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      Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement

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          Abstract

          Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

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          Most cited references29

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          Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

          The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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            European system for cardiac operative risk evaluation (EuroSCORE).

            To construct a scoring system for the prediction of early mortality in cardiac surgical patients in Europe on the basis of objective risk factors. The EuroSCORE database was divided into developmental and validation subsets. In the former, risk factors deemed to be objective, credible, obtainable and difficult to falsify were weighted on the basis of regression analysis. An additive score of predicted mortality was constructed. Its calibration and discrimination characteristics were assessed in the validation dataset. Thresholds were defined to distinguish low, moderate and high risk groups. The developmental dataset had 13,302 patients, calibration by Hosmer Lemeshow Chi square was (8) = 8.26 (P 200 micromol/l (2), active endocarditis (3) and critical preoperative state (3). Cardiac factors were unstable angina on intravenous nitrates (2), reduced left ventricular ejection fraction (30-50%: 1, 60 mmHg (2). Operation-related factors were emergency (2), other than isolated coronary surgery (2), thoracic aorta surgery (3) and surgery for postinfarct septal rupture (4). The scoring system was then applied to three risk groups. The low risk group (EuroSCORE 1-2) had 4529 patients with 36 deaths (0.8%), 95% confidence limits for observed mortality (0.56-1.10) and for expected mortality (1.27-1.29). The medium risk group (EuroSCORE 3-5) had 5977 patients with 182 deaths (3%), observed mortality (2.62-3.51), predicted (2.90-2.94). The high risk group (EuroSCORE 6 plus) had 4293 patients with 480 deaths (11.2%) observed mortality (10.25-12.16), predicted (10.93-11.54). Overall, there were 698 deaths in 14,799 patients (4.7%), observed mortality (4.37-5.06), predicted (4.72-4.95). EuroSCORE is a simple, objective and up-to-date system for assessing heart surgery, soundly based on one of the largest, most complete and accurate databases in European cardiac surgical history. We recommend its widespread use.
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              Cardiovascular disease risk profiles.

              This article presents prediction equations for several cardiovascular disease endpoints, which are based on measurements of several known risk factors. Subjects (n = 5573) were original and offspring subjects in the Framingham Heart Study, aged 30 to 74 years, and initially free of cardiovascular disease. Equations to predict risk for the following were developed: myocardial infarction, coronary heart disease (CHD), death from CHD, stroke, cardiovascular disease, and death from cardiovascular disease. The equations demonstrated the potential importance of controlling multiple risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, glucose intolerance, and left ventricular hypertrophy) as opposed to focusing on one single risk factor. The parametric model used was seen to have several advantages over existing standard regression models. Unlike logistic regression, it can provide predictions for different lengths of time, and probabilities can be expressed in a more straightforward way than the Cox proportional hazards model.
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                Author and article information

                Journal
                Br J Cancer
                Br. J. Cancer
                British Journal of Cancer
                Nature Publishing Group
                0007-0920
                1532-1827
                20 January 2015
                06 January 2015
                20 January 2015
                : 112
                : 2
                : 251-259
                Affiliations
                [1 ]Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford , Oxford OX3 7LD, UK
                [2 ]Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht , PO Box 85500, 3508GA Utrecht, The Netherlands
                Author notes
                Article
                bjc2014639
                10.1038/bjc.2014.639
                4454817
                25562432
                ea55fc30-7da9-4643-9cf7-6f417beb07ea
                Copyright © 2015 Cancer Research UK

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                Categories
                Guideline

                Oncology & Radiotherapy
                prediction models,diagnostic,prognostic,model development,model validation,transparent reporting

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