期刊
CRITICAL CARE MEDICINE
卷 46, 期 6, 页码 915-925出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/CCM.0000000000003084
关键词
cluster analysis; gene expression; machine learning; precision medicine; sepsis
资金
- Defense Advanced Research Projects Agency
- Army Research Office [W911NF-15-1-0107]
- National Institutes of Health [U01AI066569, P20RR016480, HHSN266200400064C]
- National Institutes of Health (NIH)
- Instituto de Salud Carlos III [EMER07/050, PI13/02110, PI16/01156]
- National Institute of General Medical Sciences [R01GM099773, R01GM108025]
- Bill & Melinda Gates Foundation
- NIH
- Defense Advanced Research Projects Agency (DARPA)
- OncocellMDx
- InfiniaML
- Defense Advanced Research Projects Agency Army Research Office
- National Center for Advancing Translational Sciences [UL1 TR000127, KL2 TR000126]
- DARPA
- Novartis Vaccines and Diagnostics
- bioMerieux
- BioFire Diagnostics
- National Center for Advancing Translational Sciences of the NIH [UL1TR001417]
- National Institute for Allergy and Infectious Diseases [1U19AI109662, U19AI057229, U54I117925]
- Inflammatix
Objectives: To find and validate generalizable sepsis subtypes using data-driven clustering. Design: We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700). Setting: Retrospective analysis. Subjects: Persons admitted to the hospital with bacterial sepsis. Interventions: None. Measurements and Main Results: A 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. Conclusions: The 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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