Format

Send to

Choose Destination
Sci Transl Med. 2015 May 13;7(287):287ra71. doi: 10.1126/scitranslmed.aaa5993.

A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set.

Author information

1
Department of Surgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA. Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA 94305, USA. tes17@stanford.edu pkhatri@stanford.edu.
2
Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA 94305, USA.
3
Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, 3333 Burnet Avenue, Cincinnati, OH 45223, USA. Department of Pediatrics, University of Cincinnati College of Medicine, 231 Albert Sabin Way, Cincinnati, OH 45267, USA.
4
Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA 94305, USA. Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Palo Alto, CA 94305, USA. tes17@stanford.edu pkhatri@stanford.edu.

Abstract

Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter-data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.

PMID:
25972003
PMCID:
PMC4734362
DOI:
10.1126/scitranslmed.aaa5993
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center