| 周锦辉,齐力,王君,刘思馨,石文惠,叶丽红,张振伟,张曾航,孟熙,崔佳,陈晨,吕跃斌,施小明.中国65岁及以上老年人6年虚弱发生风险预测模型研究[J].Chinese journal of Epidemiology,2024,45(6):809-816 |
| 中国65岁及以上老年人6年虚弱发生风险预测模型研究 |
| Prediction model related to 6‑year risk of frailty in older adults aged 65 years or above in China |
| Received:December 05, 2023 |
| DOI：10.3760/cma.j.cn112338-20231205-00333 |
| KeyWord: 虚弱 老年人 关键因素 预测模型 |
| English Key Word: Frailty Older adults Key factors Prediction model |
| FundProject:国家自然科学基金（82230111，82025030，82222063）；中国科学技术协会（YESS20200046） |
| Author Name | Affiliation | E-mail | | 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 | lvyuebin@nieh.chinacdc.cn | | 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 | shixm@chinacdc.cn |
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| Abstract: |
| 目的 建立适用于中国≥65岁老年人的6年虚弱发生风险预测工具。方法 数据源于2002-2018年中国老年健康影响因素跟踪调查，纳入13 676名≥65岁基线无虚弱的老年人，通过最小绝对收缩和选择算子（LASSO）方法进行虚弱的关键预测因素识别，利用Cox比例风险回归模型建立虚弱发生风险预测模型，采用Bootstrap 2 000次重复抽样方法进行模型内部验证，分别使用受试者工作特征曲线下面积（AUC）和校准曲线评价预测模型区分能力和校准能力，通过决策曲线对建立的预测工具开展净效益评估。结果 研究对象年龄M（Q1，Q3）为81.0（71.0，90.0）岁。随访时间M（Q1，Q3）为6.0（4.1，9.2）年，期间共4 126名（30.2%）老年人发生虚弱，发病密度为41.8/1 000人年。LASSO筛选纳入15个关键的虚弱预测因素，包括年龄、性别、民族、受教育年限、肉类摄入、饮茶、做家务、饲养家禽/家畜、打牌/麻将、基线视力功能、日常生活自理能力评分、器具性日常生活自理能力评分、高血压、心脏病和自评健康状态。预测模型内部验证的AUC值为0.802，最大约登指数值为0.467，对应风险切点为19.0%。校准曲线提示，预测的虚弱发生概率和实际观测概率一致性较高。决策曲线提示在风险阈值<59%时，基于预测模型干预获得的净效益较全部干预或全部不干预更高，风险阈值为19.0%时，基于预测模型干预的净效益为0.10。结论 基于问卷和体检等易获得信息构建的中国老年人6年虚弱发生风险预测模型效能好，具有筛选虚弱发生高危人群的潜在应用价值。 |
| English Abstract: |
| Objective To develop a prediction tool for 6-year incident risk of frailty among Chinese older adults aged 65 years or above. Methods Data 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. Results The 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%. Conclusions The 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. |
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