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

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 (0.778) and the second-highest NPV (0.950). The maximum creatinine level, maximum blood urea nitrogen level, minimum creatinine level, and history of renal disease were found to be the four most significant parameters through SHAP analysis. The AUCs were higher than 0.75 in predicting ARC through subgroup analysis. The XGBoost ML prediction model might help clinicians to predict the onset of ARC early among septic patients and make timely dose adjustments to avoid therapeutic failure.