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Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care

OBJECTIVES: Pneumonia is the most common bacterial infection in children at the emergency department (ED). Clinical prediction models for childhood pneumonia have been developed (using chest x-ray as their reference standard), but without implementation in clinical practice. Given current insights i...

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Autores principales: van de Maat, Josephine, Nieboer, Daan, Thompson, Matthew, Lakhanpaul, Monica, Moll, Henriette, Oostenbrink, Rianne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563975/
https://www.ncbi.nlm.nih.gov/pubmed/31194750
http://dx.doi.org/10.1371/journal.pone.0217570
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author van de Maat, Josephine
Nieboer, Daan
Thompson, Matthew
Lakhanpaul, Monica
Moll, Henriette
Oostenbrink, Rianne
author_facet van de Maat, Josephine
Nieboer, Daan
Thompson, Matthew
Lakhanpaul, Monica
Moll, Henriette
Oostenbrink, Rianne
author_sort van de Maat, Josephine
collection PubMed
description OBJECTIVES: Pneumonia is the most common bacterial infection in children at the emergency department (ED). Clinical prediction models for childhood pneumonia have been developed (using chest x-ray as their reference standard), but without implementation in clinical practice. Given current insights in the diagnostic limitations of chest x-ray, this study aims to validate these prediction models for a clinical diagnosis of pneumonia, and to explore their potential to guide decisions on antibiotic treatment at the ED. METHODS: We systematically identified clinical prediction models for childhood pneumonia and assessed their quality. We evaluated the validity of these models in two populations, using a clinical reference standard (1. definite/probable bacterial, 2. bacterial syndrome, 3. unknown bacterial/viral, 4. viral syndrome, 5. definite/probable viral), measuring performance by the ordinal c-statistic (ORC). Validation populations included prospectively collected data of children aged 1 month to 5 years attending the ED of Rotterdam (2012–2013) or Coventry (2005–2006) with fever and cough or dyspnoea. RESULTS: We identified eight prediction models and could evaluate the validity of seven, with original good performance. In the Dutch population 22/248 (9%) had a bacterial infection, in Coventry 53/301 (17%), antibiotic prescription was 21% and 35% respectively. Three models predicted a higher risk in children with bacterial infections than in those with viral disease (ORC ≥0.55) and could identify children at low risk of bacterial infection. CONCLUSIONS: Three clinical prediction models for childhood pneumonia could discriminate fairly well between a clinical reference standard of bacterial versus viral infection. However, they all require the measurement of biomarkers, raising questions on the exact target population when implementing these models in clinical practice. Moreover, choosing optimal thresholds to guide antibiotic prescription is challenging and requires careful consideration of potential harms and benefits.
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spelling pubmed-65639752019-06-20 Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care van de Maat, Josephine Nieboer, Daan Thompson, Matthew Lakhanpaul, Monica Moll, Henriette Oostenbrink, Rianne PLoS One Research Article OBJECTIVES: Pneumonia is the most common bacterial infection in children at the emergency department (ED). Clinical prediction models for childhood pneumonia have been developed (using chest x-ray as their reference standard), but without implementation in clinical practice. Given current insights in the diagnostic limitations of chest x-ray, this study aims to validate these prediction models for a clinical diagnosis of pneumonia, and to explore their potential to guide decisions on antibiotic treatment at the ED. METHODS: We systematically identified clinical prediction models for childhood pneumonia and assessed their quality. We evaluated the validity of these models in two populations, using a clinical reference standard (1. definite/probable bacterial, 2. bacterial syndrome, 3. unknown bacterial/viral, 4. viral syndrome, 5. definite/probable viral), measuring performance by the ordinal c-statistic (ORC). Validation populations included prospectively collected data of children aged 1 month to 5 years attending the ED of Rotterdam (2012–2013) or Coventry (2005–2006) with fever and cough or dyspnoea. RESULTS: We identified eight prediction models and could evaluate the validity of seven, with original good performance. In the Dutch population 22/248 (9%) had a bacterial infection, in Coventry 53/301 (17%), antibiotic prescription was 21% and 35% respectively. Three models predicted a higher risk in children with bacterial infections than in those with viral disease (ORC ≥0.55) and could identify children at low risk of bacterial infection. CONCLUSIONS: Three clinical prediction models for childhood pneumonia could discriminate fairly well between a clinical reference standard of bacterial versus viral infection. However, they all require the measurement of biomarkers, raising questions on the exact target population when implementing these models in clinical practice. Moreover, choosing optimal thresholds to guide antibiotic prescription is challenging and requires careful consideration of potential harms and benefits. Public Library of Science 2019-06-13 /pmc/articles/PMC6563975/ /pubmed/31194750 http://dx.doi.org/10.1371/journal.pone.0217570 Text en © 2019 van de Maat et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
van de Maat, Josephine
Nieboer, Daan
Thompson, Matthew
Lakhanpaul, Monica
Moll, Henriette
Oostenbrink, Rianne
Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care
title Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care
title_full Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care
title_fullStr Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care
title_full_unstemmed Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care
title_short Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care
title_sort can clinical prediction models assess antibiotic need in childhood pneumonia? a validation study in paediatric emergency care
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563975/
https://www.ncbi.nlm.nih.gov/pubmed/31194750
http://dx.doi.org/10.1371/journal.pone.0217570
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