Comparative evaluation of 11 machine learning-based risk prediction models for vancomycin-associated acute kidney injury during initial dosing

Aim : Early vancomycin-associated acute kidney injury (AKI) identification is important during the initial dosing period when non–steady-state pharmacokinetic exposure may influence nephrotoxicity risk. We aimed to conduct a multicentre retrospective cohort study of hospitalised adult patients who received vancomycin at three tertiary care hospitals in Japan between April 2019 and March 2024. Methods : Early area under the concentration–time curve (AUC) metrics, trough concentrations, concomitant medications, and comorbidities were used to develop and compare machine learning models to predict vancomycin-associated AKI. Eleven algorithms were evaluated using a nested cross-validation framework to minimise optimistic bias and prevent data leakage. Results : Among 442 patients (median age 72 years), AKI occurred in 5.7%. Across models, day 2 AUC and trough concentration consistently emerged as the most influential predictors, followed by concomitant use of piperacillin/tazobactam, diuretics, and vasopressors. The random forest model showed the best overall discriminative performance (area under the receiver operating characteristic curve 0.89; area under the precision–recall curve 0.47). Shapley additive explanations revealed nonlinear increases in AKI risk at approximately day 2 AUC ≥ 550 mg·h/L and trough concentration ≥ 20 μg/mL. These findings indicate that early vancomycin exposure, particularly cumulative exposure within the first 48 h, is strongly associated with AKI risk. Conclusion : Machine learning models incorporating early AUC metrics with trough concentrations may provide a clinically informative framework for early risk stratification during vancomycin therapy and support further evaluation of early AUC-guided dosing strategies with trough concentrations as complementary exposure markers.