Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis

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 inad-
equate 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 prom-
ise 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.