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. 2010 Feb 15;181(4):315-23.
doi: 10.1164/rccm.200906-0896OC. Epub 2009 Nov 5.

Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program

Collaborators, Affiliations

Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program

Wendy C Moore et al. Am J Respir Crit Care Med. .

Abstract

Rationale: The Severe Asthma Research Program cohort includes subjects with persistent asthma who have undergone detailed phenotypic characterization. Previous univariate methods compared features of mild, moderate, and severe asthma.

Objectives: To identify novel asthma phenotypes using an unsupervised hierarchical cluster analysis.

Methods: Reduction of the initial 628 variables to 34 core variables was achieved by elimination of redundant data and transformation of categorical variables into ranked ordinal composite variables. Cluster analysis was performed on 726 subjects.

Measurements and main results: Five groups were identified. Subjects in Cluster 1 (n = 110) have early onset atopic asthma with normal lung function treated with two or fewer controller medications (82%) and minimal health care utilization. Cluster 2 (n = 321) consists of subjects with early-onset atopic asthma and preserved lung function but increased medication requirements (29% on three or more medications) and health care utilization. Cluster 3 (n = 59) is a unique group of mostly older obese women with late-onset nonatopic asthma, moderate reductions in FEV(1), and frequent oral corticosteroid use to manage exacerbations. Subjects in Clusters 4 (n = 120) and 5 (n = 116) have severe airflow obstruction with bronchodilator responsiveness but differ in to their ability to attain normal lung function, age of asthma onset, atopic status, and use of oral corticosteroids.

Conclusions: Five distinct clinical phenotypes of asthma have been identified using unsupervised hierarchical cluster analysis. All clusters contain subjects who meet the American Thoracic Society definition of severe asthma, which supports clinical heterogeneity in asthma and the need for new approaches for the classification of disease severity in asthma.

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Figures

Figure 1.
Figure 1.
Tree analysis. Using three variables (baseline FEV1 [with a bronchodilator withhold], maximal “Max” FEV1 after six to eight puffs of albuterol, and age of onset of asthma), subjects can be assigned to the five clusters that range from milder asthma (Cluster 1) to more severe disease (Clusters 4 and 5).
Figure 2.
Figure 2.
Tree performance. Using the algorithm generated by the tree analysis, 80% of subjects are assigned to the correct cluster of asthma severity. Colors are maintained from the tree diagram (blue = mild atopic asthma; green = mild to moderate atopic asthma; yellow = late-onset nonatopic asthma; orange = severe atopic asthma; red = severe asthma with fixed airflow). Individual figure size is proportional to the frequency of a specific cluster. The percentage of subjects from that cluster that are correctly assigned is indicated numerically within the shape.

Comment in

References

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