• Rombout B E van Amstel, Jason N Kennedy, Brendon P Scicluna, BosLieuwe D JLDJDepartment of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.Laboratory of Experimental Intensive Care and Anesthesiology (L.E.I.C.A.), Amsterdam UMC, Location , Hessel Peters-Sengers, Joe M Butler, Eddie Cano-Gamez, Julian C Knight, VlaarAlexander P JAPJDepartment of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.Laboratory of Experimental Intensive Care and Anesthesiology (L.E.I.C.A.), Amsterdam UMC, Loca, Olaf L Cremer, Derek C Angus, Tom van der Poll, Christopher W Seymour, Lonneke A van Vught, and MARS Consortium.
• Department of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands. r.b.vanamstel@amsterdamumc.nl.
• Intensive Care Med. 2023 Nov 1; 49 (11): 136013691360-1369.

PurposeThe heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies.MethodsThis secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, β, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles.ResultsAll four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made.ConclusionAmong critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.© 2023. The Author(s).

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