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Introduction: Appropriate vancomycin trough levels are crucial for ensuring therapeutic efficacy while minimizing toxicity. The aim of this study is to identify clinical factors that influence the steady-state trough concentration of vancomycin and to establish a machine learning model for accurately predicting vancomycin's steady-state trough concentration. Methods: This study is a single-center, retrospective, observational investigation involving 546 hospitalized patients who received intravenous vancomycin therapy. A total of 57 clinical indicators were collected from the subjects. Random forest models were constructed and validated using internal and external datasets, with performance compared to a Bayesian PopPK model. Results: The random forest model incorporated a comprehensive set of clinical indicators, including creatinine clearance, C-reactive protein (CRP), B-type natriuretic peptide (BNP), high-density lipoprotein cholesterol (HDL-C), and daily vancomycin dose, collected 48 hours before steady-state concentration assessment. The random forest regression model achieved correlation coefficients of 0.94 for the training set and 0.81 for the test set, respectively. The random forest classification model demonstrated impressive accuracy rates of 0.99 for the training set and 0.84 for the test set. External validation further confirmed the model's generalization capabilities, with a predictive accuracy of 0.83, surpassing the Bayesian PopPK model's 0.57 accuracy. Discussion: This study presents a robust random forest model that predicts vancomycin steady-state trough concentrations with high accuracy, offering a significant advantage over existing Bayesian PopPK model. By integrating diverse clinical indicators, the model supports personalized medicine approaches and has the potential to improve clinical outcomes by facilitating more precise dosing strategies.
Hu et al. (Thu,) studied this question.