Development of a real-world, therapeutic drug monitoring–informed model to predict teicoplanin daily dose in pediatric intensive care unit patients with bacterial infections

INTRODUCTION: Teicoplanin is commonly used to treat Gram-positive bacterial infections in the intensive care unit (ICU). However, evidence to support individualized therapeutic drug monitoring (TDM)-guided daily dosing of teicoplanin in pediatric ICU patients remains limited despite substantial interpatient variability in pharmacokinetics and clinical response. AIM: To develop and validate a real-world TDM-informed machine learning model to predict physician-adjusted teicoplanin daily dose in pediatric ICU patients, with the goal of supporting individualized dosing decisions in clinical pharmacy practice. METHOD: ), root mean square error (RMSE), and mean absolute error (MAE). RESULTS: = 0.82, RMSE = 53.96 mg/day, MAE = 39.93 mg/day). The proportion of predictions within ± 30% of the observed daily dose was 81.51%. CONCLUSION: This real-world TDM-informed TabNet model shows strong performance in predicting the daily dose of clinician-adjusted teicoplanin in pediatric ICU patients. The model may serve as a clinical decision-support tool for pharmacists and physicians to assist individualized teicoplanin dosing within routine TDM workflows, potentially improving dosing consistency, and supporting safe and effective antimicrobial therapy.