Prediction model related to 6-year risk of frailty in older adults aged 65 years or above in China
Zhou Jinhui, Qi Li, Wang Jun, Liu Sixin, Shi Wenhui, Ye Lihong, Zhang Zhenwei, Zhang Zenghang, Meng Xi, Cui Jia, Chen Chen, Lyu Yuebin, Shi Xiaoming
Abstract
ObjectiveTo develop a prediction tool for 6-year incident risk of frailty among Chinese older adults aged 65 years or above.
MethodsData from the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018 was used, including 13 676 older adults aged 65 years or above who were free of frailty at baseline. Key predictors of frailty were identified via the least absolute shrinkage and selection operator (LASSO) method, and were thereafter used to predict the incident frailty based on the Cox proportional hazards regression model. The model was internally validated by 2 000 Bootstrap resamples and evaluated for the performance of discrimination and calibration using the area under the receiver operating characteristic curve (AUC) and calibration curve, respectively. The net benefit of the developed prediction tool was evaluated by decision-curve analysis.
ResultsThe M(Q1, Q3) age and follow-up time of the participants were 81.0 (71.0, 90.0) years and 6.0 (4.1, 9.2) years, respectively. A total of 4 126 older persons (30.2%) were recorded with frailty incidents during the follow-up, with the corresponding incidence density of 41.8/1 000 person-years. A total of 15 key predictors of frailty were selected by LASSO, namely, age, sex, race, education years, meat consumption, tea drinking, performing housework, raising domestic animals, playing cards or mahjong, and baseline status of visual function, activities of the daily living score, instrumental activities of the daily living score, hypertension, heart disease, and self-rated health. The prediction model was internally validated with an AUC of 0.802, with the max Youden's index of 0.467 at a risk threshold of 19.0%. The calibration curve showed high consistency between predicted probabilities and observed proportions of frailty events. The decision curve indicated that higher net benefits could be obtained via the prediction model than did strategies based on intervention in all or none participants for any risk threshold less than 59%, and the model-based net benefit was estimated to be 0.10 at a risk threshold of 19.0%.
ConclusionsThe herein developed 6-year incident risk prediction model of frailty, based on easily accessible questionnaires and physical examination variables, has good predictive performance. It has application potential in identifying populations at high risk of incident frailty.
Key words:
Frailty; Older adults; Key factors; Prediction model
Contributor Information
Zhou Jinhui
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Qi Li
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Beijing Center for Disease Prevention and Control, Beijing 100020, China
Wang Jun
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Liu Sixin
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
Shi Wenhui
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Ye Lihong
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Zhang Zhenwei
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Editorial Department for Chinese Journal of Preventive Medicine, Chinese Medical Association Publishing House, Beijing 100052, China
Zhang Zenghang
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Meng Xi
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Cui Jia
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Chen Chen
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Lyu Yuebin
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Shi Xiaoming
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
