Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases.
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Misdiagnosis of enteric fever is a major global health problem, resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine learning algorithm to host gene expression profiles, we identified a diagnostic signature, which could distinguish culture-confirmed enteric fever cases from other febrile illnesses (area under receiver operating characteristic curve > 95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR, highlighting their utility as PCR-based diagnostics for use in endemic settings.
Date Issued
2019-10-01
Date Acceptance
2019-08-09
Citation
EMBO Molecular Medicine, 2019, 11 (10), pp.1-16
ISSN
1757-4676
Publisher
EMBO Press
Start Page
1
End Page
16
Journal / Book Title
EMBO Molecular Medicine
Volume
11
Issue
10
Copyright Statement
© 2019 The Authors. Published under the terms of the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/31468702
Subjects
biomarker
enteric fever
machine learning
transcriptomics
Publication Status
Published online
Coverage Spatial
England
Article Number
e10431
Date Publish Online
2019-08-30