The risk prediction models for cognitive frailty in the older people in China: a systematic review and meta-analysis.

Abstract

Background: Recently, many risk prediction models for Cognitive Frailty (CF) in older people in China have been developed. However, there is a shortage of large-scale systematic and comprehensive studies of the methods, quality, and predictors involved in model development.

Aims: To systematically assess the risk prediction model of CF in older people in China and to conduct a meta-analysis of its predictors.

Methods: PubMed, Cochrane Library, EMbase, Web of Science, CNKI, Wanfang, VIP, and SinoMed were searched from the inception to April 30, 2024. Two researchers independently screened the literature and extracted data. The quality of studies was assessed using the PROBAST tool. Additionally, Stata 18.0 software and MedCalc software were employed to perform a meta-analysis of the modeled predictors and area under the curve (AUC).

Results: 17 articles were included, encompassing 22 CF risk prediction models, involving 9,614 participants, of which 2488 (25.9%) were diagnosed with CF. 15 models reported discrimination by AUC (0.710 to 0.991). 8 models conducted internal validation, while 7 models performed external validation. PROBAST evaluation results found that 15 articles (15/17, 88.24%) exhibited a high risk of bias (ROB). The most common predictors were advanced age, irregular exercise, malnutrition, depression, Barthel Index score, female gender, and Instrumental Activities of Daily Living (IADL) score.

Conclusion: Due to imprecise modeling methods, incomplete presentation, and lack of external validation, the models' usefulness still needs to be determined. Seven predictive factors are established predictors for CF among older people, including advanced age and so on, but the roles of educational level and fall incidents warrant further investigation.