Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.

ABSTRACT: This study aims to establish and validate prediction models based on novel machine learning (ML) algorithms for augmented renal clearance (ARC) in critically ill patients with sepsis. Patients with sepsis were extracted from the Medical Information Mart for Intensive Care IV (MIMICIV) database. Seven ML algorithms were applied for model construction. The Shapley Additive Explanations (SHAP) method was used to explore the significant characteristics. Subgroup analysis was conducted to verify the robustness of the model. A total of 2673 septic patients were included in the analysis, of which 518 patients (19.4%) developed ARC within one week after ICU admission. The Extreme Gradient Boosting (XGBoost) model had the best predictive performance (AUC: 0.841) with the highest balanced accuracy (

SUBMITTER: Wu T 

PROVIDER: S-EPMC12274384 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature