BACKGROUND: Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation 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 wall 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.
BACKGROUND:Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation 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 COPDpatients (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 COPDpatients (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 wall 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.
Authors: Peter J Castaldi; Marta Benet; Hans Petersen; Nicholas Rafaels; James Finigan; Matteo Paoletti; H Marike Boezen; Judith M Vonk; Russell Bowler; Massimo Pistolesi; Milo A Puhan; Josep Anto; Els Wauters; Diether Lambrechts; Wim Janssens; Francesca Bigazzi; Gianna Camiciottoli; Michael H Cho; Craig P Hersh; Kathleen Barnes; Stephen Rennard; Meher Preethi Boorgula; Jennifer Dy; Nadia N Hansel; James D Crapo; Yohannes Tesfaigzi; Alvar Agusti; Edwin K Silverman; Judith Garcia-Aymerich Journal: Thorax Date: 2017-06-21 Impact factor: 9.139
Authors: Peter J Castaldi; Jennifer Dy; James Ross; Yale Chang; George R Washko; Douglas Curran-Everett; Andre Williams; David A Lynch; Barry J Make; James D Crapo; Russ P Bowler; Elizabeth A Regan; John E Hokanson; Greg L Kinney; Meilan K Han; Xavier Soler; Joseph W Ramsdell; R Graham Barr; Marilyn Foreman; Edwin van Beek; Richard Casaburi; Gerald J Criner; Sharon M Lutz; Steven I Rennard; Stephanie Santorico; Frank C Sciurba; Dawn L DeMeo; Craig P Hersh; Edwin K Silverman; Michael H Cho Journal: Thorax Date: 2014-02-21 Impact factor: 9.139
Authors: Peter J Castaldi; Adel Boueiz; Jeong Yun; Raul San Jose Estepar; James C Ross; George Washko; Michael H Cho; Craig P Hersh; Gregory L Kinney; Kendra A Young; Elizabeth A Regan; David A Lynch; Gerald J Criner; Jennifer G Dy; Stephen I Rennard; Richard Casaburi; Barry J Make; James Crapo; Edwin K Silverman; John E Hokanson Journal: Chest Date: 2019-12-28 Impact factor: 9.410
Authors: Michael H Cho; George R Washko; Thomas J Hoffmann; Gerard J Criner; Eric A Hoffman; Fernando J Martinez; Nan Laird; John J Reilly; Edwin K Silverman Journal: Respir Res Date: 2010-03-16
Authors: Shin Matsuoka; George R Washko; Mark T Dransfield; Tsuneo Yamashiro; Raul San Jose Estepar; Alejandro Diaz; Edwin K Silverman; Samuel Patz; Hiroto Hatabu Journal: Acad Radiol Date: 2009-09-30 Impact factor: 3.173