Résumé
Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingly, definitive conclusions regarding the efficacy of TDM remain 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, collected through a randomized controlled trial (DRKS00011159; 10th October 2016). Our findings reveal that machine learning algorithms can successfully 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” Sequential Organ Failure Assessment (SOFA) score. The positive impact of TDM on patient recovery rates is demonstrated by unraveling the intricate connections between therapy effectiveness and clinically relevant data via machine learning.
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•The impact of TDM on patient recovery is measured using machine learning•The proposed data-driven framework enables dynamic tracking of patient drug response•The study uses data from a clinical trial involving 248 patients with sepsis•TDM-guided piperacillin/tazobactam therapy is proven to improve recovery rates
Ates et al. propose a machine learning approach to measure the impact of therapeutic drug monitoring (TDM) on sepsis recovery. Their framework dynamically tracks treatment efficacy and patient response, using clinical trial data comparing TDM-guided piperacillin/tazobactam therapy to fixed dosing, demonstrating TDM’s positive impact on patient recovery.