Identification of Five Chronic Obstructive Pulmonary Disease Subgroups with Different Prognoses in the ECLIPSE Cohort Using Cluster Analysis

Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that likely includes clinically relevant subgroups. Objectives: To identify subgroups of COPD in ECLIPSE subjects using unsupervised 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 COPD patients to 13 main factors, and the variables with the highest loading were used for unsupervised cluster analysis. Clusters were then 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. Importantly, these groups differed with regard to outcomes. Cluster A included milder patients 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: Unsupervised 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.