Early identification of disability risk in community-dwelling older adults has emerged as a critical public health priority. An increasing number of studies have focused on developing predictive models for disability risk among community-dwelling older adults. The quality, risk of bias and applicability of these models remain unclear. To systematically review and critically assess the existing disability risk prediction models in community-dwelling older adults. PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP) Database and Wanfang Database were searched from inception to February 28, 2025. Two reviewers independently identified eligible articles and extracted data using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The methodological quality, risk of bias, and applicability of the included studies were assessed with the updated Prediction Model Risk of Bias Assessment Tool integrated with artificial intelligence (PROBAST + AI). A total of 19 studies involving 19 prediction models were included. 19 models evaluated model discrimination, with the AUC or c-index ranging from 0.620 to 0.853 in the model development section and from 0.650 to 0.804 in the model validation section. And the most common predictors included age, physical function, cognitive function, cardiovascular disease, and sex. According to the PROBAST + AI, eighteen models were rated as high quality concerns in the development section and sixteen models were rated as high risk of bias in the evaluation section, alongside fifteen models were rated as high concerns of applicability. Current disability risk prediction models for community-dwelling older adults demonstrate good discriminatory performance but are characterized by significant methodological limitations and high risk of bias. Future research should rigorously adhere to reporting standards for risk prediction models in both developing and evaluating such models for older community populations, with further validation of their clinical applicability in practice. PROSPERO registration number: CRD420251001479.
Zhang et al. (Mon,) studied this question.