Trial reportCell reports. Medicine2024
Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis.
Trial report in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study.Frontiers in medicine · 2025Pooled it
- Article
- Data-driven spatial-temporal framework for exploring the heterogeneity and temporality of sepsis.Chinese medical journal · 2026Review
- Next-generation hybrid bioanalytical platforms and clinical integration of TDM technologies for precision monitoring to optimize last-resort antibiotic therapy.Frontiers in pharmacology · 2026Review
- Prediction of Meropenem Target Attainment in Critically Ill Patients Using Routine Clinical Data from a Single Center: A Machine Learning Approach.Infection and drug resistance · 2026Article
- Machine Learning Models Reveal New Risk Factors for Sub-/Supra-Therapeutic Concentrations of Sirolimus in Children with Vascular Anomalies.Drug design, development and therapy · 2026Article
- Machine learning-integrated electrochemical sensing of ciprofloxacin for digital point-of-care therapeutic drug monitoring.Mikrochimica acta · 2025Article
- Prognostic Value of the Charlson Comorbidity Index for Mortality and Machine Learning-Based Prediction in Critically Ill Patients with Paralytic Ileus: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Treatment effects of Xuebijing injection in patients with sepsis by clinical phenotype: a post hoc analysis of the EXIT-SEP trial.EClinicalMedicine · 2025Article
- Predicting outcomes using neural networks in the intensive care unit.World journal of clinical cases · 2025Article
- Artificial Intelligence in Sepsis Management: An Overview for Clinicians.Journal of clinical medicine · 2025Review
- lncRNA FENDRR targets miR-3614-5p to promote sepsis-induced acute kidney injuryCentral-European journal of immunology · 2025Article
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.