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.
BMJ (Clinical research ed.). 2024 Apr 16*** epublish ***
Gary S Collins, Karel G M Moons, Paula Dhiman, Richard D Riley, Andrew L Beam, Ben Van Calster, Marzyeh Ghassemi, Xiaoxuan Liu, Johannes B Reitsma, Maarten van Smeden, Anne-Laure Boulesteix, Jennifer Catherine Camaradou, Leo Anthony Celi, Spiros Denaxas, Alastair K Denniston, Ben Glocker, Robert M Golub, Hugh Harvey, Georg Heinze, Michael M Hoffman, André Pascal Kengne, Emily Lam, Naomi Lee, Elizabeth W Loder, Lena Maier-Hein, Bilal A Mateen, Melissa D McCradden, Lauren Oakden-Rayner, Johan Ordish, Richard Parnell, Sherri Rose, Karandeep Singh, Laure Wynants, Patricia Logullo
Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK., Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands., Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK., Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA., Department of Development and Regeneration, KU Leuven, Leuven, Belgium., Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA., Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK., Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University of Munich and Munich Centre of Machine Learning, Germany., Patient representative, Health Data Research UK patient and public involvement and engagement group., Beth Israel Deaconess Medical Center, Boston, MA, USA., Institute of Health Informatics, University College London, London, UK., National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK., Department of Computing, Imperial College London, London, UK., Northwestern University Feinberg School of Medicine, Chicago, IL, USA., Hardian Health, Haywards Heath, UK., Section for Clinical Biometrics, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria., Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada., Department of Medicine, University of Cape Town, Cape Town, South Africa., National Institute for Health and Care Excellence, London, UK., The BMJ, London, UK., Department of Intelligent Medical Systems, German Cancer Research Centre, Heidelberg, Germany., Department of Bioethics, Hospital for Sick Children Toronto, ON, Canada., Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia., Medicines and Healthcare products Regulatory Agency, London, UK., Department of Health Policy and Center for Health Policy, Stanford University, Stanford, CA, USA., Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands.