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HomeArticlesVol 36,2026 No.9Detail

Interpretation of a measurement tool to assess systematic reviews/Meta-analyses of prognostic factor studies (AMSTAR-PF)

Published on Sep. 29, 2026Total Views: 561 timesTotal Downloads: 209 timesDownloadMobile

Author: ZHANG Yaling 1, 2 ZHANG Qiang 2, 3, 4 DENG Xiuxiu 5 GUO Wenyan 6 LI Dongdong 7 ZHAO Xinke 5 LU Cuncun 8

Affiliation: 1.Department of Hematology and Oncology, The First Affiliated Hospital of Henan University of CM, Zhengzhou 450000, China 2.Collaborative Innovation Center of Prevention and Treatment of Major Diseases by Chinese and Western Medicine, Zhengzhou 450000, China 3.Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Henan University of CM, Zhengzhou 450000, China 4.Henan Academy of Chinese Medical Sciences, Zhengzhou 451163, China 5.Institute of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou 730101, China 6.The First School of Clinical Medicine, Gansu University of Chinese Medicine, Lanzhou 730101, China 7.Department of Oncology, Henan Province Hospital of Traditional Chinese Medicine, Zhengzhou 450003, China 8.School of Nursing, North Sichuan Medical College, Nanchong 637000, Sichuan Province, China

Keywords: Prognostic study Systematic review Meta-analysis Methodological quality Quality assessment tool

DOI: 10.12173/j.issn.1004-5511.202604065

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Abstract

Systematic reviews and Meta-analyses of prognostic factor studies are critical for synthesizing evidence on disease prognostic factors and informing clinical decision-making. However, their methodological quality varies widely, and existing generic quality assessment tools fail to adequately cover the specific methodological features of this type of research. In response, an international team recently developed and published AMSTAR-PF in the BMJ—the first tool dedicated to evaluating the methodological quality of systematic reviews and Meta-analyses of prognostic factor studies. This article systematically outlines the development background and process of AMSTAR-PF, interprets its items with illustrative examples, and aims to offer methodological guidance for Chinese researchers conducting high-quality systematic reviews and Meta-analyses in this field, thereby facilitating the standardized generation and effective translation of prognostic evidence.

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References

1. 王勤荣. 疾病预后评价——基层医疗中不可忽视的问题[J]. 全科医学临床与教育, 2004, (3): 147-149.WangQR. The assessment of disease prognosis-a noticeable problem for primary care[J]. Clinical Education of General Practice, 2004, (3): 147-149.

2. HemingwayH, CroftP, PerelP, et al. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes[J]. BMJ, 2013, 346: e5595.

3. RileyRD, HaydenJA, SteyerbergEW, et al. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research[J]. PLoS Med,2013, 10(2): e1001380.

4. RileyRD, MoonsKGM, SnellKIE, et al. A guide to systematic review and Meta-analysis of prognostic factor studies[J]. BMJ,2019,364: k4597.

5. HenryML, O'ConnellNE, RileyRD, et al. AMSTAR-PF: a critical appraisal tool for systematic reviews of prognostic factor studies[J]. BMJ, 2025, 391: e085718.

6. ShenY, YuJ, ZhouJ, et al. Twenty-five years of evolution and hurdles in electronic health records and interoperability in medical research: comprehensive review[J]. J Med Internet Res, 2025, 27: e59024.

7. 王道洋, 胡建颖, 宁雪丽, 等. 大模型驱动的多组学队列数据整合与疾病预测[J]. 生命科学, 2025, 37(12): 1634-1644.WangDY, HuJY, NingXL, et al. Large model-driven integration of multi-omics cohort data and disease prediction[J]. Chinese Bulletin of Life Sciences, 2025, 37(12): 1634-1644.

8. HoffmannF, AllersK, RombeyT, et al. Nearly 80 systematic reviews were published each day: observational study on trends in epidemiology and reporting over the years 2000-2019[J]. J Clin Epidemiol, 2021, 138: 1-11.

9. HaydenJA, van der WindtDA, CartwrightJL, et al. Assessing bias in studies of prognostic factors[J]. Ann Intern Med, 2013, 158(4): 280-286.

10. OuS, LuoJ, JiangQ. Overlapping systematic reviews on the same topic: a systematic literature review of quantitative research[J]. J Eval Clin Pract, 2025, 31(4): e70148.

11. KwokW, DallantT, MartinG, et al. Systematic reviews on the same topic are common but often fail to meet key methodological standards: a research-on-research study[J]. J Clin Epidemiol, 2026, 189: 112018.

12. HelbachJ, HoffmannF, PieperD, et al. Reporting according to the preferred reporting items for systematic reviews and Meta-analyses for abstracts (PRISMA-A) depends on abstract length[J]. J Clin Epidemiol, 2023, 154: 167-177.

13. 王子怡, 卢存存, 黄家艺, 等. 中文期刊发表的预测模型系统评价文献调查与评价: 方法学质量和报告质量[J]. 协和医学杂志, 2024, 15(4): 927-935.WangZY, LuCC, HuangJY, et al. Investigation and evaluation of systematic reviews of prediction models published in Chinese journals: methodological and reporting quality[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(4): 927-935.

14. WhitingP, SavovićJ, HigginsJP, et al. ROBIS: a new tool to assess risk of bias in systematic reviews was developed[J]. J Clin Epidemiol, 2016, 69: 225-234.

15. SheaBJ, ReevesBC, WellsG, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both[J]. BMJ, 2017, 358: j4008.

16. FernandoSM, TranA, ChengW, et al. Pre-arrest and intra-arrest prognostic factors associated with survival after in-hospital cardiac arrest: systematic review and Meta-analysis[J]. BMJ, 2019, 367: l6373.

17. FerreiraRT, ReisMQ, LangeMHP, et al. What evidence syntheses reveal about PROSPERO, INPLASY, OSF, the Research Registry, and protocols.io: a Meta-research study[J]. Front Res Metr Anal, 2026, 11: 1738112.

18. HartlingL, FeatherstoneR, NusplM, et al. Grey literature in systematic reviews: a cross-sectional study of the contribution of non-English reports, unpublished studies and dissertations to the results of Meta-analyses in child-relevant reviews[J]. BMC Med Res Methodol, 2017, 17(1): 64.

19. XuC, YuT, Furuya-KanamoriL, et al. Validity of data extraction in evidence synthesis practice of adverse events: reproducibility study[J]. BMJ, 2022, 377: e069155.

20. NikolakopoulouA, MavridisD, SalantiG. How to interpret Meta-analysis models: fixed effect and random effects Meta-analyses[J]. Evid Based Ment Health, 2014, 17(2): 64.

21. HongC, SalantiG, MortonSC, et al. Testing small study effects in multivariate Meta-analysis[J]. Biometrics, 2020, 76(4): 1240-1250.

22. HakoumMB, AnoutiS, Al-GibbawiM, et al. Reporting of financial and non-financial conflicts of interest by authors of systematic reviews: a methodological survey[J]. BMJ Open, 2016, 6(8): e011997.

23. HansfordHJ, RichardsGC, PageMJ, et al. Reporting health and medical research[J]. BMJ Evid Based Med, 2024, 29(6): 358-362.

24. 张强, 赵文霞, 王子怡, 等. 结局测量工具系统评价的报告规范(PRISMA-COSMIN for OMIs 2024)解读[J]. 中国全科医学, 2026, 29(13): 1766-1775.ZhangQ, ZhaoWX, WangZY, et al. Interpretation of the reporting guideline for systematic reviews of outcome measurement instruments (PRISMA-COSMIN for OMIs 2024) [J]. Chinese General Practice, 2026, 29(13): 1766-1775.

25. ChinJM, GrownsB, SebastianJ, et al. The transparency and reproducibility of systematic reviews in forensic science[J]. Forensic Sci Int, 2022, 340: 111472.

26. MartinezJ, ManjalyC, ManjalyP, et al. Janus kinase inhibitors and adverse events of acne: a systematic review and Meta-analysis[J]. JAMA Dermatol, 2023, 159(12): 1339-1345.

27. 石舒原, 周庆欣, 孙凤, 等. 糖尿病肾病可变风险因素证据图谱构建[J]. 中华流行病学杂志, 2025, 46(11): 2055-2064.ShiSY, ZhouQX, SunF, et al. Construction of evidence graph of modifiable risk factors for diabetic nephropathy[J]. Chinese Journal of Epidemiology, 2025, 46(11): 2055-2064.

28. KimJH, KimJY, LeeS, et al. Environmental risk factors, protective factors, and biomarkers for postpartum depressive symptoms: an umbrella review[J]. Neurosci Biobehav Rev, 2022, 140: 104761.

29. ChaiKEK, LinesRLJ, GucciardiDF, et al. Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews[J]. Syst Rev, 2021, 10(1): 93.

30. CourvoisierDS, Buitrago-GarciaD, BuclinCP, et al. Beyond human gold standards: a multimodel framework for automated abstract classification and information extraction[J]. Res Synth Methods, 2026, 17(2): 365-377.

31. Marques-CruzM, PintoF, VieiraRJ, et al. Use of artificial intelligence to support the assessment of the methodological quality of systematic reviews[J]. J Clin Epidemiol, 2025, 187: 111944.

32. XiaD, LaiH, ZhaoW, et al. Assessing risk of bias of cohort studies with large language models[J]. Res Synth Methods, 2025, 16(6): 990-1004.

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