Causal Machine Learning (ML) framework outperformed random forest model in identifying patients with AKI and sepsis who benefit from restrictive fluid therapy. This provides a data-driven approach for personalized fluid management and merits prospective evaluation in clinical trials.

In this retrospective cohort study, we developed and externally validated a causal-ML model to identify septic patients with AKI who would benefit from restrictive IV fluid therapy.

Those who received the model-recommended restrictive IV fluids experienced significantly higher rates of early AKI reversal (53.9 % vs. 33.2 %; p<0.001) and lower rates of MAKE 30 (17.1% vs 34.6%, p=0.003). Causal forest model outperformed random forest in identifying HTE of restrictive IV fluids with AUTOC 0.15 vs. -0.02 in external validation cohort. Among 1,931 patients in external validation cohort, the model recommended restrictive fluids for 68.9%.

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