败血症和AKI患者的个性化液体管理:一个政策树方法
medRxiv : the preprint server for health sciences
|August 16, 2024
概括
机器学习可以识别患有败血症和急性损伤 (AKI) 的患者,他们可以从限制性流体策略中受益. 这种方法可以改善脏的早期恢复,并减少不良事件,但需要前性验证.
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.
- 数据科学数据科学数据科学
背景情况:
- 静脉注射的液体对于败血症引起的急性损伤 (AKI) 至关重要,但风险是液体过载.
- 在AKI中识别受益于限制性流体策略的患者是具有挑战性的.
研究的目的:
- 开发和验证一种机器学习算法,用于识别因败血症引起的AKI患者,这些患者可以从限制性流体策略中受益.
主要方法:
- 使用因果森林和政策树算法来估计个别治疗效应.
- 该算法使用MIMIC-IV数据库开发,并在SICdb数据库上验证.
- 包括患有败血症的患者,在ICU入院后48小时内发生AKI.
主要成果:
- 政策树建议在验证队列中66.7%的患者使用限制性液体.
- 限制性流体策略与早期AKI逆转率 (47.1%对31.7%) 和持续AKI逆转率 (28.7%对17.5%) 的显着较高相关.
- 接受限制性液体治疗的患者在30天内出现严重脏不良事件的比率显著降低 (23.0%vs37.1%).
结论:
- 基于因果机器学习的策略树可以识别AKI的败血症患者,他们可以从限制性流体管理中受益.
- 这种新的方法需要通过前性临床试验进行验证.
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