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Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach
Heidi Makrinioti1,2, Paraskevi Maggina3, John Lakoumentas3
1West Middlesex University Hospital, Chelsea and Westminster Foundation Trust, Isleworth, United Kingdom.
Insights
Machine learning models can predict wheeze exacerbations in infants after hospitalization for acute bronchiolitis. Rhinovirus and clinical severity are key predictors of persistent wheezing in these children.
Area of Science:
- Pediatrics
- Infectious Diseases
- Machine Learning in Healthcare
Background:
- Acute bronchiolitis is a common infant respiratory infection.
- Hospitalized infants with bronchiolitis have an increased risk of developing wheeze exacerbations.
- Predicting persistent wheezing is crucial for early intervention and management.
Purpose of the Study:
- To develop machine learning models for predicting the incidence and persistence of wheeze exacerbations.
- To identify key predictors of wheeze exacerbations following a first hospitalized episode of acute bronchiolitis.
Main Methods:
- Prospective cohort study of 131 hospitalized infants with bronchiolitis and 73 controls.
- 3-year follow-up with 6-monthly telephone reviews.
- Principal Component Analysis (PCA) and Random Forest classification were used to develop prediction models.
Main Results:
- PCA identified two clusters of outcomes, with Cluster 1 associated with more wheeze exacerbations.
- Rhinovirus (RV) detection was more common in Cluster 1 and linked to higher clinical severity.
- A prediction model using virus type and clinical severity achieved 75.56% sensitivity and 91.86% specificity for Cluster 1.
Conclusions:
- A prediction model incorporating virus type and clinical severity can effectively predict wheeze exacerbations after hospitalization for bronchiolitis.
- Rhinovirus emerged as the strongest predictor of wheeze exacerbations.
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.
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