Cluster Analysis and Clinical Asthma Phenotypes
By P. Haldar, I. Pavord, D. Shaw, M. Berry, M. Thomas, C. Brightling, A. Wardlaw, R. Green. American Journal of Respiratory and Critical Care Medicine, 2008
1,893 citations recorded by the source corpus.
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.
Subfields: Immunology, Physiology, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/2a27925fd5c8d917634208210918cf5d82879246
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Immunology
In Physiology
In Pulmonary and Respiratory Medicine
Citations in the corpus
- International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma (2013, methodology): “There is increasing evidence that severe asthma phenotypes are related to genetic factors, age of asthma onset, disease duration, exacerbations, sinus disease and inflammatory characteristics [13, 26, 27, 29-31].”
- A summary of the new GINA strategy: a roadmap to asthma control (2015, background): “Discordance in treatment response between symptoms and risk can be biologically informative about the underlying mechanism [27].”
- Epidemiology of Asthma in Children and Adults (2019, methodology): “While this review did not find significant sex-related differences, female obesity has been associated with a pauci-eosinophil and nonatopic asthma endotype that is symptom-predominant and less steroid-responsive in previous cluster and LCA (16, 28).”
- An official American Thoracic Society/European Respiratory Society statement: asthma control and exacerbations: standardizing endpoints for clinical asthma trials and clinical practice. (2009, background): “Although the majority of patients in primary care demonstrate concordance between symptoms and eosinophilic airway inflammation, significant discordance is seen in patients referred for secondary care (288).”
- Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program. (2010, positive): “Body mass index (BMI) 29 (8) 27 (5) 28 (8) 33 (9) 31 (9) 31 (7) ,0.”