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New guideline for the reporting of studies developing, validating, or updating a prediction model.

In medicine, patients and their care providers are confronted with making numerous decisions that are commonly, if not always, made on the basis of a probability--a probability that a specific disease or condition is present (diagnostic setting) or a specific event or outcome will occur in the future (prognostic setting). In the diagnostic setting, the probability that a particular disease is present is used, for example, to inform the referral of patients for further testing, initiate treatment directly, or reassure patients that a serious cause for their complaint is unlikely. In the prognostic setting, predictions are used for planning lifestyle or therapeutic decisions based on the probability of developing a particular outcome or health state within a specific time period. Prognostic probabilities can be estimated from ill or healthy individuals, and simply refer to the prediction of an outcome in the future in individuals at risk for that outcome.

In practice, diagnostic and prognostic probability estimations are rarely based on a single test result (or single predictor), since such information is often insufficient to provide reliable estimates. Hence, to guide practitioners and patients in these probability estimations, so-called multivariable prediction models are developed. Prediction models convert multiple (2 or more) pieces of information from the patient--e.g., a patient's age, sex, symptoms, signs, and laboratory and imaging test results--into a diagnostic or prognostic probability.

Prediction models are becoming increasingly abundant in the medical literature, and policymakers are increasingly recommending their use in clinical practice guidelines. In virtually all medical domains, prediction models are being developed, evaluated (validated), and implemented. For some specific diseases, there are even an overwhelming number of competing prediction models for the same outcome or target population. It is therefore important that these clinical prediction models and the research done to develop or evaluate these models be transparently reported. 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, which has included clinicians, statisticians, epidemiologists, and journal editors, has produced a guideline for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes (1). The TRIPOD Statement is a checklist of 22 items deemed essential for transparent reporting of any prediction model study, regardless of the study methods used. The TRIPOD Statement is accompanied by an Explanation and Elaboration article (2) that describes the rationale for the checklist, clarifies the meaning of each item, and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of a prediction model. Each item of the TRIPOD checklist is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies.

The endorsement and use of this checklist by researchers and medical journal editors will help ensure that medical research findings are complete and accurately reported, understood by readers, and ultimately used by medical practitioners. The TRIPOD checklist is downloadable (3).

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: Consensus meeting was partially funded by the Netherlands Organization for Scientific Research (ZONMW grants 918.10.615 and 91208004) and by the Medical Research Council (grant G1100513).

Expert Testimony: None declared.

Patents: None declared.


(1.) Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement.Ann Intern Med 2015;162:55-63.

(2.) Moons KGM, Altman DG, ReitsmaJB, IoannidisJPA, Macaskill P, Steyerberg EW, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015;162:W1W73.

(3.) TRIPOD Statement. (Accessed December 2014).

Karel G.M. Moons, [1] * Douglas G. Altman, [2] Johannes B. Reitsma, [1] and Gary S. Collins [2]

[1] Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; [2] Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.

* Address correspondence to this author at: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500,3508 GA Utrecht, The Netherlands. E-mail

Received December 29,2014; accepted December 29,2014.

DOI: 10.1373/clinchem.2014.237883
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Title Annotation:News & Views
Author:Moons, Karel G.M.; Altman, Douglas G.; Reitsma, Johannes B.; Collins, Gary S.
Publication:Clinical Chemistry
Geographic Code:1USA
Date:Mar 1, 2015
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