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Statistical cluster analysis of the British Thoracic Society Severe refractory Asthma Registry: clinical outcomes and phenotype stability.

Newby, Chris and Heaney, Liam G and Menzies-Gow, Andrew and Niven, Rob M and Mansur, Adel H and Bucknall, Christine and Chaudhuri, Rekha and Thompson, John and 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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Official URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC410996...

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.heartofengland.nhs.uk/id/eprint/938

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