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Renal replacement therapy (RRT) was overall associated with higher hospital mortality in critically ill patients, but substantial heterogeneity in treatment effects exists.
Machine learning methods identified patient subgroups with potential mortality benefit from RRT, particularly those with elevated serum creatinine, reduced urine output, and high blood urea nitrogen levels.
Patients with relatively preserved renal function experienced potential harm, underscoring the need for more individualized treatment decisions.
These findings support a data-driven, nuanced approach to optimize RRT initiation strategies in ICU settings.
