; Timmerman, D; Fieuws, S; D'Hooghe, T

2013-05-01

Can the ability of the endometriosis fertility index (EFI) to predict non-assisted reproductive technology (ART) pregnancy after endometriosis surgery be confirmed by an external validation study? The significant relationship between the EFI score and the time to non-ART pregnancy observed in our study represents an external validation of this scoring system. The EFI was previously developed and tested prospectively in a single center, but up to now no external validation has been published. Our data provide validation of the EFI in an external fertility unit on a robust scientific basis, to identify couples with a good prognosis for spontaneous conception who can therefore defer ART treatment, regardless of their revised American Fertility Society (rAFS) endometriosis staging. Retrospective cohort study where the EFI was calculated based on history and detailed surgical findings, and related to pregnancy outcome in 233 women attempting non-ART conception immediately after surgery; all data used for EFI calculation and analysis of reproductive outcome had been collected prospectively as part of another study. The EFI score was calculated (score 0-10) for 233 women with all rAFS endometriosis stages (minimal-mild, n = 75; moderate-severe, n = 158) after endometriosis surgery (1 September 2006-30 September 2010) in a university hospital-based reproductive medicine unit with combined expertise in reproductive surgery and medically assisted reproduction. All participants attempted non-ART conception immediately after surgery by natural intercourse, ovulation induction with timed intercourse or intrauterine insemination (with or without ovulation induction or controlled ovarian stimulation). All analyses were performed for three different definitions of pregnancy [overall (any HCG >25 IU/l), clinical and ongoing >20 weeks]. Six groups were distinguished (EFI scores 1-3, 4, 5, 6, 7+8, 9+10), and Kaplan-Meier (K-M) estimates for cumulative pregnancy rate were calculated

The development of a self-administered dementia checklist: the examination of concurrent validity and discriminant validity.

Science.gov (United States)

Miyamae, Fumiko; Ura, Chiaki; Sakuma, Naoko; Niikawa, Hirotoshi; Inagaki, Hiroki; Ijuin, Mutsuo; Okamura, Tsuyoshi; Sugiyama, Mika; Awata, Shuichi

2016-01-01

The present study aims to develop a self-administered dementia checklist to enable community-residing older adults to realize their declining functions and start using necessary services. A previous study confirmed the factorial validity and internal reliability of the checklist. The present study examined its concurrent validity and discriminant validity. The authors conducted a 3-step study (a self-administered survey including the checklist, interviews by nurses, and interviews by doctors and psychologists) of 7,682 community-residing individuals who were over 65 years of age. The authors calculated Spearman's correlation coefficients between the scores of the checklist and the results of a psychological test to examine the concurrent validity. They also compared the average total scores of the checklist between groups with different Clinical Dementia Rating (CDR) scores to examine discriminant validity and conducted a receiver operating characteristic analysis to examine the discriminative power for dementia. The authors analyzed the data of 131 respondents who completed all 3 steps. The checklist scores were significantly correlated with the respondents' Mini-Mental State Examination and Frontal Assessment Battery scores. The checklist also significantly discriminated the patients with dementia (CDR = 1+) from those without dementia (CDR = 0 or 0.5). The optimal cut-off point for the two groups was 17/18 (sensitivity, 72.0%; specificity, 69.2%; positive predictive value, 69.2%; negative predictive value, 72.0%). This study confirmed the concurrent validity and discriminant validity of the self-administered dementia checklist. However, due to its insufficient discriminative power as a screening tool for older people with declining cognitive functions, the checklist is only recommended as an educational and public awareness tool.

Predictive validity of childhood oppositional defiant disorder and conduct disorder: implications for the DSM-V.

Science.gov (United States)

Burke, Jeffrey D; Waldman, Irwin; Lahey, Benjamin B

2010-11-01

Data are presented from 3 studies of children and adolescents to evaluate the predictive validity of childhood oppositional defiant disorder (ODD) and conduct disorder (CD) as defined in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV; American Psychiatric Association, 1994) and the International Classification of Diseases, Version 10 (ICD-10; World Health Organization, 1992). The present analyses strongly support the predictive validity of these diagnoses by showing that they predict both future psychopathology and enduring functional impairment. Furthermore, the present findings generally support the hierarchical developmental hypothesis in DSM-IV that some children with ODD progress to childhood-onset CD, and some youth with CD progress to antisocial personality disorder (APD). Nonetheless, they reveal that CD does not always co-occur with ODD, particularly during adolescence. Importantly, the present findings suggest that ICD-10 diagnostic criteria for ODD, which treat CD symptoms as ODD symptoms when diagnostic criteria for CD are not met, identify more functionally impaired children than the more restrictive DSM-IV definition of ODD. Filling this "hole" in the DSM-IV criteria for ODD should be a priority for the DSM-V. In addition, the present findings suggest that although the psychopathic trait of interpersonal callousness in childhood independently predicts future APD, these findings do not confirm the hypothesis that callousness distinguishes a subset of children with CD with an elevated risk for APD. PsycINFO Database Record (c) 2010 APA, all rights reserved

Multisite external validation of a risk prediction model for the diagnosis of blood stream infections in febrile pediatric oncology patients without severe neutropenia.

Science.gov (United States)

Esbenshade, Adam J; Zhao, Zhiguo; Aftandilian, Catherine; Saab, Raya; Wattier, Rachel L; Beauchemin, Melissa; Miller, Tamara P; Wilkes, Jennifer J; Kelly, Michael J; Fernbach, Alison; Jeng, Michael; Schwartz, Cindy L; Dvorak, Christopher C; Shyr, Yu; Moons, Karl G M; Sulis, Maria-Luisa; Friedman, Debra L

2017-10-01

Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness. A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables. From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI. The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society. © 2017 American Cancer Society.

Readmissions and death after ICU discharge: development and validation of two predictive models.

Directory of Open Access Journals (Sweden)

Omar Badawi

Full Text Available INTRODUCTION: Early discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS to assist ICU discharge planning. METHODS: Retrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011. EXCLUSION CRITERIA: DNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2∶1 to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge. RESULTS: 469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups. CONCLUSIONS: Our models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although

Development and validation of a predictive technology for creep closure of underground rooms in salt

International Nuclear Information System (INIS)

Munson, D.E.; DeVries, K.L.

1991-07-01

Because of the concern for public health and safety, when compared to normal engineering practice, radioactive waste repositories have quite unusual requirements governing performance assessment. In part, performance assessment requires prediction of time-dependent or creep response of the repository hundreds to thousands of years into the future. In salt, one specific need is to predict, with confidence, the time at which the repository rooms creep closed sufficiently to encapsulate the waste and seal the repository. Thus, a major task of the Waste Isolation Pilot Plant (WIPP) Program is to develop and validate this predictive technology to calculate creep of repository rooms in the bedded salt deposits of Southeastern New Mexico. 19 refs., 15 figs., 2 tabs

The diagnostic value of specific IgE to Ara h 2 to predict peanut allergy in children is comparable to a validated and updated diagnostic prediction model.

Science.gov (United States)

Klemans, Rob J B; Otte, Dianne; Knol, Mirjam; Knol, Edward F; Meijer, Yolanda; Gmelig-Meyling, Frits H J; Bruijnzeel-Koomen, Carla A F M; Knulst, André C; Pasmans, Suzanne G M A

2013-01-01

A diagnostic prediction model for peanut allergy in children was recently published, using 6 predictors: sex, age, history, skin prick test, peanut specific immunoglobulin E (sIgE), and total IgE minus peanut sIgE. To validate this model and update it by adding allergic rhinitis, atopic dermatitis, and sIgE to peanut components Ara h 1, 2, 3, and 8 as candidate predictors. To develop a new model based only on sIgE to peanut components. Validation was performed by testing discrimination (diagnostic value) with an area under the receiver operating characteristic curve and calibration (agreement between predicted and observed frequencies of peanut allergy) with the Hosmer-Lemeshow test and a calibration plot. The performance of the (updated) models was similarly analyzed. Validation of the model in 100 patients showed good discrimination (88%) but poor calibration (P original model: sex, skin prick test, peanut sIgE, and total IgE minus sIgE. When building a model with sIgE to peanut components, Ara h 2 was the only predictor, with a discriminative ability of 90%. Cutoff values with 100% positive and negative predictive values could be calculated for both the updated model and sIgE to Ara h 2. In this way, the outcome of the food challenge could be predicted with 100% accuracy in 59% (updated model) and 50% (Ara h 2) of the patients. Discrimination of the validated model was good; however, calibration was poor. The discriminative ability of Ara h 2 was almost comparable to that of the updated model, containing 4 predictors. With both models, the need for peanut challenges could be reduced by at least 50%. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

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

Science.gov (United States)

Agüero, Gonzalo; Davenport, María C; Del Valle, María de la P; Gallegos, Paulina; Kannemann, Ana L; Bokser, Vivian; Ferrero, Fernando

2010-02-01

Despite most meningitis are not bacterial, antibiotics are usually administered on admission because bacterial meningitis is difficult to be rule-out. Distinguishing bacterial from aseptic meningitis on admission could avoid inappropriate antibiotic use and hospitalization. We aimed to validate a clinical prediction rule to distinguish bacterial from aseptic meningitis in children, on arriving to the emergency room. This prospective study included patients aged or = 1000 cells/mm(3), CSF protein > or = 80 mg/dl, peripheral blood absolute neutrophil count > or = 10.000/mm(3), seizure = 1 point each. Sensitivity (S), specificity (E), positive and negative predictive values (PPV and NPV), positive and negative likelihood ratios (PLR and NLR) of the BMS to predict bacterial meningitis were calculated. Seventy patients with meningitis were included (14 bacterial meningitis). When BMS was calculated, 25 patients showed a BMS= 0 points, 11 BMS= 1 point, and 34 BMS > or = 2 points. A BMS = 0 showed S: 100%, E: 44%, VPP: 31%, VPN: 100%, RVP: 1,81 RVN: 0. A BMS > or = 2 predicted bacterial meningitis with S: 100%, E: 64%, VPP: 41%, VPN: 100%, PLR: 2.8, NLR:0. Using BMS was simple, and allowed identifying children with very low risk of bacterial meningitis. It could be a useful tool to assist clinical decision making.

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

Science.gov (United States)

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

2014-09-01

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

On the Predictability of Computer simulations: Advances in Verification and Validation

KAUST Repository

Prudhomme, Serge

2014-01-06

We will present recent advances on the topics of Verification and Validation in order to assess the reliability and predictability of computer simulations. The first part of the talk will focus on goal-oriented error estimation for nonlinear boundary-value problems and nonlinear quantities of interest, in which case the error representation consists of two contributions: 1) a first contribution, involving the residual and the solution of the linearized adjoint problem, which quantifies the discretization or modeling error; and 2) a second contribution, combining higher-order terms that describe the linearization error. The linearization error contribution is in general neglected with respect to the discretization or modeling error. However, when nonlinear effects are significant, it is unclear whether ignoring linearization effects may produce poor convergence of the adaptive process. The objective will be to show how both contributions can be estimated and employed in an adaptive scheme that simultaneously controls the two errors in a balanced manner. In the second part of the talk, we will present novel approach for calibration of model parameters. The proposed inverse problem not only involves the minimization of the misfit between experimental observables and their theoretical estimates, but also an objective function that takes into account some design goals on specific design scenarios. The method can be viewed as a regularization approach of the inverse problem, one, however, that best respects some design goals for which mathematical models are intended. The inverse problem is solved by a Bayesian method to account for uncertainties in the data. We will show that it shares the same structure as the deterministic problem that one would obtain by multi-objective optimization theory. The method is illustrated on an example of heat transfer in a two-dimensional fin. The proposed approach has the main benefit that it increases the confidence in predictive

Reliability, Validity, and Predictive Utility of the 25-Item Criminogenic Cognitions Scale (CCS)

OpenAIRE

Tangney, June Price; Stuewig, Jeffrey; Furukawa, Emi; Kopelovich, Sarah; Meyer, Patrick; Cosby, Brandon

2012-01-01

Theory, research, and clinical reports suggest that moral cognitions play a role in initiating and sustaining criminal behavior. The 25 item Criminogenic Cognitions Scale (CCS) was designed to tap 5 dimensions: Notions of entitlement; Failure to Accept Responsibility; Short-Term Orientation; Insensitivity to Impact of Crime; and Negative Attitudes Toward Authority. Results from 552 jail inmates support the reliability, validity, and predictive utility of the measure. The CCS was linked to cri...

DES Prediction of Cavitation Erosion and Its Validation for a Ship Scale Propeller

Science.gov (United States)

Ponkratov, Dmitriy, Dr

2015-12-01

Lloyd's Register Technical Investigation Department (LR TID) have developed numerical functions for the prediction of cavitation erosion aggressiveness within Computational Fluid Dynamics (CFD) simulations. These functions were previously validated for a model scale hydrofoil and ship scale rudder [1]. For the current study the functions were applied to a cargo ship's full scale propeller, on which the severe cavitation erosion was reported. The performed Detach Eddy Simulation (DES) required a fine computational mesh (approximately 22 million cells), together with a very small time step (2.0E-4 s). As the cavitation for this type of vessel is primarily caused by a highly non-uniform wake, the hull was also included in the simulation. The applied method under predicted the cavitation extent and did not fully resolve the tip vortex; however, the areas of cavitation collapse were captured successfully. Consequently, the developed functions showed a very good prediction of erosion areas, as confirmed by comparison with underwater propeller inspection results.

[Risk Prediction Using Routine Data: Development and Validation of Multivariable Models Predicting 30- and 90-day Mortality after Surgical Treatment of Colorectal Cancer].

Science.gov (United States)

Crispin, Alexander; Strahwald, Brigitte; Cheney, Catherine; Mansmann, Ulrich

2018-06-04

Quality control, benchmarking, and pay for performance (P4P) require valid indicators and statistical models allowing adjustment for differences in risk profiles of the patient populations of the respective institutions. Using hospital remuneration data for measuring quality and modelling patient risks has been criticized by clinicians. Here we explore the potential of prediction models for 30- and 90-day mortality after colorectal cancer surgery based on routine data. Full census of a major statutory health insurer. Surgical departments throughout the Federal Republic of Germany. 4283 and 4124 insurants with major surgery for treatment of colorectal cancer during 2013 and 2014, respectively. Age, sex, primary and secondary diagnoses as well as tumor locations as recorded in the hospital remuneration data according to §301 SGB V. 30- and 90-day mortality. Elixhauser comorbidities, Charlson conditions, and Charlson scores were generated from the ICD-10 diagnoses. Multivariable prediction models were developed using a penalized logistic regression approach (logistic ridge regression) in a derivation set (patients treated in 2013). Calibration and discrimination of the models were assessed in an internal validation sample (patients treated in 2014) using calibration curves, Brier scores, receiver operating characteristic curves (ROC curves) and the areas under the ROC curves (AUC). 30- and 90-day mortality rates in the learning-sample were 5.7 and 8.4%, respectively. The corresponding values in the validation sample were 5.9% and once more 8.4%. Models based on Elixhauser comorbidities exhibited the highest discriminatory power with AUC values of 0.804 (95% CI: 0.776 -0.832) and 0.805 (95% CI: 0.782-0.828) for 30- and 90-day mortality. The Brier scores for these models were 0.050 (95% CI: 0.044-0.056) and 0.067 (95% CI: 0.060-0.074) and similar to the models based on Charlson conditions. Regardless of the model, low predicted probabilities were well calibrated, while

Psychometric properties and convergent and predictive validity of an executive function test battery for two-year-olds

Directory of Open Access Journals (Sweden)

Hanna eMulder

2014-07-01

Full Text Available Executive function (EF is an important predictor of numerous developmental outcomes, such as academic achievement and behavioral adjustment. Although a plethora of measurement instruments exists to assess executive function in children, only few of these are suitable for toddlers, and even fewer have undergone psychometric evaluation. The present study evaluates the psychometric properties and validity of an assessment battery for measuring EF in two-year-olds. A sample of 2437 children were administered the assessment battery at a mean age of 2;4 years (SD = 0;3 years in a large-scale field study. Measures of both hot EF (snack and gift delay tasks and cool EF (six boxes, memory for location, and visual search task were included. Confirmatory Factor Analyses showed that a two-factor hot and cool EF model fitted the data better than a one-factor model. Measurement invariance was supported across groups differing in age, gender, socioeconomic status (SES, home language, and test setting. Criterion and convergent validity were evaluated by examining relationships between EF and age, gender, SES, home language, and parent and teacher reports of children’s attention and inhibitory control. Predictive validity of the test battery was investigated by regressing children’s pre-academic skills and behavioral problems at age three on the latent hot and cool EF factors at age two years. The test battery showed satisfactory psychometric quality and criterion, convergent, and predictive validity. Whereas cool EF predicted both pre-academic skills and behavior problems one year later, hot EF predicted behavior problems only. These results show that EF can be assessed with psychometrically sound instruments in children as young as two years, and that EF tasks can be reliably applied in large scale field research. The current instruments offer new opportunities for investigating EF in early childhood, and for evaluating interventions targeted at improving

Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

NARCIS (Netherlands)

Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.

Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to

Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

NARCIS (Netherlands)

Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.

2017-01-01

Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to

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

Science.gov (United States)

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

2013-09-01

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

Radar investigations at the Saltsjoetunnel - predictions and validation

International Nuclear Information System (INIS)

Olsson, Olle; Palmqvist, Kai

1989-01-01

Borehole radar investigations have been performed in two boreholes drilled along the extent of the Saltsjoe tunnel in Stockholm, Sweden. The objective of the project was to test investigate the capabilities of the borehole radar technique to predict geological structures prior to tunnel excavation. Singlehole and crosshole radar measurements were made in the two boreholes which outlined and equilateral triangle. The crosshole data was used to produce tomograms showing the distribution of radar attenuation and slowness (inverse of velocity) in the plane between the boreholes. The radar model of the site contained one major feature which was identified as a fracture zone. The intersection of the fracture zone with the tunnel was extrapolated from the radar data and found to be in agreement with observations in the tunnel. At the intersection of the fracture zone with the tunnel grouting had to be applied. It has also been found that the radar identifies a number of smaller features which are of practically no significance with respect to tunnel construction. There is general agreement between the radar model of the site and the geologic-tectonic model of the site. This project has demonstrated the capability of the boreholes radar technique to predict the existence, location, and orientation of geologic features (e.g. fracture zones) which can be of significance to the cost and safety when excavating a tunnel. However, further development is needed to be able to use the technique cost effectively for continuous prediction ahead of the tunnel front. (authors) (17 figs., 1 tab.)

Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions.

Directory of Open Access Journals (Sweden)

Tomislav Hengl

Full Text Available 80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na. We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring

Predictive validity of the GOSLON Yardstick index in patients with unilateral cleft lip and palate: A systematic review.

Directory of Open Access Journals (Sweden)

Cindy Buj-Acosta

Full Text Available Among the various indices developed for measuring the results of treatment in patients born with unilateral cleft lip and palate (UCLP, the GOSLON Yardstick index is the most widely used to assess the efficacy of treatment and treatment outcomes, which in UCLP cases are closely linked to jaw growth. The aim of this study was to conduct a systematic review to validate the predictability of growth using the GOSLON Yardstick in patients born with UCLP. A systematic literature review was conducted in four Internet databases: Medline, Cochrane Library, Scopus and Embase, complemented by a manual search and a further search in the databases of the leading journals that focus on this topic. An electronic search was also conducted among grey literature. The search identified a total of 131 articles. Duplicated articles were excluded and after reading titles and abstracts, any articles not related to the research objective were excluded, leaving a total of 21 texts. After reading the complete text, only three articles fulfilled the inclusion criteria. The results showed a predictive validity of between 42.2% and 64.7%, which points to a lack of evidence in the literature for the predictive validity of the GOSLON Yardstick index used in children born with UCLP.

Predictive Validity of the HKT-R Risk Assessment Tool: Two and 5-Year Violent Recidivism in a Nationwide Sample of Dutch Forensic Psychiatric Patients.

Science.gov (United States)

Bogaerts, Stefan; Spreen, Marinus; Ter Horst, Paul; Gerlsma, Coby

2018-06-01

This study has examined the predictive validity of the Historical Clinical Future [ Historisch Klinisch Toekomst] Revised risk assessment scheme in a cohort of 347 forensic psychiatric patients, which were discharged between 2004 and 2008 from any of 12 highly secure forensic centers in the Netherlands. Predictive validity was measured 2 and 5 years after release. Official reconviction data obtained from the Dutch Ministry of Security and Justice were used as outcome measures. Violent reoffending within 2 and 5 years after discharge was assessed. With regard to violent reoffending, results indicated that the predictive validity of the Historical domain was modest for 2 (area under the curve [AUC] = .75) and 5 (AUC = .74) years. The predictive validity of the Clinical domain was marginal for 2 (admission: AUC = .62; discharge: AUC = .63) and 5 (admission: AUC = .69; discharge: AUC = .62) years after release. The predictive validity of the Future domain was modest (AUC = .71) for 2 years and low for 5 (AUC = .58) years. The total score of the instrument was modest for 2 years (AUC = .78) and marginal for 5 (AUC = .68) years. Finally, the Final Risk Judgment was modest for 2 years (AUC = .78) and marginal for 5 (AUC = .63) years time at risk. It is concluded that this risk assessment instrument appears to be a satisfactory instrument for risk assessment.

Validated Loads Prediction Models for Offshore Wind Turbines for Enhanced Component Reliability

DEFF Research Database (Denmark)

Koukoura, Christina

To improve the reliability of offshore wind turbines, accurate prediction of their response is required. Therefore, validation of models with site measurements is imperative. In the present thesis a 3.6MW pitch regulated-variable speed offshore wind turbine on a monopole foundation is built...... are used for the modification of the sub-structure/foundation design for possible material savings. First, the background of offshore wind engineering, including wind-wave conditions, support structure, blade loading and wind turbine dynamics are presented. Second, a detailed description of the site...

Gene expression signatures predict outcome in non-muscle invasive bladder carcinoma - a multi-center validation study

DEFF Research Database (Denmark)

Andersen, Lars Dyrskjøt; Zieger, Karsten; Real, Francisco X.

2007-01-01

and carcinoma in situ (CIS) and for predicting disease recurrence and progression. EXPERIMENTAL DESIGN: We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain, and France using custom microarrays. Molecular classifications were compared with pathologic....... CONCLUSION: This multicenter validation study confirms in an independent series the clinical utility of molecular classifiers to predict the outcome of patients initially diagnosed with non-muscle-invasive bladder cancer. This information may be useful to better guide patient treatment....

The derivation and validation of a simple model for predicting in-hospital mortality of acutely admitted patients to internal medicine wards.

Science.gov (United States)

Sakhnini, Ali; Saliba, Walid; Schwartz, Naama; Bisharat, Naiel

2017-06-01

Limited information is available about clinical predictors of in-hospital mortality in acute unselected medical admissions. Such information could assist medical decision-making.To develop a clinical model for predicting in-hospital mortality in unselected acute medical admissions and to test the impact of secondary conditions on hospital mortality.This is an analysis of the medical records of patients admitted to internal medicine wards at one university-affiliated hospital. Data obtained from the years 2013 to 2014 were used as a derivation dataset for creating a prediction model, while data from 2015 was used as a validation dataset to test the performance of the model. For each admission, a set of clinical and epidemiological variables was obtained. The main diagnosis at hospitalization was recorded, and all additional or secondary conditions that coexisted at hospital admission or that developed during hospital stay were considered secondary conditions.The derivation and validation datasets included 7268 and 7843 patients, respectively. The in-hospital mortality rate averaged 7.2%. The following variables entered the final model; age, body mass index, mean arterial pressure on admission, prior admission within 3 months, background morbidity of heart failure and active malignancy, and chronic use of statins and antiplatelet agents. The c-statistic (ROC-AUC) of the prediction model was 80.5% without adjustment for main or secondary conditions, 84.5%, with adjustment for the main diagnosis, and 89.5% with adjustment for the main diagnosis and secondary conditions. The accuracy of the predictive model reached 81% on the validation dataset.A prediction model based on clinical data with adjustment for secondary conditions exhibited a high degree of prediction accuracy. We provide a proof of concept that there is an added value for incorporating secondary conditions while predicting probabilities of in-hospital mortality. Further improvement of the model performance

Predicting Likelihood of Having Four or More Positive Nodes in Patient With Sentinel Lymph Node-Positive Breast Cancer: A Nomogram Validation Study

International Nuclear Information System (INIS)

Unal, Bulent; Gur, Akif Serhat; Beriwal, Sushil; Tang Gong; Johnson, Ronald; Ahrendt, Gretchen; Bonaventura, Marguerite; Soran, Atilla

2009-01-01

Purpose: Katz suggested a nomogram for predicting having four or more positive nodes in sentinel lymph node (SLN)-positive breast cancer patients. The findings from this formula might influence adjuvant radiotherapy decisions. Our goal was to validate the accuracy of the Katz nomogram. Methods and Materials: We reviewed the records of 309 patients with breast cancer who had undergone completion axillary lymph node dissection. The factors associated with the likelihood of having four or more positive axillary nodes were evaluated in patients with one to three positive SLNs. The nomogram developed by Katz was applied to our data set. The area under the curve of the corresponding receiver operating characteristics curve was calculated for the nomogram. Results: Of the 309 patients, 80 (25.9%) had four or more positive axillary lymph nodes. On multivariate analysis, the number of positive SLNs (p < .0001), overall metastasis size (p = .019), primary tumor size (p = .0001), and extracapsular extension (p = .01) were significant factors predicting for four or more positive nodes. For patients with <5% probability, 90.3% had fewer than four positive nodes and 9.7% had four or more positive nodes. The negative predictive value was 91.7%, and sensitivity was 80%. The nomogram was accurate and discriminating (area under the curve, .801). Conclusion: The probability of four or more involved nodes is significantly greater in patients who have an increased number of positive SLNs, increased overall metastasis size, increased tumor size, and extracapsular extension. The Katz nomogram was validated in our patients. This nomogram will be helpful to clinicians making adjuvant treatment recommendations to their patients.

Predicting asthma in preschool children with asthma-like symptoms : Validating and updating the PIAMA risk score

NARCIS (Netherlands)

Hafkamp-de Groen, Esther; Lingsma, Hester F.; Caudri, Daan; Levie, Deborah; Wijga, Alet; Koppelman, Gerard H.; Duijts, Liesbeth; Jaddoe, Vincent W. V.; Smit, Henriette A.; Kerkhof, Marjan; Moll, Henriette A.; Hofman, Albert; Steyerberg, Ewout W.; de Jongste, Johan C.; Raat, Hein

2013-01-01

Background: The Prevention and Incidence of Asthma and Mite Allergy (PIAMA) risk score predicts the probability of having asthma at school age among preschool children with suggestive symptoms. Objective: We sought to externally validate the PIAMA risk score at different ages and in ethnic and

Development and validation of risk prediction equations to estimate survival in patients with colorectal cancer: cohort study

OpenAIRE

Hippisley-Cox, Julia; Coupland, Carol

2017-01-01

Objective: To develop and externally validate risk prediction equations to estimate absolute and conditional survival in patients with colorectal cancer. \\ud \\ud Design: Cohort study.\\ud \\ud Setting: General practices in England providing data for the QResearch database linked to the national cancer registry.\\ud \\ud Participants: 44 145 patients aged 15-99 with colorectal cancer from 947 practices to derive the equations. The equations were validated in 15 214 patients with colorectal cancer ...

Validity of a simple Internet-based outcome-prediction tool in patients with total hip replacement: a pilot study.

Science.gov (United States)

Stöckli, Cornel; Theiler, Robert; Sidelnikov, Eduard; Balsiger, Maria; Ferrari, Stephen M; Buchzig, Beatus; Uehlinger, Kurt; Riniker, Christoph; Bischoff-Ferrari, Heike A

2014-04-01

We developed a user-friendly Internet-based tool for patients undergoing total hip replacement (THR) due to osteoarthritis to predict their pain and function after surgery. In the first step, the key questions were identified by statistical modelling in a data set of 375 patients undergoing THR. Based on multiple regression, we identified the two most predictive WOMAC questions for pain and the three most predictive WOMAC questions for functional outcome, while controlling for comorbidity, body mass index, age, gender and specific comorbidities relevant to the outcome. In the second step, a pilot study was performed to validate the resulting tool against the full WOMAC questionnaire among 108 patients undergoing THR. The mean difference between observed (WOMAC) and model-predicted value was -1.1 points (95% confidence interval, CI -3.8, 1.5) for pain and -2.5 points (95% CI -5.3, 0.3) for function. The model-predicted value was within 20% of the observed value in 48% of cases for pain and in 57% of cases for function. The tool demonstrated moderate validity, but performed weakly for patients with extreme levels of pain and extreme functional limitations at 3 months post surgery. This may have been partly due to early complications after surgery. However, the outcome-prediction tool may be useful in helping patients to become better informed about the realistic outcome of their THR.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy.

Science.gov (United States)

Liu, Yan-De; Ying, Yi-Bin; Fu, Xia-Ping

2005-03-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.

Predicting Environmental Suitability for a Rare and Threatened Species (Lao Newt, Laotriton laoensis) Using Validated Species Distribution Models

Science.gov (United States)

Chunco, Amanda J.; Phimmachak, Somphouthone; Sivongxay, Niane; Stuart, Bryan L.

2013-01-01

The Lao newt (Laotriton laoensis) is a recently described species currently known only from northern Laos. Little is known about the species, but it is threatened as a result of overharvesting. We integrated field survey results with climate and altitude data to predict the geographic distribution of this species using the niche modeling program Maxent, and we validated these predictions by using interviews with local residents to confirm model predictions of presence and absence. The results of the validated Maxent models were then used to characterize the environmental conditions of areas predicted suitable for L. laoensis. Finally, we overlaid the resulting model with a map of current national protected areas in Laos to determine whether or not any land predicted to be suitable for this species is coincident with a national protected area. We found that both area under the curve (AUC) values and interview data provided strong support for the predictive power of these models, and we suggest that interview data could be used more widely in species distribution niche modeling. Our results further indicated that this species is mostly likely geographically restricted to high altitude regions (i.e., over 1,000 m elevation) in northern Laos and that only a minute fraction of suitable habitat is currently protected. This work thus emphasizes that increased protection efforts, including listing this species as endangered and the establishment of protected areas in the region predicted to be suitable for L. laoensis, are urgently needed. PMID:23555808

Validity of Bioelectrical Impedance Analysis to Estimation Fat-Free Mass in the Army Cadets.

Science.gov (United States)

Langer, Raquel D; Borges, Juliano H; Pascoa, Mauro A; Cirolini, Vagner X; Guerra-Júnior, Gil; Gonçalves, Ezequiel M

2016-03-11

Bioelectrical Impedance Analysis (BIA) is a fast, practical, non-invasive, and frequently used method for fat-free mass (FFM) estimation. The aims of this study were to validate predictive equations of BIA to FFM estimation in Army cadets and to develop and validate a specific BIA equation for this population. A total of 396 males, Brazilian Army cadets, aged 17-24 years were included. The study used eight published predictive BIA equations, a specific equation in FFM estimation, and dual-energy X-ray absorptiometry (DXA) as a reference method. Student's t-test (for paired sample), linear regression analysis, and Bland-Altman method were used to test the validity of the BIA equations. Predictive BIA equations showed significant differences in FFM compared to DXA (p FFM variance. Specific BIA equations showed no significant differences in FFM, compared to DXA values. Published BIA predictive equations showed poor accuracy in this sample. The specific BIA equations, developed in this study, demonstrated validity for this sample, although should be used with caution in samples with a large range of FFM.

Predicting the need for institutional care shortly after admission to rehabilitation: Rasch analysis and predictive validity of the BRASS Index.

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Panella, L; La Porta, F; Caselli, S; Marchisio, S; Tennant, A

2012-09-01

Effective discharge planning is increasingly recognised as a critical component of hospital-based Rehabilitation. The BRASS index is a risk screening tool for identification, shortly after hospital admission, of patients who are at risk of post-discharge problems. To evaluate the internal construct validity and reliability of the Blaylock Risk Assessment Screening Score (BRASS) within the rehabilitation setting. Observational prospective study. Rehabilitation ward of an Italian district hospital. One hundred and four consecutively admitted patients. Using classical psychometric methods and Rasch analysis (RA), the internal construct validity and reliability of the BRASS were examined. Also, external and predictive validity of the Rasch-modified BRASS (RMB) score were determined. Reliability of the original BRASS was low (Cronbach's alpha=0.595) and factor analyses showed that it was clearly multidimensional. A RA, based on a reduced 7-BRASS item set (RMB), satisfied model's expectations. Reliability was 0.777. The RMB scores strongly correlated with the original BRASS (rho=0.952; P28 days (RR=7.6, 95%CI=1.8-31.9). This study demonstrated that the original BRASS was multidimensional and unreliable. However, the RMB holds adequate internal construct validity and is sufficiently reliable as a predictor of discharge problems for group, but not individual use. The application of tools and methods (such as the BRASS Index) developed under the biomedical paradigm in a Physical and Rehabilitation Medicine setting may have limitations. Further research is needed to develop, within the rehabilitation setting, a valid measuring tool of risk of post-discharge problems at the individual level.

Validity of the MicroDYN Approach: Complex Problem Solving Predicts School Grades beyond Working Memory Capacity

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Schweizer, Fabian; Wustenberg, Sascha; Greiff, Samuel

2013-01-01

This study examines the validity of the complex problem solving (CPS) test MicroDYN by investigating a) the relation between its dimensions--rule identification (exploration strategy), rule knowledge (acquired knowledge), rule application (control performance)--and working memory capacity (WMC), and b) whether CPS predicts school grades in…

A Study of the Predictive Validity of the Children's Depression Inventory for Major Depression Disorder in Puerto Rican Adolescents

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Rivera-Medina, Carmen L.; Bernal, Guillermo; Rossello, Jeannette; Cumba-Aviles, Eduardo

2010-01-01

This study aims to evaluate the predictive validity of the Children's Depression Inventory items for major depression disorder (MDD) in an outpatient clinic sample of Puerto Rican adolescents. The sample consisted of 130 adolescents, 13 to 18 years old. The five most frequent symptoms of the Children's Depression Inventory that best predict the…

Reliability, validity, and significance of assessment of sense of contribution in the workplace.

Science.gov (United States)

Takaki, Jiro; Taniguchi, Toshiyo; Fujii, Yasuhito

2014-01-29

The purpose of this study was to assess the validity and reliability of the Sense of Contribution Scale (SCS), a newly developed, 7-item questionnaire used to measure sense of contribution in the workplace. Workers at 272 organizations answered questionnaires that included the SCS. Because of non-participation or missing data, the number of subjects included in the analyses for internal consistency and validity varied from 1,675 to 2,462 (response rates 54.6%-80.2%). Fifty-four workers were included in the analysis of test-retest reliability (response rate, 77.1%). The SCS showed high internal consistency (Cronbach's α coefficients in men and women were 0.85 and 0.86, respectively) and test-retest reliability (intraclass correlation coefficient = 0.91). Significant (p workplace bullying, and procedural and interactional justice. The SCS is a psychometrically satisfactory measure of sense of contribution in the workplace. The SCS provides a new and useful instrument to measure sense of contribution, which is independently associated with mental health in workers, for studies in organizational science, occupational health psychology and occupational medicine.

A Critical Analysis and Validation of the Accuracy of Wave Overtopping Prediction Formulae for OWECs

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David Gallach-Sánchez

2018-01-01

Full Text Available The development of wave energy devices is growing in recent years. One type of device is the overtopping wave energy converter (OWEC, for which the knowledge of the wave overtopping rates is a basic and crucial aspect in their design. In particular, the most interesting range to study is for OWECs with steep slopes to vertical walls, and with very small freeboards and zero freeboards where the overtopping rate is maximized, and which can be generalized as steep low-crested structures. Recently, wave overtopping prediction formulae have been published for this type of structures, although their accuracy has not been fully assessed, as the overtopping data available in this range is scarce. We performed a critical analysis of the overtopping prediction formulae for steep low-crested structures and the validation of the accuracy of these formulae, based on new overtopping data for steep low-crested structures obtained at Ghent University. This paper summarizes the existing knowledge about average wave overtopping, describes the physical model tests performed, analyses the results and compares them to existing prediction formulae. The new dataset extends the wave overtopping data towards vertical walls and zero freeboard structures. In general, the new dataset validated the more recent overtopping formulae focused on steep slopes with small freeboards, although the formulae are underpredicting the average overtopping rates for very small and zero relative crest freeboards.

Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient.

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Chirico, Nicola; Gramatica, Paola

2011-09-26

The main utility of QSAR models is their ability to predict activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. As a measure for such evaluation the OECD guidelines have proposed the predictive squared correlation coefficient Q(2)(F1) (Shi et al.). However, other validation criteria have been proposed by other authors: the Golbraikh-Tropsha method, r(2)(m) (Roy), Q(2)(F2) (Schüürmann et al.), Q(2)(F3) (Consonni et al.). In QSAR studies these measures are usually in accordance, though this is not always the case, thus doubts can arise when contradictory results are obtained. It is likely that none of the aforementioned criteria is the best in every situation, so a comparative study using simulated data sets is proposed here, using threshold values suggested by the proponents or those widely used in QSAR modeling. In addition, a different and simple external validation measure, the concordance correlation coefficient (CCC), is proposed and compared with other criteria. Huge data sets were used to study the general behavior of validation measures, and the concordance correlation coefficient was shown to be the most restrictive. On using simulated data sets of a more realistic size, it was found that CCC was broadly in agreement, about 96% of the time, with other validation measures in accepting models as predictive, and in almost all the examples it was the most precautionary. The proposed concordance correlation coefficient also works well on real data sets, where it seems to be more stable, and helps in making decisions when the validation measures are in conflict. Since it is conceptually simple, and given its stability and restrictiveness, we propose the concordance correlation coefficient as a complementary, or alternative, more prudent measure of a QSAR model to be externally predictive.

Self-perceived Coparenting of Nonresident Fathers: Scale Development and Validation.

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Dyer, W Justin; Fagan, Jay; Kaufman, Rebecca; Pearson, Jessica; Cabrera, Natasha

2017-11-16

This study reports on the development and validation of the Fatherhood Research and Practice Network coparenting perceptions scale for nonresident fathers. Although other measures of coparenting have been developed, this is the first measure developed specifically for low-income, nonresident fathers. Focus groups were conducted to determine various aspects of coparenting. Based on this, a scale was created and administered to 542 nonresident fathers. Participants also responded to items used to examine convergent and predictive validity (i.e., parental responsibility, contact with the mother, father self-efficacy and satisfaction, child behavior problems, and contact and engagement with the child). Factor analyses and reliability tests revealed three distinct and reliable perceived coparenting factors: undermining, alliance, and gatekeeping. Validity tests suggest substantial overlap between the undermining and alliance factors, though undermining was uniquely related to child behavior problems. The alliance and gatekeeping factors showed strong convergent validity and evidence for predictive validity. Taken together, results suggest this relatively short measure (11 items) taps into three coparenting dimensions significantly predictive of aspects of individual and family life. © 2017 Family Process Institute.

Development and Validation of a Predictive Model for Functional Outcome After Stroke Rehabilitation: The Maugeri Model.

Science.gov (United States)

Scrutinio, Domenico; Lanzillo, Bernardo; Guida, Pietro; Mastropasqua, Filippo; Monitillo, Vincenzo; Pusineri, Monica; Formica, Roberto; Russo, Giovanna; Guarnaschelli, Caterina; Ferretti, Chiara; Calabrese, Gianluigi

2017-12-01

Prediction of outcome after stroke rehabilitation may help clinicians in decision-making and planning rehabilitation care. We developed and validated a predictive tool to estimate the probability of achieving improvement in physical functioning (model 1) and a level of independence requiring no more than supervision (model 2) after stroke rehabilitation. The models were derived from 717 patients admitted for stroke rehabilitation. We used multivariable logistic regression analysis to build each model. Then, each model was prospectively validated in 875 patients. Model 1 included age, time from stroke occurrence to rehabilitation admission, admission motor and cognitive Functional Independence Measure scores, and neglect. Model 2 included age, male gender, time since stroke onset, and admission motor and cognitive Functional Independence Measure score. Both models demonstrated excellent discrimination. In the derivation cohort, the area under the curve was 0.883 (95% confidence intervals, 0.858-0.910) for model 1 and 0.913 (95% confidence intervals, 0.884-0.942) for model 2. The Hosmer-Lemeshow χ 2 was 4.12 ( P =0.249) and 1.20 ( P =0.754), respectively. In the validation cohort, the area under the curve was 0.866 (95% confidence intervals, 0.840-0.892) for model 1 and 0.850 (95% confidence intervals, 0.815-0.885) for model 2. The Hosmer-Lemeshow χ 2 was 8.86 ( P =0.115) and 34.50 ( P =0.001), respectively. Both improvement in physical functioning (hazard ratios, 0.43; 0.25-0.71; P =0.001) and a level of independence requiring no more than supervision (hazard ratios, 0.32; 0.14-0.68; P =0.004) were independently associated with improved 4-year survival. A calculator is freely available for download at https://goo.gl/fEAp81. This study provides researchers and clinicians with an easy-to-use, accurate, and validated predictive tool for potential application in rehabilitation research and stroke management. © 2017 American Heart Association, Inc.

The validation of the turnover intention scale

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Chris F.C. Bothma

2013-04-01

Full Text Available Orientation: Turnover intention as a construct has attracted increased research attention in the recent past, but there are seemingly not many valid and reliable scales around to measure turnover intention. Research purpose: This study focused on the validation of a shortened, six-item version of the turnover intention scale (TIS-6. Motivation for the study: The research question of whether the TIS-6 is a reliable and a valid scale for measuring turnover intention and for predicting actual turnover was addressed in this study. Research design, approach and method: The study was based on a census-based sample (n= 2429 of employees in an information, communication and technology (ICT sector company (N= 23 134 where the TIS-6 was used as one of the criterion variables. The leavers (those who left the company in this sample were compared with the stayers (those who remained in the employ of the company in this sample in respect of different variables used in the study. Main findings: It was established that the TIS-6 could measure turnover intentions reliably (α= 0.80. The TIS-6 could significantly distinguish between leavers and stayers (actual turnover, thereby confirming its criterion-predictive validity. The scale also established statistically significant differences between leavers and stayers in respect of a number of the remaining theoretical variables used in the study, thereby also confirming its differential validity. These comparisons were conducted for both the 4-month and the 4-year period after the survey was conducted. Practical/managerial implications: Turnover intention is related to a number of variables in the study which necessitates a reappraisal and a reconceptualisation of existing turnover intention models. Contribution/value-add: The TIS-6 can be used as a reliable and valid scale to assess turnover intentions and can therefore be used in research to validly and reliably assess turnover intentions or to

Using deuterated PAH amendments to validate chemical extraction methods to predict PAH bioavailability in soils

International Nuclear Information System (INIS)

Gomez-Eyles, Jose L.; Collins, Chris D.; Hodson, Mark E.

2011-01-01

Validating chemical methods to predict bioavailable fractions of polycyclic aromatic hydrocarbons (PAHs) by comparison with accumulation bioassays is problematic. Concentrations accumulated in soil organisms not only depend on the bioavailable fraction but also on contaminant properties. A historically contaminated soil was freshly spiked with deuterated PAHs (dPAHs). dPAHs have a similar fate to their respective undeuterated analogues, so chemical methods that give good indications of bioavailability should extract the fresh more readily available dPAHs and historic more recalcitrant PAHs in similar proportions to those in which they are accumulated in the tissues of test organisms. Cyclodextrin and butanol extractions predicted the bioavailable fraction for earthworms (Eisenia fetida) and plants (Lolium multiflorum) better than the exhaustive extraction. The PAHs accumulated by earthworms had a larger dPAH:PAH ratio than that predicted by chemical methods. The isotope ratio method described here provides an effective way of evaluating other chemical methods to predict bioavailability. - Research highlights: → Isotope ratios can be used to evaluate chemical methods to predict bioavailability. → Chemical methods predicted bioavailability better than exhaustive extractions. → Bioavailability to earthworms was still far from that predicted by chemical methods. - A novel method using isotope ratios to assess the ability of chemical methods to predict PAH bioavailability to soil biota.

Using deuterated PAH amendments to validate chemical extraction methods to predict PAH bioavailability in soils

Energy Technology Data Exchange (ETDEWEB)

Gomez-Eyles, Jose L., E-mail: j.l.gomezeyles@reading.ac.uk [University of Reading, School of Human and Environmental Sciences, Soil Research Centre, Reading, RG6 6DW Berkshire (United Kingdom); Collins, Chris D.; Hodson, Mark E. [University of Reading, School of Human and Environmental Sciences, Soil Research Centre, Reading, RG6 6DW Berkshire (United Kingdom)

2011-04-15

Validating chemical methods to predict bioavailable fractions of polycyclic aromatic hydrocarbons (PAHs) by comparison with accumulation bioassays is problematic. Concentrations accumulated in soil organisms not only depend on the bioavailable fraction but also on contaminant properties. A historically contaminated soil was freshly spiked with deuterated PAHs (dPAHs). dPAHs have a similar fate to their respective undeuterated analogues, so chemical methods that give good indications of bioavailability should extract the fresh more readily available dPAHs and historic more recalcitrant PAHs in similar proportions to those in which they are accumulated in the tissues of test organisms. Cyclodextrin and butanol extractions predicted the bioavailable fraction for earthworms (Eisenia fetida) and plants (Lolium multiflorum) better than the exhaustive extraction. The PAHs accumulated by earthworms had a larger dPAH:PAH ratio than that predicted by chemical methods. The isotope ratio method described here provides an effective way of evaluating other chemical methods to predict bioavailability. - Research highlights: > Isotope ratios can be used to evaluate chemical methods to predict bioavailability. > Chemical methods predicted bioavailability better than exhaustive extractions. > Bioavailability to earthworms was still far from that predicted by chemical methods. - A novel method using isotope ratios to assess the ability of chemical methods to predict PAH bioavailability to soil biota.

Validation of models with multivariate output

International Nuclear Information System (INIS)

Rebba, Ramesh; Mahadevan, Sankaran

2006-01-01

This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading

Development and validation of optimal cut-off value in inter-arm systolic blood pressure difference for prediction of cardiovascular events.

Science.gov (United States)

Hirono, Akira; Kusunose, Kenya; Kageyama, Norihito; Sumitomo, Masayuki; Abe, Masahiro; Fujinaga, Hiroyuki; Sata, Masataka

2018-01-01

An inter-arm systolic blood pressure difference (IAD) is associated with cardiovascular disease. The aim of this study was to develop and validate the optimal cut-off value of IAD as a predictor of major adverse cardiac events in patients with arteriosclerosis risk factors. From 2009 to 2014, 1076 patients who had at least one cardiovascular risk factor were included in the analysis. We defined 700 randomly selected patients as a development cohort to confirm that IAD was the predictor of cardiovascular events and to determine optimal cut-off value of IAD. Next, we validated outcomes in the remaining 376 patients as a validation cohort. The blood pressure (BP) of both arms measurements were done simultaneously using the ankle-brachial blood pressure index (ABI) form of automatic device. The primary endpoint was the cardiovascular event and secondary endpoint was the all-cause mortality. During a median period of 2.8 years, 143 patients reached the primary endpoint in the development cohort. In the multivariate Cox proportional hazards analysis, IAD was the strong predictor of cardiovascular events (hazard ratio: 1.03, 95% confidence interval: 1.01-1.05, p=0.005). The receiver operating characteristic curve revealed that 5mmHg was the optimal cut-off point of IAD to predict cardiovascular events (p<0.001). In the validation cohort, the presence of a large IAD (IAD ≥5mmHg) was significantly associated with the primary endpoint (p=0.021). IAD is significantly associated with future cardiovascular events in patients with arteriosclerosis risk factors. The optimal cut-off value of IAD is 5mmHg. Copyright © 2017 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

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Emma Mares-García

2017-06-01

Full Text Available Background Other studies have assessed nonadherence to proton pump inhibitors (PPIs, but none has developed a screening test for its detection. Objectives To construct and internally validate a predictive model for nonadherence to PPIs. Methods This prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count. Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC, was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android. Results The points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83–0.91], p < 0.001. The test yielded a sensitivity of 0.80 (95% CI [0.70–0.87] and a specificity of 0.82 (95% CI [0.76–0.87]. The three parameters were very similar in the bootstrap validation. Conclusions A points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.

Validation of the Web-Based IBTR! 2.0 Nomogram to Predict for Ipsilateral Breast Tumor Recurrence After Breast-Conserving Therapy.

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Kindts, Isabelle; Laenen, Annouschka; Peeters, Stephanie; Janssen, Hilde; Depuydt, Tom; Nevelsteen, Ines; Van Limbergen, Erik; Weltens, Caroline

2016-08-01

To evaluate the IBTR! 2.0 nomogram, which predicts 10-year ipsilateral breast tumor recurrence (IBTR) after breast-conserving therapy with and without radiation therapy for breast cancer, by using a large, external, and independent cancer center database. We retrospectively identified 1898 breast cancer cases, treated with breast-conserving therapy and radiation therapy at the University Hospital Leuven from 2000 to 2007, with requisite data for the nomogram variables. Clinicopathologic factors were assessed. Two definitions of IBTR were considered where simultaneous regional or distant recurrence were either censored (conform IBTR! 2.0) or included as event. Validity of the prediction algorithm was tested in terms of discrimination and calibration. Discrimination was assessed by the concordance probability estimate and Harrell's concordance index. The mean predicted and observed 10-year estimates were compared for the entire cohort and for 4 risk groups predefined by nomogram-predicted IBTR risks, and a calibration plot was drawn. Median follow-up was 10.9 years. The 10-year IBTR rates were 1.3% and 2.1%, according to the 2 definitions of IBTR. The validation cohort differed from the development cohort with respect to the administration of hormonal therapy, surgical section margins, lymphovascular invasion, and tumor size. In univariable analysis, younger age (P=.002) and a positive nodal status (P=.048) were significantly associated with IBTR, with a trend for the omission of hormonal therapy (P=.061). The concordance probability estimate and concordance index varied between 0.57 and 0.67 for the 2 definitions of IBTR. In all 4 risk groups the model overestimated the IBTR risk. In particular, between the lowest-risk groups a limited differentiation was suggested by the calibration plot. The IBTR! 2.0 predictive model for IBTR in breast cancer patients shows substandard discriminative ability, with an overestimation of the risk in all subgroups. Copyright �

Validation of the Web-Based IBTR! 2.0 Nomogram to Predict for Ipsilateral Breast Tumor Recurrence After Breast-Conserving Therapy

International Nuclear Information System (INIS)

Kindts, Isabelle; Laenen, Annouschka; Peeters, Stephanie; Janssen, Hilde; Depuydt, Tom; Nevelsteen, Ines; Van Limbergen, Erik; Weltens, Caroline

2016-01-01

Purpose: To evaluate the IBTR! 2.0 nomogram, which predicts 10-year ipsilateral breast tumor recurrence (IBTR) after breast-conserving therapy with and without radiation therapy for breast cancer, by using a large, external, and independent cancer center database. Methods and Materials: We retrospectively identified 1898 breast cancer cases, treated with breast-conserving therapy and radiation therapy at the University Hospital Leuven from 2000 to 2007, with requisite data for the nomogram variables. Clinicopathologic factors were assessed. Two definitions of IBTR were considered where simultaneous regional or distant recurrence were either censored (conform IBTR! 2.0) or included as event. Validity of the prediction algorithm was tested in terms of discrimination and calibration. Discrimination was assessed by the concordance probability estimate and Harrell's concordance index. The mean predicted and observed 10-year estimates were compared for the entire cohort and for 4 risk groups predefined by nomogram-predicted IBTR risks, and a calibration plot was drawn. Results: Median follow-up was 10.9 years. The 10-year IBTR rates were 1.3% and 2.1%, according to the 2 definitions of IBTR. The validation cohort differed from the development cohort with respect to the administration of hormonal therapy, surgical section margins, lymphovascular invasion, and tumor size. In univariable analysis, younger age (P=.002) and a positive nodal status (P=.048) were significantly associated with IBTR, with a trend for the omission of hormonal therapy (P=.061). The concordance probability estimate and concordance index varied between 0.57 and 0.67 for the 2 definitions of IBTR. In all 4 risk groups the model overestimated the IBTR risk. In particular, between the lowest-risk groups a limited differentiation was suggested by the calibration plot. Conclusions: The IBTR! 2.0 predictive model for IBTR in breast cancer patients shows substandard discriminative ability, with an

Validation of the Web-Based IBTR! 2.0 Nomogram to Predict for Ipsilateral Breast Tumor Recurrence After Breast-Conserving Therapy

Energy Technology Data Exchange (ETDEWEB)

Kindts, Isabelle, E-mail: Isabelle.kindts@uzleuven.be [Department of Oncology, KU Leuven - University of Leuven, Leuven (Belgium); Department of Radiation Oncology, University Hospitals Leuven, Leuven (Belgium); Laenen, Annouschka [Leuven Biostatistics and Statistical Bioinformatics Center (L-Biostat), KU Leuven - University of Leuven, Leuven (Belgium); Peeters, Stephanie; Janssen, Hilde; Depuydt, Tom [Department of Oncology, KU Leuven - University of Leuven, Leuven (Belgium); Department of Radiation Oncology, University Hospitals Leuven, Leuven (Belgium); Nevelsteen, Ines [Department of Oncology, KU Leuven - University of Leuven, Leuven (Belgium); Department of Surgical Oncology, University Hospitals Leuven, Leuven (Belgium); Van Limbergen, Erik; Weltens, Caroline [Department of Oncology, KU Leuven - University of Leuven, Leuven (Belgium); Department of Radiation Oncology, University Hospitals Leuven, Leuven (Belgium)

2016-08-01

Purpose: To evaluate the IBTR! 2.0 nomogram, which predicts 10-year ipsilateral breast tumor recurrence (IBTR) after breast-conserving therapy with and without radiation therapy for breast cancer, by using a large, external, and independent cancer center database. Methods and Materials: We retrospectively identified 1898 breast cancer cases, treated with breast-conserving therapy and radiation therapy at the University Hospital Leuven from 2000 to 2007, with requisite data for the nomogram variables. Clinicopathologic factors were assessed. Two definitions of IBTR were considered where simultaneous regional or distant recurrence were either censored (conform IBTR! 2.0) or included as event. Validity of the prediction algorithm was tested in terms of discrimination and calibration. Discrimination was assessed by the concordance probability estimate and Harrell's concordance index. The mean predicted and observed 10-year estimates were compared for the entire cohort and for 4 risk groups predefined by nomogram-predicted IBTR risks, and a calibration plot was drawn. Results: Median follow-up was 10.9 years. The 10-year IBTR rates were 1.3% and 2.1%, according to the 2 definitions of IBTR. The validation cohort differed from the development cohort with respect to the administration of hormonal therapy, surgical section margins, lymphovascular invasion, and tumor size. In univariable analysis, younger age (P=.002) and a positive nodal status (P=.048) were significantly associated with IBTR, with a trend for the omission of hormonal therapy (P=.061). The concordance probability estimate and concordance index varied between 0.57 and 0.67 for the 2 definitions of IBTR. In all 4 risk groups the model overestimated the IBTR risk. In particular, between the lowest-risk groups a limited differentiation was suggested by the calibration plot. Conclusions: The IBTR! 2.0 predictive model for IBTR in breast cancer patients shows substandard discriminative ability, with an

[Reliability and validity of the Braden Scale for predicting pressure sore risk].

Science.gov (United States)

Boes, C

2000-12-01

For more accurate and objective pressure sore risk assessment various risk assessment tools were developed mainly in the USA and Great Britain. The Braden Scale for Predicting Pressure Sore Risk is one such example. By means of a literature analysis of German and English texts referring to the Braden Scale the scientific control criteria reliability and validity will be traced and consequences for application of the scale in Germany will be demonstrated. Analysis of 4 reliability studies shows an exclusive focus on interrater reliability. Further, even though examination of 19 validity studies occurs in many different settings, such examination is limited to the criteria sensitivity and specificity (accuracy). The range of sensitivity and specificity level is 35-100%. The recommended cut off points rank in the field of 10 to 19 points. The studies prove to be not comparable with each other. Furthermore, distortions in these studies can be found which affect accuracy of the scale. The results of the here presented analysis show an insufficient proof for reliability and validity in the American studies. In Germany, the Braden scale has not yet been tested under scientific criteria. Such testing is needed before using the scale in different German settings. During the course of such testing, construction and study procedures of the American studies can be used as a basis as can the problems be identified in the analysis presented below.

A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation

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Luis Cláudio Lemos Correia

Full Text Available Abstract Background: Currently, there is no validated multivariate model to predict probability of obstructive coronary disease in patients with acute chest pain. Objective: To develop and validate a multivariate model to predict coronary artery disease (CAD based on variables assessed at admission to the coronary care unit (CCU due to acute chest pain. Methods: A total of 470 patients were studied, 370 utilized as the derivation sample and the subsequent 100 patients as the validation sample. As the reference standard, angiography was required to rule in CAD (stenosis ≥ 70%, while either angiography or a negative noninvasive test could be used to rule it out. As predictors, 13 baseline variables related to medical history, 14 characteristics of chest discomfort, and eight variables from physical examination or laboratory tests were tested. Results: The prevalence of CAD was 48%. By logistic regression, six variables remained independent predictors of CAD: age, male gender, relief with nitrate, signs of heart failure, positive electrocardiogram, and troponin. The area under the curve (AUC of this final model was 0.80 (95% confidence interval [95%CI] = 0.75 - 0.84 in the derivation sample and 0.86 (95%CI = 0.79 - 0.93 in the validation sample. Hosmer-Lemeshow's test indicated good calibration in both samples (p = 0.98 and p = 0.23, respectively. Compared with a basic model containing electrocardiogram and troponin, the full model provided an AUC increment of 0.07 in both derivation (p = 0.0002 and validation (p = 0.039 samples. Integrated discrimination improvement was 0.09 in both derivation (p < 0.001 and validation (p < 0.0015 samples. Conclusion: A multivariate model was derived and validated as an accurate tool for estimating the pretest probability of CAD in patients with acute chest pain.

Validation of the Manchester scoring system for predicting BRCA1/2 mutations in 9,390 families suspected of having hereditary breast and ovarian cancer.

Science.gov (United States)

Kast, Karin; Schmutzler, Rita K; Rhiem, Kerstin; Kiechle, Marion; Fischer, Christine; Niederacher, Dieter; Arnold, Norbert; Grimm, Tiemo; Speiser, Dorothee; Schlegelberger, Brigitte; Varga, Dominic; Horvath, Judit; Beer, Marit; Briest, Susanne; Meindl, Alfons; Engel, Christoph

2014-11-15

The Manchester scoring system (MSS) allows the calculation of the probability for the presence of mutations in BRCA1 or BRCA2 genes in families suspected of having hereditary breast and ovarian cancer. In 9,390 families, we determined the predictive performance of the MSS without (MSS-2004) and with (MSS-2009) consideration of pathology parameters. Moreover, we validated a recalibrated version of the MSS-2009 (MSS-recal). Families were included in the registry of the German Consortium for Hereditary Breast and Ovarian Cancer, using defined clinical criteria. Receiver operating characteristics (ROC) analysis was used to determine the predictive performance. The recalibrated model was developed using logistic regression analysis and tested using an independent random validation sample. The area under the ROC curves regarding a mutation in any of the two BRCA genes was 0.77 (95%CI 0.75-0.79) for MSS-2004, 0.80 (95%CI 0.78-0.82) for MSS-2009, and 0.82 (95%CI 0.80-0.83) for MSS-recal. Sensitivity at the 10% mutation probability cutoff was similar for all three models (MSS-2004 92.2%, MSS-2009 92.2%, and MSS-recal 90.3%), but specificity of MSS-recal (46.0%) was considerably higher than that of MSS-2004 (25.4%) and MSS-2009 (32.3%). In the MSS-recal model, almost all predictors of the original MSS were significantly predictive. However, the score values of some predictors, for example, high grade triple negative breast cancers, differed considerably from the originally proposed score values. The original MSS performed well in our sample of high risk families. The use of pathological parameters increased the predictive performance significantly. Recalibration improved the specificity considerably without losing much sensitivity. © 2014 UICC.

A Parsimonious Instrument for Predicting Students' Intent to Pursue a Sales Career: Scale Development and Validation

Science.gov (United States)

Peltier, James W.; Cummins, Shannon; Pomirleanu, Nadia; Cross, James; Simon, Rob

2014-01-01

Students' desire and intention to pursue a career in sales continue to lag behind industry demand for sales professionals. This article develops and validates a reliable and parsimonious scale for measuring and predicting student intention to pursue a selling career. The instrument advances previous scales in three ways. The instrument is…

Prediction of flow and drawdown for the site characterization and validation site in the Stripa Mine

International Nuclear Information System (INIS)

Long, J.C.S.; Mauldon, A.D.; Nelson, K.; Martel, S.; Fuller, P.; and Karasaki, K.

1992-01-01

Geophysical and hydrologic data from a location in the Stripa Mine in Sweden, called the Site Characterization and Validation (SCV) block, has been used to create a series of models for flow through the fracture network. The models can be characterized as ''equivalent discontinuum'' models. Equivalent discontinuum models are derived starting from a specified lattice or 6 ''template''. An inverse analysis called ''Simulated Annealing'' is used to make a random search through the elements of the lattice to find a configuration that can reproduce the measured responses. Evidence at Stripa points to hydrology which is dominated by fracture zones. These have been identified and located through extensive characterization efforts. Lattice templates were arranged to lie on the fracture zones identified by Black and Olsson. The fundamental goal of this project was to build a fracture flow model based an initial data set, and use this model to make predictions of the flow behavior during a new test. Then given data from the new test, predict a second test, etc. The first data set was an interference test called C1-2. Both a two-dimensional and a three-dimensional model were annealed to the C1-2 data and use this model to predict the behavior of the Simulated Drift Experiment (SDE). The SDE measured the flow into, and drawdown due to reducing the pressure in a group of 6 parallel boreholes. Then both the C1-2 and SDE data were used to predict the flow into and drawdown due to an excavation, the Validation Drift (VD), made through the boreholes. Finally, all the data was used to predict the hydrologic response to opening another hole, T1

Measuring Gambling Reinforcers, Over Consumption and Fallacies: The Psychometric Properties and Predictive Validity of the Jonsson-Abbott Scale.

Science.gov (United States)

Jonsson, Jakob; Abbott, Max W; Sjöberg, Anders; Carlbring, Per

2017-01-01

Traditionally, gambling and problem gambling research relies on cross-sectional and retrospective designs. This has compromised identification of temporal relationships and causal inference. To overcome these problems a new questionnaire, the Jonsson-Abbott Scale (JAS), was developed and used in a large, prospective, general population study, The Swedish Longitudinal Gambling Study (Swelogs). The JAS has 11 items and seeks to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. The aims of the study were to examine psychometric properties of the JAS (internal consistency and dimensionality) and predictive validity with respect to increased gambling risk and problem gambling onset. The results are based on repeated interviews with 3818 participants. The response rate from the initial baseline wave was 74%. The original sample consisted of a random, stratified selection from the Swedish population register aged between 16 and 84. The results indicate an acceptable fit of a three-factor solution in a confirmatory factor analysis with 'Over consumption,' 'Gambling fallacies,' and 'Reinforcers' as factors. Reinforcers, Over consumption and Gambling fallacies were significant predictors of gambling risk potential and Gambling fallacies and Over consumption were significant predictors of problem gambling onset (incident cases) at 12 month follow up. When controlled for risk potential measured at baseline, the predictor Over consumption was not significant for gambling risk potential at follow up. For incident cases, Gambling fallacies and Over consumption remained significant when controlled for risk potential. Implications of the results for the development of problem gambling, early detection, prevention, and future research are discussed.

Estimating uncertainty of inference for validation

Energy Technology Data Exchange (ETDEWEB)

Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM

2010-09-30

We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study.

Science.gov (United States)

Hu, Zhongkai; Hao, Shiying; Jin, Bo; Shin, Andrew Young; Zhu, Chunqing; Huang, Min; Wang, Yue; Zheng, Le; Dai, Dorothy; Culver, Devore S; Alfreds, Shaun T; Rogow, Todd; Stearns, Frank; Sylvester, Karl G; Widen, Eric; Ling, Xuefeng

2015-09-22

The increasing rate of health care expenditures in the United States has placed a significant burden on the nation's economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation. This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups. In the HealthInfoNet, Maine's health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient's next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree-based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013. Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine. The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes.

Reliability, Validity, and Predictive Utility of the 25-Item Criminogenic Cognitions Scale (CCS).

Science.gov (United States)

Tangney, June Price; Stuewig, Jeffrey; Furukawa, Emi; Kopelovich, Sarah; Meyer, Patrick; Cosby, Brandon

2012-10-01

Theory, research, and clinical reports suggest that moral cognitions play a role in initiating and sustaining criminal behavior. The 25 item Criminogenic Cognitions Scale (CCS) was designed to tap 5 dimensions: Notions of entitlement; Failure to Accept Responsibility; Short-Term Orientation; Insensitivity to Impact of Crime; and Negative Attitudes Toward Authority. Results from 552 jail inmates support the reliability, validity, and predictive utility of the measure. The CCS was linked to criminal justice system involvement, self-report measures of aggression, impulsivity, and lack of empathy. Additionally, the CCS was associated with violent criminal history, antisocial personality, and clinicians' ratings of risk for future violence and psychopathy (PCL:SV). Furthermore, criminogenic thinking upon incarceration predicted subsequent official reports of inmate misconduct during incarceration. CCS scores varied somewhat by gender and race. Research and applied uses of CCS are discussed.

Validation and Refinement of Prediction Models to Estimate Exercise Capacity in Cancer Survivors Using the Steep Ramp Test.

Science.gov (United States)

Stuiver, Martijn M; Kampshoff, Caroline S; Persoon, Saskia; Groen, Wim; van Mechelen, Willem; Chinapaw, Mai J M; Brug, Johannes; Nollet, Frans; Kersten, Marie-José; Schep, Goof; Buffart, Laurien M

2017-11-01

To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo 2peak ) and peak power output (W peak ). Cross-sectional study. Multicenter. Cancer survivors (N=283) in 2 randomized controlled exercise trials. Not applicable. Prediction model accuracy was assessed by intraclass correlation coefficients (ICCs) and limits of agreement (LOA). Multiple linear regression was used for model extension. Clinical performance was judged by the percentage of accurate endurance exercise prescriptions. ICCs of SRT-predicted Vo 2peak and W peak with these values as obtained by the cardiopulmonary exercise test were .61 and .73, respectively, using the previously published prediction models. 95% LOA were ±705mL/min with a bias of 190mL/min for Vo 2peak and ±59W with a bias of 5W for W peak . Modest improvements were obtained by adding body weight and sex to the regression equation for the prediction of Vo 2peak (ICC, .73; 95% LOA, ±608mL/min) and by adding age, height, and sex for the prediction of W peak (ICC, .81; 95% LOA, ±48W). Accuracy of endurance exercise prescription improved from 57% accurate prescriptions to 68% accurate prescriptions with the new prediction model for W peak . Predictions of Vo 2peak and W peak based on the SRT are adequate at the group level, but insufficiently accurate in individual patients. The multivariable prediction model for W peak can be used cautiously (eg, supplemented with a Borg score) to aid endurance exercise prescription. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Prediction of new brain metastases after radiosurgery: validation and analysis of performance of a multi-institutional nomogram.

Science.gov (United States)

Ayala-Peacock, Diandra N; Attia, Albert; Braunstein, Steve E; Ahluwalia, Manmeet S; Hepel, Jaroslaw; Chung, Caroline; Contessa, Joseph; McTyre, Emory; Peiffer, Ann M; Lucas, John T; Isom, Scott; Pajewski, Nicholas M; Kotecha, Rupesh; Stavas, Mark J; Page, Brandi R; Kleinberg, Lawrence; Shen, Colette; Taylor, Robert B; Onyeuku, Nasarachi E; Hyde, Andrew T; Gorovets, Daniel; Chao, Samuel T; Corso, Christopher; Ruiz, Jimmy; Watabe, Kounosuke; Tatter, Stephen B; Zadeh, Gelareh; Chiang, Veronica L S; Fiveash, John B; Chan, Michael D

2017-11-01

Stereotactic radiosurgery (SRS) without whole brain radiotherapy (WBRT) for brain metastases can avoid WBRT toxicities, but with risk of subsequent distant brain failure (DBF). Sole use of number of metastases to triage patients may be an unrefined method. Data on 1354 patients treated with SRS monotherapy from 2000 to 2013 for new brain metastases was collected across eight academic centers. The cohort was divided into training and validation datasets and a prognostic model was developed for time to DBF. We then evaluated the discrimination and calibration of the model within the validation dataset, and confirmed its performance with an independent contemporary cohort. Number of metastases (≥8, HR 3.53 p = 0.0001), minimum margin dose (HR 1.07 p = 0.0033), and melanoma histology (HR 1.45, p = 0.0187) were associated with DBF. A prognostic index derived from the training dataset exhibited ability to discriminate patients' DBF risk within the validation dataset (c-index = 0.631) and Heller's explained relative risk (HERR) = 0.173 (SE = 0.048). Absolute number of metastases was evaluated for its ability to predict DBF in the derivation and validation datasets, and was inferior to the nomogram. A nomogram high-risk threshold yielding a 2.1-fold increased need for early WBRT was identified. Nomogram values also correlated to number of brain metastases at time of failure (r = 0.38, p < 0.0001). We present a multi-institutionally validated prognostic model and nomogram to predict risk of DBF and guide risk-stratification of patients who are appropriate candidates for radiosurgery versus upfront WBRT.

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

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

2017-04-01

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

Validation of the FAST skating protocol to predict aerobic power in ice hockey players.

Science.gov (United States)

Petrella, Nicholas J; Montelpare, William J; Nystrom, Murray; Plyley, Michael; Faught, Brent E

2007-08-01

Few studies have reported a sport-specific protocol to measure the aerobic power of ice hockey players using a predictive process. The purpose of our study was to validate an ice hockey aerobic field test on players of varying ages, abilities, and levels. The Faught Aerobic Skating Test (FAST) uses an on-ice continuous skating protocol on a course measuring 160 feet (48.8 m) using a CD to pace the skater with a beep signal to cross the starting line at each end of the course. The FAST incorporates the principle of increasing workload at measured time intervals during a continuous skating exercise. Step-wise multiple regression modelling was used to determine the estimate of aerobic power. Participants completed a maximal aerobic power test using a modified Bruce incremental treadmill protocol, as well as the on-ice FAST. Normative data were collected on 406 ice hockey players (291 males, 115 females) ranging in age from 9 to 25 y. A regression to predict maximum aerobic power was developed using body mass (kg), height (m), age (y), and maximum completed lengths of the FAST as the significant predictors of skating aerobic power (adjusted R2 = 0.387, SEE = 7.25 mL.kg-1.min-1, p < 0.0001). These results support the application of the FAST in estimating aerobic power among male and female competitive ice hockey players between the ages of 9 and 25 years.

Validity of Bioelectrical Impedance Analysis to Estimation Fat-Free Mass in the Army Cadets

Directory of Open Access Journals (Sweden)

Raquel D. Langer

2016-03-01

Full Text Available Background: Bioelectrical Impedance Analysis (BIA is a fast, practical, non-invasive, and frequently used method for fat-free mass (FFM estimation. The aims of this study were to validate predictive equations of BIA to FFM estimation in Army cadets and to develop and validate a specific BIA equation for this population. Methods: A total of 396 males, Brazilian Army cadets, aged 17–24 years were included. The study used eight published predictive BIA equations, a specific equation in FFM estimation, and dual-energy X-ray absorptiometry (DXA as a reference method. Student’s t-test (for paired sample, linear regression analysis, and Bland–Altman method were used to test the validity of the BIA equations. Results: Predictive BIA equations showed significant differences in FFM compared to DXA (p < 0.05 and large limits of agreement by Bland–Altman. Predictive BIA equations explained 68% to 88% of FFM variance. Specific BIA equations showed no significant differences in FFM, compared to DXA values. Conclusion: Published BIA predictive equations showed poor accuracy in this sample. The specific BIA equations, developed in this study, demonstrated validity for this sample, although should be used with caution in samples with a large range of FFM.

Multilevel Assessment of the Predictive Validity of Teacher Made Tests in the Zimbabwean Primary Education Sector

Science.gov (United States)

Machingambi, Zadzisai

2017-01-01

The principal focus of this study was to undertake a multilevel assessment of the predictive validity of teacher made tests in the Zimbabwean primary education sector. A correlational research design was adopted for the study, mainly to allow for statistical treatment of data and subsequent classical hypotheses testing using the spearman's rho.…

Validity of bacterial pneumonia score for predicting bacteremia in children with pneumonia

Directory of Open Access Journals (Sweden)

Rosalia Theodosia Daten Beyeng

2011-12-01

Full Text Available Background Bacteremia in children with pneumonia reflects a severe condition, with longer duration of hospital care and potentially lethal complications. Early detection of bacteremia in patients with pneumonia may reduce serious complications. Few bacteremia screening tools have been widely used in chidren with pneumonia. One of those tools is the bacterial pneumonia score (BPS. Objective To assess the validity of the bacterial pneumonia score for predicting bacteremia in pediatric patients with pneumonia. Methods A diagnostic test was conducted on children aged 1 to 60 months hospitalized with pneumonia from December 2009 to August 2010. Subjects were collected consecutively. Pneumonia was diagnosed using the World Healt Organization (WHO criteria. Subjects underwent complete blood counts and blood culture examinations at admission. Statistical analyses included sensitivity, specificity, positive and negative predictive value (PPV/NPV, positive and negative likelihood ratio (PLR/NLR, and post-test probability. Results Our study included 229 children. Based on BPS with a cut-off score of ≥ 4, the sensitivity was 83.3%, specificity 49.7%, PPV 8.4%, NPV 98.2%, PLR 1.66, NLR 0.31, and post-test probability 8.4% for detecting bacteremia in pediatric pneumonia patients. Conclusion BPS can not be used for predicting bacteremia in pediatric patients with pneumonia.

Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

NARCIS (Netherlands)

Roelen, Corne A. M.; Stapelfeldt, Christina M.; Heymans, Martijn W.; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V.; Bultmann, Ute; Jensen, Chris

Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. Methods 2,562 municipal eldercare

Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

NARCIS (Netherlands)

Roelen, C.A.M.; Stapelfeldt, C.M.; Heijmans, M.W.; van Rhenen, W.; Labriola, M.; Nielsen, C.V.; Bultmann, U.; Jensen, C.

2015-01-01

Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models’ risk discrimination was also investigated. Methods 2,562 municipal eldercare

Determining the validity of exposure models for environmental epidemiology : predicting electromagnetic fields from mobile phone base stations

NARCIS (Netherlands)

Beekhuizen, Johan|info:eu-repo/dai/nl/34472641X

2014-01-01

One of the key challenges in environmental epidemiology is the exposure assessment of large populations. Spatial exposure models have been developed that predict exposure to the pollutant of interest for large study sizes. However, the validity of these exposure models is often unknown. In this

Validation of adult height prediction based on automated bone age determination in the Paris Longitudinal Study of healthy children

Energy Technology Data Exchange (ETDEWEB)

Martin, David D. [Tuebingen University Children' s Hospital, Tuebingen (Germany); Filderklinik, Filderstadt (Germany); Schittenhelm, Jan [Tuebingen University Children' s Hospital, Tuebingen (Germany); Thodberg, Hans Henrik [Visiana, Holte (Denmark)

2016-02-15

An adult height prediction model based on automated determination of bone age was developed and validated in two studies from Zurich, Switzerland. Varied living conditions and genetic backgrounds might make the model less accurate. To validate the adult height prediction model on children from another geographical location. We included 51 boys and 58 girls from the Paris Longitudinal Study of children born 1953 to 1958. Radiographs were obtained once or twice a year in these children from birth to age 18. Bone age was determined using the BoneXpert method. Radiographs in children with bone age greater than 6 years were considered, in total 1,124 images. The root mean square deviation between the predicted and the observed adult height was 2.8 cm for boys in the bone age range 6-15 years and 3.1 cm for girls in the bone age range 6-13 years. The bias (the average signed difference) was zero, except for girls below bone age 12, where the predictions were 0.8 cm too low. The accuracy of the BoneXpert method in terms of root mean square error was as predicted by the model, i.e. in line with what was observed in the Zurich studies. (orig.)

Validation of adult height prediction based on automated bone age determination in the Paris Longitudinal Study of healthy children

International Nuclear Information System (INIS)

Martin, David D.; Schittenhelm, Jan; Thodberg, Hans Henrik

2016-01-01

An adult height prediction model based on automated determination of bone age was developed and validated in two studies from Zurich, Switzerland. Varied living conditions and genetic backgrounds might make the model less accurate. To validate the adult height prediction model on children from another geographical location. We included 51 boys and 58 girls from the Paris Longitudinal Study of children born 1953 to 1958. Radiographs were obtained once or twice a year in these children from birth to age 18. Bone age was determined using the BoneXpert method. Radiographs in children with bone age greater than 6 years were considered, in total 1,124 images. The root mean square deviation between the predicted and the observed adult height was 2.8 cm for boys in the bone age range 6-15 years and 3.1 cm for girls in the bone age range 6-13 years. The bias (the average signed difference) was zero, except for girls below bone age 12, where the predictions were 0.8 cm too low. The accuracy of the BoneXpert method in terms of root mean square error was as predicted by the model, i.e. in line with what was observed in the Zurich studies. (orig.)

External Validity of a Risk Stratification Score Predicting Early Distant Brain Failure and Salvage Whole Brain Radiation Therapy After Stereotactic Radiosurgery for Brain Metastases.

Science.gov (United States)

Press, Robert H; Boselli, Danielle M; Symanowski, James T; Lankford, Scott P; McCammon, Robert J; Moeller, Benjamin J; Heinzerling, John H; Fasola, Carolina E; Burri, Stuart H; Patel, Kirtesh R; Asher, Anthony L; Sumrall, Ashley L; Curran, Walter J; Shu, Hui-Kuo G; Crocker, Ian R; Prabhu, Roshan S

2017-07-01

A scoring system using pretreatment factors was recently published for predicting the risk of early (≤6 months) distant brain failure (DBF) and salvage whole brain radiation therapy (WBRT) after stereotactic radiosurgery (SRS) alone. Four risk factors were identified: (1) lack of prior WBRT; (2) melanoma or breast histologic features; (3) multiple brain metastases; and (4) total volume of brain metastases external patient population. We reviewed the records of 247 patients with 388 brain metastases treated with SRS between 2010 at 2013 at Levine Cancer Institute. The Press (Emory) risk score was calculated and applied to the validation cohort population, and subsequent risk groups were analyzed using cumulative incidence. The low-risk (LR) group had a significantly lower risk of early DBF than did the high-risk (HR) group (22.6% vs 44%, P=.004), but there was no difference between the HR and intermediate-risk (IR) groups (41.2% vs 44%, P=.79). Total lesion volume externally valid, but the model was able to stratify between 2 levels (LR and not-LR [combined IR and HR]) for early (≤6 months) DBF. These results reinforce the importance of validating predictive models in independent cohorts. Further refinement of this scoring system with molecular information and in additional contemporary patient populations is warranted. Copyright © 2017 Elsevier Inc. All rights reserved.

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

Science.gov (United States)

Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

External Validation and Recalibration of Risk Prediction Models for Acute Traumatic Brain Injury among Critically Ill Adult Patients in the United Kingdom.

Science.gov (United States)

Harrison, David A; Griggs, Kathryn A; Prabhu, Gita; Gomes, Manuel; Lecky, Fiona E; Hutchinson, Peter J A; Menon, David K; Rowan, Kathryn M

2015-10-01

This study validates risk prediction models for acute traumatic brain injury (TBI) in critical care units in the United Kingdom and recalibrates the models to this population. The Risk Adjustment In Neurocritical care (RAIN) Study was a prospective, observational cohort study in 67 adult critical care units. Adult patients admitted to critical care following acute TBI with a last pre-sedation Glasgow Coma Scale score of less than 15 were recruited. The primary outcomes were mortality and unfavorable outcome (death or severe disability, assessed using the Extended Glasgow Outcome Scale) at six months following TBI. Of 3626 critical care unit admissions, 2975 were analyzed. Following imputation of missing outcomes, mortality at six months was 25.7% and unfavorable outcome 57.4%. Ten risk prediction models were validated from Hukkelhoven and colleagues, the Medical Research Council (MRC) Corticosteroid Randomisation After Significant Head Injury (CRASH) Trial Collaborators, and the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) group. The model with the best discrimination was the IMPACT "Lab" model (C index, 0.779 for mortality and 0.713 for unfavorable outcome). This model was well calibrated for mortality at six months but substantially under-predicted the risk of unfavorable outcome. Recalibration of the models resulted in small improvements in discrimination and excellent calibration for all models. The risk prediction models demonstrated sufficient statistical performance to support their use in research and audit but fell below the level required to guide individual patient decision-making. The published models for unfavorable outcome at six months had poor calibration in the UK critical care setting and the models recalibrated to this setting should be used in future research.

Predictive validity of callous-unemotional traits measured in early adolescence with respect to multiple antisocial outcomes.

Science.gov (United States)

McMahon, Robert J; Witkiewitz, Katie; Kotler, Julie S

2010-11-01

This study investigated the predictive validity of youth callous-unemotional (CU) traits, as measured in early adolescence (Grade 7) by the Antisocial Process Screening Device (APSD; Frick & Hare, 2001), in a longitudinal sample (N = 754). Antisocial outcomes, assessed in adolescence and early adulthood, included self-reported general delinquency from 7th grade through 2 years post-high school, self-reported serious crimes through 2 years post-high school, juvenile and adult arrest records through 1 year post-high school, and antisocial personality disorder symptoms and diagnosis at 2 years post-high school. CU traits measured in 7th grade were highly predictive of 5 of the 6 antisocial outcomes-general delinquency, juvenile and adult arrests, and early adult antisocial personality disorder criterion count and diagnosis-over and above prior and concurrent conduct problem behavior (i.e., criterion counts of oppositional defiant disorder and conduct disorder) and attention-deficit/hyperactivity disorder (criterion count). Incorporating a CU traits specifier for those with a diagnosis of conduct disorder improved the positive prediction of antisocial outcomes, with a very low false-positive rate. There was minimal evidence of moderation by sex, race, or urban/rural status. Urban/rural status moderated one finding, with being from an urban area associated with stronger relations between CU traits and adult arrests. Findings clearly support the inclusion of CU traits as a specifier for the diagnosis of conduct disorder, at least with respect to predictive validity. PsycINFO Database Record (c) 2010 APA, all rights reserved

Predictive validity of the tobacco marketing receptivity index among non-smoking youth.

Science.gov (United States)

Braun, Sandra; Abad-Vivero, Erika Nayeli; Mejía, Raúl; Barrientos, Inti; Sargent, James D; Thrasher, James F

2018-05-01

In a previous cross-sectional study of early adolescents, we developed a marketing receptivity index (MRI) that integrates point-of-sale (PoS) marketing exposures, brand recall, and ownership of branded merchandise. The MRI had independent, positive associations with smoking susceptibility among never smokers and with current smoking behavior. The current longitudinal study assessed the MRI's predictive validity among adolescents who have never smoked cigarettes METHODS: Data come from a longitudinal, school-based survey of 33 secondary schools in Argentina. Students who had never smoked at baseline were followed up approximately 17months later (n=1700). Questions assessed: PoS marketing exposure by querying frequency of going to stores where tobacco is commonly sold; cued recall of brand names for 3 cigarette packages from dominant brands but with the brand name removed; and ownership of branded merchandise. A four-level MRI was derived: 1.low PoS marketing exposure only; 2. high PoS exposure or recall of 1 brand; 3. recall of 2 or more brands; and 4. ownership of branded merchandise. Logistic regression models regressed smoking initiation by follow up survey on the MRI, each of its components, and students' willingness to try a brand, adjusting for sociodemographics, social network smoking, and sensation seeking. The MRI had an independent positive association with smoking initiation. When analyzed separately, each MRI component was associated with outcomes except branded merchandise ownership. The MRI and its components were associated with smoking initiation, except for branded merchandise ownership, which may better predict smoking progression than initiation. The MRI appears valid and useful for future studies. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

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

2017-06-06

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

External validation of two prediction models identifying employees at risk of high sickness absence : cohort study with 1-year follow-up

NARCIS (Netherlands)

Roelen, Corne A. M.; Bultmann, Ute; van Rhenen, Willem; van der Klink, Jac J. L.; Twisk, Jos W. R.; Heymans, Martijn W.

2013-01-01

Background: Two models including age, self-rated health (SRH) and prior sickness absence (SA) were found to predict high SA in health care workers. The present study externally validated these prediction models in a population of office workers and investigated the effect of adding gender as a

External validation and clinical utility of a prediction model for 6-month mortality in patients undergoing hemodialysis for end-stage kidney disease.

Science.gov (United States)

Forzley, Brian; Er, Lee; Chiu, Helen Hl; Djurdjev, Ognjenka; Martinusen, Dan; Carson, Rachel C; Hargrove, Gaylene; Levin, Adeera; Karim, Mohamud

2018-02-01

End-stage kidney disease is associated with poor prognosis. Health care professionals must be prepared to address end-of-life issues and identify those at high risk for dying. A 6-month mortality prediction model for patients on dialysis derived in the United States is used but has not been externally validated. We aimed to assess the external validity and clinical utility in an independent cohort in Canada. We examined the performance of the published 6-month mortality prediction model, using discrimination, calibration, and decision curve analyses. Data were derived from a cohort of 374 prevalent dialysis patients in two regions of British Columbia, Canada, which included serum albumin, age, peripheral vascular disease, dementia, and answers to the "the surprise question" ("Would I be surprised if this patient died within the next year?"). The observed mortality in the validation cohort was 11.5% at 6 months. The prediction model had reasonable discrimination (c-stat = 0.70) but poor calibration (calibration-in-the-large = -0.53 (95% confidence interval: -0.88, -0.18); calibration slope = 0.57 (95% confidence interval: 0.31, 0.83)) in our data. Decision curve analysis showed the model only has added value in guiding clinical decision in a small range of threshold probabilities: 8%-20%. Despite reasonable discrimination, the prediction model has poor calibration in this external study cohort; thus, it may have limited clinical utility in settings outside of where it was derived. Decision curve analysis clarifies limitations in clinical utility not apparent by receiver operating characteristic curve analysis. This study highlights the importance of external validation of prediction models prior to routine use in clinical practice.

Validating severity of illness scoring systems in the prediction of outcomes in Staphylococcus aureus bacteremia.

Science.gov (United States)

Sharma, Mamta; Szpunar, Susan; Khatib, Riad

2013-08-01

Severity of illness scores are helpful in predicting mortality; however, no standardized scoring system has been validated in patients with Staphylococcus aureus bacteremia (SAB). The modified Rapid Emergency Medicine Score (REMS), the CURB-65 (confusion, urea, respiratory rate, blood pressure and age 65) and the Charlson weighted index of comorbidity (CWIC) were compared in predicting outcomes at the onset of SAB. All adult inpatients with SAB from July 15, 2008, to December 31, 2009, were prospectively assessed. The 3 scoring systems were applied: REMS, CURB-65 and CWIC. The end points were attributable and overall mortality. A total of 241 patients with SAB were reviewed during the study period. The all-cause mortality rate was 22.8% and attributable mortality 14.1%. Patients who died had higher mean CURB-65 score and REMS than those who lived, whereas the difference in the CWIC score was not significant. Two logistic regression models based on CURB-65 score or REMS, after controlling for CWIC, revealed that both scores were independent predictors of mortality, with an odds ratio of 3.38 (P < 0.0001) and 1.45 (P < 0.0001) for CURB-65 and REMS, respectively. Receiver operating characteristic analysis revealed that a cutoff point of 3.0 (CURB-65) and 6.0 (REMS) provided the highest sensitivity and specificity. The area under the curves for all-cause mortality were 0.832 and 0.806, and for attributable mortality 0.845 and 0.819, for CURB-65 and REMS, respectively. REMS and CURB-65 scores outperformed CWIC as predictors of mortality in SAB and may be effective in predicting the severity of illness at the onset of bacteremia.

Prediction models for successful external cephalic version: a systematic review.

Science.gov (United States)

Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

2015-12-01

To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

Validation of a zero-dimensional model for prediction of NOx and engine performance for electronically controlled marine two-stroke diesel engines

International Nuclear Information System (INIS)

Scappin, Fabio; Stefansson, Sigurður H.; Haglind, Fredrik; Andreasen, Anders; Larsen, Ulrik

2012-01-01

The aim of this paper is to derive a methodology suitable for energy system analysis for predicting the performance and NO x emissions of marine low speed diesel engines. The paper describes a zero-dimensional model, evaluating the engine performance by means of an energy balance and a two zone combustion model using ideal gas law equations over a complete crank cycle. The combustion process is divided into intervals, and the product composition and flame temperature are calculated in each interval. The NO x emissions are predicted using the extended Zeldovich mechanism. The model is validated using experimental data from two MAN B and W engines; one case being data subject to engine parameter changes corresponding to simulating an electronically controlled engine; the second case providing data covering almost all model input and output parameters. The first case of validation suggests that the model can predict specific fuel oil consumption and NO x emissions within the 95% confidence intervals given by the experimental measurements. The second validation confirms the capability of the model to match measured engine output parameters based on measured engine input parameters with a maximum 5% deviation. - Highlights: ► A fast realistic model of a marine two-stroke low speed diesel engine was derived. ► The model is fast and accurate enough for future complex energy systems analysis. ► The effects of engine tuning were validated with experimental tests. ► The model was validated while constrained by experimental input and output data.

Development and Validation of a Scoring System to Predict Outcomes of Patients With Primary Biliary Cirrhosis Receiving Ursodeoxycholic Acid Therapy.

Science.gov (United States)

Lammers, Willem J; Hirschfield, Gideon M; Corpechot, Christophe; Nevens, Frederik; Lindor, Keith D; Janssen, Harry L A; Floreani, Annarosa; Ponsioen, Cyriel Y; Mayo, Marlyn J; Invernizzi, Pietro; Battezzati, Pier M; Parés, Albert; Burroughs, Andrew K; Mason, Andrew L; Kowdley, Kris V; Kumagi, Teru; Harms, Maren H; Trivedi, Palak J; Poupon, Raoul; Cheung, Angela; Lleo, Ana; Caballeria, Llorenç; Hansen, Bettina E; van Buuren, Henk R

2015-12-01

Approaches to risk stratification for patients with primary biliary cirrhosis (PBC) are limited, single-center based, and often dichotomous. We aimed to develop and validate a better model for determining prognoses of patients with PBC. We performed an international, multicenter meta-analysis of 4119 patients with PBC treated with ursodeoxycholic acid at liver centers in 8 European and North American countries. Patients were randomly assigned to derivation (n = 2488 [60%]) and validation cohorts (n = 1631 [40%]). A risk score (GLOBE score) to predict transplantation-free survival was developed and validated with univariate and multivariable Cox regression analyses using clinical and biochemical variables obtained after 1 year of ursodeoxycholic acid therapy. Risk score outcomes were compared with the survival of age-, sex-, and calendar time-matched members of the general population. The prognostic ability of the GLOBE score was evaluated alongside those of the Barcelona, Paris-1, Rotterdam, Toronto, and Paris-2 criteria. Age (hazard ratio = 1.05; 95% confidence interval [CI]: 1.04-1.06; P 0.30 had significantly shorter times of transplant-free survival than matched healthy individuals (P ursodeoxycholic acid-treated patients with PBC. This score might be used to select strategies for treatment and care. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Validation of the FACT-B+4-UL questionnaire and exploration of its predictive value in women submitted to surgery for breast cancer.

Science.gov (United States)

Andrade Ortega, Juan Alfonso; Millán Gómez, Ana Pilar; Ribeiro González, Marisa; Martínez Piró, Pilar; Jiménez Anula, Juan; Sánchez Andújar, María Belén

2017-06-21

The early detection of upper limb complications is important in women operated on for breast cancer. The "FACT-B+4-UL" questionnaire, a specific variant of the Functional Assessment of Cancer Therapy-Breast (FACT-B) is available among others to measure the upper limb function. The Spanish version of the upper limb subscale of the FACT-B+4 was validated in a prospective cohort of 201 women operated on for breast cancer (factor analysis, internal consistency, test-retest reliability, construct validity and sensitivity to change were determined). Its predictive capacity of subsequent lymphoedema and other complications in the upper limb was explored using logistic regression. This subscale is unifactorial and has a great internal consistency (Cronbach's alpha: 0.87), its test-retest reliability and construct validity are strong (intraclass correlation coefficient: 0.986; Pearson's R with "Quick DASH": 0.81) as is its sensitivity to change. It didn't predict the onset of lymphedema. Its predictive capacity for other upper limb complications is low. FACT-B+4-UL is useful in measuring upper limb disability in women surgically treated for breast cancer; but it does not predict the onset of lymphoedema and its predictive capacity for others complications in the upper limb is low. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

Development and validation of a prediction algorithm for the onset of common mental disorders in a working population.

Science.gov (United States)

Fernandez, Ana; Salvador-Carulla, Luis; Choi, Isabella; Calvo, Rafael; Harvey, Samuel B; Glozier, Nicholas

2018-01-01

Common mental disorders are the most common reason for long-term sickness absence in most developed countries. Prediction algorithms for the onset of common mental disorders may help target indicated work-based prevention interventions. We aimed to develop and validate a risk algorithm to predict the onset of common mental disorders at 12 months in a working population. We conducted a secondary analysis of the Household, Income and Labour Dynamics in Australia Survey, a longitudinal, nationally representative household panel in Australia. Data from the 6189 working participants who did not meet the criteria for a common mental disorders at baseline were non-randomly split into training and validation databases, based on state of residence. Common mental disorders were assessed with the mental component score of 36-Item Short Form Health Survey questionnaire (score ⩽45). Risk algorithms were constructed following recommendations made by the Transparent Reporting of a multivariable prediction model for Prevention Or Diagnosis statement. Different risk factors were identified among women and men for the final risk algorithms. In the training data, the model for women had a C-index of 0.73 and effect size (Hedges' g) of 0.91. In men, the C-index was 0.76 and the effect size was 1.06. In the validation data, the C-index was 0.66 for women and 0.73 for men, with positive predictive values of 0.28 and 0.26, respectively Conclusion: It is possible to develop an algorithm with good discrimination for the onset identifying overall and modifiable risks of common mental disorders among working men. Such models have the potential to change the way that prevention of common mental disorders at the workplace is conducted, but different models may be required for women.

A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

Energy Technology Data Exchange (ETDEWEB)

Oberije, Cary, E-mail: cary.oberije@maastro.nl [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); De Ruysscher, Dirk [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Universitaire Ziekenhuizen Leuven, KU Leuven (Belgium); Houben, Ruud [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Heuvel, Michel van de; Uyterlinde, Wilma [Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Deasy, Joseph O. [Memorial Sloan Kettering Cancer Center, New York (United States); Belderbos, Jose [Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Dingemans, Anne-Marie C. [Department of Pulmonology, University Hospital Maastricht, Research Institute GROW of Oncology, Maastricht (Netherlands); Rimner, Andreas; Din, Shaun [Memorial Sloan Kettering Cancer Center, New York (United States); Lambin, Philippe [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands)

2015-07-15

Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

External Validation and Evaluation of Reliability and Validity of the Modified Seoul National University Renal Stone Complexity Scoring System to Predict Stone-Free Status After Retrograde Intrarenal Surgery.

Science.gov (United States)

Park, Juhyun; Kang, Minyong; Jeong, Chang Wook; Oh, Sohee; Lee, Jeong Woo; Lee, Seung Bae; Son, Hwancheol; Jeong, Hyeon; Cho, Sung Yong

2015-08-01

The modified Seoul National University Renal Stone Complexity scoring system (S-ReSC-R) for retrograde intrarenal surgery (RIRS) was developed as a tool to predict stone-free rate (SFR) after RIRS. We externally validated the S-ReSC-R. We retrospectively reviewed 159 patients who underwent RIRS. The S-ReSC-R was assigned from 1 to 12 according to the location and number of sites involved. The stone-free status was defined as no evidence of a stone or with clinically insignificant residual fragment stones less than 2 mm. Interobserver and test-retest reliabilities were evaluated. Statistical performance of the prediction model was assessed by its predictive accuracy, predictive probability, and clinical usefulness. Overall SFR was 73.0%. The SFRs were 86.7%, 70.2%, and 48.6% in low-score (1-2), intermediate-score (3-4), and high-score (5-12) groups, respectively (pR revealed an area under the curve (AUC) of 0.731 (95% CI 0.650-0.813). The AUC of the three-titered S-ReSC-R was 0.701 (95% CI 0.609-0.794). The calibration plot showed that the predicted probability of SFR had a concordance comparable to that of observed frequency. The Hosmer-Lemeshow goodness of fit test revealed a p-value of 0.01 for the S-ReSC-R and 0.90 for the three-titered S-ReSC-R. Interobserver and test-retest reliabilities revealed an almost perfect level of agreement. The present study proved the predictive value of S-ReSC-R to predict SFR following RIRS in an independent cohort. Interobserver and test-retest reliabilities confirmed that S-ReSC-R was reliable and valid.

Computational Prediction and Rationalization, and Experimental Validation of Handedness Induction in Helical Aromatic Oligoamide Foldamers.

Science.gov (United States)

Liu, Zhiwei; Hu, Xiaobo; Abramyan, Ara M; Mészáros, Ádám; Csékei, Márton; Kotschy, András; Huc, Ivan; Pophristic, Vojislava

2017-03-13

Metadynamics simulations were used to describe the conformational energy landscapes of several helically folded aromatic quinoline carboxamide oligomers bearing a single chiral group at either the C or N terminus. The calculations allowed the prediction of whether a helix handedness bias occurs under the influence of the chiral group and gave insight into the interactions (sterics, electrostatics, hydrogen bonds) responsible for a particular helix sense preference. In the case of camphanyl-based and morpholine-based chiral groups, experimental data confirming the validity of the calculations were already available. New chiral groups with a proline residue were also investigated and were predicted to induce handedness. This prediction was verified experimentally through the synthesis of proline-containing monomers, their incorporation into an oligoamide sequence by solid phase synthesis and the investigation of handedness induction by NMR spectroscopy and circular dichroism. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Measuring Gambling Reinforcers, Over Consumption and Fallacies: The Psychometric Properties and Predictive Validity of the Jonsson-Abbott Scale

Directory of Open Access Journals (Sweden)

Jakob Jonsson

2017-10-01

Full Text Available Traditionally, gambling and problem gambling research relies on cross-sectional and retrospective designs. This has compromised identification of temporal relationships and causal inference. To overcome these problems a new questionnaire, the Jonsson-Abbott Scale (JAS, was developed and used in a large, prospective, general population study, The Swedish Longitudinal Gambling Study (Swelogs. The JAS has 11 items and seeks to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. The aims of the study were to examine psychometric properties of the JAS (internal consistency and dimensionality and predictive validity with respect to increased gambling risk and problem gambling onset. The results are based on repeated interviews with 3818 participants. The response rate from the initial baseline wave was 74%. The original sample consisted of a random, stratified selection from the Swedish population register aged between 16 and 84. The results indicate an acceptable fit of a three-factor solution in a confirmatory factor analysis with ‘Over consumption,’ ‘Gambling fallacies,’ and ‘Reinforcers’ as factors. Reinforcers, Over consumption and Gambling fallacies were significant predictors of gambling risk potential and Gambling fallacies and Over consumption were significant predictors of problem gambling onset (incident cases at 12 month follow up. When controlled for risk potential measured at baseline, the predictor Over consumption was not significant for gambling risk potential at follow up. For incident cases, Gambling fallacies and Over consumption remained significant when controlled for risk potential. Implications of the results for the development of problem gambling, early detection, prevention, and future research are discussed.

Validation of a model for predicting smear-positive active pulmonary tuberculosis in patients with initial acid-fast bacilli smear-negative sputum

Energy Technology Data Exchange (ETDEWEB)

Yeh, Jun-Jun [Department of Chest Medicine, Section of Thoracic Imaging, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City (China); Chia Nan University of Pharmacy and Science, Tainan (China); Meiho University, Pingtung (China); Pingtung Christian Hospital, Pingtung (China); Heng Chun Christian Hospital, Pingtung (China)

2018-01-15

The objective of this study was to develop a predictive model for final smear-positive (SP) active pulmonary tuberculosis (aPTB) in patients with initial negative acid fast bacilli (AFB) sputum smears (iSN-SP-aPTB) based on high-resolution computed tomography (HRCT). Eighty (126, 21) patients of iSN-SP-aPTB and 402 (459, 876) patients of non-initial positive acid fast bacilli (non-iSP) pulmonary disease without iSN-SP-aPTB were included in a derivation (validation, prospective) cohort. HRCT characteristics were analysed, and multivariable regression and receiver operating characteristic (ROC) curve analysis was performed to develop a score predictive of iSN-SP-aPTB. The derivation cohort showed clusters of nodules/mass of the right upper lobe or left upper lobe were independent predictors of iSN-SP-aPTB, while bronchiectasis in the right middle lobe or left lingual lobe were negatively associated with iSN-SP-aPTB. A predictive score for iSN-SP-aPTB based on these findings was tested in the validation and prospective cohorts. With an ideal cut-off score = 1, the sensitivity, specificity, positive predictive value, and negative predictive value of the prediction model were 87.5% (90%, 90.5%), 99% (97.1%, 98.4%), 94.6% (81.3%, 57.5%), and 97.6% (97%, 99.8%) in the derivation (validation, prospective) cohorts, respectively. The model may help identify iSN-SP-aPTB among patients with non-iSP pulmonary diseases. circle Smear-positive active pulmonary tuberculosis that is initial smear-negative (iSN-SP-aPTB) is infectious. (orig.)

International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer

DEFF Research Database (Denmark)

Meretoja, Tuomo J; Leidenius, Marjut H K; Heikkilä, Päivi S

2012-01-01

predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other...... centers. All statistical tests were two-sided. Results Nine tumor- and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series...... resulted in an AUC of 0.714 (95% confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95% CI = 0.689 to 0.750). The model was well calibrated in the external validation series. Conclusions We present a novel, international, multicenter, predictive tool...

Dimensionality and predictive validity of the HAM-Nat, a test of natural sciences for medical school admission.

Science.gov (United States)

Hissbach, Johanna C; Klusmann, Dietrich; Hampe, Wolfgang

2011-10-14

Knowledge in natural sciences generally predicts study performance in the first two years of the medical curriculum. In order to reduce delay and dropout in the preclinical years, Hamburg Medical School decided to develop a natural science test (HAM-Nat) for student selection. In the present study, two different approaches to scale construction are presented: a unidimensional scale and a scale composed of three subject specific dimensions. Their psychometric properties and relations to academic success are compared. 334 first year medical students of the 2006 cohort responded to 52 multiple choice items from biology, physics, and chemistry. For the construction of scales we generated two random subsamples, one for development and one for validation. In the development sample, unidimensional item sets were extracted from the item pool by means of weighted least squares (WLS) factor analysis, and subsequently fitted to the Rasch model. In the validation sample, the scales were subjected to confirmatory factor analysis and, again, Rasch modelling. The outcome measure was academic success after two years. Although the correlational structure within the item set is weak, a unidimensional scale could be fitted to the Rasch model. However, psychometric properties of this scale deteriorated in the validation sample. A model with three highly correlated subject specific factors performed better. All summary scales predicted academic success with an odds ratio of about 2.0. Prediction was independent of high school grades and there was a slight tendency for prediction to be better in females than in males. A model separating biology, physics, and chemistry into different Rasch scales seems to be more suitable for item bank development than a unidimensional model, even when these scales are highly correlated and enter into a global score. When such a combination scale is used to select the upper quartile of applicants, the proportion of successful completion of the curriculum

Dimensionality and predictive validity of the HAM-Nat, a test of natural sciences for medical school admission

Directory of Open Access Journals (Sweden)

Hissbach Johanna C

2011-10-01

Full Text Available Abstract Background Knowledge in natural sciences generally predicts study performance in the first two years of the medical curriculum. In order to reduce delay and dropout in the preclinical years, Hamburg Medical School decided to develop a natural science test (HAM-Nat for student selection. In the present study, two different approaches to scale construction are presented: a unidimensional scale and a scale composed of three subject specific dimensions. Their psychometric properties and relations to academic success are compared. Methods 334 first year medical students of the 2006 cohort responded to 52 multiple choice items from biology, physics, and chemistry. For the construction of scales we generated two random subsamples, one for development and one for validation. In the development sample, unidimensional item sets were extracted from the item pool by means of weighted least squares (WLS factor analysis, and subsequently fitted to the Rasch model. In the validation sample, the scales were subjected to confirmatory factor analysis and, again, Rasch modelling. The outcome measure was academic success after two years. Results Although the correlational structure within the item set is weak, a unidimensional scale could be fitted to the Rasch model. However, psychometric properties of this scale deteriorated in the validation sample. A model with three highly correlated subject specific factors performed better. All summary scales predicted academic success with an odds ratio of about 2.0. Prediction was independent of high school grades and there was a slight tendency for prediction to be better in females than in males. Conclusions A model separating biology, physics, and chemistry into different Rasch scales seems to be more suitable for item bank development than a unidimensional model, even when these scales are highly correlated and enter into a global score. When such a combination scale is used to select the upper quartile of

Prediction and Validation of Heat Release Direct Injection Diesel Engine Using Multi-Zone Model

Science.gov (United States)

Anang Nugroho, Bagus; Sugiarto, Bambang; Prawoto; Shalahuddin, Lukman

2014-04-01

The objective of this study is to develop simulation model which capable to predict heat release of diesel combustion accurately in efficient computation time. A multi-zone packet model has been applied to solve the combustion phenomena inside diesel cylinder. The model formulations are presented first and then the numerical results are validated on a single cylinder direct injection diesel engine at various engine speed and timing injections. The model were found to be promising to fulfill the objective above.

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

NARCIS (Netherlands)

Lamain-de Ruiter, M.; Kwee, A.; Naaktgeboren, C.A.; Groot, I. de; Evers, I.M.; Groenendaal, F.; Hering, Y.R.; Huisjes, A.J.M.; Kirpestein, C.; Monincx, W.M.; Siljee, J.E.; Zelfde, A. van't; Oirschot, C.M. van; Vankan-Buitelaar, S.A.; Vonk, M.A.A.W.; Wiegers, T.A.; Zwart, J.J.; Franx, A.; Moons, K.G.M.; Koster, M.P.H.

2016-01-01

Objective: To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy. Design: External validation of all published prognostic models in

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics.

Science.gov (United States)

Devillers, J; Pandard, P; Richard, B

2013-01-01

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.

Prospective validation of immunological infiltrate for prediction of response to neoadjuvant chemotherapy in HER2-negative breast cancer--a substudy of the neoadjuvant GeparQuinto trial.

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Issa-Nummer, Yasmin; Darb-Esfahani, Silvia; Loibl, Sibylle; Kunz, Georg; Nekljudova, Valentina; Schrader, Iris; Sinn, Bruno Valentin; Ulmer, Hans-Ullrich; Kronenwett, Ralf; Just, Marianne; Kühn, Thorsten; Diebold, Kurt; Untch, Michael; Holms, Frank; Blohmer, Jens-Uwe; Habeck, Jörg-Olaf; Dietel, Manfred; Overkamp, Friedrich; Krabisch, Petra; von Minckwitz, Gunter; Denkert, Carsten

2013-01-01

We have recently described an increased lymphocytic infiltration rate in breast carcinoma tissue is a significant response predictor for anthracycline/taxane-based neoadjuvant chemotherapy (NACT). The aim of this study was to prospectively validate the tumor-associated lymphocyte infiltrate as predictive marker for response to anthracycline/taxane-based NACT. The immunological infiltrate was prospectively evaluated in a total of 313 core biopsies from HER2 negative patients of the multicenter PREDICT study, a substudy of the neoadjuvant GeparQuinto study. Intratumoral lymphocytes (iTuLy), stromal lymphocytes (strLy) as well as lymphocyte-predominant breast cancer (LPBC) were evaluated by histopathological assessment. Pathological complete response (pCR) rates were analyzed and compared between the defined subgroups using the exact test of Fisher. Patients with lymphocyte-predominant breast cancer (LPBC) had a significantly increased pCR rate of 36.6%, compared to non-LPBC patients (14.3%, pimmunological infiltrate in breast tumor tissue is predictive for response to anthracycline/taxane-based NACT. Patients with LPBC and increased stromal lymphocyte infiltration have significantly increased pCR rates. The lymphocytic infiltrate is a promising additional parameter for histopathological evaluation of breast cancer core biopsies.

A systematic approach to obtain validated Partial Least Square models for predicting lipoprotein subclasses from serum NMR spectra

NARCIS (Netherlands)

Mihaleva, V.V.; van Schalkwijk, D.B.; de Graaf, A.A.; van Duynhoven, J.; van Dorsten, F.A.; Vervoort, J.; Smilde, A.; Westerhuis, J.A.; Jacobs, D.M.

2014-01-01

A systematic approach is described for building validated PLS models that predict cholesterol and triglyceride concentrations in lipoprotein subclasses in fasting serum from a normolipidemic, healthy population. The PLS models were built on diffusion-edited 1H NMR spectra and calibrated on

A systematic approach to obtain validated partial least square models for predicting lipoprotein subclasses from serum NMR spectra

NARCIS (Netherlands)

Mihaleva, V.V.; Schalkwijk, van D.B.; Graaf, de A.A.; Duynhoven, van J.P.M.; Dorsten, van F.A.; Vervoort, J.J.M.; Smilde, A.K.; Westerhuis, J.A.; Jacobs, D.M.

2014-01-01

A systematic approach is described for building validated PLS models that predict cholesterol and triglyceride concentrations in lipoprotein subclasses in fasting serum from a normolipidemic, healthy population. The PLS models were built on diffusion-edited (1)H NMR spectra and calibrated on

A systematic approach to obtain validated partial least square models for predicting lipoprotein subclasses from serum nmr spectra

NARCIS (Netherlands)

Mihaleva, V.V.; Schalkwijk, D.B. van; Graaf, A.A. de; Duynhoven, J. van; Dorsten, F.A. van; Vervoort, J.; Smilde, A.; Westerhuis, J.A.; Jacobs, D.M.

2014-01-01

A systematic approach is described for building validated PLS models that predict cholesterol and triglyceride concentrations in lipoprotein subclasses in fasting serum from a normolipidemic, healthy population. The PLS models were built on diffusion-edited 1H NMR spectra and calibrated on

Incremental validity of positive orientation: predictive efficiency beyond the five-factor model

Directory of Open Access Journals (Sweden)

Łukasz Roland Miciuk

2016-05-01

Full Text Available Background The relation of positive orientation (a basic predisposition to think positively of oneself, one’s life and one’s future and personality traits is still disputable. The purpose of the described research was to verify the hypothesis that positive orientation has predictive efficiency beyond the five-factor model. Participants and procedure One hundred and thirty participants (at the mean age M = 24.84 completed the following questionnaires: the Self-Esteem Scale (SES, the Satisfaction with Life Scale (SWLS, the Life Orientation Test-Revised (LOT-R, the Positivity Scale (P-SCALE, the NEO Five Factor Inventory (NEO-FFI, the Self-Concept Clarity Scale (SCC, the Generalized Self-Efficacy Scale (GSES and the Life Engagement Test (LET. Results The introduction of positive orientation as an additional predictor in the second step of regression analyses led to better prediction of the following variables: purpose in life, self-concept clarity and generalized self-efficacy. This effect was the strongest for predicting purpose in life (i.e. 14% increment of the explained variance. Conclusions The results confirmed our hypothesis that positive orientation can be characterized by incremental validity – its inclusion in the regression model (in addition to the five main factors of personality increases the amount of explained variance. These findings may provide further evidence for the legitimacy of measuring positive orientation and personality traits separately.

Liver stiffness value-based risk estimation of late recurrence after curative resection of hepatocellular carcinoma: development and validation of a predictive model.

Directory of Open Access Journals (Sweden)

Kyu Sik Jung

Full Text Available Preoperative liver stiffness (LS measurement using transient elastography (TE is useful for predicting late recurrence after curative resection of hepatocellular carcinoma (HCC. We developed and validated a novel LS value-based predictive model for late recurrence of HCC.Patients who were due to undergo curative resection of HCC between August 2006 and January 2010 were prospectively enrolled and TE was performed prior to operations by study protocol. The predictive model of late recurrence was constructed based on a multiple logistic regression model. Discrimination and calibration were used to validate the model.Among a total of 139 patients who were finally analyzed, late recurrence occurred in 44 patients, with a median follow-up of 24.5 months (range, 12.4-68.1. We developed a predictive model for late recurrence of HCC using LS value, activity grade II-III, presence of multiple tumors, and indocyanine green retention rate at 15 min (ICG R15, which showed fairly good discrimination capability with an area under the receiver operating characteristic curve (AUROC of 0.724 (95% confidence intervals [CIs], 0.632-0.816. In the validation, using a bootstrap method to assess discrimination, the AUROC remained largely unchanged between iterations, with an average AUROC of 0.722 (95% CIs, 0.718-0.724. When we plotted a calibration chart for predicted and observed risk of late recurrence, the predicted risk of late recurrence correlated well with observed risk, with a correlation coefficient of 0.873 (P<0.001.A simple LS value-based predictive model could estimate the risk of late recurrence in patients who underwent curative resection of HCC.

Mining, Validation, and Clinical Significance of Colorectal Cancer (CRC)-Associated lncRNAs.

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Sun, Xiangwei; Hu, Yingying; Zhang, Liang; Hu, Changyuan; Guo, Gangqiang; Mao, Chenchen; Xu, Jianfeng; Ye, Sisi; Huang, Guanli; Xue, Xiangyang; Guo, Aizhen; Shen, Xian

2016-01-01

Colorectal cancer (CRC) is one of the deadliest tumours, but its pathogenesis remains unclear. The involvement of differentially expressed long non-coding RNAs (lncRNAs) in CRC tumorigenesis makes them suitable tumour biomarkers. Here, we screened 150 cases of CRC and 85 cases of paracancerous tissues in the GEO database for differentially expressed lncRNAs. The levels of lncRNA candidates in 84 CRC and paracancerous tissue samples were validated by qRT-PCR and their clinical significance was analyzed. We identified 15 lncRNAs with differential expression in CRC tumours; among them, AK098081 was significantly up-regulated, whereas AK025209, BC040303, BC037331, AK026659, and CR749831 were down-regulated in CRC. In a receiver operating characteristic curve analysis, the area under the curve for the six lncRNAs was 0.914. High expression of AK098081 and low expression of BC040303, CR749831, and BC037331 indicated poor CRC differentiation. CRC patients with lymph node metastasis had lower expression of BC037331. In addition, the group with high AK098081 expression presented significantly lower overall survival and disease-free survival rates than the low-expression group, confirming AK098081 as an independent risk factor for CRC patients. In conclusion, we have identified multiple CRC-associated lncRNAs from microarray expression profiles that can serve as novel biomarkers for the diagnosis and prognosis of CRC.

Validating a model that predicts daily growth and feed quality of New Zealand dairy pastures.

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Woodward, S J

2001-09-01

The Pasture Quality (PQ) model is a simple, mechanistic, dynamical system model that was designed to capture the essential biological processes in grazed grass-clover pasture, and to be optimised to derive improved grazing strategies for New Zealand dairy farms. While the individual processes represented in the model (photosynthesis, tissue growth, flowering, leaf death, decomposition, worms) were based on experimental data, this did not guarantee that the assembled model would accurately predict the behaviour of the system as a whole (i.e., pasture growth and quality). Validation of the whole model was thus a priority, since any strategy derived from the model could impact a farm business in the order of thousands of dollars per annum if adopted. This paper describes the process of defining performance criteria for the model, obtaining suitable data to test the model, and carrying out the validation analysis. The validation process highlighted a number of weaknesses in the model, which will lead to the model being improved. As a result, the model's utility will be enhanced. Furthermore, validation was found to have an unexpected additional benefit, in that despite the model's poor initial performance, support was generated for the model among field scientists involved in the wider project.

Proactive, Reactive, and Romantic Relational Aggression in Adulthood: Measurement, Predictive Validity, Gender Differences, and Association with Intermittent Explosive Disorder

OpenAIRE

Murray-Close, Dianna; Ostrov, Jamie M.; Nelson, David A.; Crick, Nicki R.; Coccaro, Emil F.

2009-01-01

The psychometric properties of a recently introduced adult self-report of relational aggression are presented. Specifically, the predictive utility of proactive and reactive peer-directed relational aggression, as well as romantic relational aggression, are explored in a large (N = 1387) study of adults. The measure had adequate reliability and validity and the subscales demonstrated unique predictive abilities for a number of dependent variables. In particular, reactive but not proactive rel...

Prediction of cognitive and motor development in preterm children using exhaustive feature selection and cross-validation of near-term white matter microstructure.

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Schadl, Kornél; Vassar, Rachel; Cahill-Rowley, Katelyn; Yeom, Kristin W; Stevenson, David K; Rose, Jessica

2018-01-01

Advanced neuroimaging and computational methods offer opportunities for more accurate prognosis. We hypothesized that near-term regional white matter (WM) microstructure, assessed on diffusion tensor imaging (DTI), using exhaustive feature selection with cross-validation would predict neurodevelopment in preterm children. Near-term MRI and DTI obtained at 36.6 ± 1.8 weeks postmenstrual age in 66 very-low-birth-weight preterm neonates were assessed. 60/66 had follow-up neurodevelopmental evaluation with Bayley Scales of Infant-Toddler Development, 3rd-edition (BSID-III) at 18-22 months. Linear models with exhaustive feature selection and leave-one-out cross-validation computed based on DTI identified sets of three brain regions most predictive of cognitive and motor function; logistic regression models were computed to classify high-risk infants scoring one standard deviation below mean. Cognitive impairment was predicted (100% sensitivity, 100% specificity; AUC = 1) by near-term right middle-temporal gyrus MD, right cingulate-cingulum MD, left caudate MD. Motor impairment was predicted (90% sensitivity, 86% specificity; AUC = 0.912) by left precuneus FA, right superior occipital gyrus MD, right hippocampus FA. Cognitive score variance was explained (29.6%, cross-validated Rˆ2 = 0.296) by left posterior-limb-of-internal-capsule MD, Genu RD, right fusiform gyrus AD. Motor score variance was explained (31.7%, cross-validated Rˆ2 = 0.317) by left posterior-limb-of-internal-capsule MD, right parahippocampal gyrus AD, right middle-temporal gyrus AD. Search in large DTI feature space more accurately identified neonatal neuroimaging correlates of neurodevelopment.

Spatially resolved flux measurements of NOx from London suggest significantly higher emissions than predicted by inventories.

Science.gov (United States)

Vaughan, Adam R; Lee, James D; Misztal, Pawel K; Metzger, Stefan; Shaw, Marvin D; Lewis, Alastair C; Purvis, Ruth M; Carslaw, David C; Goldstein, Allen H; Hewitt, C Nicholas; Davison, Brian; Beevers, Sean D; Karl, Thomas G

2016-07-18

To date, direct validation of city-wide emissions inventories for air pollutants has been difficult or impossible. However, recent technological innovations now allow direct measurement of pollutant fluxes from cities, for comparison with emissions inventories, which are themselves commonly used for prediction of current and future air quality and to help guide abatement strategies. Fluxes of NOx were measured using the eddy-covariance technique from an aircraft flying at low altitude over London. The highest fluxes were observed over central London, with lower fluxes measured in suburban areas. A footprint model was used to estimate the spatial area from which the measured emissions occurred. This allowed comparison of the flux measurements to the UK's National Atmospheric Emissions Inventory (NAEI) for NOx, with scaling factors used to account for the actual time of day, day of week and month of year of the measurement. The comparison suggests significant underestimation of NOx emissions in London by the NAEI, mainly due to its under-representation of real world road traffic emissions. A comparison was also carried out with an enhanced version of the inventory using real world driving emission factors and road measurement data taken from the London Atmospheric Emissions Inventory (LAEI). The measurement to inventory agreement was substantially improved using the enhanced version, showing the importance of fully accounting for road traffic, which is the dominant NOx emission source in London. In central London there was still an underestimation by the inventory of 30-40% compared with flux measurements, suggesting significant improvements are still required in the NOx emissions inventory.

Differential Predictive Validity of High School GPA and College Entrance Test Scores for University Students in Yemen

Science.gov (United States)

Al-Hattami, Abdulghani Ali Dawod

2012-01-01

High school grade point average and college entrance test scores are two admission criteria that are currently used by most colleges in Yemen to select their prospective students. Given their widespread use, it is important to investigate their predictive validity to ensure the accuracy of the admission decisions in these institutions. This study…

Incremental Criterion Validity of the WJ-III COG Clinical Clusters: Marginal Predictive Effects beyond the General Factor

Science.gov (United States)

McGill, Ryan J.

2015-01-01

The current study examined the incremental validity of the clinical clusters from the Woodcock-Johnson III Tests of Cognitive Abilities (WJ-III COG) for predicting scores on the Woodcock-Johnson III Tests of Achievement (WJ-III ACH). All participants were children and adolescents (N = 4,722) drawn from the nationally representative WJ-III…

Validation of a Web-Based Tool to Predict the Ipsilateral Breast Tumor Recurrence (IBTR! 2.0) after Breast-Conserving Therapy for Korean Patients.

Science.gov (United States)

Jung, Seung Pil; Hur, Sung Mo; Lee, Se Kyung; Kim, Sangmin; Choi, Min-Young; Bae, Soo Youn; Kim, Jiyoung; Kim, Min Kuk; Kil, Won Ho; Choe, Jun-Ho; Kim, Jung-Han; Kim, Jee Soo; Nam, Seok Jin; Bae, Jeoung Won; Lee, Jeong Eon

2013-03-01

IBTR! 2.0 is a web-based nomogram that predicts the 10-year ipsilateral breast tumor recurrence (IBTR) rate after breast-conserving therapy. We validated this nomogram in Korean patients. The nomogram was tested for 520 Korean patients, who underwent breast-conserving surgery followed by radiation therapy. Predicted and observed 10-year outcomes were compared for the entire cohort and for each group, predefined by nomogram-predicted risks: group 1, 10%. In overall patients, the overall 10 year predicted and observed estimates of IBTR were 5.22% and 5.70% (p=0.68). In group 1, (n=124), the predicted and observed estimates were 2.25% and 1.80% (p=0.73), in group 2 (n=177), 3.95% and 3.90% (p=0.97), in group 3 (n=181), 7.14% and 8.80% (p=0.42), and in group 4 (n=38), 11.66% and 14.90% (p=0.73), respectively. In a previous validation of this nomogram based on American patients, nomogram-predicted IBTR rates were overestimated in the high-risk subgroup. However, our results based on Korean patients showed that the observed IBTR was higher than the predicted estimates in groups 3 and 4. This difference may arise from ethnic differences, as well as from the methods used to detect IBTR and the healthcare environment. IBTR! 2.0 may be considered as an acceptable nomogram in Korean patients with low- to moderate-risk of in-breast recurrence. Before widespread use of this nomogram, the IBTR! 2.0 needs a larger validation study and continuous modification.

Validation of a new mortality risk prediction model for people 65 years and older in northwest Russia: The Crystal risk score.

Science.gov (United States)

Turusheva, Anna; Frolova, Elena; Bert, Vaes; Hegendoerfer, Eralda; Degryse, Jean-Marie

2017-07-01

Prediction models help to make decisions about further management in clinical practice. This study aims to develop a mortality risk score based on previously identified risk predictors and to perform internal and external validations. In a population-based prospective cohort study of 611 community-dwelling individuals aged 65+ in St. Petersburg (Russia), all-cause mortality risks over 2.5 years follow-up were determined based on the results obtained from anthropometry, medical history, physical performance tests, spirometry and laboratory tests. C-statistic, risk reclassification analysis, integrated discrimination improvement analysis, decision curves analysis, internal validation and external validation were performed. Older adults were at higher risk for mortality [HR (95%CI)=4.54 (3.73-5.52)] when two or more of the following components were present: poor physical performance, low muscle mass, poor lung function, and anemia. If anemia was combined with high C-reactive protein (CRP) and high B-type natriuretic peptide (BNP) was added the HR (95%CI) was slightly higher (5.81 (4.73-7.14)) even after adjusting for age, sex and comorbidities. Our models were validated in an external population of adults 80+. The extended model had a better predictive capacity for cardiovascular mortality [HR (95%CI)=5.05 (2.23-11.44)] compared to the baseline model [HR (95%CI)=2.17 (1.18-4.00)] in the external population. We developed and validated a new risk prediction score that may be used to identify older adults at higher risk for mortality in Russia. Additional studies need to determine which targeted interventions improve the outcomes of these at-risk individuals. Copyright © 2017 Elsevier B.V. All rights reserved.

Meta-Analysis of Predictive Significance of the Black Hole Sign for Hematoma Expansion in Intracerebral Hemorrhage.

Science.gov (United States)

Zheng, Jun; Yu, Zhiyuan; Guo, Rui; Li, Hao; You, Chao; Ma, Lu

2018-04-27

Hematoma expansion is related to unfavorable prognosis in intracerebral hemorrhage (ICH). The black hole sign is a novel marker on non-contrast computed tomography for predicting hematoma expansion. However, its predictive values are different in previous studies. Thus, this meta-analysis was conducted to evaluate the predictive significance of the black hole sign for hematoma expansion in ICH. A systematic literature search was performed. Original researches on the association between the black hole sign and hematoma expansion in ICH were included. Sensitivity and specificity were pooled to assess the predictive accuracy. Summary receiver operating characteristics curve (SROC) was developed. Deeks' funnel plot asymmetry test was used to assess the publication bias. Five studies with a total of 1495 patients were included in this study. The pooled sensitivity and specificity of the black hole sign for predicting hematoma expansion were 0.30 and 0.91, respectively. The area under the curve was 0.78 in SROC curve. There was no significant publication bias. This meta-analysis shows that the black hole sign is a helpful imaging marker for predicting hematoma expansion in ICH. Although the black hole sign has a relatively low sensitivity, its specificity is relatively high. Copyright © 2018 Elsevier Inc. All rights reserved.