Development and validation of a predictive model for cognitive frailty in community-dwelling older adults: a cross-sectional study

Liwei Sun^1,2Yu Zhang^1,2Wenting Ji^1,2Jia Zheng^1,2Guo Hua Zheng^1*Jie Xia^1*

• ¹Shanghai University of Medicine and Health Sciences, Shanghai, China
• ²Shanghai University of Traditional Chinese Medicine, Shanghai, China

Abstract Background Cognitive frailty (CF) is a geriatric syndrome involving both physical frailty and cognitive impairment, increasing vulnerability to adverse health outcomes. However, practical prediction models integrating easily accessible, modifiable risk factors for community settings are lacking. This study aimed to develop and validate a predictive model for CF in community-dwelling older adults. Methods A cross-sectional study was conducted from September 2022 to May 2024 in Pudong New District, Shanghai, with 979 participants aged 60 and above. Data on sociodemographic characteristics, behavioural factors, nutritional status, sleep quality, depression, as well as lifestyle, health-related factors, physical frailty, and cognitive function were collected through questionnaires administered to older adults, with 20 indicators analyzed. The population was divided into a 7:3 ratio for training and validation. LASSO regression and multivariate logistic regression identified risk factors, and a nomogram prediction model was developed. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Results Of the 979 participants, 31.1% were diagnosed with CF. Seven predictors, including marital status, smoking, Timed Up and Go test (TUGT), depression, sleep quality, nutrition, and medication count, were identified to construct the model. Together, these variables provide a comprehensive assessment of the risk of cognitive frailty in older adults. The model exhibited good predictive performance, with AUC values of 0.753 and 0.733 for the development and validation sets, respectively. The P-values for the Hosmer-Lemeshow test were 0.507 and 0.537 for the training and validation cohorts, respectively, indicating a notable calibration curve fit. The DCA curves also show that the model has good predictive ability and stability. Conclusions Community-dwelling older adults have a higher incidence of cognitive frailty. This study developed an effective, low-cost, and non-invasive model with promising predictive capabilities that can be used as a screening tool to identify community-dwelling older adults at high risk for cognitive frailty in clinical practice. This model is expected to assist healthcare professionals in improving the effectiveness of prevention of cognitive frailty in community-dwelling older adults.