Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach

Heidi Makrinioti; Paraskevi Maggina; John Lakoumentas; Paraskevi Xepapadaki; Stella Taka; Spyridon Megremis; Maria Manioudaki; Sebastian L Johnston; Maria Tsolia; Vassiliki Papaevangelou; Nikolaos G Papadopoulos

doi:10.3389/falgy.2021.728389

Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach

Front Allergy. 2021 Nov 2:2:728389. doi: 10.3389/falgy.2021.728389. eCollection 2021.

Authors

Affiliations

¹ West Middlesex University Hospital, Chelsea and Westminster Foundation Trust, Isleworth, United Kingdom.
² Centre for Paediatrics and Child Health, Imperial College London, London, United Kingdom.
³ Allergy and Clinical Immunology Laboratory, Second Department of Pediatrics, National and Kapodistrian University of Athens (NKUA), School of Medicine, P. and A. Kyriakou Children's Hospital, Athens, Greece.
⁴ Division of Evolution, Infection and Genomics, University of Manchester, Manchester, United Kingdom.
⁵ National Heart and Lung Institute, Imperial College London, London, United Kingdom.
⁶ Second Department of Pediatrics, National and Kapodistrian University of Athens (NKUA), School of Medicine, P. and A. Kyriakou Children's Hospital, Athens, Greece.
⁷ Third Department of Paediatrics, Attikon University General Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
⁸ Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, United Kingdom.

Abstract

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.

Keywords: bronchiolitis; machine learning; rhinovirus; virus; wheeze.