A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections

Michael B. Mayhew, Ljubomir Buturovic, Roland Luethy, Uros Midic, Andrew R. Moore, Jonasel A. Roque, Brian D. Shaller, Tola Asuni, David Rawling, Melissa Remmel, Kirindi Choi, James Wacker, Purvesh Khatri, Angela J. Rogers and Timothy E. Sweeney ()
Additional contact information
Michael B. Mayhew: Inflammatix, Inc.
Ljubomir Buturovic: Inflammatix, Inc.
Roland Luethy: Inflammatix, Inc.
Uros Midic: Inflammatix, Inc.
Andrew R. Moore: Stanford University
Jonasel A. Roque: Stanford University
Brian D. Shaller: Stanford University
Tola Asuni: Stanford University
David Rawling: Inflammatix, Inc.
Melissa Remmel: Inflammatix, Inc.
Kirindi Choi: Inflammatix, Inc.
James Wacker: Inflammatix, Inc.
Purvesh Khatri: Stanford University
Angela J. Rogers: Stanford University
Timothy E. Sweeney: Inflammatix, Inc.

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90–0.93) and a viral-vs-other AUROC 0.92 (95% CI 0.90–0.93). We then apply this classifier, inflammatix-bacterial-viral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77–0.93), and viral-vs.-other 0.85 (95% CI 0.76–0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83–0.99), and viral-vs.-other 0.91 (95% CI 0.82–0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-14975-w 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:11:y:2020:i:1:d:10.1038_s41467-020-14975-w

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

DOI: 10.1038/s41467-020-14975-w

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 ().