Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing.

Abstract

Selecting an appropriate population pharmacokinetic (PK) model for individual patients in model-informed precision dosing (MIPD) can be challenging, particularly in the absence of therapeutic drug monitoring (TDM) samples. We developed a machine learning (ML) model to guide individualized PK model selection for a priori MIPD of vancomycin based on routinely recorded patient characteristics. This retrospective analysis included 343,636 vancomycin TDM records, each from a distinct adult patient across 156 healthcare centers, along with a priori predictions from six PK models. A multi-label classification approach was applied, labeling PK model predictions based on whether they fell within 80%-125% of observed TDM values. Various modeling strategies were evaluated using XGBoost as the base algorithm, with binary relevance selected for the final model. At the prediction stage, PK models were ranked and averaged for each patient based on ML-predicted probabilities that predictions would fall within 80%-125% of the observed concentration. Selecting the highest ranked PK model for each patient and ML-based model averaging outperformed all single PK models, body mass index-based selection, and naive averaging. On a population level, these ML approaches resulted in more accurate predictions, a higher proportion of predictions within 80%-125% of observed vancomycin concentrations, and no systematic bias. Predictive performance declined with lower ML-assigned rankings, and selecting the lowest-ranked PK model for each patient resulted in worse performance than the worst-performing single PK model. By guiding the selection of appropriate models and avoiding less suitable ones, ML approaches for a priori MIPD may improve early dosing decisions.