患有败血症和急性损伤的患者的个性化液体管理:一种随意的机器学习方法
Wonsuk Oh1,2,3, Kullaya Takkavatakarn1,4, Zainab Al-Taie5
1Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
Critical care explorations
|December 8, 2025
概括
一个新的因果机器学习 (ML) 框架有效地识别了因败血症引起的急性损伤 (AKI) 的患者,他们受益于限制性IV液体治疗,改善AKI逆转和减少不良事件.
科学领域:
- 关键护理医学 关键护理医学
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗保健中的机器学习
背景情况:
- 静脉注射 (IV) 液体对于治疗败血症引起的急性损伤 (AKI) 至关重要,但液体过载具有重大风险.
- 识别受益于限制性流体策略的患者是具有挑战性的.
- 因果机器学习 (ML) 提供了一种新的方法来估计IV液体的异质治疗效应 (HTEs).
研究的目的:
- 开发和验证一种因果性ML框架,用于识别AKI和败血症患者,这些患者受益于限制性液体管理 (在24小时内<500毫升).
主要方法:
- 在ICU入院后48小时内发生败血症和AKI的患者的回顾性研究.
- 开发并验证了一种因果性ML模型,以估计流体治疗的个性化治疗效果.
- 将模型性能与使用目标操作员特征曲线 (AUTOC) 下面面积的随机森林模型进行比较.
主要成果:
- 与随机森林相比,因果森林模型在识别限制性IV液体的HTE方面表现优越 (外部验证中AUTOC 0.15与-0.02).
- 该模型在外部验证队列中的68.9%的患者推限制性液体.
- 接受限制性液体治疗的患者显著增加了早期AKI逆转 (53.9%与33.2%相比),持续AKI逆转 (34.2%与18.0%相比),并降低了MAKE30率 (17.1%与34.6%相比).
结论:
- 开发的因果性ML框架有效地识别了AKI的败血症患者,这些患者可以从限制性流体治疗中受益.
- 这种数据驱动的方法可以实现个性化的流体管理.
- 未来的临床试验有必要进一步评估这一框架.
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