Affiliation:
1. School of Nursing, Peking University, Beijing, China
2. School of Public Health, Peking University, Beijing, China
Abstract
This study aimed to develop and validate prediction models for incident reversible cognitive frailty (RCF) based on social-ecological predictors. Older adults aged ≥60 years from China Health and Retirement Longitudinal Study (CHARLS) 2011–2013 survey were included as training set ( n = 1230). The generalized linear mixed model (GLMM), eXtreme Gradient Boosting, support vector machine, random forest, and Binary Mixed Model forest were used to develop prediction models. All models were evaluated internally with 5-fold cross-validation and evaluated externally via CHARLS 2013–2015 survey ( n = 1631). Only GLMM showed good discrimination (AUC = 0.765, 95% CI = 0.736, 0.795) in training set, and all models showed fair discrimination (AUC = 0.578–0.667, 95% CI = 0.545, 0.725) in internal and external validation. All models showed acceptable calibration, overall prediction performance, and clinical usefulness in training and validation sets. Older adults were divided into three groups using risk score based on GLMM, which could assist healthcare providers to predict incident RCF, facilitating early identification of high-risk population.
Funder
National Natural Science Foundation of China
Incubation Fund for Scientific Research and Innovation of Leading Talents of Unnamed Nursing of Peking University
Key Laboratory of Geriatric Long-term Care (Naval Medical University), Ministry of Education
Major Project of the National Social Science Fund of China