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

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 fie...

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Vydané v:BMJ (Online) Ročník 385; s. e078378
Hlavní autori: 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
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England British Medical Journal Publishing Group 16.04.2024
BMJ Publishing Group LTD
Predmet:
Artificial intelligence
Calibration
Check lists
Checklist
Decision Support Techniques
Deep learning
Humans
Learning algorithms
Machine learning
Medical imaging
Models, Statistical
Open access
Prediction models
Prognosis
Regression analysis
Research Methods & Reporting
Validation studies
ISSN:1756-1833, 1756-1833
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1756-1833
1756-1833
DOI:10.1136/bmj-2023-078378