Open AccessDiagnostic host gene signature for distinguishing enteric fever from other febrile diseases
Author(s) -
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
Publication year - 2019
Publication title -
embo molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.923
H-Index - 107
eISSN - 1757-4684
pISSN - 1757-4676
DOI - 10.15252/emmm.201910431
Subject(s) - medicine , library science , research centre , family medicine , computer science
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.