• Timothy E Sweeney, Tej D Azad, Michele Donato, Winston A Haynes, Thanneer M Perumal, Ricardo Henao, Jesús F Bermejo-Martin, Raquel Almansa, Eduardo Tamayo, Judith A Howrylak, Augustine Choi, Grant P Parnell, Benjamin Tang, Marshall Nichols, Christopher W Woods, Geoffrey S Ginsburg, Stephen F Kingsmore, Larsson Omberg, Lara M Mangravite, Hector R Wong, Ephraim L Tsalik, Raymond J Langley, and Purvesh Khatri.
• Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA.
• Crit. Care Med. 2018 Jun 1; 46 (6): 915-925.

ObjectivesTo find and validate generalizable sepsis subtypes using data-driven clustering.DesignWe used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700).SettingRetrospective analysis.SubjectsPersons admitted to the hospital with bacterial sepsis.InterventionsNone.Measurements And Main ResultsA unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed "Inflammopathic, Adaptive, and Coagulopathic." We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts.ConclusionsThe three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

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