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Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children

BACKGROUND: Health-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI). The diagnostic accuracy of IMCI in detecting children with SBI i...

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Autores principales: Keitel, Kristina, Kilowoko, Mary, Kyungu, Esther, Genton, Blaise, D’Acremont, Valérie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6724300/
https://www.ncbi.nlm.nih.gov/pubmed/31481123
http://dx.doi.org/10.1186/s12879-019-4371-y
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author Keitel, Kristina
Kilowoko, Mary
Kyungu, Esther
Genton, Blaise
D’Acremont, Valérie
author_facet Keitel, Kristina
Kilowoko, Mary
Kyungu, Esther
Genton, Blaise
D’Acremont, Valérie
author_sort Keitel, Kristina
collection PubMed
description BACKGROUND: Health-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI). The diagnostic accuracy of IMCI in detecting children with SBI is unknown. Prediction rules and guidelines for SBI from well-resourced countries at outpatient level may help to improve current guidelines; however, their diagnostic performance has not been evaluated in resource-limited countries, where clinical conditions, access to care, and diagnostic capacity differ. The aim of this study was to estimate the diagnostic accuracy of existing prediction rules and clinical guidelines in identifying children with SBI in a cohort of febrile children attending outpatient health facilities in Tanzania. METHODS: Structured literature review to identify available prediction rules and guidelines aimed at detecting SBI and retrospective, external validation on a dataset containing 1005 febrile Tanzanian children with acute infections. The reference standard, SBI, was established based on rigorous clinical and microbiological criteria. RESULTS: Four prediction rules and five guidelines, including IMCI, could be validated. All examined rules and guidelines had insufficient diagnostic accuracy for ruling-in or ruling-out SBI with positive and negative likelihood ratios ranging from 1.04–1.87 to 0.47–0.92, respectively. IMCI had a sensitivity of 36.7% (95% CI 29.4–44.6%) at a specificity of 70.3% (67.1–73.4%). Rules that use a combination of clinical and laboratory testing had better performance compared to rules and guidelines using only clinical and or laboratory elements. CONCLUSIONS: Currently applied guidelines for managing children with febrile illness have insufficient diagnostic accuracy in detecting children with SBI. Revised clinical algorithms including simple point-of-care tests with improved accuracy for detecting SBI targeting in tropical resource-poor settings are needed. They should undergo careful external validation against clinical outcome before implementation, given the inherent limitations of gold standards for SBI. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-019-4371-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-67243002019-09-10 Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children Keitel, Kristina Kilowoko, Mary Kyungu, Esther Genton, Blaise D’Acremont, Valérie BMC Infect Dis Research Article BACKGROUND: Health-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI). The diagnostic accuracy of IMCI in detecting children with SBI is unknown. Prediction rules and guidelines for SBI from well-resourced countries at outpatient level may help to improve current guidelines; however, their diagnostic performance has not been evaluated in resource-limited countries, where clinical conditions, access to care, and diagnostic capacity differ. The aim of this study was to estimate the diagnostic accuracy of existing prediction rules and clinical guidelines in identifying children with SBI in a cohort of febrile children attending outpatient health facilities in Tanzania. METHODS: Structured literature review to identify available prediction rules and guidelines aimed at detecting SBI and retrospective, external validation on a dataset containing 1005 febrile Tanzanian children with acute infections. The reference standard, SBI, was established based on rigorous clinical and microbiological criteria. RESULTS: Four prediction rules and five guidelines, including IMCI, could be validated. All examined rules and guidelines had insufficient diagnostic accuracy for ruling-in or ruling-out SBI with positive and negative likelihood ratios ranging from 1.04–1.87 to 0.47–0.92, respectively. IMCI had a sensitivity of 36.7% (95% CI 29.4–44.6%) at a specificity of 70.3% (67.1–73.4%). Rules that use a combination of clinical and laboratory testing had better performance compared to rules and guidelines using only clinical and or laboratory elements. CONCLUSIONS: Currently applied guidelines for managing children with febrile illness have insufficient diagnostic accuracy in detecting children with SBI. Revised clinical algorithms including simple point-of-care tests with improved accuracy for detecting SBI targeting in tropical resource-poor settings are needed. They should undergo careful external validation against clinical outcome before implementation, given the inherent limitations of gold standards for SBI. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-019-4371-y) contains supplementary material, which is available to authorized users. BioMed Central 2019-09-03 /pmc/articles/PMC6724300/ /pubmed/31481123 http://dx.doi.org/10.1186/s12879-019-4371-y Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Keitel, Kristina
Kilowoko, Mary
Kyungu, Esther
Genton, Blaise
D’Acremont, Valérie
Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children
title Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children
title_full Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children
title_fullStr Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children
title_full_unstemmed Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children
title_short Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children
title_sort performance of prediction rules and guidelines in detecting serious bacterial infections among tanzanian febrile children
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6724300/
https://www.ncbi.nlm.nih.gov/pubmed/31481123
http://dx.doi.org/10.1186/s12879-019-4371-y
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