Diagnostic host gene signature to accurately distinguish 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 accurately disting...
| Үндсэн зохиолчид: | , , , , , , , , , , , , , , , , , |
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| Формат: | Journal article |
| Хэл сонгох: | English |
| Хэвлэсэн: |
EMBO Press
2019
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| Тойм: | 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. |
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