RT Journal Article
SR Electronic
T1 Mortality prediction in sepsis via gene expression analysis: a community approach
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 095489
DO 10.1101/095489
A1 Timothy E Sweeney
A1 Thanneer M Perumal
A1 Ricardo Henao
A1 Marshall Nichols
A1 Judith A Howrylak
A1 Augustine M Choi
A1 Jesús F Bermejo-Martin
A1 Raquel Almansa
A1 Eduardo Tamayo
A1 Emma E Davenport
A1 Katie L Burnham
A1 Charles J Hinds
A1 Julian C Knight
A1 Christopher W Woods
A1 Stephen F Kingsmore
A1 Geoffrey S Ginsburg
A1 Hector R Wong
A1 Grant P Parnell
A1 Benjamin Tang
A1 Lyle L Moldawer
A1 Frederick E Moore
A1 Larsson Omberg
A1 Purvesh Khatri
A1 Ephraim L Tsalik
A1 Lara M Mangravite
A1 Raymond J Langley
YR 2016
UL http://biorxiv.org/content/early/2016/12/19/095489.abstract
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.