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Updated: Jun 30, 2025
Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
Published on: October 25, 2013
Determining steady-state trough range in vancomycin drug dosing using machine learning
M Samie Tootooni1, Erin F Barreto2, Phichet Wutthisirisart3
1Department of Health Informatics and Data Science, Loyola University Chicago, Maywood, IL, United States of America.
Background:
Vancomycin 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.
Methods:
Observed 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.
Result:
The 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.
Conclusion:
We 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.
Insights
Machine learning models predict vancomycin dosing risks in intensive care units (ICU). These models identify sub- and supra-therapeutic vancomycin trough levels, improving drug dosing accuracy for critically ill patients.
Area of Science:
- Pharmacology
- Clinical Pharmacy
- Machine Learning in Medicine
Background:
- Vancomycin, a critical antibiotic in ICUs, has a narrow therapeutic window and is nephrotoxic.
- Achieving therapeutic vancomycin trough levels is crucial for efficacy and safety.
- Predicting and managing vancomycin dosing is essential for critically ill patients.
Purpose of the Study:
- To develop and compare machine learning models for predicting vancomycin trough levels.
- To assess the risk of sub-therapeutic and supra-therapeutic vancomycin levels.
- To improve the accuracy of vancomycin dosing in intensive care unit (ICU) patients.
Main Methods:
- Utilized a cohort of 5337 vancomycin courses from adult ICU patients (2007-2017).
- Trained and compared various classification models using categorized vancomycin trough levels (sub-therapeutic, therapeutic, supra-therapeutic).
- Evaluated model performance using AUC-ROC, specificity, and sensitivity.
Main Results:
- XGBoost models demonstrated superior performance compared to other machine learning approaches.
- Achieved AUC-ROC of 0.85 and 0.83 for sub- and supra-therapeutic levels, respectively.
- Key predictors included kinetic estimated glomerular filtration rate, vancomycin regimen, comorbidities, BMI, age, sex, and blood pressure.
Conclusions:
- Developed predictive models to identify risks of inappropriate vancomycin trough levels.
- The models aid in optimizing vancomycin dosing strategies for ICU patients.
- Enhanced accuracy in drug dosing can improve patient outcomes in critical care settings.
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