Prediction of frailty in community older adults based on machine learning
A systematic review and meta-analysis
Bibliographic Data
| ID | 22089331 |
|---|---|
| Authors | Yifan Ou (0000-0002-4654-6936, University of South China), Dandan Jiang (0000-0003-0517-3042, University of South China), Pan Li (0000-0002-0316-0355), Wen Zhang (0000-0002-8166-2879), William Zhang (0000-0002-8850-5235, University of South China), Yutong Zhou (0000-0002-3518-1441, University of South China), Yao Chen (0000-0002-3823-9485, University of South China), Xinhong Yin (University of South China, corresponding author) |
| Year | 2026 |
| Volume | 13 |
| Pages | 1667792-1667792 |
| Publication date | 2026-01-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1667792 |
| PMID | 41602088 |
| OpenAlex | W7122733289 |
| Language | EN |
| Citations received | 1 |
| References cited | 76 |
Background: An increasing number of predictive models for frailty in community-dwelling older adults are now being developed using machine learning methods. Differences between model performances limit their practical application. Therefore, we conducted a systematic review and meta-analysis to summarize and evaluate the performance and clinical applicability of these risk prediction models. Methods: PubMed, Web of Science, Embase, Cochrane Library, Scopus, CINAHL, SinoMed, VIP, CNKI, and Wanfang were searched. The search time was from the database establishment to June 10, 2025. The PROBAST+AI assessment tool was used to assess the study quality, and a meta-analysis of the area under the curve (AUC) was performed using Stata18.0 software. Results: A total of 10 studies were included, and 45 Machine Learning (ML) models were developed, of which 36 models were developed for internal validation and 9 for external validation. In the internal validation set, the pooled AUC for baseline frailty prediction studies was 0.878 (95% CI 0.799, 0.958), while the pooled AUC for longitudinal frailty prediction studies was 0.730 (0.670, 0.790). When all studies were pooled without distinguishing prediction time points, the overall pooled AUC was 0.786 (95% CI 0.697, 0.875). Conclusion: Although most of the included models had good discrimination and calibration, the overall quality and applicability of the current study are still problematic. In future studies, researchers should follow the TRIPOD+AI statement and the PROBAST+AI list to construct high-quality, more applicable predictive models. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251071061, identifier CRD420251071061
Frailty Index · Frailty syndrome · MEDLINE · Older people · Chronic Disease Management Strategies · Frailty in Older Adults · Heart Failure Treatment and Management
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Frailty
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To Explain or to Predict?
The cost of dichotomising continuous variables
The PRISMA 2020 statement
Determinants and risk prediction models for frailty among community-living older adults in eastern China
Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty
Identification and prediction of frailty among community-dwelling older Japanese adults based on Bayesian network analysis
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |