An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of deaths globally each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole blood transcriptomics for stratification of patients with severe infection by integrating data from 3,149 samples of sepsis patients and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 19-gene signature. Finally, we built a machine learning framework, SepstratifieR, to deploy SRSq in sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, thus bringing us closer to precision medicine in infection.