ArticleIntensive care medicine experimental2025
29-mRNA host response signatures for classification of bacterial infection, viral infection and disease progression in COVID-19 pneumonia: a post hoc analysis of the SAVE-MORE randomized clinical trial.
Article in Intensive care medicine experimental, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Real-world clinical evaluation of the Inflammatix TriVerity test for sepsis at the emergency department: an Acutelines study.Internal and emergency medicine · 2026Article
- Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.Journal of personalized medicine · 2026Review
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Authors and funding
41 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiomarkers based on host response signatures are currently under development for the critically ill. We applied a 29-mRNA classifier for the diagnosis and prognosis of suspected acute infection and sepsis (TriVerity
methodsWe applied three scores from locked classifiers (IMX-BVN-4 and IMX-SEV-4) from the 29-mRNA TriVerity
resultsA total of 471 patients were studied. At baseline nearly 90% had a Very Low or Low IMX-BVN-4 Bacterial Score and Moderate, High or Very High IMX-BVN-4 Viral Score. Anakinra treatment had an effect on the expression of genes indicating IMX-SEV-4 High or Very High scores after a 7 day treatment compared to baseline (12.9% of anakinra-treated patients continued being classified as high severity vs 20.4% of placebo-treated patients, p 0.046).
conclusionsThe classifiers were well tested in COVID-19 pneumonia and may become a useful tool for hospitalized patients.
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