This article aims to introduce the research methodology for systematic reviews and Meta-analyses of prediction models, detailing its six key steps (research question formulation, search strategy development, literature screening and risk of bias assessment, data extraction and preparation, evidence synthesis and Meta-analysis, and result interpretation and reporting), as well as commonly used tools (PROBAST risk of bias assessment tool and TRIPOD-SRMA reporting guidelines). This article uses the chronic hepatitis B-related hepatocellular carcinoma risk prediction model as an example to conduct a Meta-analysis of the 3-year predictive performance of the REACH-B model. This provides a systematic solution and technical approach for evidence synthesis in clinical prediction models.

1.van Smeden M, Reitsma JB, Riley RD, et al. Clinical prediction models: diagnosis versus prognosis[J]. J Clin Epidemiol, 2021,  132: 142-145. DOI: 10.1016/j.jclinepi.2021.01.009.

2.Bellou V, Belbasis L, Konstantinidis AK, et al. Prognostic models for outcome prediction in patients with chronic obst ructive pulmonary disease: systematic review and critical appraisal[J]. BMJ, 2018, 367: l5358. DOI: 10.1136/bmj.l5358.

3.Puhan MA, Garcia-Aymerich J, Frey M, et al. Expansion of the prognostic assessment of patients with chronic obstru ctive pulmonary disease: the updated BODE index and the ADO index[J]. Lancet, 2009, 374(9691): 704-711. DOI: 10.1016/S0140-6736(09)61301-5.

4.Boeck L, Soriano JB, Brusse-Keizer M, et al. Prognostic assessment in COPD without lung function: the B-AE-D indices[J]. Eur Respir J, 2016, 47(6):1635-1644. DOI: 10.1183/13993003.01485-2015.

5.Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and Meta-analysis of prediction model performance[J]. BMJ, 2017, 356: i6460. DOI: 10.1136/bmj.i6460.

6.Damen JA, Moons KG, Smeden M, et al. How to conduct a systematic review and Meta-analysis of prognostic model studies[J]. Clin Microbiol Infect, 2023, 29(4): 434-440. DOI: 10.1016/j.cmi.2022.07.019.

7.Geersing GJ, Bouwmeester W, Zuithoff P, et al. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews[J]. PLoS One, 2012, 7(2): e32844. DOI: 10.1371/journal.pone.0032844.

8.Wolff RF, Moons KG, Riley RD, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies[J]. Ann Intern Med, 2019, 170(1): 51-58. DOI: 10.7326/M18-1377.

9.Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist[J]. PLoS Med, 2014, 11(10): e1001744. DOI: 10.1371/journal.pmed.1001744.

10.Debray TP, Damen JA, Riley RD, et al. A framework for Meta-analysis of prediction model studies with binary and time-to-event outcomes[J]. Stat Methods Med Res, 2019, 28(9): 2768-2786. DOI: 10.1177/0962280218785504.

11.Foroutan F, Guyatt G, Zuk V, et al. GRADE Guidelines 28: Use of GRADE for the assessment of evidence about prognostic factors: rating certainty in identification of groups of patients with different absolute risks[J]. J Clin Epidemiol, 2020, 121: 62-70. DOI: 10.1016/j.jclinepi.2019.12.023.

12.Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement[J]. BMJ, 2015, 350: g7594. DOI: 10.1136/bmj.g7594.

13.Snell K, Levis B, Damen J, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and Meta-analyses (TRIPOD-SRMA)[J]. BMJ, 2023, 381: e73538. DOI: 10.1136/bmj-2022-073538.

14.Wang F, Fan J, Zhang Z, et al. The global burden of liver disease: the major impact of China[J]. Hepatology, 2014, 60(6): 2099-2108. DOI: 10.1002/hep.27406.

15.Liu J, Zhang S, Wang Q, et al. Seroepidemiology of hepatitis B virus infection in 2 million men aged 21-49 years in rural China: a population-based, cross-sectional study[J]. Lancet Infect Dis, 2016, 16(1): 80-86. DOI: 10.1016/s1473-3099(15)00218-2.

16.Wu S, Zeng N, Sun F, et al. Hepatocellular carcinoma prediction models in chronic hepatitis B: a systematic review of 14 models and external validation[J]. Clin Gastroenterol Hepatol, 2021, 19(12): 2499-2513. DOI: 10.1016/j.cgh.2021.02.040.

17.Yuen MF, Tanaka Y, Fong DY, et al. Independent risk factors and predictive score for the development of hepatocellular carcinoma in chronic hepatitis B[J]. Journal of hepatology, 2009, 50(1): 80-88. DOI: 10.1016/j.jhep.2008.07.023.

18.Yang HI, Sherman M, Su J, et al. Nomograms for risk of hepatocellular carcinoma in patients with chronic hepatitis B virus infection[J]. J Clin Oncol, 2010, 28(14): 2437-2444. DOI: 10.1200/jco.2009.27.4456.

19.Wong VW, Chan SL, Mo F, et al. Clinical scoring system to predict hepatocellular carcinoma in chronic hepatitis B carriers[J]. J Clin Oncol, 2010, 28(10): 1660-1665. DOI: 10.1200/jco.2009.26.2675.

20.Yang HI, Yuen MF, Chan HL, et al. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B): development and validation of a predictive score[J]. Lancet Oncol, 2011, 12(6): 568-574. DOI: 10.1016/s1470-2045(11)70077-8.

21.Wong GL, Chan HL, Wong CK, et al. Liver stiffness-based optimization of hepatocellular carcinoma risk score in patients with chronic hepatitis B[J]. J Hepatol, 2014, 60(2): 339-345. DOI: 10.1016/j.jhep.2013.09.029.

22.Lee HW, Yoo EJ, Kim BK, et al. Prediction of development of liver-related events by transient elastography in hepatitis B patients with complete virological response on antiviral therapy[J]. Am J Gastroenterol, 2014, 109(8): 1241-1249. DOI: 10.1038/ajg.2014.157.

23.Papatheodoridis G, Dalekos G, Sypsa V, et al. PAGE-B predicts the risk of developing hepatocellular carcinoma in Caucasians with chronic hepatitis B on 5-year antiviral therapy[J]. J Hepatol, 2016, 64(4): 800-806. DOI: 10.1016/j.jhep.2015.11.035.

24.Poh Z, Shen L, Yang HI, et al. Real-world risk score for hepatocellular carcinoma (RWS-HCC): a clinically practical risk predictor for HCC in chronic hepatitis B[J]. Gut, 2016, 65(5): 887-888. DOI: 10.1136/gutjnl-2015-310818.

25.Kim JH, Kim YD, Lee M, et al. Modified PAGE-B score predicts the risk of hepatocellular carcinoma in Asians with chronic hepatitis B on antiviral therapy[J]. J Hepatol, 2018, 69(5): 1066-1073. DOI: 10.1016/j.jhep.2018.07.018.

26.Hsu YC, Yip TC, Ho HJ, et al. Development of a scoring system to predict hepatocellular carcinoma in Asians on antivirals for chronic hepatitis B[J]. J Hepatol, 2018, 69(2): 278-285. DOI: 10.1016/j.jhep.2018.02.032.

27.Yu JH, Suh YJ, Jin YJ, et al. Prediction model for hepatocellular carcinoma risk in treatment-naive chronic hepatitis B patients receiving entecavir/tenofovir[J]. Eur J Gastroenterol Hepatol, 2019, 31(7): 865-872. DOI: 10.1097/meg.0000000000001357.

28.Yang HI, Yeh ML, Wong GL, et al. Real-world effectiveness from the Asia Pacific rim liver consortium for HBV risk score for the prediction of hepatocellular carcinoma in chronic hepatitis B patients treated with oral antiviral therapy[J]. J Infect Dis, 2020, 221(3): 389-399. DOI: 10.1093/infdis/jiz477.

29.Yang HI, Tseng TC, Liu J, et al. Incorporating serum level of hepatitis B surface antigen or omitting level of hepatitis B virus DNA does not affect calculation of risk for hepatocellular carcinoma in patients without cirrhosis[J]. Clin Gastroenterol Hepatol, 2016, 14(3): 461-468. e462. DOI: 10.1016/j.cgh.2015.10.033.

30.陈薇,陈靖,郑琦,等. 乙型肝炎病毒相关性肝细胞癌预测模型的验证[J]. 中华肝脏病杂志, 2015, 23(7): 507-511. [Chen  W, Chen J, Zheng Q, et al. Validation study of prediction models of hepatitis B virus-related hepatocellular carcinoma[J]. Chinese Journal of Hepatology, 2015, 23(7): 507-511.] DOI: 10.3760/cma.j.issn.1007-3418.2015.07.008.

31.Kim WR, Loomba R, Berg T, et al. Impact of long-term tenofovir disoproxil fumarate on incidence of hepatocellular carcinoma in patients with chronic hepatitis B[J]. Cancer, 2015, 121(20): 3631-3638. DOI: 10.1002/cncr.29537.

32.Seo YS, Jang BK, Um SH, et al. Validation of risk prediction models for the development of HBV-related HCC: a retrospective multi-center 10-year follow-up cohort study[J]. Oncotarget, 2017, 8(68): 113213-113224. DOI: 10.18632/oncotarget.22375.

33.Jeon MY, Lee HW, Kim SU, et al. Feasibility of dynamic risk prediction for hepatocellular carcinoma development in patients with chronic hepatitis B[J]. Liver Int, 2018, 38(4): 676-686. DOI: 10.1111/liv.13583.

34.Kim MN, Hwang SG, Rim KS, et al. Validation of PAGE-B model in Asian chronic hepatitis B patients receiving entecavir or tenofovir[J]. Liver Int, 2017, 37(12): 1788-1795. DOI: 10.1111/liv.13450.

35.Ahn J, Lim JK, Lee HM, et al. Lower observed hepatocellular carcinoma incidence in chronic hepatitis B patients treated with entecavir: results of the ENUMERATE study[J]. Am J Gastroenterol, 2016, 111(9): 1297-1304. DOI: 10.1038/ajg.2016.257.

36.Wong GL, Chan HL, Chan HY, et al. Accuracy of risk scores for patients with chronic hepatitis B receiving entecavir treatment[J]. Gastroenterology, 144(5): 933-944. DOI: 10.1053/j.gastro.2013.02.002.

37.Coffin CS, Rezaeeaval M, Pang JX, et al. The incidence of hepatocellular carcinoma is reduced in patients with chronic hepatitis B on long-term nucleos(t)ide analogue therapy[J]. Aliment Pharmacol Ther, 2014, 40(11-12): 1262-1269. DOI: 10.1111/apt.12990.

38.Arends P, Sonneveld MJ, Zoutendijk R, et al. Entecavir treatment does not eliminate the risk of hepatocellular carc inoma in chronic hepatitis B: limited role for risk scores in Caucasians[J]. Gut, 64(8): 1289-1295. DOI: 10.1136/gutjnl-2014-307023.

39.Kim GA, Lee HC, Kim MJ, et al. Incidence of hepatocellular carcinoma after HBsAg seroclearance in chronic hepatitis B patients: a need for surveillance[J]. J Hepatol, 2015, 62(5): 1092-1099. DOI: 10.1016/j.jhep.2014.11.031.

40.Papatheodoridis GV, Dalekos GN, Yurdaydin C, et al. Incidence and predictors of hepatocellular carcinoma in Caucasian chronic hepatitis B patients receiving entecavir or tenofovir[J]. J Hepatol, 2015, 62(2): 363-370. DOI: 10.1016/j.jhep.2014.08.045.

41.Abu-Amara M, Cerocchi O, Malhi G, et al. The applicability of hepatocellular carcinoma risk prediction scores in a North American patient population with chronic hepatitis B infecti on[J]. Gut, 2016, 65(8): 1347-1358. DOI: 10.1136/gutjnl-2014-309099.

42.Tawada A, Chiba T, Saito T, et al. Utility of prediction scores for hepatocellular carcinoma in patients with chronic hepatitis B treated with nucleos(t)ide analogues[J]. Oncology, 2016, 90(4): 199-208. DOI: 10.1159/000444392.

43.Brouwer WP, van der Meer AJP, Boonstra A, et al. Prediction of long-term clinical outcome in a diverse chronic hepatitis B population: role of the PAGE-B score[J]. J Viral Hepat, 2017, 24(11): 1023-1031. DOI: 10.1111/jvh.12727.

44.Daheim M, Lang S, Goeser T, et al. Real-world risk score for hepatocellular carcinoma risk prediction in CHBV: a validation outside of Asia[J]. Gut, 2017, 66(7): 1346-1347. DOI: 10.1136/gutjnl-2016-312993.

45.Riveiro-Barciela M, Tabernero D, Calleja JL, et al. Effectiveness and safety of entecavir or tenofovir in a Spanish cohort of chronic hepatitis B patients: validation of the Page-B score to predict hepatocellular carcinoma[J]. Dig Dis Sci, 2017, 62(3): 784-793. DOI: 10.1007/s10620-017-4448-7.

46.Yip TC, Wong GL, Wong VW, et al. Reassessing the accuracy of PAGE-B-related scores to predict hepatocel lular carcinoma development in patients with chronic hepatitis B[J]. J Hepatol, 2020, 72(5): 847-854. DOI: 10.1016/j.jhep.2019.12.005.