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. Moldawer, Frederick E. Moore, Larsson Omberg, Purvesh Khatri, Ephraim L. Tsalik, Lara M. Mangravite and Raymond J. Langley ()
Additional contact information
Timothy E. Sweeney: Stanford University School of Medicine
Thanneer M. Perumal: Sage Bionetworks
Ricardo Henao: Duke University
Marshall Nichols: Duke University
Judith A. Howrylak: Penn State Milton S. Hershey Medical Center
Augustine M. Choi: Cornell Medical Center
Jesús F. Bermejo-Martin: Hospital Clínico Universitario de Valladolid/IECSCYL
Raquel Almansa: Hospital Clínico Universitario de Valladolid/IECSCYL
Eduardo Tamayo: Hospital Clínico Universitario de Valladolid/IECSCYL
Emma E. Davenport: Harvard Medical School
Katie L. Burnham: University of Oxford
Charles J. Hinds: Queen Mary University
Julian C. Knight: University of Oxford
Christopher W. Woods: Duke University
Stephen F. Kingsmore: Rady Children’s Institute for Genomic Medicine
Geoffrey S. Ginsburg: Duke University
Hector R. Wong: Cincinnati Children’s Hospital Medical Center and Cincinnati Children’s Research Foundation
Grant P. Parnell: Westmead Institute for Medical Research
Benjamin Tang: Westmead Institute for Medical Research
Lyle L. Moldawer: University of Florida College of Medicine
Frederick E. Moore: University of Florida College of Medicine
Larsson Omberg: Sage Bionetworks
Purvesh Khatri: Stanford University School of Medicine
Ephraim L. Tsalik: Duke University
Lara M. Mangravite: Sage Bionetworks
Raymond J. Langley: University of South Alabama

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-018-03078-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03078-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-03078-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().