Systematic review of risk prediction models for sarcopenia in community-dwelling older adults
10.3760/cma.j.cn115682-20241015-05647
- VernacularTitle:社区老年人肌少症风险预测模型的系统评价
- Author:
Yuanyue PANG
1
;
Tongtong CAO
1
;
Xue DONG
1
Author Information
1. 长春中医药大学护理学院，长春 130117
- Publication Type:Journal Article
- Keywords:
Aged;
Community;
Sarcopenia;
Prediction model;
Systematic review
- From:
Chinese Journal of Modern Nursing
2025;31(22):3017-3024
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically review risk prediction models for sarcopenia in community-dwelling older adults.Methods:Literature on risk prediction models for sarcopenia in community-dwelling older adults was retrieved from China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine disc, VIP, PubMed, Embase, Cochrane Library, and Web of Science. The search period was from database inception to October 24, 2024. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies using the Prediction Model Risk of Bias Assessment Tool (PROBAST) .Results:A total of 17 articles were included, encompassing 24 risk prediction models for sarcopenia in community-dwelling older adults. The area under the receiver operating characteristic curve ( AUC) reported in 16 studies ranged from 0.710 to 0.983. Age, sex, body mass index, body weight, calf circumference, and physical activity habits were the most frequently identified predictors. The overall risk of bias in the 17 articles was high, while applicability was generally good. Conclusions:Although the risk prediction models for sarcopenia in community-dwelling older adults show good predictive performance, methodological flaws in model development and a high risk of bias are prevalent. Future studies should strictly adhere to the PROBAST framework, optimize model construction and validation processes, and improve data handling strategies to enhance model performance and applicability.
