在重症监护中比较高流量鼻和非侵入性通风:深度反事实推断的见解
Xiaolei Lu1, Michael Miller1, Alex K Pearce1
1Department of Biomedical Informatics (Xiaolei Lu, Jonathan Y. Lam, Aaron E. Boussina, and Shamim Nemati), the Division of Pulmonary, Critical Care, and Sleep Medicine (Michael Miller, Alex K. Pearce, Atul Malhotra), University of California, San Diego, La Jolla, CA; Department of Informatics (Kai Zheng), University of California, Irvine, Irvine, CA.
针对急性呼吸衰竭 (ARF) 的高流量鼻腔管 (HFNC) 或非侵入性正压通风 (NIV) 的个体化治疗改善了结果. 模型导向呼吸支减少了ICU患者的侵入性机械通风和死亡率.
科学领域:
- 关键护理医学 关键护理医学
- 呼吸系统医学 呼吸系统医学
- 医疗保健中的人工智能
背景情况:
- 随机试验为急性呼吸衰竭 (ARF) 提供了人口水平的指导,但不考虑个体患者的变化.
- 识别具有侵入性机械通风 (IMV) 高风险的患者对于优化呼吸支至关重要.
- 高流量鼻腔管 (HFNC) 和非侵入性正压通风 (NIV) 是ARF的常见干预措施.
研究的目的:
- 开发和验证一个深度的反事实模型来估计HFNC与NIV在ARF患者的个性化治疗效应 (ITE).
- 评估以模型为指导,一致治疗对IMV需要和患者结果的影响.
- 评估模型在不同医疗保健环境中的通用性.
主要方法:
- 对重症监护病房 (ICU) 患者进行了回顾性队列研究,这些患者被确定为高风险的IMV.
- 开发一个深度的反事实模型,包括表示学习,条件规范流和混调整.
- 使用多变量逻辑回归评估治疗一致性及其与IMV,死亡率或住院出院的关联.
主要成果:
- 在加州大学圣地亚哥医疗中心 (UC San Diego Health) 的协同治疗 (与给药疗法一致的模型建议) 与IMV的几率显著降低有关 (OR=0.661对于NIV;0.677对于HFNC).
- 在加州大学圣地亚哥医疗中心,协同治疗还降低了死亡率或临终关怀退院的几率 (OR=0.679对于NIV;0.749对于HFNC).
- 在 UC Irvine Health,一致的NIV和HFNC显示出死亡率或临终关怀出院的大幅降低 (OR=0.092对NIV;0.088对HFNC).
结论:
- 个性化,以模型为指导的呼吸支策略可以显著改善ARF重病患者的结果.
- 与深度反事实模型的治疗一致性预测了IMV的需求减少以及较低的死亡率或临终关怀出院率.
- 这种方法提供了一种有希望的方法,可以在个体患者层面优化呼吸支决策.
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