PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods—Chinese interpretation_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WANG Xingmeng ¹ ,  DAI Guohua ² ,  GAO Wulin ² , GUAN Hui ² , REN Lili ³ , CHEN Chen ¹ , TAN Xiaoyang ¹ , LIN Yiming ¹

1. First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China;
2. Department of Geriatric Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China;
3. Department of Critical Care Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China;

Corresponding?author：

DAI Guohua, Email: daigh2004@163.com; GAO Wulin, Email: gaowulin05@sina.com

Keywords：

PROBAST+AI; artificial intelligence; machine learning; clinical prediction model; systematic review

DOI：

10.7507/1007-4848.202507088

Video：

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The development and validation of clinical prediction models based on artificial intelligence (AI) and machine learning (ML) methods have become increasingly widespread. However, the prediction model bias risk and applicability evaluation tool developed in 2019 (i.e., PROBAST-2019) has shown significant limitations. Therefore, an expanded and updated version of the PROBAST-2019 tool was released in 2025, known as the PROBAST+AI tool. The tool is divided into two parts including model development and model evaluation. It aims to comprehensively and systematically evaluate potential methodological quality issues in model development, bias risks in model evaluation, and the applicability of models, regardless of the modeling method used. This paper provides a systematic interpretation of the PROBAST+AI tool's items and case analyses, with the aim of guiding and assisting researchers engaged in related studies and promoting the high-quality development of clinical predictive model research.

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6.	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 (Clinical research ed. ), 2015, 350: g7594.
7.	Collins GS, Moons K GM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods BMJ (Clinical research ed. ), 2024, 385: e078378.
8.	周小芹, 劉慧珍, 王婷, 等. 基于大語言模型的臨床預測模型研究報告指南(TRIPOD-LLM)解讀. 中國胸心血管外科臨床雜志, 2025, 32(7): 940-946.Zhou XQ, Liu HZ, Wang T, et al. Interpretation of the TRIPOD-LLM reporting guideline for studies using large language models. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(7): 940-946.
9.	Gallifant J, Afshar M, Ameen S, et al. The TRIPOD-LLM reporting guideline for studies using large language models. Nat Med, 2025, 31(1): 60-69.
10.	Andaur Navarro CL, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ (Clinical research ed.), 2021, 375: n2281.
11.	Andaur Navarro CL, Damen JAA, Takada T, et al. Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Med Res Methodol, 2022, 22(1): 12.
12.	Cai Y, Cai YQ, Tang LY, et al. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med, 2024, 22: 56.
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14.	Moons KGM, Damen JAA, Kaul T, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ, 2025: e082505.
15.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
16.	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.
17.	Riley RD, Snell KI, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med, 2019, 38(7): 1276-1296.
18.	Riley RD, Snell KIE, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part I–Continuous outcomes. Stat Med, 2019, 38(7): 1262-1275.
19.	Riley RD, Ensor J, KIE S, et al. Calculating the sample size required for developing a clinical prediction model. BMJ (Clinical research ed. ), 2020, 368: m441.
20.	Sisk R, Sperrin M, Peek N, et al. Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study. Stat Methods Med Res, 2023, 32(8): 1461-1477.
21.	?inkovec H, Heinze G, Blagus R, et al. To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets. BMC Med Res Methodol, 2021, 21(1): 199.
22.	Riley RD, Snell KIE, Martin GP, et al. Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small. J Clin Epidemiol, 2021, 132: 88-96.
23.	Van Calster B, Van Smeden M, De Cock B, et al. Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study. Stat Methods Med Res, 2020, 29(11): 3166-3178.
24.	曾竟, 何小龍, 胡華娟, 等. 基于機器學習構建急性心力衰竭患者易損期死亡或再入院風險預測模型. 陸軍軍醫大學學報, 2024, 46(7): 738-745.Zeng J, He XL, Hu HJ, et al. Construction of a risk prediction model for predicting death or admission in acute heart failure patients during vulnerable phase based on machine learning. J Army Med Univ, 2024, 46(7): 738-745.

1. 谷鴻秋, 周支瑞, 章仲恒, 等. 臨床預測模型: 基本概念、應用場景及研究思路. 中國循證心血管醫學雜志, 2018, 10(12): 1454-1456+1462.Gu HQ, Zhou ZR, Zhang ZH, et al. Clinical prediction models: basic concepts, application scenarios, and research strategies. Chin J Evid Based Cardiovasc Med, 2018, 10(12): 1454-1456, 1462.
2. 孫聰, 戴國華, 侯曉銘, 等. 中醫預警因素在疾病風險預后研究中的選擇思路與方法. 中西醫結合心腦血管病雜志, 2021, 19(17): 3033-3035.Sun C, Dai GH, Hou XM, et al. Selection criteria and methods for traditional chinese medicine risk factors in disease risk prognosis research. Chin J Integr Med Cardio-Cerebrovasc Dis, 2021, 19(17): 3033-3035.
3. 魯小丹, 衛建華, 沈建通, 等. 預測模型系統評價的制作方法與步驟. 中國循證醫學雜志, 2023, 23(5): 602-609.Lu XD, Wei JH, Shen JT, et al. Methods and processes for producing a systematic review of predictive model studies. Chin J Evid-Based Med, 2023, 23(5): 602-609.
4. 景城陽, 馮琳, 李嘉琛, 等. 中醫臨床預測模型研究的概況性綜述. 中華中醫藥雜志, 2024, 39(12): 6815-6820.Jing CY, Feng L, Li JC, et al. Scoping review of study on clinical prediction model of traditional Chinese medicine. China J Tradit Chin Med, 2024, 39(12): 6815-6820.
5. Wessler BS, Nelson J, Park JG, et al. External validations of cardiovascular clinical prediction models: a large-scale review of the literature. Circ Cardiovasc Qual Outcomes, 2021, 14(8): e007858.
6. 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 (Clinical research ed. ), 2015, 350: g7594.
7. Collins GS, Moons K GM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods BMJ (Clinical research ed. ), 2024, 385: e078378.
8. 周小芹, 劉慧珍, 王婷, 等. 基于大語言模型的臨床預測模型研究報告指南(TRIPOD-LLM)解讀. 中國胸心血管外科臨床雜志, 2025, 32(7): 940-946.Zhou XQ, Liu HZ, Wang T, et al. Interpretation of the TRIPOD-LLM reporting guideline for studies using large language models. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(7): 940-946.
9. Gallifant J, Afshar M, Ameen S, et al. The TRIPOD-LLM reporting guideline for studies using large language models. Nat Med, 2025, 31(1): 60-69.
10. Andaur Navarro CL, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ (Clinical research ed.), 2021, 375: n2281.
11. Andaur Navarro CL, Damen JAA, Takada T, et al. Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Med Res Methodol, 2022, 22(1): 12.
12. Cai Y, Cai YQ, Tang LY, et al. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med, 2024, 22: 56.
13. 李澤宇, 詹正哲, 程嘉儀, 等. 基于人工智能的臨床預測模型研究報告規范(TRIPOD+AI)中文解讀. 中國循證醫學雜志, 2025, 25(03): 339-343.Li ZY, Zhan ZZ, Cheng JY, et al. A Chinese introduction to TRIPOD+AI statement: transparent reporting of multivariable prediction models for individual prognosis or diagnosis that use artificial intelligence. Chin J Evid-Based Med, 2025, 25(03): 339-343.
14. Moons KGM, Damen JAA, Kaul T, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ, 2025: e082505.
15. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
16. 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.
17. Riley RD, Snell KI, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med, 2019, 38(7): 1276-1296.
18. Riley RD, Snell KIE, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part I–Continuous outcomes. Stat Med, 2019, 38(7): 1262-1275.
19. Riley RD, Ensor J, KIE S, et al. Calculating the sample size required for developing a clinical prediction model. BMJ (Clinical research ed. ), 2020, 368: m441.
20. Sisk R, Sperrin M, Peek N, et al. Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study. Stat Methods Med Res, 2023, 32(8): 1461-1477.
21. ?inkovec H, Heinze G, Blagus R, et al. To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets. BMC Med Res Methodol, 2021, 21(1): 199.
22. Riley RD, Snell KIE, Martin GP, et al. Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small. J Clin Epidemiol, 2021, 132: 88-96.
23. Van Calster B, Van Smeden M, De Cock B, et al. Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study. Stat Methods Med Res, 2020, 29(11): 3166-3178.
24. 曾竟, 何小龍, 胡華娟, 等. 基于機器學習構建急性心力衰竭患者易損期死亡或再入院風險預測模型. 陸軍軍醫大學學報, 2024, 46(7): 738-745.Zeng J, He XL, Hu HJ, et al. Construction of a risk prediction model for predicting death or admission in acute heart failure patients during vulnerable phase based on machine learning. J Army Med Univ, 2024, 46(7): 738-745.

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