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BioMed Research International
Volume 2014 (2014), Article ID 420134, 9 pages
Review Article

Identification of Clinical Phenotypes Using Cluster Analyses in COPD Patients with Multiple Comorbidities

1Service de Pneumologie, Hôpital Cochin, Assistance Publique Hôpitaux de Paris, 27 rue du Faubourg St. Jacques, 75014 Paris, France
2Université Paris Descartes, Sorbonne Paris Cité, 75014 Paris, France
3Initiatives BPCO Study Group, France
4EFFI-STAT, 75004 Paris, France

Received 10 November 2013; Accepted 2 January 2014; Published 10 February 2014

Academic Editor: Wim Janssens

Copyright © 2014 Pierre-Régis Burgel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Chronic obstructive pulmonary disease (COPD) is characterized by persistent airflow limitation, the severity of which is assessed using forced expiratory volume in 1 sec (FEV1, % predicted). Cohort studies have confirmed that COPD patients with similar levels of airflow limitation showed marked heterogeneity in clinical manifestations and outcomes. Chronic coexisting diseases, also called comorbidities, are highly prevalent in COPD patients and likely contribute to this heterogeneity. In recent years, investigators have used innovative statistical methods (e.g., cluster analyses) to examine the hypothesis that subgroups of COPD patients sharing clinically relevant characteristics (phenotypes) can be identified. The objectives of the present paper are to review recent studies that have used cluster analyses for defining phenotypes in observational cohorts of COPD patients. Strengths and weaknesses of these statistical approaches are briefly described. Description of the phenotypes that were reasonably reproducible across studies and received prospective validation in at least one study is provided, with a special focus on differences in age and comorbidities (including cardiovascular diseases). Finally, gaps in current knowledge are described, leading to proposals for future studies.