Heterogeneity of Severe Asthma in Childhood: Confirmation by Cluster Analysis of Children in the NIH/NHLBI Severe Asthma Research Program (SARP)

• Authors: Anne M Fitzpatrick, Emory University; W. Gerald Teague, University of Virginia; Deborah A. Meyers, Wake Forest University; Stephen P. Peters, Wake Forest University; Xingnan Li, Wake Forest University; Huashi Li, Wake Forest University; Sally E. Wenzel, University of Pittsburgh; Shean Aujla, University of Pittsburgh; Mario Castro, Washington University; Leonard B. Bacharier, Washington University; Benjamin M. Gaston, University of Virginia; Eugene R. Bleecker, Wake Forest University; Wendy C. Moore, Wake Forest University
• Journal Title: Journal of Allergy and Clinical Immunology: In Practice
• Volume: Volume 127, Number 2
• Publisher: Elsevier: 12 months | 2011-02, Pages 382-389.e1-13
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.jaci.2010.11.015
• Subjects: Health Sciences, Immunology
• Keywords: Allergic sensitization; Asthma; Severe asthma; Asthma guidelines; Children; Cluster analysis; Lung function; Phenotype

Abstract

Background Asthma in children is a heterogeneous disorder with many phenotypes. Although unsupervised cluster analysis is a useful tool for identifying phenotypes, it has not been applied to school-age children with persistent asthma across a wide range of severities. Objectives This study determined how children with severe asthma are distributed across a cluster analysis and how well these clusters conform to current definitions of asthma severity. Methods Cluster analysis was applied to 12 continuous and composite variables from 161 children at 5 centers enrolled in the Severe Asthma Research Program (SARP). Results Four clusters of asthma were identified. Children in Cluster 1 (n = 48) had relatively normal lung function and less atopy, while children in Cluster 2 (n = 52) had slightly lower lung function, more atopy, and increased symptoms and medication usage. Cluster 3 (n = 32) had greater co-morbidity, increased bronchial responsiveness and lower lung function. Cluster 4 (n = 29) had the lowest lung function and the greatest symptoms and medication usage. Predictors of cluster assignment were asthma duration, the number of asthma controller medications, and baseline lung function. Children with severe asthma were present in all clusters, and no cluster corresponded to definitions of asthma severity provided in asthma treatment guidelines. Conclusions Severe asthma in children is highly heterogeneous. Unique phenotypic clusters previously identified in adults can also be identified in children, but with important differences. Larger validation and longitudinal studies are needed to determine the baseline and predictive validity of these phenotypic clusters in the larger clinical setting.