Diagnostic host gene signature for distinguishing 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; et al. (Wiley, 2019)
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.