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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Veröffentlicht in:BMJ (Online) Jg. 385; S. e078378
Hauptverfasser: 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
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England British Medical Journal Publishing Group 16.04.2024
BMJ Publishing Group LTD
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ISSN:1756-1833, 1756-1833
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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.
AbstractList 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 Boulesteix, Anne-Laure
Denniston, Alastair K
Lam, Emily
Oakden-Rayner, Lauren
Dhiman, Paula
Beam, Andrew L
van Smeden, Maarten
Reitsma, Johannes B
Harvey, Hugh
Collins, Gary S
Moons, Karel G M
Logullo, Patricia
Hoffman, Michael M
Golub, Robert M
McCradden, Melissa D
Heinze, Georg
Maier-Hein, Lena
Parnell, Richard
Kengne, André Pascal
Ghassemi, Marzyeh
Camaradou, Jennifer Catherine
Loder, Elizabeth W
Rose, Sherri
Van Calster, Ben
Wynants, Laure
Celi, Leo Anthony
Lee, Naomi
Liu, Xiaoxuan
Ordish, Johan
Singh, Karandeep
Denaxas, Spiros
Mateen, Bilal A
Riley, Richard D
Glocker, Ben
Author_xml – sequence: 1
  givenname: Gary S
  orcidid: 0000-0002-2772-2316
  surname: Collins
  fullname: Collins, Gary S
  email: gary.collins@csm.ox.ac.uk
  organization: Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
– sequence: 2
  givenname: Karel G M
  orcidid: 0000-0003-2118-004X
  surname: Moons
  fullname: Moons, Karel G M
  organization: Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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  givenname: Paula
  orcidid: 0000-0002-0989-0623
  surname: Dhiman
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  organization: Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
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  givenname: Richard D
  orcidid: 0000-0001-8699-0735
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  givenname: Andrew L
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  orcidid: 0000-0003-1613-7450
  surname: Van Calster
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  organization: Department of Biomedical Data Science, Leiden University Medical Centre, Leiden, Netherlands
– sequence: 7
  givenname: Marzyeh
  orcidid: 0000-0001-6349-7251
  surname: Ghassemi
  fullname: Ghassemi, Marzyeh
  organization: Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
– sequence: 8
  givenname: Xiaoxuan
  orcidid: 0000-0002-1286-0038
  surname: Liu
  fullname: Liu, Xiaoxuan
  organization: University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
– sequence: 9
  givenname: Johannes B
  orcidid: 0000-0003-4026-4345
  surname: Reitsma
  fullname: Reitsma, Johannes B
  organization: Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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  orcidid: 0000-0002-5529-1541
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  orcidid: 0000-0002-2729-0947
  surname: Boulesteix
  fullname: Boulesteix, Anne-Laure
  organization: Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University of Munich and Munich Centre of Machine Learning, Germany
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  givenname: Jennifer Catherine
  orcidid: 0000-0002-5742-2840
  surname: Camaradou
  fullname: Camaradou, Jennifer Catherine
  organization: Patient representative, University of East Anglia, Faculty of Health Sciences, Norwich Research Park, Norwich, UK
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  givenname: Leo Anthony
  orcidid: 0000-0001-6712-6626
  surname: Celi
  fullname: Celi, Leo Anthony
  organization: Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
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  surname: Denaxas
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  organization: British Heart Foundation Data Science Centre, London, UK
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  orcidid: 0000-0001-7849-0087
  surname: Denniston
  fullname: Denniston, Alastair K
  organization: Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
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  orcidid: 0000-0002-4897-9356
  surname: Glocker
  fullname: Glocker, Ben
  organization: Department of Computing, Imperial College London, London, UK
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  orcidid: 0000-0001-7881-1207
  surname: Golub
  fullname: Golub, Robert M
  organization: Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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  orcidid: 0000-0003-4528-1312
  surname: Harvey
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  organization: Hardian Health, Haywards Heath, UK
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  givenname: Georg
  orcidid: 0000-0003-1147-8491
  surname: Heinze
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  organization: Section for Clinical Biometrics, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria
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  givenname: Michael M
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  surname: Hoffman
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  organization: Vector Institute for Artificial Intelligence, Toronto, ON, Canada
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  givenname: André Pascal
  orcidid: 0000-0002-5183-131X
  surname: Kengne
  fullname: Kengne, André Pascal
  organization: Department of Medicine, University of Cape Town, Cape Town, South Africa
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  surname: Lam
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  organization: Patient representative, Health Data Research UK patient and public involvement and engagement group
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  surname: Lee
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  organization: National Institute for Health and Care Excellence, London, UK
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  organization: Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
BackLink https://www.ncbi.nlm.nih.gov/pubmed/38626948$$D View this record in MEDLINE/PubMed
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38626949 - BMJ. 2024 Apr 16;385:q824. doi: 10.1136/bmj.q824.
38636956 - BMJ. 2024 Apr 18;385:q902. doi: 10.1136/bmj.q902.
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Snippet The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the...
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StartPage e078378
SubjectTerms 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
Title TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
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https://www.ncbi.nlm.nih.gov/pubmed/38626948
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Volume 385
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