Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement

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 http://www.tripod-statement.org).

Can We Estimate Short- and Intermediate-term Survival in Patients Undergoing Surgery for Metastatic Bone Disease?

“…Importantly, the decision curves do not estimate the likelihood of survival (the PATHFx models serve this purpose), but rather help determine which model(s) should and should not be used in certain clinical situations represented by a given surgeon's threshold. Wherever possible, we included information recommended by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) [1]. In addition to these, we believe decision analytic techniques, such as those used in this study, are important requirements for any model destined for the clinical setting [3].…”

Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings

“…8 Also, in the TRIPOD statement, there is an item on the reporting of treatment received among participants of a study developing or validating a multivariable prediction model for diagnosis or prognosis. 9 In this article, we evaluate these different methods in situations that aim to develop a prognostic model generating predictions in case individuals were to remain untreated, which serve as input for treatment decisions. In particular, we examine how the methods impact upon the predictive performance and proportion of correct indications of treatment of a prognostic model being developed using data from a randomised or observational study.…”

A model to predict risk of blood transfusion after gynecologic surgery

“…Guidelines for the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis statement were used in this study. 20 Eligible patients were identified if they had gynecologic surgery through review of their Current Procedural Terminology codes during the study period. Patients who underwent obstetric surgical procedures were excluded.…”

“…This method is stronger methodologically than randomly splitting a data set. 20 However, there are no guidelines on how to choose the time cut points, so we arbitrarily chose to divide the total cohort into thirds by using twothirds to build and internally validate and one-third to temporally validate the model using patients who underwent surgery in a time period after the development cohort. The total cohort was split into a model training cohort of 12,219 surgeries performed from January 2010 through December 2012 and a separate validation cohort of 6100 surgeries performed from January 2013 through June 2014.…”