• M Samie Tootooni, Erin F Barreto, Phichet Wutthisirisart, Kianoush B Kashani, and Kalyan S Pasupathy.
• Department of Health Informatics and Data Science, Loyola University Chicago, Maywood, IL, United States of America. Electronic address: mtootooni@luc.edu.
• J Crit Care. 2024 Mar 18; 82: 154784154784.

BackgroundVancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate dosing for each ICU patient.MethodsObserved vancomycin trough levels were categorized into sub-therapeutic, therapeutic, and supra-therapeutic levels to train and compare different classification models. We included adult ICU patients (≥ 18 years) with at least one vancomycin concentration measurement during hospitalization at Mayo Clinic, Rochester, MN, from January 2007 to December 2017.ResultThe final cohort consisted of 5337 vancomycin courses. The XGBoost models outperformed other machine learning models with the AUC-ROC of 0.85 and 0.83, specificity of 53% and 47%, and sensitivity of 94% and 94% for sub- and supra-therapeutic categories, respectively. Kinetic estimated glomerular filtration rate and other creatinine-based measurements, vancomycin regimen (dose and interval), comorbidities, body mass index, age, sex, and blood pressure were among the most important variables in the models.ConclusionWe developed models to assess the risk of sub- and supra-therapeutic vancomycin trough levels to improve the accuracy of drug dosing in critically ill patients.Copyright © 2023. Published by Elsevier Inc.

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