• Timothy E Sweeney, Aaditya Shidham, Hector R Wong, and Purvesh Khatri.
• 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.
• Sci Transl Med. 2015 May 13;7(287):287ra71.

AbstractAlthough 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.Copyright © 2015, American Association for the Advancement of Science.

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