Diagnostic host gene signature to accurately distinguish enteric fever from other febrile diseases
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.
<jats:title>ABSTRACT</jats:title><jats:p>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.</jats:p>