Development and validation of prediction model for older adults with cognitive frailty

Abstract

Abstract Objective This study sought to develop and validate a 6-year risk prediction model in older adults with cognitive frailty (CF). Methods In the secondary analysis of Chinese Longitudinal Healthy Longevity Survey (CLHLS), participants from the 2011–2018 cohort were included to develop the prediction model. The CF was assessed by the Chinese version of Mini-Mental State Exam (CMMSE) and the modified Fried criteria. The stepwise regression was used to select predictors, and the logistic regression analysis was conducted to construct the model. The model was externally validated using the temporal validation method via the 2005–2011 cohort. The discrimination was measured by the area under the curve (AUC), and the calibration was measured by the calibration plot. A nomogram was conducted to vividly present the prediction model. Results The development dataset included 2420 participants aged 60 years or above, and 243 participants suffered from CF during a median follow-up period of 6.91 years (interquartile range 5.47–7.10 years). Six predictors, namely, age, sex, residence, body mass index (BMI), exercise, and physical disability, were finally used to develop the model. The model performed well with the AUC of 0.830 and 0.840 in the development and external validation datasets, respectively. Conclusion The study could provide a practical tool to identify older adults with a high risk of CF early. Furthermore, targeting modifiable factors could prevent about half of the new-onset CF during a 6-year follow-up.