使用基于深度学习的因果推断方法确定在重症监护室患者个性化心房动治疗的最佳策略:节奏和/或节率控制
Min Woo Kang1, Shin Young Ahn1, Yoonjin Kang2,3
1Department of Internal Medicine, Korea University Guro Hospital, Seoul 08308, Korea.
Journal of the American Medical Informatics Association : JAMIA
|November 29, 2025
概括
在重症监护室 (ICU) 管理心房动 (AF) 是至关重要的. 节律和节律控制策略都显著降低了ICU患者的死亡率,特定的患者个人资料从节律控制中获益更多.
科学领域:
- 关键护理医学 关键护理医学
- 心脏病学 心脏病学
- 医疗保健中的人工智能
背景情况:
- 前庭动 (AF) 在重症监护室 (ICU) 普遍存在,但最佳管理策略仍在争论中.
- 目前对AF管理的指导方针通常基于门诊数据,需要对ICU进行特定研究.
研究的目的:
- 评估不同AF管理策略 (节律控制,节律控制或无控制) 在降低ICU患者死亡率方面的有效性.
- 通过使用深度学习因果推理模型,确定从节奏控制与节奏控制中获益最多的患者子组.
主要方法:
- 使用了MIMIC-III和MIMIC-IV数据库中的数据,用于记录AF的ICU入院.
- 采用基于深度学习的因果推理模型来分析节奏和节奏控制策略的治疗效应.
- 使用后勤回归识别了与差异化治疗效果相关的患者特征.
主要成果:
- 所有的AF管理策略 (节奏和速率控制,仅节奏,仅速率) 与没有控制相比,显著降低了住院死亡率.
- 仅速率控制显示出最大的死亡率降低 (-9.11%),其次是仅节律控制 (-2.32%) 和结合节律/速率控制 (-1.23%).
- 节律控制在老年患者,心率较高的患者,新发动心肌梗塞以及特定的并发症/疾病中更有效.
结论:
- 基于深度学习的因果推断有效量化了ICU中AF管理策略的死亡率降低.
- 确定了特定的患者特征,这些特征可以预测节奏控制对节奏控制的更好的结果.
- 调查结果提供了基于证据的见解,用于在重症患者中个性化AF管理.
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