학술논문
Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases
Document Type
Report
Author
Blohmke, Christoph J.; Muller, Julius; Gibani, Malick M.; Dobinson, Hazel; Shrestha, Sonu; Perinparajah, Soumya; Jin, Celina; Hughes, Harri; Blackwell, Luke; Dongol, Sabina; Karkey, Abhilasha; Schreiber, Fernanda; Pickard, Derek; Basnyat, Buddha; Dougan, Gordon; Baker, Stephen; Pollard, Andrew J.; Darton, Thomas C.
Source
EMBO Molecular Medicine. August 30, 2019, Vol. 11 Issue 10
Subject
Language
English
ISSN
1757-4676
Abstract
Introduction Enteric fever, a disease caused by systemic infection with Salmonella enterica serovars Typhi or Paratyphi A, accounts for 13.5–26.9 million illness episodes worldwide each year (Buckle et al,; Mogasale [...]
: 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.
: 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.