Related Experiment Video
Updated: Aug 5, 2026
06:58
Frailty Assessment in an Aging Mouse Model
Published on: September 23, 2025
Predictive Risk Models for Frailty Onset in Older Adults: A Scoping Review of Methodological Trends, Model
Jiechenming Xiao1, Weihong Zhang1, Dan Xu1
1Department of Nursing, Taizhou First People's Hospital, Taizhou, Zhejiang, People's Republic of China.
Clinical Interventions in Aging
|August 4, 2026
Summary
Identifying frailty in older adults is key for intervention. This review found prediction models lack rigorous validation and face implementation barriers, hindering clinical use.
Area of Science:
- Gerontology
- Predictive Modeling
- Public Health
Background:
- Early identification of frailty in older adults is critical for timely interventions.
- Numerous prediction models exist, but a comprehensive overview of their methods and implementation challenges is lacking.
Purpose of the Study:
- To systematically review methodological trends in frailty onset prediction models for older adults.
- To assess model performance and identify barriers to clinical implementation.
Main Methods:
- A systematic scoping review following Arksey and O'Malley's framework and PRISMA-ScR guidelines.
- Searched six Chinese and English databases from inception to February 2026.
- Included 30 studies reporting 31 frailty prediction models (2018-2026).
Main Results:
- Most studies originated from China (73.33%) focusing on community-dwelling older adults (73.33%).
- Machine learning adoption increased (38.7%) but showed no superior performance over logistic regression (AUC 0.813 vs 0.860).
- Calibration reporting was low (50% for ML models), external validation and TRIPOD+AI adherence were limited (22.6%), and no studies assessed economic outcomes.
Conclusions:
- Frailty prediction models face challenges including poor calibration reporting, limited external validation, and significant translation barriers.
- Future research must focus on rigorous external validation, adherence to reporting guidelines like TRIPOD+AI, and developing clinically integrated digital tools.
