Identification of five chronic obstructive pulmonary disease subgroups with different prognoses in the ECLIPSE cohort using cluster analysis.

Nicholas Locantore, Bruno Delafont, Jørgen Vestbo, Ignacio Coca, Y Ivanov (Collaborator), K Kostov (Collaborator), J Bourbeau (Collaborator), M Fitzgerald (Collaborator), P Hernández (Collaborator), K Killian (Collaborator), R Levy (Collaborator), F Maltais (Collaborator), D O'Donnell (Collaborator), J Krepelka (Collaborator), J Vestbo (Collaborator), E Wouters (Collaborator), D Quinn (Collaborator), P Bakke (Collaborator), M Kosnik (Collaborator), A Agusti (Collaborator)Y Feschenko (Collaborator), V Gavrisyuk (Collaborator), L Yashina (Collaborator), W MacNee (Collaborator), D Singh (Collaborator), J Wedzicha (Collaborator), A Anzueto (Collaborator), S Braman (Collaborator), R Casaburi (Collaborator), B Celli (Collaborator), G Giessel (Collaborator), M Gotfried (Collaborator), G Greenwald (Collaborator), N Hanania (Collaborator), D Mahler (Collaborator), B Make (Collaborator), S Rennard (Collaborator), C Rochester (Collaborator), P Scanlon (Collaborator), D Schuller (Collaborator), F Sciurba (Collaborator), A Sharafkhaneh (Collaborator), T Siler (Collaborator), E Silverman (Collaborator), A Wanner (Collaborator), R Wide (Collaborator), R ZuWallack (Collaborator), R Tal-Singer (Collaborator), H Coxson (Collaborator), C Crim (Collaborator), L Edwards (Collaborator), D Lomas (Collaborator), J Yates (Collaborator), A Agustí (Collaborator), P Calverley (Collaborator), B Miller (Collaborator)

    Research output: Contribution to journalArticlepeer-review


    RATIONALE: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that likely includes clinically relevant subgroups. OBJECTIVES: To identify subgroups of COPD in ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) subjects using cluster analysis and to assess clinically meaningful outcomes of the clusters during 3 years of longitudinal follow-up. METHODS: Factor analysis was used to reduce 41 variables determined at recruitment in 2,164 patients with COPD to 13 main factors, and the variables with the highest loading were used for cluster analysis. Clusters were evaluated for their relationship with clinically meaningful outcomes during 3 years of follow-up. The relationships among clinical parameters were evaluated within clusters. MEASUREMENTS AND MAIN RESULTS: Five subgroups were distinguished using cross-sectional clinical features. These groups differed regarding outcomes. Cluster A included patients with milder disease and had fewer deaths and hospitalizations. Cluster B had less systemic inflammation at baseline but had notable changes in health status and emphysema extent. Cluster C had many comorbidities, evidence of systemic inflammation, and the highest mortality. Cluster D had low FEV1, severe emphysema, and the highest exacerbation and COPD hospitalization rate. Cluster E was intermediate for most variables and may represent a mixed group that includes further clusters. The relationships among clinical variables within clusters differed from that in the entire COPD population. CONCLUSIONS: Cluster analysis using baseline data in ECLIPSE identified five COPD subgroups that differ in outcomes and inflammatory biomarkers and show different relationships between clinical parameters, suggesting the clusters represent clinically and biologically different subtypes of COPD.
    Original languageEnglish
    JournalAnnals of the American Thoracic Society
    Issue number3
    Publication statusPublished - Mar 2015


    • chronic obstructive pulmonary disease
    • cluster analysis
    • longitudinal outcomes


    Dive into the research topics of 'Identification of five chronic obstructive pulmonary disease subgroups with different prognoses in the ECLIPSE cohort using cluster analysis.'. Together they form a unique fingerprint.

    Cite this