Identification of a predominant COPD phenotype in clinical practice

Background: Chronic obstructive pulmonary disease (COPD) is characterized by airflow [imitation caused by small airways increased resistance and/or terminal airspaces emphysematous destruction. Spirometric detection of not fully reversible airflow limitation unifies under the acronym COPD, a spectrum of heterogeneous conditions, whose clinical presentations may be substantially different. In a cross-sectional study we aimed to ascertain whether COPD phenotypes reflecting different mechanisms of airflow limitation could be clinically identified. Methods: Multidimensional scaling was used to visualize as a single point in a two-dimension space the multidimensional variables derived from each of 322 COPD patients (derivation set) by clinical, functional, and chest radiographic evaluation. Cluster analysis assigned then a cluster membership to each patient data point. Finally, using cluster membership as dependent variable and all data acquired as independent variables, we developed multivariate models to prospectively classify another group of 93 COPD patients (validation set) in whom high-resolution computerized tomography (HRCT) density parameters were measured. Results: A multivariate model based on nine variables acquired from the derivation set by history (sputum characteristics), physical examination (adventitious sounds, hyperresonance), FEV1/VC, and chest radiography (increased vascular markings, bronchial watt thickening, increased lung volume, reduced lung density) partitioned the validation set into two groups whose clinical, functional, chest radiographic, and HRCT characteristics corresponded to either an airways obstructive or a parenchymal destructive COPD phenotype. Conclusion: Patients with COPD can be assigned a clinical phenotype reflecting the prevalent mechanism of airflow limitation. The standardized identification of the predominant phenotype may permit to clinically characterize COPD beyond its unifying spirometric definition. (C) 2007 Elsevier Ltd. All rights reserved.