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开发和验证一种风险预测模型,用于慢性疾病的中国老年人轻度认知障碍的风险预测

Lulu Yan^1,2, Yuanyuan Peng³, Chenjiao Guo³

¹Health Science Center, Yangtze University, Jingzhou City, Hubei Province, 434023, China.

|January 6, 2026

概括

一个新的预测模型有助于识别患有慢性疾病的老年人,他们面临轻度认知障碍 (MCI) 的风险. 关键因素包括年龄,教育和抑郁症状,有助于早期干预策略.

科学领域:

老年学是一门学科.
神经学神经学
公共卫生公共卫生

背景情况:

人口老龄化面临轻度认知障碍 (MCI) 的增长率.
慢性疾病与老年人中MCI风险增加有关.
在这个人口群体中识别MCI预测因素对于公共卫生至关重要.

研究的目的:

在患有慢性疾病的中国老年人中开发MCI的预测模型.
在这个人群中确定与MCI发展相关的关键因素.
验证模型的性能,以确定其临床适用性.

主要方法:

利用了来自中国健康与退休长度研究 (CHARLS) 和医院基队列的数据.
使用后勤回归,LASSO和随机森林来选择9个最佳预测因素.
使用ROC曲线,校准曲线和决策曲线分析 (DCA) 评估模型性能.

主要成果:

确定年龄,教育,孩子的满意度,婚姻状况,抑郁症状,ADL得分,收入,SCD和慢性疾病的数量作为重要预测因素.
在培训,内部和外部验证集中达到超过0.7的AUC值.
通过DCA证明了强大的校准和显著的临床实用性.

结论:

开发的诺莫格拉姆模型显示,在患有慢性疾病的老年人中,MCI的预测性能良好.
这个工具可以帮助识别有针对性的干预的高风险个体.
研究结果支持个性化风险评估和MCI早期管理策略.

关键词:

查尔斯查尔斯查尔斯查尔斯查尔斯查尔斯慢性疾病慢性疾病慢性疾病在MCI中,MCI是MCI.年龄较大的成年人.预测模型是一个预测模型.

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