Unraveling the Impact of Therapeutic Drug Monitoring via Machine Learning

Abstract

AbstractClinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients have been hindered by small patient group, variability between studies, patient heterogeneity and inadequate use of TDM. Accordingly, definitive conclusions regarding the efficacy of TDM have remained elusive. To address these challenges, we propose an innovative approach that leverages data-driven methods to unveil the concealed connections between therapy effectiveness and patient data. Our findings reveal that machine learning algorithms can identify informative features that distinguish between healthy and sick states. These hold promise as potential markers for disease classification and severity stratification, as well as offering a continuous and data-driven “multidimensional” SOFA score. The implementation of TDM-guided therapy was associated with improved recovery rates particularly during the critical 72 hours after sepsis onset. Providing the first-ever quantification of the impact of TDM, our approach has the potential to revolutionize the way TDM applied in critical care.