TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models That Use Regression or Machine Learning Methods

DAgostino Sr R, Vasan R, Pencina M, Wolf P, Cobain M, Massaro J . General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008; 117(6):743-53. DOI: 10.1161/CIRCULATIONAHA.107.699579. View

van Smeden M, Moons K, de Groot J, Collins G, Altman D, Eijkemans M . Sample size for binary logistic prediction models: Beyond events per variable criteria. Stat Methods Med Res. 2018; 28(8):2455-2474. PMC: 6710621. DOI: 10.1177/0962280218784726. View

de Hond A, Leeuwenberg A, Hooft L, Kant I, Nijman S, van Os H . Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med. 2022; 5(1):2. PMC: 8748878. DOI: 10.1038/s41746-021-00549-7. View

Ibrahim H, Liu X, Zariffa N, Morris A, Denniston A . Health data poverty: an assailable barrier to equitable digital health care. Lancet Digit Health. 2021; 3(4):e260-e265. DOI: 10.1016/S2589-7500(20)30317-4. View

Van Calster B, Steyerberg E, Wynants L, van Smeden M . There is no such thing as a validated prediction model. BMC Med. 2023; 21(1):70. PMC: 9951847. DOI: 10.1186/s12916-023-02779-w. View

Riley R, Snell K, Ensor J, Burke D, Harrell Jr F, Moons K . Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Stat Med. 2018; 38(7):1262-1275. DOI: 10.1002/sim.7993. View

Chen I, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M . Ethical Machine Learning in Healthcare. Annu Rev Biomed Data Sci. 2021; 4:123-144. PMC: 8362902. DOI: 10.1146/annurev-biodatasci-092820-114757. View

Wessler B, Nelson J, Park J, McGinnes H, Gulati G, Brazil R . External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature. Circ Cardiovasc Qual Outcomes. 2021; 14(8):e007858. PMC: 8366535. DOI: 10.1161/CIRCOUTCOMES.121.007858. View

Stevens L, Mortazavi B, Deo R, Curtis L, Kao D . Recommendations for Reporting Machine Learning Analyses in Clinical Research. Circ Cardiovasc Qual Outcomes. 2020; 13(10):e006556. PMC: 8320533. DOI: 10.1161/CIRCOUTCOMES.120.006556. View

10.

Nashef S, Roques F, Sharples L, Nilsson J, Smith C, Goldstone A . EuroSCORE II. Eur J Cardiothorac Surg. 2012; 41(4):734-44. DOI: 10.1093/ejcts/ezs043. View

11.

Olczak J, Pavlopoulos J, Prijs J, IJpma F, Doornberg J, Lundstrom C . Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthop. 2021; 92(5):513-525. PMC: 8519529. DOI: 10.1080/17453674.2021.1918389. View

12.

Vasey B, Nagendran M, Campbell B, Clifton D, Collins G, Denaxas S . Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022; 28(5):924-933. DOI: 10.1038/s41591-022-01772-9. View

13.

Munguia-Realpozo P, Etchegaray-Morales I, Mendoza-Pinto C, Mendez-Martinez S, Osorio-Pena A, Ayon-Aguilar J . Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review. Autoimmun Rev. 2023; 22(5):103294. DOI: 10.1016/j.autrev.2023.103294. View

14.

Snell K, Levis B, Damen J, Dhiman P, Debray T, Hooft L . Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ. 2023; 381:e073538. PMC: 10155050. DOI: 10.1136/bmj-2022-073538. View

15.

Sendak M, Gao M, Brajer N, Balu S . Presenting machine learning model information to clinical end users with model facts labels. NPJ Digit Med. 2020; 3:41. PMC: 7090057. DOI: 10.1038/s41746-020-0253-3. View

16.

Glasziou P, Altman D, Bossuyt P, Boutron I, Clarke M, Julious S . Reducing waste from incomplete or unusable reports of biomedical research. Lancet. 2014; 383(9913):267-76. DOI: 10.1016/S0140-6736(13)62228-X. View

17.

Wolff R, Moons K, Riley R, Whiting P, Westwood M, Collins G . PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med. 2019; 170(1):51-58. DOI: 10.7326/M18-1376. View

18.

Collins G, Dhiman P, Ma J, Schlussel M, Archer L, Van Calster B . Evaluation of clinical prediction models (part 1): from development to external validation. BMJ. 2024; 384:e074819. PMC: 10772854. DOI: 10.1136/bmj-2023-074819. View

19.

Gail M, Brinton L, Byar D, Corle D, Green S, Schairer C . Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989; 81(24):1879-86. DOI: 10.1093/jnci/81.24.1879. View

20.

Rech M, de Macedo Filho L, White A, Perez-Vega C, Samson S, Chaichana K . Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence. Brain Sci. 2023; 13(3). PMC: 10046799. DOI: 10.3390/brainsci13030495. View