PT - JOURNAL ARTICLE AU - Timothy E Sweeney AU - Thanneer M Perumal AU - Ricardo Henao AU - Marshall Nichols AU - Judith A Howrylak AU - Augustine M Choi AU - Jesús F Bermejo-Martin AU - Raquel Almansa AU - Eduardo Tamayo AU - Emma E Davenport AU - Katie L Burnham AU - Charles J Hinds AU - Julian C Knight AU - Christopher W Woods AU - Stephen F Kingsmore AU - Geoffrey S Ginsburg AU - Hector R Wong AU - Grant P Parnell AU - Benjamin Tang AU - Lyle L Moldawer AU - Frederick E Moore AU - Larsson Omberg AU - Purvesh Khatri AU - Ephraim L Tsalik AU - Lara M Mangravite AU - Raymond J Langley TI - Mortality prediction in sepsis via gene expression analysis: a community approach AID - 10.1101/095489 DP - 2016 Jan 01 TA - bioRxiv PG - 095489 4099 - http://biorxiv.org/content/early/2016/12/19/095489.short 4100 - http://biorxiv.org/content/early/2016/12/19/095489.full AB - Improved risk stratification and prognosis in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here three scientific groups were invited to independently generate prognostic models for 30-day mortality using 12 discovery cohorts (N=650) containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance was validated in 5 cohorts of community-onset sepsis patients (N=189) in which the models showed summary AUROCs ranging from 0.765-0.89. Similar performance was observed in 4 cohorts of hospital-acquired sepsis (N=282). Combining the new gene-expression-based prognostic models with prior clinical severity scores led to significant improvement in prediction of 30-day mortality (p<0.01). These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis, improving both resource allocation and prognostic enrichment in clinical trials.