Oh Wonsuk, Takkavatakarn Kullaya, Kittrell Hannah, Shawwa Khaled, Gomez Hernando, Sawant Ashwin S, Tandon Pranai, Kumar Gagan, Sterling Michael, Hofer Ira, Chan Lili, Oropello John, Kohli-Seth Roopa, Charney Alexander W, Kraft Monica, Kovatch Patricia, Kellum John A, Nadkarni Girish N, Sakhuja Ankit
medRxiv. 2025 Jan 23:2024.08.06.24311556. doi: 10.1101/2024.08.06.24311556.
Intravenous fluids are mainstay of management of acute kidney injury (AKI) after sepsis but can cause fluid overload. Recent literature shows that restrictive fluid strategy may be beneficial in some patients with AKI, however, identifying these patients is challenging.
We aimed to develop and validate a machine learning algorithm to identify patients who would benefit from a restrictive fluid strategy.
We included patients with sepsis who developed AKI within 48 hours of ICU admission and defined restrictive fluid strategy as receiving <500mL fluids within 24 hours after AKI. Our primary outcome was early AKI reversal at 24 hours of AKI onset, and secondary outcomes included sustained AKI reversal and major adverse kidney events by 30 days (MAKE30). We used a causal forest, a machine learning algorithm to estimate individual treatment effects and policy tree algorithm to identify patients who would benefit by restrictive fluid strategy. We developed the algorithm in MIMIC-IV and validated it in SICdb databases.
Among 2,044 patients in the external validation cohort, policy tree recommended restrictive fluids for 66.7%. Among these, patients who received restrictive fluids demonstrated significantly higher rate of early AKI reversal (47.1% vs 31.7%,p=0.004), sustained AKI reversal (28.7% vs 17.5%, p=0.013) and lower rates of MAKE30 (23.0% vs 37.1%, p=0.011). These results were consistent in adjusted analysis.
Policy tree based on causal machine learning can identify septic patients with AKI who benefit from a restrictive fluid strategy. This approach needs to be validated in prospective trials.
静脉输液是脓毒症后急性肾损伤（AKI）治疗的主要手段，但可能导致液体超负荷。最近的文献表明，限制性液体策略可能对某些AKI患者有益，然而，识别这些患者具有挑战性。
我们旨在开发并验证一种机器学习算法，以识别将从限制性液体策略中获益的患者。
我们纳入了在重症监护病房（ICU）入院后48小时内发生AKI的脓毒症患者，并将限制性液体策略定义为在AKI发生后24小时内接受<500mL液体。我们的主要结局是AKI发作24小时时早期AKI逆转，次要结局包括持续AKI逆转和30天时的主要不良肾脏事件（MAKE30）。我们使用因果森林（一种用于估计个体治疗效果的机器学习算法）和策略树算法来识别将从限制性液体策略中获益的患者。我们在MIMIC-IV中开发该算法，并在SICdb数据库中进行验证。
在外部验证队列的2044例患者中，策略树推荐66.7%的患者采用限制性液体治疗。在这些患者中，接受限制性液体治疗的患者早期AKI逆转率显著更高（47.1%对31.7%，p=0.004），持续AKI逆转率更高（28.7%对17.5%，p=0.013），MAKE30发生率更低（23.0%对37.1%，p=0.011）。这些结果在调整分析中是一致的。
基于因果机器学习的策略树可以识别出从限制性液体策略中获益的脓毒症合并AKI患者。这种方法需要在前瞻性试验中进行验证。
