Diagnostic host gene signature to accurately distinguish enteric fever from other febrile diseases

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 disting...

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Bibliographic Details
Main Authors: Blohmke, CJ, Muller, J, Gibani, MM, Dobinson, H, Shrestha, S, Perinparajah, S, Jin, C, Hughes, H, Blackwell, L, Dongol, S, Karkey, A, Schreiber, F, Pickard, D, Basnyat, B, Dougan, G, Baker, S, Pollard, AJ, Darton, TC
Format: Journal article
Language:English
Published: EMBO Press 2019
Description
Summary: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.