Diagnostic host gene signature to accurately distinguish enteric fever from other febrile diseases
File(s)
Working paper
Author(s)
Type
Working Paper
Abstract
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 accurately distinguish culture-confirmed enteric fever cases from other febrile illnesses (AUROC<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 diagnostic for use in endemic settings.
Date Issued
2018-05-21
Citation
2018
Publisher
Cold Spring Harbor Labratory
Copyright Statement
© 2020 The Author(s). It is made available under a CC BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
https://www.biorxiv.org/content/10.1101/327429v1
Publication Status
Published