A community approach to mortality prediction in sepsis via gene expression analysis

Timothy E. Sweeney, Thanneer M. Perumal, Ricardo Henao, Marshall Nichols, Judith A. Howrylak, Augustine M. Choi, Jesús F. Bermejo-Martin, Raquel Almansa, Eduardo Tamayo, Emma E. Davenport, Katie L. Burnham, Charles J. Hinds, Julian C. Knight, Christopher W. Woods, Stephen F. Kingsmore, Geoffrey S. Ginsburg, Hector R. Wong, Grant P. Parnell, Benjamin Tang, Lyle L. MoldawerFrederick E. Moore, Larsson Omberg, Purvesh Khatri, Ephraim L. Tsalik, Lara M. Mangravite, Raymond J. Langley

Research output: Contribution to journalArticlepeer-review

137 Scopus citations


Improved risk stratification and prognosis prediction 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, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.
Original languageEnglish (US)
JournalNature Communications
Issue number1
StatePublished - Dec 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Chemistry
  • General Physics and Astronomy


Dive into the research topics of 'A community approach to mortality prediction in sepsis via gene expression analysis'. Together they form a unique fingerprint.

Cite this