Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/32353
Title: Statistical Cluster Analysis of the British Thoracic Society Severe Refractory Asthma Registry: Clinical Outcomes and Phenotype Stability
Authors: Newby, Chris
Heaney, L. G.
Menzies-Gow, A.
Niven, R. M.
Mansur, A.
Bucknall, C.
Chaudhuri, R.
Thompson, J.
Burton, P.
Brightling, Chris
British Thoracic Society Severe Refractory Asthma Network
First Published: 24-Jul-2014
Publisher: Public Library of Science
Citation: PLoS One, 2014, 9 (7), p. e102987
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. 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). 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. 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.
DOI Link: 10.1371/journal.pone.0102987
ISSN: 1932-6203
eISSN: 1932-6203
Links: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0102987
http://hdl.handle.net/2381/32353
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: © 2014 Newby et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: PMCID: PMC4109965
Appears in Collections:Published Articles, Dept. of Infection, Immunity and Inflammation

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