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Crit Care Med. 2018 Jun;46(6):915-925. doi: 10.1097/CCM.0000000000003084.

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters.

Author information

1
Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA.
2
Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA.
3
Sage Bionetworks, Seattle, WA.
4
Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC.
5
Department of Electrical and Computer Engineering, Duke University, Durham, NC.
6
Bio Sepsis, Hospital ClĂ­nico Universitario de Valladolid/IECSCYL, Valladolid, Spain.
7
Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Penn State Milton S. Hershey Medical Center, Hershey, PA.
8
Department of Medicine, Cornell Medical Center, New York, NY.
9
Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Sydney, NSW, Australia.
10
Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, NC.
11
Durham Veteran's Affairs Health Care System, Durham, NC.
12
Rady Children's Institute for Genomic Medicine, San Diego, CA.
13
Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, OH.
14
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH.
15
Department of Pharmacology, University of South Alabama, Mobile, AL.

Abstract

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.

PMID:
29537985
PMCID:
PMC5953807
[Available on 2019-06-01]
DOI:
10.1097/CCM.0000000000003084

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