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J Allergy Clin Immunol. 2011 Feb;127(2):382-389.e1-13. doi: 10.1016/j.jaci.2010.11.015. Epub 2010 Dec 31.

Heterogeneity of severe asthma in childhood: confirmation by cluster analysis of children in the National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program.

Author information

1
Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA. anne.fitzpatrick@emory.edu

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.

RESULTS:

Four clusters of asthma were identified. Children in cluster 1 (n = 48) had relatively normal lung function and less atopy. Children in cluster 2 (n = 52) had slightly lower lung function, more atopy, and increased symptoms and medication use. Cluster 3 (n = 32) had greater comorbidity, increased bronchial responsiveness, and lower lung function. Cluster 4 (n = 29) had the lowest lung function and the greatest symptoms and medication use. 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.

CONCLUSION:

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.

PMID:
21195471
PMCID:
PMC3060668
DOI:
10.1016/j.jaci.2010.11.015
[Indexed for MEDLINE]
Free PMC Article

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