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

Chris Newby, Liam G Heaney, Andrew Menzies-Gow, Rob M Niven, Adel Mansur, Christine Bucknall, Rekha Chaudhuri, John Thompson, Paul Burton, Chris Brightling, British Thoracic Society Severe Refractory Asthma Network

Research output: Contribution to journalArticle (Academic Journal)peer-review

85 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)e102987
JournalPLoS ONE
Volume9
Issue number7
DOIs
Publication statusPublished - 2014

Keywords

  • Adolescent
  • Adrenal Cortex Hormones
  • Adult
  • Anti-Asthmatic Agents
  • Asthma
  • Body Mass Index
  • Child
  • Child, Preschool
  • Cluster Analysis
  • Eosinophils
  • Female
  • Follow-Up Studies
  • Great Britain
  • Humans
  • Male
  • Middle Aged
  • Phenotype
  • Registries
  • Severity of Illness Index
  • Societies, Medical
  • Treatment Outcome

Fingerprint

Dive into the research topics of 'Statistical cluster analysis of the British Thoracic Society Severe refractory Asthma Registry: clinical outcomes and phenotype stability'. Together they form a unique fingerprint.

Cite this