南方医科大学学报

南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (12): 2639-2645.doi: 10.12122/j.issn.1673-4254.2025.12.11

• • 上一篇    

中国农村社区老年人认知障碍预测模型的构建与验证——基于中国健康与养老追踪调查数据库

王飞1(), 李蔚然1, 尚祥1, 李飞2()   

  1. 1.安徽中医药大学第一临床医学院，安徽 合肥 230031
    2.安徽中医药大学第二附属医院 安徽 合肥 230061
  • 收稿日期:2025-06-29 出版日期:2025-12-20 发布日期:2025-12-22
  • 通讯作者: 李飞 E-mail:wangr05@qq.com;leagcen@163.com
  • 作者简介:王 飞，在读博士研究生，E-mail: wangr05@qq.com
  • 基金资助:
    国家青年岐黄学者项目(国中医药人教函[2022]256);安徽省自然科学基金面上项目(2308085MH297);安徽高校自然科学重大研究项目(2023AH040099)

Development and validation of a risk prediction model for cognitive impairment in rural elderly Chinese populations: evidence from the CHARLS study

Fei WANG1(), Weiran LI1, Xiang SHANG1, Fei LI2()   

  1. 1.First Clinical Medical College, Anhui University of Chinese Medicine, Hefei 230061, China
    2.Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China
  • Received:2025-06-29 Online:2025-12-20 Published:2025-12-22
  • Contact: Fei LI E-mail:wangr05@qq.com;leagcen@163.com

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摘要：

目的 构建并验证一套适用于中国农村社区老年人群的认知障碍风险预测模型。 方法 基于2011年中国健康与养老追踪调查横断面数据，纳入年龄≥60岁的农村社区老年人共2228例，按7∶3比例随机分为训练集（1560例）和内部验证集（668例）。收集包括人口社会学特征、生活方式与行为习惯、慢性病史、身体功能与主观健康状态等38个候选变量。采用最小绝对收缩与选择算子（LASSO）回归筛选特征变量，再以多因素Logistic回归确定独立危险因素，并据此构建认知障碍预测列线图。通过受试者工作特征曲线（ROC）和校准曲线评估模型的判别力与拟合度，采用决策曲线分析（DCA）评价其临床应用价值。 结果 LASSO及多因素Logistic回归分析显示，年龄、受教育年限、饮酒情况、收缩压、握力及抑郁状态为认知障碍的独立影响因素。所构建的列线图在训练集和验证集中的ROC曲线下面积分别为0.839（95% CI： 0.814~0.864）和0.840（95% CI： 0.801~0.879），显示出良好的预测性能。校准曲线提示模型拟合良好，DCA结果进一步证实该模型具备较高的临床实用性。 结论 本研究构建的基于LASSO筛选变量的认知障碍预测列线图具有较高的预测准确性、判别能力及潜在临床应用价值，可为中国农村老年人群的认知障碍风险早期识别与干预提供参考工具。

关键词: 认知障碍, 老年人, 风险预测模型, 列线图, 中国健康与养老追踪调查

Abstract:

Objective To develop and validate a risk prediction model for cognitive impairment in community-dwelling elderly individuals in China. Methods This cross-sectional study was based on data from the 2011 China Health and Retirement Longitudinal Study (CHARLS), and the data of 2228 individuals aged ≥60 years were analyzed. The participants were randomly divided into a training set (n=1560) and an internal validation set (n=668) in a 7∶3 ratio. Thirty-eight candidate variables were collected, covering sociodemographic characteristics, lifestyle and behavioral habits, chronic disease history, physical function, and self-rated health status. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression to identify independent risk factors for cognitive impairment. A nomogram was constructed based on these factors, its discrimination power and calibration were assessed using the receiver operating characteristic (ROC) curve and calibration plot, respectively, and its clinical utility was evaluated using decision curve analysis (DCA). Results Age, years of education, alcohol consumption, systolic blood pressure, grip strength, and depressive symptoms were identified as independent predictors of cognitive impairment in Chinese elderly individuals. The area under the ROC curve of the constructed nomogram was 0.839 (95% CI: 0.814-0.864) in the training set and 0.840 (95% CI: 0.801-0.879) in the validation set, indicating good predictive performance of the model. The calibration plots demonstrated good agreement between the predicted and observed outcomes, and the DCA showed good clinical utility of the model. Conclusion The nomogram developed in this study based on LASSO-selected predictors demonstrates high accuracy, discrimination power, and potential clinical applicability to facilitate early identification and intervention of cognitive impairment among rural elderly individuals in China.

Key words: cognitive impairment, elderly individuals, risk prediction model, nomogram, China Health and Retirement Longitudinal Study