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目的:构建适用于中国65岁及以上老年人日常生活自理能力（ADL）受损的6年风险简易预测工具。方法:以中国老年健康影响因素跟踪调查项目的34 349名65岁及以上老年人为对象，通过面对面访谈收集对象人口学特征、生活方式和慢性病患病情况等资料，采用器具性日常生活自理能力（IADL）量表评价对象的功能状态，采用简易精神状态评价量表评价对象的心理健康状况，通过体格检查获取对象的身高、体重和血压等资料并计算体质指数（BMI），基线和随访时均采用Katz量表评价ADL状况。以ADL状态为因变量，以Lasso回归筛选得到的关键预测因素为自变量，构建Cox比例风险回归模型并采用列线图工具可视化。采用受试者工作特征曲线下面积（AUC）和校准曲线分别评估模型的区分度和校准度。采用不bootstrap 200次重复抽样进行模型的内部验证。采用敏感性分析评价模型的稳健性。结果:研究对象年龄［n M（n Q1，n Q3）］为86（75，94）岁，其中男性9 774名（46.0%）。共随访112 606人年，发生ADL受损4 578例，发病密度为40.7/1 000人年。Cox比例风险回归模型分析显示：年龄增长、BMI升高、女性、高血压和脑血管疾病史可使ADL受损发生风险升高［n HR（95%n CI）分别为1.06（1.05~1.06）、1.05（1.04~1.06）、1.17（1.10~1.25）、1.07（1.01~1.13）和1.41（1.23~1.62）］；少数民族、连续行走1 km、独自乘坐公共交通工具、几乎每天做家务可使ADL受损发生风险降低［n HR（95%n CI）分别为0.71（0.62~0.80）、0.72（0.65~0.80）、0.74（0.68~0.82）和0.69（0.64~0.74）］。模型对ADL受损发生风险预测的AUC值为0.853，校准曲线显示预测概率和实际观测概率具有较好的一致性。剔除不可干预因素（年龄、性别和民族）后，模型对ADL受损发生风险预测的AUC值为0.779；65~74岁和75岁及以上老年人群中AUC值分别为0.634和0.765；基于IADL中连续行走1 km和独自乘坐公共交通工具2个变量构建的模型与基于IADL综合得分构建的模型的AUC值分别为0.853和 0.851。n 结论:所建立的ADL受损风险预测模型性能良好且稳健。“,”Objective:To construct an easy-to-use risk prediction tool for 6-year risk of activities of daily living(ADL) disability among Chinese elderly aged 65 and above.Methods:A total of 34 349 elderly aged 65 and above were recruited from the Chinese Longitudinal Healthy Longevity Survey. Demographic characteristics, lifestyle and chronic diseases of the elderly were collected through face-to-face interviews. The functional status of the elderly was evaluated by the instrumental activities of daily living(IADL) scale. The mental health status of the elderly was evaluated by the Mini-Mental State Examination. The height, weight, blood pressure and other information of the subjects were obtained through physical examination and body mass index(BMI) was calculated. The ADL status was evaluated by Katz Scale at baseline and follow-up surveys. Taking ADL status as the dependent variable and the key predictors were selected from Lasso regression as the independent variables, a Cox proportional risk regression model was constructed and visualized by the nomogram tool. Area under the receiver operating characteristic curve(AUC) and calibration curve were used to evaluate the discrimination and calibration of the model. A total of 200 bootstrap resamples were used for internal validation of the model. Sensitivity analysis was used to evaluate the robustness of the model.Results:The n M(Q1, n Q3) of subjects′ age as 86(75, 94) years old, of which 9 774(46.0%) were males. A total of 112 606 person-years were followed up, 4 578 cases of ADL disability occurred and the incidence density was 40.7/1 000 person-years. Cox proportional risk regression model analysis showed that older age, higher BMI, female, hypertension and history of cerebrovascular disease were associated with higher risk of ADL disability [n HR(95%n CI) were 1.06(1.05-1.06), 1.05(1.04-1.06), 1.17(1.10-1.25),1.07(1.01-1.13) and 1.41(1.23-1.62), respectively.]; Ethnic minorities, walking 1 km continuously, taking public transportation alone and doing housework almost every day were associated with lower risk of ADL disability [n HR(95%n CI): 0.71(0.62-0.80), 0.72(0.65-0.80), 0.74(0.68-0.82) and 0.69(0.64-0.74), respectively]. The AUC value of the model was 0.853, and the calibration curve showed that the predicted probability was highly consistent with the observed probability. After excluding non-intervening factors(age, sex and ethnicity), the AUC value of the model for predicting the risk of ADL disability was 0.779. The AUC values of 65-74 years old and 75 years old and above were 0.634 and 0.765, respectively. The AUC values of the model based on walking 1 km continuous and taking public transport alone in IADL and the model based on comprehensive score of IADL were 0.853 and 0.851, respectively.n Conclusion:The risk prediction model of ADL disability established in this study has good performance and robustness.