A Chinese introduction to TRIPOD+AI statement: transparent reporting of multivariable prediction models for individual prognosis or diagnosis that use artificial intelligence_Chinese Journal of Evidence-Based Medicine

Authors：

LI Zeyu , ZHAN Zhengzhe , CHENG Jiayi , LIU Wei ,  TANG Wei ,  LIU Chang

State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, P. R. China;

Corresponding?author：

TANG Wei, Email: mydrtw@vip.sina.com; LIU Chang, Email: liu_chang_92@sina.com

Keywords：

Clinical prediction model; Prognostic model; Diagnostic model; Artificial intelligence; Machine learning; TRIPOD; Reporting guideline

DOI：

10.7507/1672-2531.202408089

Video：

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In recent years, the TRIPOD 2015 statement has shown significant limitations with the gradual application of machine learning methods in the development and evaluation of clinical prediction models. Therefore, TRIPOD 2015 statement has been updated in 2024 as the TRIPOD+AI statement entitled "TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods", aiming to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. This article interprets the key contents and items of the TRIPOD+AI in order to provide aids for clinical researchers.

Citation： LI Zeyu, ZHAN Zhengzhe, CHENG Jiayi, LIU Wei, TANG Wei, LIU Chang. A Chinese introduction to TRIPOD+AI statement: transparent reporting of multivariable prediction models for individual prognosis or diagnosis that use artificial intelligence. Chinese Journal of Evidence-Based Medicine, 2025, 25(3): 339-343. doi: 10.7507/1672-2531.202408089 Copy

1.	Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med, 2013, 10(2): e1001381.
2.	Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how. BMJ, 2009, 338: b375.
3.	Bedogni G. Clinical prediction models: a practical approach to development, validation and updating. J R Stat Soc Ser A Stat Soc, 2009, 172: 944.
4.	Mallett S, Royston P, Dutton S, et al. Reporting methods in studies developing prognostic models in cancer: a review. BMC Med, 2010, 8: 20.
5.	Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
6.	曹煜隆, 單嬌, 龔志忠, 等. 個體預后與診斷預測模型研究報告規范—TRIPOD聲明解讀. 中國循證醫學雜志, 2020, 20(4): 492-496.
7.	Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med, 2015, 162(1): W1-W73.
8.	Heus P, Reitsma JB, Collins GS, et al. Transparent reporting of multivariable prediction models in journal and conference abstracts: TRIPOD for abstracts. Ann Intern Med, 2020.
9.	Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist. BMJ, 2023, 380: e071018.
10.	Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ, 2023, 380: e071058.
11.	陶立元, 劉玨. 基于多源數據的個體預后或診斷多因素預測模型報告規范(TRIPOD-Cluster)解讀. 中華醫學雜志, 2023, 103(36): 2893-2897.
12.	韓莎莎, 吳鵬, 王則一, 等. 基于群數據科學研究的報告規范: TRIPOD-Cluster指南清單解讀及拓展. 數字醫學與健康, 2023, 1(1): 12-21.
13.	Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 2023, 381: e073538.
14.	王子怡, 盧存存, 張晶磊, 等. 個體預后與診斷多變量預測模型系統評價/Meta分析報告規范(TRIPOD-SRMA)解讀. 中國循證醫學雜志, 2024, 24(2): 202-210.
15.	李戈, 章萌, 夏鴿拉·買代提, 等. 個體預后或診斷預測模型系統綜述和Meta分析報告規范(TRIPOD-SRMA)案例解讀. 數字醫學與健康, 2023, 1(2): 69-76.
16.	Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
17.	Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 2021, 11(7): e048008.
18.	Schuit E, Groenwold RH, Harrell FE, et al. Unexpected predictor-outcome associations in clinical prediction research: causes and solutions. CMAJ, 2013, 185(10): E499-E505.
19.	Rosenblatt M, Tejavibulya L, Jiang R, et al. Data leakage inflates prediction performance in connectome-based machine learning models. Nat Commun, 2024, 15(1): 1829.
20.	Buda M, Maki A, Mazurowski MA. A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw, 2018, 106: 249-259.
21.	Ibrahim H, Liu X, Zariffa N, et al. Health data poverty: an assailable barrier to equitable digital health care. Lancet Digit Health, 2021, 3(4): e260-e265.
22.	Bertram MG, Sundin J, Roche DG, et al. Open science. Curr Biol, 2023, 33(15): R792-R797.
23.	Thibault RT, Amaral OB, Argolo F, et al. Open science 2. 0: towards a truly collaborative research ecosystem. PLoS Biol, 2023, 21(10): e3002362.

1. Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med, 2013, 10(2): e1001381.
2. Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how. BMJ, 2009, 338: b375.
3. Bedogni G. Clinical prediction models: a practical approach to development, validation and updating. J R Stat Soc Ser A Stat Soc, 2009, 172: 944.
4. Mallett S, Royston P, Dutton S, et al. Reporting methods in studies developing prognostic models in cancer: a review. BMC Med, 2010, 8: 20.
5. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
6. 曹煜隆, 單嬌, 龔志忠, 等. 個體預后與診斷預測模型研究報告規范—TRIPOD聲明解讀. 中國循證醫學雜志, 2020, 20(4): 492-496.
7. Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med, 2015, 162(1): W1-W73.
8. Heus P, Reitsma JB, Collins GS, et al. Transparent reporting of multivariable prediction models in journal and conference abstracts: TRIPOD for abstracts. Ann Intern Med, 2020.
9. Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist. BMJ, 2023, 380: e071018.
10. Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ, 2023, 380: e071058.
11. 陶立元, 劉玨. 基于多源數據的個體預后或診斷多因素預測模型報告規范(TRIPOD-Cluster)解讀. 中華醫學雜志, 2023, 103(36): 2893-2897.
12. 韓莎莎, 吳鵬, 王則一, 等. 基于群數據科學研究的報告規范: TRIPOD-Cluster指南清單解讀及拓展. 數字醫學與健康, 2023, 1(1): 12-21.
13. Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 2023, 381: e073538.
14. 王子怡, 盧存存, 張晶磊, 等. 個體預后與診斷多變量預測模型系統評價/Meta分析報告規范(TRIPOD-SRMA)解讀. 中國循證醫學雜志, 2024, 24(2): 202-210.
15. 李戈, 章萌, 夏鴿拉·買代提, 等. 個體預后或診斷預測模型系統綜述和Meta分析報告規范(TRIPOD-SRMA)案例解讀. 數字醫學與健康, 2023, 1(2): 69-76.
16. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
17. Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 2021, 11(7): e048008.
18. Schuit E, Groenwold RH, Harrell FE, et al. Unexpected predictor-outcome associations in clinical prediction research: causes and solutions. CMAJ, 2013, 185(10): E499-E505.
19. Rosenblatt M, Tejavibulya L, Jiang R, et al. Data leakage inflates prediction performance in connectome-based machine learning models. Nat Commun, 2024, 15(1): 1829.
20. Buda M, Maki A, Mazurowski MA. A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw, 2018, 106: 249-259.
21. Ibrahim H, Liu X, Zariffa N, et al. Health data poverty: an assailable barrier to equitable digital health care. Lancet Digit Health, 2021, 3(4): e260-e265.
22. Bertram MG, Sundin J, Roche DG, et al. Open science. Curr Biol, 2023, 33(15): R792-R797.
23. Thibault RT, Amaral OB, Argolo F, et al. Open science 2. 0: towards a truly collaborative research ecosystem. PLoS Biol, 2023, 21(10): e3002362.

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A Chinese introduction to TRIPOD+AI statement: transparent reporting of multivariable prediction models for individual prognosis or diagnosis that use artificial intelligence

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