Model‐informed vancomycin precision dosing by population pharmacokinetics combined with machine learning algorithms

AIMS: Achieving the target AUC/MIC remains a critical challenge in vancomycin therapeutic drug monitoring, with traditional empirical dosing regimens often leading to suboptimal outcomes. This study aimed to develop and validate a novel population pharmacokinetic (PopPK)-informed machine learning model to optimize TDM-guided dose adjustments. By accurately predicting the 24-h area under the curve (AUC), the model assists clinicians in rapidly individualizing regimens, thereby improving target attainment, reducing subsequent modifications and minimizing toxicity. METHODS: , RMSE, Lin's concordance correlation coefficient (CCC), mean percentage error (MPE) and mean absolute percentage error (MAPE). The top-performing model (ANN) was subsequently validated on an independent test set. Its performance, interpretability and clinical safety were further examined using SHAP analysis, Bland-Altman plots, error grid analysis (EGA) and ROC curve evaluation. RESULTS: (0.920 ± 0.027) and CCC (0.960 ± 0.011), coupled with the lowest RMSE (0.498 ± 0.076 L/h) and minimal systematic bias (MPE: 1.290 ± 1.774). SHAP analysis identified DV (measured concentration), Cys (mg/L) and Last dose as the three most influential predictive features. CONCLUSIONS: The ANN model achieved exceptional accuracy, robustness and clinical safety in predicting individualized vancomycin clearance. This model is deployable as an instantaneous, point-of-care CDSS tool, offering a powerful new pathway to solve the long-standing challenges of accessibility and accuracy in routine TDM practice.