Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases

Diagnostic host gene signature for distinguishing 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 distinguish cult...

Ausführliche Beschreibung

Bibliographische Detailangaben
Hauptverfasser: 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, A, Darton, T
Format: Journal article
Sprache:English
Veröffentlicht: Wiley 2019
Beschreibung
Zusammenfassung: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.

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