Related Experiment Video
Updated: Sep 9, 2025
Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
Published on: July 24, 2013
Development and validation of a machine learning-based prediction model for frailty in older adults with diabetes: a
An Luo1, Yiting Pan1, Yaqing Liu1
1School of Nursing, University of South China, Hengyang, Hunan, China.
Introduction:
Frailty is a common condition in older adults with diabetes, which significantly increases the risk of adverse health outcomes. Early identification of frailty in this population is crucial for implementing timely interventions. However, there is a lack of specific prediction models for frailty in older adults with diabetes. This study aims to develop and validate a prediction model for frailty in this high-risk group.
Methods And Analysis:
This study uses data from the national follow-up of the China Health and Retirement Longitudinal Study (CHARLS), which range from 2011 to 2020. The study population includes older adults with diabetes aged 60 and above. Frailty is assessed using Fried's frailty phenotype. Potential predictors will be identified through a systematic review and expert consultation. Eight machine learning models will be developed to predict frailty, with model performance to be evaluated using receiver operating characteristic curves, calibration plots and internal validation through leave-one-out cross validation. Finally, the optimal model will be deployed via an electronic risk calculator with Shapley Additive Explanation-based visualisations.
Ethics And Dissemination:
The CHARLS was approved by the Biomedical Ethics Committee of Peking University (approval number: IRB00001052-11015), and all participants were required to sign informed consent. This study was approved by the Medical Research Ethics Committee of the University of South China (approval number: 2023NHHL006). We will disseminate results via presentations at scientific meetings and publication in peer-reviewed journals.
Prospero Registration Number:
CRD42023470933.
