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Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach

Introduction: Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, infants with hospitalized bronchiolitis are more likely to develop wheeze exacerbations during the first years of life. The objective of th...

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Autores principales: Makrinioti, Heidi, Maggina, Paraskevi, Lakoumentas, John, Xepapadaki, Paraskevi, Taka, Stella, Megremis, Spyridon, Manioudaki, Maria, Johnston, Sebastian L., Tsolia, Maria, Papaevangelou, Vassiliki, Papadopoulos, Nikolaos G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8974688/
https://www.ncbi.nlm.nih.gov/pubmed/35387034
http://dx.doi.org/10.3389/falgy.2021.728389
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author Makrinioti, Heidi
Maggina, Paraskevi
Lakoumentas, John
Xepapadaki, Paraskevi
Taka, Stella
Megremis, Spyridon
Manioudaki, Maria
Johnston, Sebastian L.
Tsolia, Maria
Papaevangelou, Vassiliki
Papadopoulos, Nikolaos G.
author_facet Makrinioti, Heidi
Maggina, Paraskevi
Lakoumentas, John
Xepapadaki, Paraskevi
Taka, Stella
Megremis, Spyridon
Manioudaki, Maria
Johnston, Sebastian L.
Tsolia, Maria
Papaevangelou, Vassiliki
Papadopoulos, Nikolaos G.
author_sort Makrinioti, Heidi
collection PubMed
description Introduction: Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, infants with hospitalized bronchiolitis are more likely to develop wheeze exacerbations during the first years of life. The objective of this prospective cohort study was to develop machine learning models to predict incidence and persistence of wheeze exacerbations following the first hospitalized episode of acute bronchiolitis. Methods: One hundred thirty-one otherwise healthy term infants hospitalized with the first episode of bronchiolitis at a tertiary pediatric hospital in Athens, Greece, and 73 age-matched controls were recruited. All patients/controls were followed up for 3 years with 6-monthly telephone reviews. Through principal component analysis (PCA), a cluster model was used to describe main outcomes. Associations between virus type and the clusters and between virus type and other clinical characteristics and demographic data were identified. Through random forest classification, a prediction model with smallest classification error was identified. Primary outcomes included the incidence and the number of caregiver-reported wheeze exacerbations. Results: PCA identified 2 clusters of the outcome measures (Cluster 1 and Cluster 2) that were significantly associated with the number of recurrent wheeze episodes over 3-years of follow-up (Chi-Squared, p < 0.001). Cluster 1 included infants who presented higher number of wheeze exacerbations over follow-up time. Rhinovirus (RV) detection was more common in Cluster 1 and was more strongly associated with clinical severity on admission (p < 0.01). A prediction model based on virus type and clinical severity could predict Cluster 1 with an overall error 0.1145 (sensitivity 75.56% and specificity 91.86%). Conclusion: A prediction model based on virus type and clinical severity of first hospitalized episode of bronchiolitis could predict sensitively the incidence and persistence of wheeze exacerbations during a 3-year follow-up. Virus type (RV) was the strongest predictor.
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spelling pubmed-89746882022-04-05 Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach Makrinioti, Heidi Maggina, Paraskevi Lakoumentas, John Xepapadaki, Paraskevi Taka, Stella Megremis, Spyridon Manioudaki, Maria Johnston, Sebastian L. Tsolia, Maria Papaevangelou, Vassiliki Papadopoulos, Nikolaos G. Front Allergy Allergy Introduction: Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, infants with hospitalized bronchiolitis are more likely to develop wheeze exacerbations during the first years of life. The objective of this prospective cohort study was to develop machine learning models to predict incidence and persistence of wheeze exacerbations following the first hospitalized episode of acute bronchiolitis. Methods: One hundred thirty-one otherwise healthy term infants hospitalized with the first episode of bronchiolitis at a tertiary pediatric hospital in Athens, Greece, and 73 age-matched controls were recruited. All patients/controls were followed up for 3 years with 6-monthly telephone reviews. Through principal component analysis (PCA), a cluster model was used to describe main outcomes. Associations between virus type and the clusters and between virus type and other clinical characteristics and demographic data were identified. Through random forest classification, a prediction model with smallest classification error was identified. Primary outcomes included the incidence and the number of caregiver-reported wheeze exacerbations. Results: PCA identified 2 clusters of the outcome measures (Cluster 1 and Cluster 2) that were significantly associated with the number of recurrent wheeze episodes over 3-years of follow-up (Chi-Squared, p < 0.001). Cluster 1 included infants who presented higher number of wheeze exacerbations over follow-up time. Rhinovirus (RV) detection was more common in Cluster 1 and was more strongly associated with clinical severity on admission (p < 0.01). A prediction model based on virus type and clinical severity could predict Cluster 1 with an overall error 0.1145 (sensitivity 75.56% and specificity 91.86%). Conclusion: A prediction model based on virus type and clinical severity of first hospitalized episode of bronchiolitis could predict sensitively the incidence and persistence of wheeze exacerbations during a 3-year follow-up. Virus type (RV) was the strongest predictor. Frontiers Media S.A. 2021-11-02 /pmc/articles/PMC8974688/ /pubmed/35387034 http://dx.doi.org/10.3389/falgy.2021.728389 Text en Copyright © 2021 Makrinioti, Maggina, Lakoumentas, Xepapadaki, Taka, Megremis, Manioudaki, Johnston, Tsolia, Papaevangelou and Papadopoulos. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Allergy
Makrinioti, Heidi
Maggina, Paraskevi
Lakoumentas, John
Xepapadaki, Paraskevi
Taka, Stella
Megremis, Spyridon
Manioudaki, Maria
Johnston, Sebastian L.
Tsolia, Maria
Papaevangelou, Vassiliki
Papadopoulos, Nikolaos G.
Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach
title Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach
title_full Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach
title_fullStr Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach
title_full_unstemmed Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach
title_short Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach
title_sort recurrent wheeze exacerbations following acute bronchiolitis—a machine learning approach
topic Allergy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8974688/
https://www.ncbi.nlm.nih.gov/pubmed/35387034
http://dx.doi.org/10.3389/falgy.2021.728389
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