TY - JOUR
T1 - TRIPOD+AI statement
T2 - updated guidance for reporting clinical prediction models that use regression or machine learning methods
AU - Collins, Gary S.
AU - Moons, Karel G.M.
AU - Dhiman, Paula
AU - Riley, Richard D.
AU - Beam, Andrew L.
AU - Van Calster, Ben
AU - Ghassemi, Marzyeh
AU - Liu, Xiaoxuan
AU - Reitsma, Johannes B.
AU - van Smeden, Maarten
AU - Boulesteix, Anne Laure
AU - Camaradou, Jennifer Catherine
AU - Celi, Leo Anthony
AU - Denaxas, Spiros
AU - Denniston, Alastair K.
AU - Glocker, Ben
AU - Golub, Robert M.
AU - Harvey, Hugh
AU - Heinze, Georg
AU - Hoffman, Michael M.
AU - Kengne, André Pascal
AU - Lam, Emily
AU - Lee, Naomi
AU - Loder, Elizabeth W.
AU - Maier-Hein, Lena
AU - Mateen, Bilal A.
AU - McCradden, Melissa D.
AU - Oakden-Rayner, Lauren
AU - Ordish, Johan
AU - Parnell, Richard
AU - Rose, Sherri
AU - Singh, Karandeep
AU - Wynants, Laure
AU - Logullo, Patricia
PY - 2024/4/16
Y1 - 2024/4/16
N2 - 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.
AB - 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.
U2 - 10.1136/bmj-2023-078378
DO - 10.1136/bmj-2023-078378
M3 - Article
SN - 0959-8146
VL - 385
JO - BMJ
JF - BMJ
M1 - 078378
ER -