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Cluster Analysis and Clinical Asthma Phenotypes

¹ Institute for Lung Health, Glenfield Hospital, Leicester, United Kingdom; and ² Department of General Practice, University of Aberdeen, Aberdeen, United Kingdom

Correspondence and requests for reprints should be addressed to Dr. P. Haldar, M.R.C.P., Institute for Lung Health, Glenfield Hospital, Leicester, LE3 9QP, UK. E-mail: ph62{at}le.ac.uk

Rationale: Heterogeneity in asthma expression is multidimensional, including variability in clinical, physiologic, and pathologic parameters. Classification requires consideration of these disparate domains in a unified model.

Objectives: To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups.

Methods: We performed k-means cluster analysis in three independent asthma populations. Clusters of a population managed in primary care (n = 184) with predominantly mild to moderate disease, were compared with a refractory asthma population managed in secondary care (n = 187). We then compared differences in asthma outcomes (exacerbation frequency and change in corticosteroid dose at 12 mo) between clusters in a third population of 68 subjects with predominantly refractory asthma, clustered at entry into a randomized trial comparing a strategy of minimizing eosinophilic inflammation (inflammation-guided strategy) with standard care.

Measurements and Main Results: Two clusters (early-onset atopic and obese, noneosinophilic) were common to both asthma populations. Two clusters characterized by marked discordance between symptom expression and eosinophilic airway inflammation (early-onset symptom predominant and late-onset inflammation predominant) were specific to refractory asthma. Inflammation-guided management was superior for both discordant subgroups leading to a reduction in exacerbation frequency in the inflammation-predominant cluster (3.53 [SD, 1.18] vs. 0.38 [SD, 0.13] exacerbation/patient/yr, P = 0.002) and a dose reduction of inhaled corticosteroid in the symptom-predominant cluster (mean difference, 1,829 µg beclomethasone equivalent/d [95% confidence interval, 307–3,349 µg]; P = 0.02).

Conclusions: Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms.

Key Words: taxonomy • corticosteroid response • multivariate classification

AT A GLANCE COMMENTARY Scientific Knowledge on the Subject Although several models of asthma classification have been proposed, a system defining the phenotypes of clinical asthma that incorporate the different aspects of the disease has not been developed. What This Study Adds to the Field Cluster analysis may be used to classify patients with asthma into phenotypic groups that exhibit clinically relevant differences in outcome with a management strategy using a measure of eosinophilic inflammation for titrating corticosteroid therapy.