基于机器学习的SHAP分析,预测患有OSA的老年人的动脉动脉样硬化,这是一个多中心研究
Wei-Meng Cai1, Dong Rui1, Li-Bo Zhao2
1Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China.
Nature and science of sleep
|October 6, 2025
概括
一种新的机器学习模型准确地预测了患有阻塞性睡眠呼吸暂停 (OSA) 的老年患者的喉动脉样硬化 (CAS). 该模型强调了夜间低血 (T90) 对心血管风险评估的传统措施的重要性.
科学领域:
- 心脏病学 心脏病学
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- Carotid 动脉样硬化 (CAS) 是患有阻塞性睡眠呼吸暂停 (OSA) 的老年患者的显著心血管风险.
- 现有的风险评估工具未能充分解决在这个脆弱群体中OSA特定的CAS预测机制.
研究的目的:
- 开发和验证一种机器学习模型,用于预测OSA老年患者的CAS.
- 确定该人群中CAS的关键预测因素,重点关注与OSA相关的因素.
主要方法:
- 一个多中心的回顾性研究,涉及1196名老年患者 (≥60岁) 已确认OSA.
- 通过LASSO回归,从18个候选变量中选择最佳预测特征.
- 开发和验证了四个机器学习算法,XGBoost因其卓越的性能而被选中;用于模型解释性,使用了SHAP分析.
主要成果:
- 开发的XGBoost模型实现了0.854的AUC,准确度为79.8%,灵敏度为81.2%,特异性为78.5%.
- 系统性血压和睡眠时间百分比与氧和率<90% (T90) 被确定为最有影响力的预测因素.
- 对于CAS风险,T90成为比传统的呼吸暂停-呼吸暂停指数更重要的预测指标.
结论:
- 该研究提出了首个基于机器学习的CAS预测模型,用于老年OSA患者,证明了临床相关的性能.
- 夜间的低血压负担 (T90) 在这个人群中,对于心血管风险分层来说,似乎比呼吸事件频率更为重要.
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