Statistical cluster analysis of the British thoracic society severe refractory asthma registry: clinical outcomes and phenotype stability

Newby, C., Heaney, L. G., Menzies-Gow, A., Niven, R. M., Mansur, A., Bucknall, C., Chaudhuri, R. , Thompson, J., Burton, P. and Brightling, C. (2014) Statistical cluster analysis of the British thoracic society severe refractory asthma registry: clinical outcomes and phenotype stability. PLoS ONE, 9(7), e102987. (doi: 10.1371/journal.pone.0102987) (PMID:25058007) (PMCID:PMC4109965)

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Abstract

Background Severe refractory asthma is a heterogeneous disease. We sought to determine statistical clusters from the British Thoracic Society Severe refractory Asthma Registry and to examine cluster-specific outcomes and stability.<p></p> Methods Factor analysis and statistical cluster modelling was undertaken to determine the number of clusters and their membership (N = 349). Cluster-specific outcomes were assessed after a median follow-up of 3 years. A classifier was programmed to determine cluster stability and was validated in an independent cohort of new patients recruited to the registry (n = 245).<p></p> Findings Five clusters were identified. Cluster 1 (34%) were atopic with early onset disease, cluster 2 (21%) were obese with late onset disease, cluster 3 (15%) had the least severe disease, cluster 4 (15%) were the eosinophilic with late onset disease and cluster 5 (15%) had significant fixed airflow obstruction. At follow-up, the proportion of subjects treated with oral corticosteroids increased in all groups with an increase in body mass index. Exacerbation frequency decreased significantly in clusters 1, 2 and 4 and was associated with a significant fall in the peripheral blood eosinophil count in clusters 2 and 4. Stability of cluster membership at follow-up was 52% for the whole group with stability being best in cluster 2 (71%) and worst in cluster 4 (25%). In an independent validation cohort, the classifier identified the same 5 clusters with similar patient distribution and characteristics.<p></p> Interpretation Statistical cluster analysis can identify distinct phenotypes with specific outcomes. Cluster membership can be determined using a classifier, but when treatment is optimised, cluster stability is poor.<p></p>

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Chaudhuri, Dr Rekha and Bucknall, Dr Christine
Authors: Newby, C., Heaney, L. G., Menzies-Gow, A., Niven, R. M., Mansur, A., Bucknall, C., Chaudhuri, R., Thompson, J., Burton, P., and Brightling, C.
College/School:College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
Journal Name:PLoS ONE
Publisher:Public Library of Science
ISSN:1932-6203
ISSN (Online):1932-6203
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in PLoS ONE 9(7):e102987
Publisher Policy:Reproduced under a Creative Commons License

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