Abstract
ObjectiveTo establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method.MethodsA retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model.ResultsThree algorithms (SVR, GBRT, and RF) with high R2 scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R2 = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors.ConclusionWe firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance.
Highlights
Teicoplanin is a glycopeptide antibiotic for the treatment of severe infections caused by Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus (MRSA) [1]
This study was performed on 279 TDM measurements obtained from 192 patients who underwent teicoplanin treatment
Our results showed higher loading dose, maintenance dose, duration of teicoplanin treatment, weight, ALB, cystatin C (Cys-C), as well as lower estimated glomerular filtration rate (eGFR), clearance rate (CLcr) and age were related to higher teicoplanin trough concentration
Summary
Teicoplanin is a glycopeptide antibiotic for the treatment of severe infections caused by Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus (MRSA) [1]. With a very highly bound to plasma albumin, teicoplanin has a very long terminal elimination half-life (ranging from 100 to 170 h) and even takes several days to achieve the effective plasma concentration, which results in a great individual variability and permitting once daily dose [3]. An initial loading dose is required to achieve effective plasma concentration rapidly [3]. Teicoplanin is highly bioavailable (>90%) and eventually excreted in urine as a prototype. Because of these pharmacokinetic characteristics, the fixed dosing regimens of teicoplanin administered to patients suffering from hypoalbuminemia [3], and/or renal insufficiency, and/or an expansion of the extracellular fluids might lead to the wide variations and fluctuations of concentrations [4]
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