Cluster Analysis and Clinical Asthma Phenotypes

TLDR

Asthma heterogeneity spans clinical, physiologic, and pathologic dimensions, and classification must integrate these domains into a unified model. The study aims to apply k‑means cluster analysis to identify distinct asthma phenotypic groups. The authors applied k‑means clustering to three independent asthma cohorts—primary‑care mild/moderate, secondary‑care refractory, and a randomized‑trial cohort—to delineate phenotypic clusters. K‑means clustering identified distinct phenotypes, including two shared clusters (early‑onset atopic and obese, non‑eosinophilic) and two refractory‑specific discordant clusters, and inflammation‑guided management reduced exacerbations in the inflammation‑predominant cluster and lowered steroid doses in the symptom‑predominant cluster.

Abstract

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.