心血管疾病和阻塞性睡眠呼吸暂停并发症的多层次表型模型:威斯康星州长度睡眠队列研究
Duy Nguyen1, Ca Hoang2, Tien Truong2
1Department of Industrial and Systems Engineering, North Carolina A&T State University, Greensboro, North Carolina, United States of America.
PloS one
|July 15, 2025
概括
这项研究引入了一种新模型,通过分析随时间推移的健康数据,预测阻塞性睡眠呼吸暂停 (OSA) 患者的心血管疾病 (CVD). 该模型识别了具有不同心血管疾病风险的不同患者群体,改进了个性化治疗策略.
科学领域:
- 心脏病学 心脏病学
- 睡眠医学 睡眠医学
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病 (CVD) 在阻塞性睡眠呼吸暂停 (OSA) 患者中很常见.
- 由于复杂的相互作用,OSA中CVD的预测建模具有挑战性.
- 现有的方法缺乏动态和纵向视角,无法准确预测心血管疾病的进展.
研究的目的:
- 开发一种新的多层次的表型模型,用于预测OSA患者的心血管疾病进展.
- 分析OSA和心血管疾病并发症的纵向进展和相互作用.
- 加强针对心血管疾病风险的OSA患者的个性化治疗策略.
主要方法:
- 利用来自威斯康星州睡眠队列的纵向队列 (n=1,123).
- 采用特征重要性分析,物流混合效应模型 (LGMM) 和t分布式随机邻居嵌入 (t-SNE) 与高斯混合模型 (GMM).
- 确定了关键预测因素,跟踪了个体的轨迹,并将患者分为不同的表型集群.
主要成果:
- 总胆固醇,LDL和糖尿病等临床指标是心血管疾病的主要预测因素.
- LGMM模型实现了高诊断精度 (0.9556).
- 现型分析揭示了具有独特风险概况的两个不同的集群;一个集群显示,由于夜间缺氧和交感激活,主要心血管不良事件 (MACE) 的风险更高.
结论:
- 这项研究提供了对动态OSA-CVD相互作用的更深入的理解.
- 这种新型模型提供了强大的工具,用于预测OSA患者的心血管疾病发作.
- 研究结果支持开发个性化治疗策略,以改善患者的治疗结果.
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