Frailty risk prediction models in maintenance hemodialysis patients: a systematic review and meta-analysis of studies from China - Zhicheng Zhang; Shuoming Wang; Ziqi Xu; Yue Sun; Xinran Zhou; Rui Zhou; Qiong Li; Guodong Wang (Renal Failure)
Objectives To systematically evaluate and meta-analyze the performance, validity, and influencing factors of frailty risk prediction models specifically developed for patients undergoing maintenance hemodialysis in China.Methods China National Knowledge Infrastructure, Wanfang Database, China Science and Technology Journal Database, SinoMed, PubMed, Web of Science, Cochrane Library, CINAHL and Embase were searched from inception to October 10, 2024. Two independent reviewers conducted literature screening, data extraction, and risk of bias assessment using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed to pool the incidence rates and identify independent predictors.Results Fourteen studies incorporating 16 distinct frailty risk prediction models were included. The predictive accuracy, measured by the area under the receiver operating characteristic curve (AUC), ranged from 0.819 to 0.998. Seven studies performed internal validation, one study executed external validation, and one study conducted both internal and external validation. All studies exhibited a high overall risk of bias. Pooled incidence of frailty among maintenance hemodialysis patients was 32.2% (95% CI: 26.9%–37.6%). Significant predictors of frailty included advanced age, hypoalbuminemia, poor nutritional status, female sex, comorbid conditions, and depression (p < 0.05).Conclusions The pooled incidence of frailty among maintenance hemodialysis patients was notably high at 32.2%, with advanced age, hypoalbuminemia, poor nutritional status, female sex, comorbid conditions, and depression emerging as significant predictors. Existing frailty prediction models for maintenance hemodialysis patients demonstrated robust predictive capacity but exhibited substantial methodological limitations, high bias and limited external validation. Future research should prioritize multicenter, large sample, validation studies to enhance applicability and reliability.