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Classification of chronic obstructive pulmonary disease (COPD) is usually based on the severity of airflow limitation, which may not reflect phenotypic heterogeneity. Here, we sought to identify COPD phenotypes using multiple clinical variables. COPD subjects recruited in a French multicentre cohort were characterised using a standardised process. Principal component analysis (PCA) was performed using eight variables selected for their relevance to COPD: age, cumulative smoking, forced expiratory volume in 1 s (FEV(1)) (% predicted), body mass index, exacerbations, dyspnoea (modified Medical Research Council scale), health status (St George's Respiratory Questionnaire) and depressive symptoms (hospital anxiety and depression scale). Patient classification was performed using cluster analysis based on PCA-transformed data. 322 COPD subjects were analysed: 77% were male; median (interquartile range) age was 65.0 (58.0-73.0) yrs; FEV(1) was 48.9 (34.1-66.3)% pred; and 21, 135, 107 and 59 subjects were classified in Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1, 2, 3 and 4, respectively. PCA showed that three independent components accounted for 61% of variance. PCA-based cluster analysis resulted in the classification of subjects into four clinical phenotypes that could not be identified using GOLD classification. Importantly, subjects with comparable airflow limitation (FEV(1)) belonged to different phenotypes and had marked differences in age, symptoms, comorbidities and predicted mortality. These analyses underscore the need for novel multidimensional COPD classification for improving patient care and quality of clinical trials.
Burgel et al. (Thu,) studied this question.