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

Five publicly available gene expression cohorts comparing SIRS/trauma to sepsis were split into time-matched subcohorts and summarized via multicohort analysis, which yielded an 11-gene set that was validated for discriminating patients with sepsis or infection in 15 independent cohorts. Uncovering the tracks of sepsis Sepsis, or systemic inflammation resulting from infection, is notoriously difficult to distinguish from other causes of inflammation, which can be a nonspecific response to noninfectious conditions. A delay in recognition of sepsis and initiation of antibiotics can be deadly. Conversely, preemptively starting antibiotics in a patient misdiagnosed with sepsis exposes the patient to side effects of the drugs. Sweeney et al. evaluated a large collection of data from patients with sepsis or sterile inflammation and identified a set of genes that distinguish the two conditions. The authors also discovered that gene expression in these conditions changes over time, and thus any attempts to identify sepsis patients by gene expression would need to consider the time since the onset of disease. 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.