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

Aug 3, 2026

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Ates et al. propose a data-driven machine learning framework to quantify how therapeutic drug monitoring (TDM) influences sepsis recovery. The study addresses limits of prior TDM evidence by using clinical trial data from 248 sepsis patients and comparing TDM-guided piperacillin/tazobactam dosing with fixed dosing. The framework dynamically models treatment efficacy and patient response, enabling continuous, multidimensional insight and supporting improved recovery outcomes.

LanguageEnglish

CategoryHealthcare

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Pages19

Reading Time48 min

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Article Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis Graphical abstract | | | | --- | --- | Highlights Authors H. Ceren Ates, Abdallah Alshanawani, Stefan Hagel, Menino O. Cotta, Jason A. Roberts, Can Dincer, CihanAtes Correspondence [dincer@imtek.de](dincer@imtek.de) (C.D.), [cihan.ates@kit.edu](cihan.ates@kit.edu) (C.A.) In brief 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 efﬁcacyand patient response, using clinical trial data comparing TDM-guided piperacillin/ tazobactam therapy to ﬁxed dosing, demonstrating TDM’s positive impact on patient recovery. d The impact of TDM on patient recovery is measured using machine learning d The proposed data-driven framework enables dynamic tracking of patient drug response d The study uses data from a clinical trial involving 248 patients with sepsis d TDM-guided piperacillin/tazobactam therapy is proven to improve recovery rates Ates et al., 2024, Cell Reports Medicine 5, 101681 August 20, 2024 ª 2024 The Authors. Published by Elsevier Inc. [https://doi.org/10.1016/j.xcrm.2024.101681](https://doi.org/10.1016/j.xcrm.2024.101681) ll ll OPEN ACCESS Article Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis H. Ceren Ates,1,2 Abdallah Alshanawani,2 Stefan Hagel,3 Menino O. Cotta,4 Jason A. Roberts,4,5,6 Can Dincer,1,2,9,* and Cihan Ates7,8,* 1University of Freiburg, FIT Freiburg Centre for Interactive Materials and Bioinspired Technology, 79110 Freiburg, Germany 2University of Freiburg, Department of Microsystems Engineering (IMTEK), 79110 Freiburg, Germany 3Institute for Infectious Diseases and Infection Control, Jena University Hospital – Friedrich Schiller University Jena, 07747 Jena, Germany 4Faculty of Medicine, University of Queensland Centre for Clinical Research, The University of Queensland, Brisbane, QLD 4006, Australia 5Departments of Intensive Care Medicine and Pharmacy, Royal Brisbane and Women’s Hospital, Brisbane, QLD 4006, Australia 6Division of Anaesthesiology Critical Care Emergency and Pain Medicine, N^ımes University Hospital, University of Montpellier, 34295 N^ımes, France 7Karlsruhe Institute of Technology (KIT), Machine Intelligence in Energy Systems, 76131 Karlsruhe, Germany 8Karlsruhe Institute of Technology (KIT), Center of Health Technologies, 76131 Karlsruhe, Germany 9Lead contact *Correspondence: [dincer@imtek.de](dincer@imtek.de) (C. D.), [cihan.ates@kit.edu](cihan.ates@kit.edu) (C.A.) [https://doi.org/10.1016/j.xcrm.2024.101681](https://doi.org/10.1016/j.xcrm.2024.101681) SUMMARY 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. INTRODUCTION Sepsis is a life-threatening condition that poses

FAQs

What is the goal of this machine learning approach in sepsis treatment?

The approach aims to measure how therapeutic drug monitoring (TDM) affects recovery in sepsis patients by linking treatment effectiveness to patient data through machine learning.

How is the TDM strategy evaluated in the study?

It uses data from a randomized controlled clinical trial with 248 sepsis patients, comparing TDM-guided piperacillin/tazobactam therapy against fixed dosing.

Why is therapeutic drug monitoring considered challenging to implement in ICUs?

Wider adoption is hindered by limited availability, operational complexity that can delay reporting, and cost considerations.

Aug 3, 2026

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