Research ArticleSepsis

Robust classification of bacterial and viral infections via integrated host gene expression diagnostics

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Science Translational Medicine  06 Jul 2016:
Vol. 8, Issue 346, pp. 346ra91
DOI: 10.1126/scitranslmed.aaf7165

Making a gene list, checking it twice

Sepsis, a severe inflammation caused by infection, is a common and deadly medical condition. Sepsis therapy combines supportive treatment with interventions directed at the underlying cause of the illness, especially antibiotics for bacterial infections. Unfortunately, it can be difficult to distinguish patients with noninfectious inflammation from those with bacterial and viral infections, and only those with bacterial sepsis derive any benefit from antibiotics. Sweeney et al. have previously analyzed large numbers of patients across many cohorts to derive a blood test identifying which patients with sepsis-like symptoms have an underlying infection. Now, the authors expanded their analysis to create an integrated score that not only identifies infected patients but also classifies their infection as bacterial or viral, suggesting appropriate treatment.


Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.

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