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Newby, Chris, Heaney, Liam G, Menzies-Gow, Andrew, Niven, Rob M, Mansur, Adel H, Bucknall, Christine, Chaudhuri, Rekha, Thompson, John, Burton, Paul and Brightling, Chris (2014) Statistical cluster analysis of the British Thoracic Society Severe refractory Asthma Registry: clinical outcomes and phenotype stability. PloS one, 9 (7). e102987. ISSN 1932-6203.
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Statistical cluster analysis of the British Thoracic Society Severe refractory Asthma Registry.pdf Download (292kB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Subjects: | WF Respiratory system. Respiratory medicine |
| Divisions: | Planned IP Care > Respiratory Medicine |
| Related URLs: | |
| Depositing User: | Mr Philip O'Reilly |
| Date Deposited: | 21 May 2015 10:47 |
| Last Modified: | 21 May 2015 10:47 |
| URI: | http://www.repository.uhblibrary.co.uk/id/eprint/938 |
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