TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
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.
Author
Collins, Gary S
Moons, Karel G M
Dhiman, Paula
Riley, Richard D
Beam, Andrew L
Van Calster, Ben
Ghassemi, Marzyeh
Liu, Xiaoxuan
Reitsma, Johannes B
van Smeden, Maarten
Boulesteix, Anne-Laure
Camaradou, Jennifer Catherine
Celi, Leo Anthony
Denaxas, Spiros
Denniston, Alastair K
Glocker, Ben
Golub, Robert M
Harvey, Hugh
Heinze, Georg
Hoffman, Michael M
Kengne, André Pascal
Lam, Emily
Lee, Naomi
Loder, Elizabeth W
Maier-Hein, Lena
Mateen, Bilal A
McCradden, Melissa D
Oakden-Rayner, Lauren
Ordish, Johan
Parnell, Richard
Rose, Sherri
Singh, Karandeep
Wynants, Laure
Logullo, Patricia
Moons, Karel G M
Dhiman, Paula
Riley, Richard D
Beam, Andrew L
Van Calster, Ben
Ghassemi, Marzyeh
Liu, Xiaoxuan
Reitsma, Johannes B
van Smeden, Maarten
Boulesteix, Anne-Laure
Camaradou, Jennifer Catherine
Celi, Leo Anthony
Denaxas, Spiros
Denniston, Alastair K
Glocker, Ben
Golub, Robert M
Harvey, Hugh
Heinze, Georg
Hoffman, Michael M
Kengne, André Pascal
Lam, Emily
Lee, Naomi
Loder, Elizabeth W
Maier-Hein, Lena
Mateen, Bilal A
McCradden, Melissa D
Oakden-Rayner, Lauren
Ordish, Johan
Parnell, Richard
Rose, Sherri
Singh, Karandeep
Wynants, Laure
Logullo, Patricia
Citations
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Date
2024-04-16
Type
Article
Subject
Public health. Health statistics. Occupational health. Health education
Collections
Citation
Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378. Erratum in: BMJ. 2024 Apr 18;385:q902.
Journal / Source Title
BMJ
DOI
10.1136/bmj-2023-078378
PMID
38626948
Publisher
British Medical Association
Publisher’s URL
http://www.bmj.com/thebmj