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

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143 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)
Journal	Nature Communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-03078-2
State	Published - Dec 1 2018
Externally published	Yes

ASJC Scopus subject areas

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

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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). https://doi.org/10.1038/s41467-018-03078-2

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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.",

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 = "Generated from Scopus record by KAUST IRTS on 2023-02-15",

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

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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. https://doi.org/10.1038/s41467-018-03078-2

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.

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15

PY - 2018/12/1

Y1 - 2018/12/1

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.

UR - https://www.nature.com/articles/s41467-018-03078-2

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U2 - 10.1038/s41467-018-03078-2

DO - 10.1038/s41467-018-03078-2

M3 - Article

SN - 2041-1723

VL - 9

JO - Nature Communications

JF - Nature Communications

IS - 1

ER -

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

Abstract

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