Prediction of Significant Creatinine Elevation in First ICU Stays with Vancomycin Use: A retrospective study through Catboost

Abstract

Background: Vancomycin, a key antibiotic for severe Gram-positive infections in ICUs, poses a high nephrotoxicity risk. Early prediction of kidney injury in critically ill patients is challenging. This study aimed to develop a machine learning model to predict vancomycin-related creatinine elevation using routine ICU data. Methods: We analyzed 10,288 ICU patients (aged 18-80) from the MIMIC-IV database who received vancomycin. Kidney injury was defined by KDIGO criteria (creatinine rise >=0.3 mg/dL within 48h or >=50% within 7d). Features were selected via SelectKBest (top 30) and Random Forest ranking (final 15). Six algorithms were tested with 5-fold cross-validation. Interpretability was evaluated using SHAP, Accumulated Local Effects (ALE), and Bayesian posterior sampling. Results: Of 10,288 patients, 2,903 (28.2%) developed creatinine elevation. CatBoost performed best (AUROC 0.818 [95% CI: 0.801-0.834], sensitivity 0.800, specificity 0.681, negative predictive value 0.900). Key predictors were phosphate, total bilirubin, magnesium, Charlson index, and APSIII. SHAP confirmed phosphate as a major risk factor. ALE showed dose-response patterns. Bayesian analysis estimated mean risk 60.5% (95% credible interval: 16.8-89.4%) in high-risk cases. Conclusions: This machine learning model predicts vancomycin-associated creatinine elevation from routine ICU data with strong accuracy and interpretability, enabling early risk detection and supporting timely interventions in critical care.