Application of Machine Learning Models to Predict Tigecycline Exposure in Critically Ill Patients Using Different Limited Sampling Strategies.

Background: The high variability in the pharmacokinetics of tigecycline may impact therapeutic efficacy, particularly in critically ill patients. This study introduces machine learning (ML) methods, aiming to clarify their effectiveness in individualized drug exposure prediction and determine the optimal modeling strategy. Methods: The least absolute shrinkage and selection operator (LASSO) method was used to screen the covariates. Four ML models, random forest, extreme gradient boosting, multilayer perceptron, and long short-term memory network (LSTM), were trained and tested using 751 concentrations from 98 patients in the pharmacokinetic dataset to predict the area under the steady-state plasma concentration-time curve within 24 hours (AUC Results: Seven covariates were screened out by LASSO method, including total bilirubin, creatinine clearance, Sequential Organ Failure Assessment (SOFA) score, body mass index, serum creatinine, age, and prothrombin time. Internal validation showed that, among the four ML models, the LSTM model performed the best, with the combination of covariates and the plasma concentration feature "C Conclusion: The LSTM model established in this study enables an accurate estimate of tigecycline AUC