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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...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783646/ https://www.ncbi.nlm.nih.gov/pubmed/31468702 http://dx.doi.org/10.15252/emmm.201910431 |
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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 |
author_facet | 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 |
author_sort | Blohmke, Christoph J |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6783646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67836462019-10-17 Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases 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 EMBO Mol Med Articles 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. John Wiley and Sons Inc. 2019-08-30 2019-10 /pmc/articles/PMC6783646/ /pubmed/31468702 http://dx.doi.org/10.15252/emmm.201910431 Text en © 2019 The Authors. Published under the terms of the CC BY 4.0 license This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles 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 Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases |
title | Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases |
title_full | Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases |
title_fullStr | Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases |
title_full_unstemmed | Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases |
title_short | Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases |
title_sort | diagnostic host gene signature for distinguishing enteric fever from other febrile diseases |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783646/ https://www.ncbi.nlm.nih.gov/pubmed/31468702 http://dx.doi.org/10.15252/emmm.201910431 |
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