在轻度认知障碍患者中,基于联合集体学习的阿尔茨海默病风险预测
Tianyuan Guan1, Lei Shang1, Peng Yang1
1Department of Health Statistics, School of Public Health, Airforce Medical University, Xian, Shaanxi, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xian, Shaanxi, China.
The journal of prevention of Alzheimer's disease
|February 6, 2025
概括
这项研究开发了考克斯比例危险回归模型,以预测轻度认知障碍 (MCI) 患者的阿尔茨海默病 (AD) 风险. 该模型提供了准确的预测,并可以帮助临床环境中的早期干预策略.
科学领域:
- 神经科学是一个神经科学.
- 老年学是一门学科.
- 生物统计学 生物统计学
背景情况:
- 早期干预对于阿尔茨海默氏病 (AD) 是至关重要的.
- 轻度认知障碍 (MCI) 是阿尔茨海默病的前身阶段.
- 在MCI患者中开发准确的AD风险预测模型对于临床指导至关重要.
研究的目的:
- 在轻度认知障碍 (MCI) 患者中建立阿尔茨海默病 (AD) 风险预测模型.
- 为初级医疗机构提供临床指导,以识别患AD进展高风险的个体.
- 评估不同预测模型对MCI的AD风险的性能.
主要方法:
- 利用来自NACC数据库的3674名患有MCI的受试者的数据.
- 采用集体学习方法 (随机生存森林,极端梯度提升) 与SHAP用于预测器选择.
- 开发并比较了Cox比例危险回归,RSF和XGBoost模型用于AD风险预测.
主要成果:
- 在MCI患者中确定了AD风险的13个关键预测因素.
- 考克斯比例危险回归模型表现出强大的预测性能 (一致性指数0.798,综合障碍得分0.087).
- 集体学习模型 (RSF,XGBoost) 在预测准确度方面没有超过考克斯模型.
结论:
- 集体学习有效地识别了MCI受试者对AD风险的预测因素.
- 考克斯的比例危险回归模型是一个有前途的工具,在完全临床验证后,在初级保健机构快速查AD风险.
- 已识别的预测因素易于解释和获取,从而促进准确的AD风险预测.
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