An international observational study validating gene-expression sepsis immune subgroups

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B. Antcliffe, David | Peronnet, Estelle | Pène, Frédéric | Strålin, Kristoffer | Brealey, David | Blein, Sophie | Cleaver, Richard | Cronhjort, Maria | Diehl, Jean-Luc | Voiriot, Guillaume | Fleurie, Aurore | Lannsjö, Claudia | Lukaszewicz, Anne-Claire | Mårtensson, Johan | Pham, Tài | de Prost, Nicolas | Ricard, Jean-Damien | Singer, Mervyn | Terraz, Gabriel | Timsit, Jean-François | Unge, Christian | Vieillard-Baron, Antoine | Rubenson Wahlin, Rebecka | Llitjos, Jean-François | C. Gordon, Anthony

Edité par CCSD ; BioMed Central -

International audience. Background Sepsis gene-expression sub-phenotypes with prognostic and theranostic potential have been discovered. These have been identified retrospectively and have not been translated to methods that could be deployed at the bedside. We aimed to identify subgroups of septic patients at high-risk of poor outcome, using a rapid, multiplex RNA-based test. Methods Adults with sepsis, in the intensive care unit (ICU) were recruited from 17 sites in the United Kingdom, Sweden and France. Blood was collected at days 2–5 (S1), 6–8 (S2) and 13–15 (S3) after ICU admission and analyzed centrally. Patients were assigned into ‘high' and ‘low' risk groups using two models previously developed for the Immune-Profiling Panel prototype on the bioMérieux FilmArray® system. Results 357 patients were recruited (March 2021–November 2022). 69% were male with a median age of 67 years, APACHE II score of 21 and a 30% 90-day mortality rate. The proportions of high-risk patients decreased over the three sampling times (model 1: 53%, 40%, 15% and model 2: 81%, 74%, 37%). In model 1, 90-day mortality was higher in a high-risk group at each time (S1: 35% vs 24%, p = 0.04; S2: 43% vs 20%, p < 0.001; S3: 52% vs 24%, p = 0.007). In model 2, mortality was only significantly different at the second sampling time (S1: 30% vs 27%, p = 0.77; S2: 34% vs 14%, p = 0.002; S3: 35% vs 23%, p = 0.13). Conclusions Gene-expression diagnostics can identify patients with sepsis at high-risk of poor outcomes and could be used to identify patients for precision medicine trials. Registration ISRCTN11364482 Registered 24th September 2020.

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