Interpretation of checklist for transparent reporting of multivariable prediction models for individual prognosis or diagnosis tailored for systematic reviews and meta-analyses (TRIPOD-SRMA)_Chinese Journal of Evidence-Based Medicine

Authors：

WANG Ziyi ^1,2 ,  LU Cuncun ³ , ZHANG Jinglei ⁴ , HUANG Jiayi ^1,2 , LIU Wendi ^1,2 , SHANG Wenru ² , CHEN Zijia ³ , CUI Lu ^1,2 , ZHAO Xiaoxiao ³ , HU Xiaoye ^1,2 , YANG Kehu ² ,  LI Xiuxia ^1,2

1. Evidence Based Social Science Research Center/Health Technology Assessment Center, School of Public Health, Lanzhou University, Lanzhou 730000, P. R. China;
2. Evidence Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, P. R. China;
3. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, P. R. China;
4. School of Health Sciences, Wuhan University, Wuhan 430062, P. R. China;

Corresponding author：

LU Cuncun, Email: cuncunlu2017@163.com; LI Xiuxia, Email: lixiuxia@lzu.edu.cn

Keywords：

TRIPOD-SRMA; Systematic review; Meta-analysis; Reporting guideline; Prediction model

DOI：

10.7507/1672-2531.202308041

Video：

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Clinical prediction models typically utilize a combination of multiple variables to predict individual health outcomes. However, multiple prediction models for the same outcome often exist, making it challenging to determine the suitable model for guiding clinical practice. In recent years, an increasing number of studies have evaluated and summarized prediction models using the systematic review/meta-analysis method. However, they often report poorly on critical information. To enhance the reporting quality of systematic reviews/meta-analyses of prediction models, foreign scholars published the TRIPOD-SRMA reporting guideline in BMJ in March 2023. As the number of such systematic reviews/meta-analyses is increasing rapidly domestically, this paper interprets the reporting guideline with a published example. This study aims to assist domestic scholars in better understanding and applying this reporting guideline, ultimately improving the overall quality of relevant research.

Citation： WANG Ziyi, LU Cuncun, ZHANG Jinglei, HUANG Jiayi, LIU Wendi, SHANG Wenru, CHEN Zijia, CUI Lu, ZHAO Xiaoxiao, HU Xiaoye, YANG Kehu, LI Xiuxia. Interpretation of checklist for transparent reporting of multivariable prediction models for individual prognosis or diagnosis tailored for systematic reviews and meta-analyses (TRIPOD-SRMA). Chinese Journal of Evidence-Based Medicine, 2024, 24(2): 202-210. doi: 10.7507/1672-2531.202308041 Copy

1.	Hendriksen JM, Geersing GJ, Moons KG, et al. Diagnostic and prognostic prediction models. J Thromb Haemost, 2013, 11(Suppl 1): 129-141.
2.	Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 2016, 353: i2416.
3.	Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ, 2017, 356: i6460.
4.	Heus P, Damen JAAG, Pajouheshnia R, et al. Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med, 2018, 16(1): 120.
5.	Belbasis L, Panagiotou OA. Reproducibility of prediction models in health services research. BMC Res Notes, 2022, 15(1): 204.
6.	Deng B, Li Y, Chen JY, et al. Prediction models of vaginal birth after cesarean delivery: a systematic review. Int J Nurs Stud, 2022, 135: 104359.
7.	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.
8.	郭淑杰, 王艳红, 赵亚楠, 等. 产后出血风险预测模型的系统评价. 中国循证医学杂志, 2022, 22(11): 1287-1300.
9.	杨晓, 伏建林, 周会兰. 基于机器学习方法构建小于胎龄儿预测模型的系统评价. 中国循证医学杂志, 2023, 23(3): 334-340.
10.	蔡真真, 陈媛, 林碧霞. 心血管疾病患者院内心脏骤停风险预测模型的系统评价. 中国循证医学杂志, 2022, 22(10): 1175-1181.
11.	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.
12.	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.
13.	Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 2021, 372: n71.
14.	Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ, 2009, 339: b2535.
15.	Joshi A, Kale S, Chandel S, et al. Likert scale: explored and explained. Br J Appl Sci Technol, 2015, 7: 396-403.
16.	Riley RD, Moons KGM, Snell KIE, et al. A guide to systematic review and meta-analysis of prognostic factor studies. BMJ, 2019, 364: k4597.
17.	Riley RD, Debray TPA, Fisher D, et al. Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: statistical recommendations for conduct and planning. Stat Med, 2020, 39(15): 2115-2137.
18.	van Giessen A, Peters J, Wilcher B, et al. Systematic review of health economic impact evaluations of risk prediction models: stop developing, start evaluating. Value Health, 2017, 20(4): 718-726.
19.	Moons KG, Altman DG, Vergouwe Y, et al. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ, 2009, 338: b606.
20.	van Beek PE, Andriessen P, Onland W, et al. Prognostic models predicting mortality in preterm infants: systematic review and meta-analysis. Pediatrics, 2021, 147(5): e2020020461.
21.	Montori VM, Wilczynski NL, Morgan D, et al. Optimal search strategies for retrieving systematic reviews from Medline: analytical survey. BMJ, 2005, 330(7482): 68.
22.	卢存存, 张强, 雷超, 等. 使用队列和常规收集数据开展随机对照试验的报告清单(CONSORT-ROUTINE2021)解读. 中国循证医学杂志, 2022, 22(6): 731-738.
23.	鲁小丹, 卫建华, 沈建通, 等. 预测模型系统评价的制作方法与步骤. 中国循证医学杂志, 2023, 23(5): 602-609.
24.	Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 2009, 339: b2700.
25.	Dwan K, Altman DG, Arnaiz JA, et al. Systematic review of the empirical evidence of study publication bias and outcome reporting bias. PLoS One, 2008, 3(8): e3081.
26.	Khalil H, Ameen D, Zarnegar A. Tools to support the automation of systematic reviews: a scoping review. J Clin Epidemiol, 2022, 144: 22-42.
27.	Mahmić-Kaknjo M, Tomić V, Ellen ME, et al. Delphi survey on the most promising areas and methods to improve systematic reviews' production and updating. Syst Rev, 2023, 12(1): 56.
28.	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
29.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
30.	de Jong Y, Ramspek CL, Zoccali C, et al. Appraising prediction research: a guide and meta-review on bias and applicability assessment using the prediction model risk of bias assessment tool (PROBAST). Nephrology (Carlton), 2021, 26(12): 939-947.
31.	Tatas Z, Koutsiouroumpa O, Seehra J, et al. Do pooled estimates from orthodontic meta-analyses change depending on the meta-analysis approach. A meta-epidemiological study. Eur J Orthod, 2023, 12: cjad031.
32.	Brozek JL, Canelo-Aybar C, Akl EA, et al. GRADE guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-an overview in the context of health decision-making. J Clin Epidemiol, 2021, 129: 138-150.
33.	Pieper D, Rombey T. Where to prospectively register a systematic review. Syst Rev, 2022, 11(1): 8.
34.	Hakoum MB, Anouti S, Al-Gibbawi M, et al. Reporting of financial and non-financial conflicts of interest by authors of systematic reviews: a methodological survey. BMJ Open, 2016, 6(8): e011997.
35.	Saldanha IJ, Smith BT, Ntzani E, et al. The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research. Syst Rev, 2019, 8(1): 334.
36.	Sahni NR, Carrus B. Artificial intelligence in U. S. health care delivery. N Engl J Med, 2023, 389(4): 348-358.

1. Hendriksen JM, Geersing GJ, Moons KG, et al. Diagnostic and prognostic prediction models. J Thromb Haemost, 2013, 11(Suppl 1): 129-141.
2. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 2016, 353: i2416.
3. Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ, 2017, 356: i6460.
4. Heus P, Damen JAAG, Pajouheshnia R, et al. Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med, 2018, 16(1): 120.
5. Belbasis L, Panagiotou OA. Reproducibility of prediction models in health services research. BMC Res Notes, 2022, 15(1): 204.
6. Deng B, Li Y, Chen JY, et al. Prediction models of vaginal birth after cesarean delivery: a systematic review. Int J Nurs Stud, 2022, 135: 104359.
7. 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.
8. 郭淑杰, 王艳红, 赵亚楠, 等. 产后出血风险预测模型的系统评价. 中国循证医学杂志, 2022, 22(11): 1287-1300.
9. 杨晓, 伏建林, 周会兰. 基于机器学习方法构建小于胎龄儿预测模型的系统评价. 中国循证医学杂志, 2023, 23(3): 334-340.
10. 蔡真真, 陈媛, 林碧霞. 心血管疾病患者院内心脏骤停风险预测模型的系统评价. 中国循证医学杂志, 2022, 22(10): 1175-1181.
11. 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.
12. 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.
13. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 2021, 372: n71.
14. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ, 2009, 339: b2535.
15. Joshi A, Kale S, Chandel S, et al. Likert scale: explored and explained. Br J Appl Sci Technol, 2015, 7: 396-403.
16. Riley RD, Moons KGM, Snell KIE, et al. A guide to systematic review and meta-analysis of prognostic factor studies. BMJ, 2019, 364: k4597.
17. Riley RD, Debray TPA, Fisher D, et al. Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: statistical recommendations for conduct and planning. Stat Med, 2020, 39(15): 2115-2137.
18. van Giessen A, Peters J, Wilcher B, et al. Systematic review of health economic impact evaluations of risk prediction models: stop developing, start evaluating. Value Health, 2017, 20(4): 718-726.
19. Moons KG, Altman DG, Vergouwe Y, et al. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ, 2009, 338: b606.
20. van Beek PE, Andriessen P, Onland W, et al. Prognostic models predicting mortality in preterm infants: systematic review and meta-analysis. Pediatrics, 2021, 147(5): e2020020461.
21. Montori VM, Wilczynski NL, Morgan D, et al. Optimal search strategies for retrieving systematic reviews from Medline: analytical survey. BMJ, 2005, 330(7482): 68.
22. 卢存存, 张强, 雷超, 等. 使用队列和常规收集数据开展随机对照试验的报告清单(CONSORT-ROUTINE2021)解读. 中国循证医学杂志, 2022, 22(6): 731-738.
23. 鲁小丹, 卫建华, 沈建通, 等. 预测模型系统评价的制作方法与步骤. 中国循证医学杂志, 2023, 23(5): 602-609.
24. Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 2009, 339: b2700.
25. Dwan K, Altman DG, Arnaiz JA, et al. Systematic review of the empirical evidence of study publication bias and outcome reporting bias. PLoS One, 2008, 3(8): e3081.
26. Khalil H, Ameen D, Zarnegar A. Tools to support the automation of systematic reviews: a scoping review. J Clin Epidemiol, 2022, 144: 22-42.
27. Mahmić-Kaknjo M, Tomić V, Ellen ME, et al. Delphi survey on the most promising areas and methods to improve systematic reviews' production and updating. Syst Rev, 2023, 12(1): 56.
28. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
29. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
30. de Jong Y, Ramspek CL, Zoccali C, et al. Appraising prediction research: a guide and meta-review on bias and applicability assessment using the prediction model risk of bias assessment tool (PROBAST). Nephrology (Carlton), 2021, 26(12): 939-947.
31. Tatas Z, Koutsiouroumpa O, Seehra J, et al. Do pooled estimates from orthodontic meta-analyses change depending on the meta-analysis approach. A meta-epidemiological study. Eur J Orthod, 2023, 12: cjad031.
32. Brozek JL, Canelo-Aybar C, Akl EA, et al. GRADE guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-an overview in the context of health decision-making. J Clin Epidemiol, 2021, 129: 138-150.
33. Pieper D, Rombey T. Where to prospectively register a systematic review. Syst Rev, 2022, 11(1): 8.
34. Hakoum MB, Anouti S, Al-Gibbawi M, et al. Reporting of financial and non-financial conflicts of interest by authors of systematic reviews: a methodological survey. BMJ Open, 2016, 6(8): e011997.
35. Saldanha IJ, Smith BT, Ntzani E, et al. The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research. Syst Rev, 2019, 8(1): 334.
36. Sahni NR, Carrus B. Artificial intelligence in U. S. health care delivery. N Engl J Med, 2023, 389(4): 348-358.

Chinese Journal of Evidence-Based Medicine

Interpretation of checklist for transparent reporting of multivariable prediction models for individual prognosis or diagnosis tailored for systematic reviews and meta-analyses (TRIPOD-SRMA)

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