Risk prediction models for frailty in maintenance hemodialysis patients: a systematic review
10.3760/cma.j.cn115682-20240503-02449
- VernacularTitle:维持性血液透析患者衰弱风险预测模型的系统评价
- Author:
Yuliang DUAN
1
;
Yunhong DU
;
Xiao ZHANG
;
Yu WANG
;
Li WANG
Author Information
1. 湖南中医药大学护理学院，长沙 410208
- Publication Type:Journal Article
- Keywords:
Hemodialysis;
Frailty;
Prediction model;
Systematic review
- From:
Chinese Journal of Modern Nursing
2025;31(7):860-867
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically review risk prediction models for frailty in maintenance hemodialysis (MHD) patients.Methods:Relevant literature on frailty risk prediction models for MHD patients were retrieved from PubMed, Web of Science, Embase, Cochrane Library, CINHAL, China National Knowledge Infrastructure, WanFang Data, VIP, and China Biology Medicine disc from inception to March 25, 2024. Two researchers independently screened the literature and extracted data. The PROBAST tool was used to assess the methodological quality of the prediction models. Meta-analysis of predictive factors was conducted using Stata 16.0 software.Results:A total of 13 studies were included, comprising 19 frailty risk prediction models for MHD patients. All studies exhibited a high risk of bias. Six studies conducted internal validation, and three studies conducted external validation. The area under the receiver operating characteristic curve ( AUC) of the included prediction models ranged from 0.720 to 0.998. Meta-analysis revealed that age [ OR=1.149, 95% CI (1.070, 1.234), P<0.001], female gender [ OR=4.472, 95% CI (1.799, 11.117), P<0.001], nutritional score [ OR=2.650, 95% CI (1.010, 6.970), P=0.048], comorbidities [ OR=1.990, 95% CI (1.500, 2.650), P<0.001], and serum albumin [ OR=0.830, 95% CI (0.790, 0.880), P<0.001] were significant influencing factors for frailty in MHD patients. Conclusions:Risk prediction models for frailty in MHD patients are still in the research stage. Healthcare professionals should pay close attention to frailty risks in older patients, females, those with malnutrition, comorbidities, and low serum albumin levels, and implement timely interventions.
