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Development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults

Predictive models have been developed for influenza but have seldom been validated. Typically they have focused on patients meeting a definition of infection that includes fever. Less is known about how models perform when more symptoms are considered. We, therefore, aimed to create and internally v...

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Autores principales: Vuichard-Gysin, Danielle, Mertz, Dominik, Pullenayegum, Eleanor, Singh, Pardeep, Smieja, Marek, Loeb, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370215/
https://www.ncbi.nlm.nih.gov/pubmed/30742654
http://dx.doi.org/10.1371/journal.pone.0212050
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author Vuichard-Gysin, Danielle
Mertz, Dominik
Pullenayegum, Eleanor
Singh, Pardeep
Smieja, Marek
Loeb, Mark
author_facet Vuichard-Gysin, Danielle
Mertz, Dominik
Pullenayegum, Eleanor
Singh, Pardeep
Smieja, Marek
Loeb, Mark
author_sort Vuichard-Gysin, Danielle
collection PubMed
description Predictive models have been developed for influenza but have seldom been validated. Typically they have focused on patients meeting a definition of infection that includes fever. Less is known about how models perform when more symptoms are considered. We, therefore, aimed to create and internally validate predictive scores of acute respiratory infection (ARI) symptoms to diagnose influenza virus infection as confirmed by polymerase chain reaction (PCR) from respiratory specimens. Data from a completed trial to study the indirect effect of influenza immunization in Hutterite communities were randomly split into two independent groups for model derivation and validation. We applied different multivariable modelling techniques and constructed Receiver Operating Characteristics (ROC) curves to determine predictive indexes at different cut-points. From 2008–2011, 3288 first seasonal ARI episodes and 321 (9.8%) influenza positive events occurred in 2202 individuals. In children up to 17 years, the significant predictors of influenza virus infection were fever, chills, and cough along with being of age 6 years and older. In adults, presence of chills and cough but not fever were highly specific for influenza virus infection (sensitivity 30%, specificity 96%). Performance of the models in the validation set was not significantly different. The predictors were consistently found to be significant irrespective of the multivariable technique. Symptomatic predictors of influenza virus infection vary between children and adults. The scores could assist clinicians in their test and treat decisions but the results need to be externally validated prior to application in clinical practice.
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spelling pubmed-63702152019-02-22 Development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults Vuichard-Gysin, Danielle Mertz, Dominik Pullenayegum, Eleanor Singh, Pardeep Smieja, Marek Loeb, Mark PLoS One Research Article Predictive models have been developed for influenza but have seldom been validated. Typically they have focused on patients meeting a definition of infection that includes fever. Less is known about how models perform when more symptoms are considered. We, therefore, aimed to create and internally validate predictive scores of acute respiratory infection (ARI) symptoms to diagnose influenza virus infection as confirmed by polymerase chain reaction (PCR) from respiratory specimens. Data from a completed trial to study the indirect effect of influenza immunization in Hutterite communities were randomly split into two independent groups for model derivation and validation. We applied different multivariable modelling techniques and constructed Receiver Operating Characteristics (ROC) curves to determine predictive indexes at different cut-points. From 2008–2011, 3288 first seasonal ARI episodes and 321 (9.8%) influenza positive events occurred in 2202 individuals. In children up to 17 years, the significant predictors of influenza virus infection were fever, chills, and cough along with being of age 6 years and older. In adults, presence of chills and cough but not fever were highly specific for influenza virus infection (sensitivity 30%, specificity 96%). Performance of the models in the validation set was not significantly different. The predictors were consistently found to be significant irrespective of the multivariable technique. Symptomatic predictors of influenza virus infection vary between children and adults. The scores could assist clinicians in their test and treat decisions but the results need to be externally validated prior to application in clinical practice. Public Library of Science 2019-02-11 /pmc/articles/PMC6370215/ /pubmed/30742654 http://dx.doi.org/10.1371/journal.pone.0212050 Text en © 2019 Vuichard-Gysin 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
Vuichard-Gysin, Danielle
Mertz, Dominik
Pullenayegum, Eleanor
Singh, Pardeep
Smieja, Marek
Loeb, Mark
Development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults
title Development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults
title_full Development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults
title_fullStr Development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults
title_full_unstemmed Development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults
title_short Development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults
title_sort development and validation of clinical prediction models to distinguish influenza from other viruses causing acute respiratory infections in children and adults
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370215/
https://www.ncbi.nlm.nih.gov/pubmed/30742654
http://dx.doi.org/10.1371/journal.pone.0212050
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