Cluster analysis and clinical asthma phenotypes

Pranab Haldar, Ian D. Pavord, Dominic E. Shaw, Michael A. Berry, Michael Thomas, Christopher E. Brightling, Andrew I. Wardlaw, Ruth H. Green

Research output: Contribution to journalArticle

1136 Citations (Scopus)

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 mu g beclomethasone equivalent/d [95% confidence interval, 307-3,349 mu g]; P = 0.02).

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

Original languageEnglish
Pages (from-to)218-224
Number of pages7
JournalAmerican Journal of Respiratory and Critical Care Medicine
Volume178
Issue number3
DOIs
Publication statusPublished - 1 Aug 2008

Keywords

  • taxonomy
  • corticosteroid response
  • multivariate classification
  • randomized controlled-trial
  • exhaled nitric-oxide
  • management
  • adults

Cite this

Haldar, P., Pavord, I. D., Shaw, D. E., Berry, M. A., Thomas, M., Brightling, C. E., ... Green, R. H. (2008). Cluster analysis and clinical asthma phenotypes. American Journal of Respiratory and Critical Care Medicine, 178(3), 218-224. https://doi.org/10.1164/rccm.200711-1754OC

Cluster analysis and clinical asthma phenotypes. / Haldar, Pranab; Pavord, Ian D.; Shaw, Dominic E.; Berry, Michael A.; Thomas, Michael; Brightling, Christopher E.; Wardlaw, Andrew I.; Green, Ruth H.

In: American Journal of Respiratory and Critical Care Medicine, Vol. 178, No. 3, 01.08.2008, p. 218-224.

Research output: Contribution to journalArticle

Haldar, P, Pavord, ID, Shaw, DE, Berry, MA, Thomas, M, Brightling, CE, Wardlaw, AI & Green, RH 2008, 'Cluster analysis and clinical asthma phenotypes' American Journal of Respiratory and Critical Care Medicine, vol. 178, no. 3, pp. 218-224. https://doi.org/10.1164/rccm.200711-1754OC
Haldar P, Pavord ID, Shaw DE, Berry MA, Thomas M, Brightling CE et al. Cluster analysis and clinical asthma phenotypes. American Journal of Respiratory and Critical Care Medicine. 2008 Aug 1;178(3):218-224. https://doi.org/10.1164/rccm.200711-1754OC
Haldar, Pranab ; Pavord, Ian D. ; Shaw, Dominic E. ; Berry, Michael A. ; Thomas, Michael ; Brightling, Christopher E. ; Wardlaw, Andrew I. ; Green, Ruth H. / Cluster analysis and clinical asthma phenotypes. In: American Journal of Respiratory and Critical Care Medicine. 2008 ; Vol. 178, No. 3. pp. 218-224.
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AU - Brightling, Christopher E.

AU - Wardlaw, Andrew I.

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N2 - 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 mu g beclomethasone equivalent/d [95% confidence interval, 307-3,349 mu g]; P = 0.02).Conclusions: Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms.

AB - 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 mu g beclomethasone equivalent/d [95% confidence interval, 307-3,349 mu g]; P = 0.02).Conclusions: Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms.

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