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

Sweeney, Timothy E

Perumal, Thanneer M

Henao, Ricardo

Nichols, Marshall

Howrylak, Judith A

Choi, Augustine M

Bermejo-Martin, Jesús F

Almansa, Raquel

Tamayo, Eduardo

Davenport, Emma E

Burnham, Katie L

Hinds, Charles J

Knight, Julian C

Woods, Christopher W

Kingsmore, Stephen F

Ginsburg, Geoffrey S

Wong, Hector R

Parnell, Grant P

Tang, Benjamin

Moldawer, Lyle L

Moore, Frederick E

Omberg, Larsson

Khatri, Purvesh

Tsalik, Ephraim L

Mangravite, Lara M

Langley, Raymond J

2023-04-01T16:24:40Z

2023-04-01T16:24:40Z

2018-02

2023-04-01T16:24:39Z

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.

10.1038/s41467-018-03078-2

2041-1723

2041-1723

https://hdl.handle.net/10161/26958

eng

Springer Science and Business Media LLC

Nature communications

10.1038/s41467-018-03078-2

Humans

Community-Acquired Infections

Cross Infection

Sepsis

Prognosis

Severity of Illness Index

Gene Expression Profiling

Models, Theoretical

Biomarkers

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

Journal article

Henao, Ricardo|0000-0003-4980-845X

Woods, Christopher W|0000-0001-7240-2453

Ginsburg, Geoffrey S|0000-0003-4739-9808

Tsalik, Ephraim L|0000-0002-6417-2042

694

1

Duke

Pratt School of Engineering

School of Medicine

School of Nursing

Nursing

Basic Science Departments

Clinical Science Departments

Institutes and Centers

Biostatistics & Bioinformatics

Electrical and Computer Engineering

Medicine

Pathology

Medicine, Cardiology

Medicine, Infectious Diseases

Duke Cancer Institute

Duke Human Vaccine Institute

Institutes and Provost's Academic Units

University Institutes and Centers

Duke Global Health Institute

Duke Center for Applied Genomics and Precision Medicine

Published

9