使用心房的负担趋势和机器学习来预测心血管住院的近期风险
James Peacock1, Evan J Stanelle2, Lawrence C Johnson2
1White Plains Hospital, NY (J.P.).
Circulation. Arrhythmia and electrophysiology
|October 24, 2024
概括
从可插入心脏监测器 (ICM) 中对心房的负担趋势的机器学习可以预测心血管住院病例 (CVH). 超出平均水平的负担与患者活动减少相结合,显著增加了CVH风险,为治疗提供了可操作的见解.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 心房动 (AF) 增加了心血管住院 (CVH) 的风险.
- 在AF负荷的动态变化可能会触发CVH.
- 需要对短期CVH进行预测模型.
研究的目的:
- 调查机器学习对预测短期CVH的有用性,使用可插入心脏监测器 (ICM) 的AF负担趋势.
- 为了识别与增加的CVH风险相关的特定AF负担模式.
主要方法:
- 使用了Optum Clinformatics Data Mart和Medtronic CareLink ICM数据库 (2007年至2019年) 的数据库.
- 分析ICM检测到的AF参数,计算简单的移动平均值以确定诊断趋势.
- 使用机器学习将诊断趋势与5天内发生的CVH事件相关联.
主要成果:
- 包括2616名患者; 76%的人经历了CVH.
- 机器学习确定了不同的风险组:低于平均负担,高于平均负担和高于平均负担,活动减少.
- 低活动的高于平均负担显示了CVH的最高相对风险 (11.15) .
- 使用负载趋势和活动数据,CVH的预测能力增加了20% (AUC为0.66对0.55).
结论:
- AF负担趋势,特别是患者活动减少的平均以上负担,与短期的CVH有很强的关联.
- 这种机器学习方法提供了可操作的数据,用于指导治疗和缓解CVH.
- 通过ICM通过动态监测AF负担可以增强心血管事件的预测能力.
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