TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

TLDR

The TRIPOD 2015 statement set minimum reporting standards for prediction model studies, but the rise of AI‑powered machine learning methods and the need for complete, accurate, and transparent reporting have highlighted gaps in that guidance. The authors update the TRIPOD statement to create TRIPOD+AI, a harmonised guidance that promotes thorough reporting of prediction model studies using regression or machine learning, and describe its expanded 27‑item checklist. The new TRIPOD+AI checklist supersedes the 2015 version, offering 27 harmonised items—including an abstract checklist—to guide reporting of prediction model studies regardless of the modelling approach.

Abstract

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed. TRIPOD+AI provides harmonised guidance for reporting prediction model studies, irrespective of whether regression modelling or machine learning methods have been used. The new checklist supersedes the TRIPOD 2015 checklist, which should no longer be used. This article describes the development of TRIPOD+AI and presents the expanded 27 item checklist with more detailed explanation of each reporting recommendation, and the TRIPOD+AI for Abstracts checklist. TRIPOD+AI aims to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. Complete reporting will facilitate study appraisal, model evaluation, and model implementation.