Prediction of vancomycin trough concentration using machine learning in the intensive care unit

Abstract Background: It is difficult to predict vancomycin trough concentrations in critically ill patients as their pharmacokinetics change with the progression of both organ failure and medical intervention. This study aims to develop a model to predict vancomycin trough concentration using machine learning (ML) and to compare its prediction accuracy with that of the population pharmacokinetic (PPK) model. Methods: A single-center retrospective observational study was conducted. Patients who had been admitted to the intensive care unit, received intravenous vancomycin, and had undergone therapeutic drug monitoring between 2013 and 2020,were included. Thereafter, ML models were developed with random forest, LightGBM, and ridge regression using 42 features. Mean absolute errors (MAE) were compared and important features were shown using LightGBM. Results: Among 335 patients, 225 were included as training data and 110 were used for test data. A significant difference was identified in the MAE by each ML model compared with PPK;4.13 ± 3.64 for random forest, 4.18 ± 3.37 for LightGBM, 4.29 ± 3.88 for ridge regression, and 6.17 ± 5.36 for PPK. The highest importance features were pH, lactate, and serum creatinine. Conclusion: This study concludes that ML may be able to more accurately predict vancomycin trough concentrations than the currently used PPK model in ICU patients.