Frailty risk prediction models in maintenance hemodialysis patients: a systematic review and meta-analysis of model performance and methodological quality - Zihan Chen; Lidan Zhu; Caixia Ma; Juanjuan Li; Le Liu; Wenyi Xiao; Yurong Ge (Renal Failure, 2025)
Background Frailty affects outcomes in maintenance hemodialysis (MHD) patients, highlighting the need for reliable predictive tools. Despite the rise of predictive models, the clinical validity and scientific quality of these models remain unknown.Objective The purpose of this systematic review is to assess the clinical usefulness, predictive accuracy, and methodological quality of the current frailty risk prediction models in patients with MHD.Methods Databases including PubMed, Embase, Cochrane Library, CNKI, and others were comprehensively searched until August 2024. Studies that created or validated frailty risk prediction models for adult MHD patients were considered. The Newcastle-Ottawa Scale (NOS) and PROBAST were used to measure quality. The meta-analysis examined common predictive factors.Results Twelve of the 824 papers that reported 14 prediction models satisfied the inclusion criteria. The most common method was logistic regression. Frailty prevalence ranged from 17.2% to 79.2%. Age, albumin, depression, and dietary condition were among the variables that were most often found. Model performance varied considerably, with area under the curve (AUC) ranging from 0.72 to 0.998. All studies had significant methodological deficiencies.Conclusions Existing frailty risk prediction models demonstrate potential utility but currently suffer from significant methodological flaws and limited external validation, impairing their clinical applicability. Future models should emphasize rigorous study design, standardized statistical methods, and robust external validation. Clinicians should cautiously interpret existing models while focusing on critical predictors such as age, albumin, depression, and nutrition for frailty management in MHD patients.