Infection and drug resistance
Prediction of Meropenem Target Attainment in Critically Ill Patients Using Routine Clinical Data from a Single Center: A Machine Learning Approach
By Lu Q, Wang Y, Huang K and 3 others.
Abstract
Objective
Meropenem pharmacokinetics vary considerably among critically ill patients, resulting in uncertainty in target concentration attainment. This study aimed to develop a predictive model using real-world therapeutic drug monitoring (TDM) data to assess target attainment and provide a reference for individualized meropenem management.
Methods
This single-center retrospective study included critically ill patients administered meropenem treatment and subjected to trough concentration monitoring from August 2023 to January 2026. Meropenem target attainment was defined as a trough concentration (Cmin) exceeding 8.0 mg/L. The analysis included 399 TDM samples from 333 patients, which were partitioned into training and test sets at an 8:2 ratio using a patient-level splitting approach. Following feature selection, eight machine learning algorithms were applied to build prediction models. Predictive performance was assessed primarily by the area under the receiver operating characteristic curve (AUC), with accuracy, sensitivity, specificity, F1 score, and calibration curves used as additional evaluation measures. SHapley Additive exPlanations (SHAP) analysis was then used to interpret the final model.