• Chris Newby, Liam G Heaney, Andrew Menzies-Gow, Rob M Niven, Adel Mansur, Christine Bucknall, Rekha Chaudhuri, John Thompson, Paul Burton, Chris Brightling, and British Thoracic Society Severe Refractory Asthma Network.
• Department of Infection, Inflammation and Immunity, Institute for Lung Health, University of Leicester, Leicester, United Kingdom; Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
• Plos One. 2014 Jan 1; 9 (7): e102987.

BackgroundSevere 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.MethodsFactor 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).FindingsFive 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.InterpretationStatistical 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.

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