Machine learning-based prediction model for teicoplanin plasma concentrations in adults with liver disease using real-world data

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Objective To construct a prediction model for teicoplanin (TEIC) plasma concentrations through machine learning and deep learning techniques in patients with liver disease using real-world clinical data. Methods A retrospective study was conducted on patients who underwent TEIC therapeutic drug monitoring at a tertiary hospital in China (January 2019–March 2025). Dataset was split into training and test sets (8:2 ratio). Feature selection combined univariate analysis and algorithm importance ranking. Missing values were imputed using random forest (RF) model. Ten machine learning algorithms, such as RF, TransTab and light gradient boosting machine (LightGBM), were employed for model development, with predictive performance evaluated through 10-fold cross-validation on the training set. The optimal model was validated its predictive performance on the test set. Results A total of 646 patients (689 TEIC concentrations) were eligible. Key variables were daily dose, hemoglobin (HGB), aspartate aminotransferase (AST), albumin (ALB), estimated glomerular filtration rate (eGFR), indirect bilirubin (IBIL), total bilirubin (TBIL), platelet count (PLT), urea and direct bilirubin (DBIL). LightGBM demonstrated superior predictive performance among ten algorithms, with a RMSE of 2.90, a R 2 of 0.80, a MAE of 2.34, and 89.13% of accurate predictions within ±30% of observed concentrations on the independent test set. Daily dose, hemoglobin, and AST emerged as the most influential features. Conclusion The LightGBM-based model integrating clinical covariates demonstrated robust predictive capability for TEIC plasma concentrations in liver disease. This tool provides real-world evidence to optimize TEIC dosing, advancing individualized treatment strategies to improve therapeutic outcomes in this population.

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