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Clinical Predictors of Shigella and Campylobacter Infection in Children in the United States

BACKGROUND: Infectious gastroenteritis is a major cause of morbidity and mortality among children worldwide. While most episodes are self-limiting, for select pathogens such as Shigella and Campylobacter, etiological diagnosis may allow effective antimicrobial therapy and aid public health intervent...

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Autores principales: Smith, Timothy, Ye, Xiangyang, Stockmann, Chris, Cohen, Daniel, Leber, Amy, Daly, Judy, Jackson, Jami, Selvarangan, Rangaraj, Kanwar, Neena, Bender, Jeffery, Bard, Jennifer Dien, Festekjian, Ara, Duffy, Susan, Larsen, Chari, Baca, Tanya, Holmberg, Kristen, Bourzac, Kevin, Chapin, Kimberle C, Pavia, Andrew, Leung, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631326/
http://dx.doi.org/10.1093/ofid/ofx163.879
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author Smith, Timothy
Ye, Xiangyang
Stockmann, Chris
Cohen, Daniel
Leber, Amy
Daly, Judy
Jackson, Jami
Selvarangan, Rangaraj
Kanwar, Neena
Bender, Jeffery
Bard, Jennifer Dien
Festekjian, Ara
Duffy, Susan
Larsen, Chari
Baca, Tanya
Holmberg, Kristen
Bourzac, Kevin
Chapin, Kimberle C
Pavia, Andrew
Leung, Daniel
author_facet Smith, Timothy
Ye, Xiangyang
Stockmann, Chris
Cohen, Daniel
Leber, Amy
Daly, Judy
Jackson, Jami
Selvarangan, Rangaraj
Kanwar, Neena
Bender, Jeffery
Bard, Jennifer Dien
Festekjian, Ara
Duffy, Susan
Larsen, Chari
Baca, Tanya
Holmberg, Kristen
Bourzac, Kevin
Chapin, Kimberle C
Pavia, Andrew
Leung, Daniel
author_sort Smith, Timothy
collection PubMed
description BACKGROUND: Infectious gastroenteritis is a major cause of morbidity and mortality among children worldwide. While most episodes are self-limiting, for select pathogens such as Shigella and Campylobacter, etiological diagnosis may allow effective antimicrobial therapy and aid public health interventions. Unfortunately, clinical predictors of such pathogens are not well established and are based on small studies using bacterial culture for identification. METHODS: We used prospectively collected data from a multi-center study of pediatric gastroenteritis employing multi-pathogen molecular diagnostics to determine clinical predictors associated with 1) Shigella and 2) Shigella or Campylobacter infection. We used machine learning algorithms for clinical predictor identification, then performed logistic regression on features extracted plus pre-selected variables of interest. RESULTS: Of 993 children enrolled with acute diarrhea, we detected Shigella spp. in 56 (5.6%) and Campylobacter spp. in 24 (2.4%). Compared with children who had neither pathogen detected (of whom, >70% had ≥1 potential pathogen identified), bloody diarrhea (odds ratio 4.0), headache (OR 2.2), fever (OR 7.1), summer (OR 3.3), and sick contact with GI illness (OR 2.2), were positively associated with Shigella, and out-of-state travel (OR 0.3) and vomiting and/or nausea (OR 0.4) were negatively associated (Table). For Shigella or Campylobacter, predictors were similar but season was no longer significantly associated with infection. CONCLUSION: These results can create prediction models and assist clinicians with identifying patients who would benefit from diagnostic testing and earlier antibiotic treatment. This may curtail unnecessary antibiotic use, and help to direct and target appropriate use of stool diagnostics. DISCLOSURES: A. Leber, BioFIre Diagnostics: Research Contractor and Scientific Advisor, Research support, Speaker honorarium and Travel expenses J. Daly, Biofire: Grant Investigator, Grant recipient R. Selvarangan, BioFire Diagnostics: Board Member and Investigator, Consulting fee and Research grant Luminex Diagnostics: Investigator, Research grant J. Dien Bard, BioFire: Consultant and Investigator, Research grant and Speaker honorarium K. Holmberg, BioFire Diagnostics: Employee, Salary K. Bourzac, BioFire Diagnostics: Employee, Salary K. C. Chapin, BioFire Diagnstics: Investigator, Research support A. Pavia, BioFire Diagnostics: Grant Investigator, Research grant
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spelling pubmed-56313262017-11-07 Clinical Predictors of Shigella and Campylobacter Infection in Children in the United States Smith, Timothy Ye, Xiangyang Stockmann, Chris Cohen, Daniel Leber, Amy Daly, Judy Jackson, Jami Selvarangan, Rangaraj Kanwar, Neena Bender, Jeffery Bard, Jennifer Dien Festekjian, Ara Duffy, Susan Larsen, Chari Baca, Tanya Holmberg, Kristen Bourzac, Kevin Chapin, Kimberle C Pavia, Andrew Leung, Daniel Open Forum Infect Dis Abstracts BACKGROUND: Infectious gastroenteritis is a major cause of morbidity and mortality among children worldwide. While most episodes are self-limiting, for select pathogens such as Shigella and Campylobacter, etiological diagnosis may allow effective antimicrobial therapy and aid public health interventions. Unfortunately, clinical predictors of such pathogens are not well established and are based on small studies using bacterial culture for identification. METHODS: We used prospectively collected data from a multi-center study of pediatric gastroenteritis employing multi-pathogen molecular diagnostics to determine clinical predictors associated with 1) Shigella and 2) Shigella or Campylobacter infection. We used machine learning algorithms for clinical predictor identification, then performed logistic regression on features extracted plus pre-selected variables of interest. RESULTS: Of 993 children enrolled with acute diarrhea, we detected Shigella spp. in 56 (5.6%) and Campylobacter spp. in 24 (2.4%). Compared with children who had neither pathogen detected (of whom, >70% had ≥1 potential pathogen identified), bloody diarrhea (odds ratio 4.0), headache (OR 2.2), fever (OR 7.1), summer (OR 3.3), and sick contact with GI illness (OR 2.2), were positively associated with Shigella, and out-of-state travel (OR 0.3) and vomiting and/or nausea (OR 0.4) were negatively associated (Table). For Shigella or Campylobacter, predictors were similar but season was no longer significantly associated with infection. CONCLUSION: These results can create prediction models and assist clinicians with identifying patients who would benefit from diagnostic testing and earlier antibiotic treatment. This may curtail unnecessary antibiotic use, and help to direct and target appropriate use of stool diagnostics. DISCLOSURES: A. Leber, BioFIre Diagnostics: Research Contractor and Scientific Advisor, Research support, Speaker honorarium and Travel expenses J. Daly, Biofire: Grant Investigator, Grant recipient R. Selvarangan, BioFire Diagnostics: Board Member and Investigator, Consulting fee and Research grant Luminex Diagnostics: Investigator, Research grant J. Dien Bard, BioFire: Consultant and Investigator, Research grant and Speaker honorarium K. Holmberg, BioFire Diagnostics: Employee, Salary K. Bourzac, BioFire Diagnostics: Employee, Salary K. C. Chapin, BioFire Diagnstics: Investigator, Research support A. Pavia, BioFire Diagnostics: Grant Investigator, Research grant Oxford University Press 2017-10-04 /pmc/articles/PMC5631326/ http://dx.doi.org/10.1093/ofid/ofx163.879 Text en © The Author 2017. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Smith, Timothy
Ye, Xiangyang
Stockmann, Chris
Cohen, Daniel
Leber, Amy
Daly, Judy
Jackson, Jami
Selvarangan, Rangaraj
Kanwar, Neena
Bender, Jeffery
Bard, Jennifer Dien
Festekjian, Ara
Duffy, Susan
Larsen, Chari
Baca, Tanya
Holmberg, Kristen
Bourzac, Kevin
Chapin, Kimberle C
Pavia, Andrew
Leung, Daniel
Clinical Predictors of Shigella and Campylobacter Infection in Children in the United States
title Clinical Predictors of Shigella and Campylobacter Infection in Children in the United States
title_full Clinical Predictors of Shigella and Campylobacter Infection in Children in the United States
title_fullStr Clinical Predictors of Shigella and Campylobacter Infection in Children in the United States
title_full_unstemmed Clinical Predictors of Shigella and Campylobacter Infection in Children in the United States
title_short Clinical Predictors of Shigella and Campylobacter Infection in Children in the United States
title_sort clinical predictors of shigella and campylobacter infection in children in the united states
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631326/
http://dx.doi.org/10.1093/ofid/ofx163.879
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