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

Journal: Cell reports. Medicine
PMID: 39127039

Abstract

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.

Authors

  • H Ceren Ates
    University of Freiburg, FIT Freiburg Centre for Interactive Materials and Bioinspired Technology, 79110 Freiburg, Germany; University of Freiburg, Department of Microsystems Engineering (IMTEK), 79110 Freiburg, Germany.
  • Abdallah Alshanawani
    University of Freiburg, Department of Microsystems Engineering (IMTEK), 79110 Freiburg, Germany.
  • Stefan Hagel
    Institute for Infectious Diseases and Infection Control, Jena University Hospital - Friedrich Schiller University Jena, 07747 Jena, Germany.
  • Menino O Cotta
    Faculty of Medicine, University of Queensland Centre for Clinical Research, The University of Queensland, Brisbane, QLD 4006, Australia.
  • Jason A Roberts
    Centre for Translational Anti-infective Pharmacodynamics, Pharmacy Australia Centre of Excellence, Brisbane, Queensland, Australia.
  • Can Dincer
    University of Freiburg, FIT Freiburg Centre for Interactive Materials and Bioinspired Technology, 79110 Freiburg, Germany; University of Freiburg, Department of Microsystems Engineering (IMTEK), 79110 Freiburg, Germany. Electronic address: dincer@imtek.de.
  • Cihan Ates
    Karlsruhe Institute of Technology (KIT), Machine Intelligence in Energy Systems, 76131 Karlsruhe, Germany; Karlsruhe Institute of Technology (KIT), Center of Health Technologies, 76131 Karlsruhe, Germany. Electronic address: cihan.ates@kit.edu.

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