A machine learning model that emulates experts decision making in vancomycin initial dose planning

Abstract

Vancomycin is a glycopeptide antibiotic that has been used primarily in the treatment of methicillin-resistant Staphylococcus aureus infections. To enhance its clinical effectiveness and prevent nephrotoxicity, therapeutic drug monitoring (TDM) of trough concentrations is recommended. Initial vancomycin dosing regimens are determined based on patient characteristics such as age, body weight, and renal function, and dosing strategies to achieve therapeutic concentration windows at initial TDM have been extensively studied. Although numerous dosing nomograms for specific populations have been developed, no comprehensive strategy exists for individually tailoring initial dosing regimens; therefore, decision making regarding initial dosing largely depends on each clinicians experience and expertise. In this study, we applied a machine-learning (ML) approach to integrate clinician knowledge into a predictive model for initial vancomycin dosing. A dataset of vancomycin initial dose plans defined by pharmacists experienced in vancomycin TDM (i.e., experts) was used to build the ML model. The target trough concentration was attained at comparable rates with the model- and expert-recommended dosing regimens, suggesting that the ML model successfully incorporated the experts knowledge. The predictive model developed here will contribute to improved decision making for initial vancomycin dosing and early attainment of therapeutic windows.