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; Raymond J. Langley

doi:10.1038/s41467-018-03078-2

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 journal › Article › peer-review

140 Scopus citations

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.

Original language	English (US)
Article number	694
Pages (from-to)	694
Journal	Nature Communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-03078-2
State	Published - Feb 15 2018

Keywords

Biomarkers/blood
Community-Acquired Infections/mortality
Cross Infection/mortality
Gene Expression Profiling
Humans
Models, Theoretical
Prognosis
Sepsis/blood
Severity of Illness Index

ASJC Scopus subject areas

Physics and Astronomy(all)
Chemistry(all)
Biochemistry, Genetics and Molecular Biology(all)

Access to Document

10.1038/s41467-018-03078-2

Cite this

Sweeney, T. E., Perumal, T. M., Henao, R., Nichols, M., Howrylak, J. A., Choi, A. M., Bermejo-Martin, J. F., Almansa, R., Tamayo, E., Davenport, E. E., Burnham, K. L., Hinds, C. J., Knight, J. C., Woods, C. W., Kingsmore, S. F., Ginsburg, G. S., Wong, H. R., Parnell, G. P., Tang, B., ... Langley, R. J. (2018). A community approach to mortality prediction in sepsis via gene expression analysis. Nature Communications, 9(1), 694. Article 694. https://doi.org/10.1038/s41467-018-03078-2

Sweeney, TE, Perumal, TM, Henao, R, Nichols, M, Howrylak, JA, Choi, AM, Bermejo-Martin, JF, Almansa, R, Tamayo, E, Davenport, EE, Burnham, KL, Hinds, CJ, Knight, JC, Woods, CW, Kingsmore, SF, Ginsburg, GS, Wong, HR, Parnell, GP, Tang, B, Moldawer, LL, Moore, FE, Omberg, L, Khatri, P, Tsalik, EL, Mangravite, LM & Langley, RJ 2018, 'A community approach to mortality prediction in sepsis via gene expression analysis', Nature Communications, vol. 9, no. 1, 694, pp. 694. https://doi.org/10.1038/s41467-018-03078-2

@article{bb6fcd25a6c64122801df9a7b10a1bd0,

title = "A community approach to mortality prediction in sepsis via gene expression analysis",

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.",

keywords = "Biomarkers/blood, Community-Acquired Infections/mortality, Cross Infection/mortality, Gene Expression Profiling, Humans, Models, Theoretical, Prognosis, Sepsis/blood, Severity of Illness Index",

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

note = "Publisher Copyright: {\textcopyright} 2018 The Author(s).",

year = "2018",

month = feb,

day = "15",

doi = "10.1038/s41467-018-03078-2",

language = "English (US)",

volume = "9",

pages = "694",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

}

TY - JOUR

T1 - A community approach to mortality prediction in sepsis via gene expression analysis

AU - Sweeney, Timothy E.

AU - Perumal, Thanneer M.

AU - Henao, Ricardo

AU - Nichols, Marshall

AU - Howrylak, Judith A.

AU - Choi, Augustine M.

AU - Bermejo-Martin, Jesús F.

AU - Almansa, Raquel

AU - Tamayo, Eduardo

AU - Davenport, Emma E.

AU - Burnham, Katie L.

AU - Hinds, Charles J.

AU - Knight, Julian C.

AU - Woods, Christopher W.

AU - Kingsmore, Stephen F.

AU - Ginsburg, Geoffrey S.

AU - Wong, Hector R.

AU - Parnell, Grant P.

AU - Tang, Benjamin

AU - Moldawer, Lyle L.

AU - Moore, Frederick E.

AU - Omberg, Larsson

AU - Khatri, Purvesh

AU - Tsalik, Ephraim L.

AU - Mangravite, Lara M.

AU - Langley, Raymond J.

PY - 2018/2/15

Y1 - 2018/2/15

N2 - 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.

AB - 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.

KW - Biomarkers/blood

KW - Community-Acquired Infections/mortality

KW - Cross Infection/mortality

KW - Gene Expression Profiling

KW - Humans

KW - Models, Theoretical

KW - Prognosis

KW - Sepsis/blood

KW - Severity of Illness Index

UR - http://www.scopus.com/inward/record.url?scp=85042210696&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042210696&partnerID=8YFLogxK

U2 - 10.1038/s41467-018-03078-2

DO - 10.1038/s41467-018-03078-2

M3 - Article

C2 - 29449546

AN - SCOPUS:85042210696

SN - 2041-1723

VL - 9

SP - 694

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 694

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this