RATIONALE: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that likely includes clinically relevant subgroups. OBJECTIVES: To identify subgroups of COPD in ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) subjects using 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 patients with COPD to 13 main factors, and the variables with the highest loading were used for cluster analysis. Clusters were 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. These groups differed regarding outcomes. Cluster A included patients with milder disease 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: 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.
RCT Entities:
RATIONALE: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that likely includes clinically relevant subgroups. OBJECTIVES: To identify subgroups of COPD in ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) subjects using 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 patients with COPD to 13 main factors, and the variables with the highest loading were used for cluster analysis. Clusters were 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. These groups differed regarding outcomes. Cluster A included patients with milder disease 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: 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.
Authors: Adel Boueiz; Yale Chang; Michael H Cho; George R Washko; Raul San José Estépar; Russell P Bowler; James D Crapo; Dawn L DeMeo; Jennifer G Dy; Edwin K Silverman; Peter J Castaldi Journal: Chest Date: 2017-09-21 Impact factor: 9.410
Authors: Ayodeji Adegunsoye; Justin M Oldham; Jonathan H Chung; Steven M Montner; Cathryn Lee; Leah J Witt; Danielle Stahlbaum; Rene S Bermea; Lena W Chen; Scully Hsu; Aliya N Husain; Imre Noth; Rekha Vij; Mary E Strek; Matthew Churpek Journal: Chest Date: 2017-09-28 Impact factor: 9.410
Authors: Michael Schivo; Timothy E Albertson; Angela Haczku; Nicholas J Kenyon; Amir A Zeki; Brooks T Kuhn; Samuel Louie; Mark V Avdalovic Journal: J Investig Med Date: 2017-03-03 Impact factor: 2.895
Authors: Nirupama Putcha; Ashraf Fawzy; Gabriel G Paul; Allison A Lambert; Kevin J Psoter; Venkataramana K Sidhaye; John Woo; J Michael Wells; Wassim W Labaki; Claire M Doerschuk; Richard E Kanner; MeiLan K Han; Carlos Martinez; Laura M Paulin; Fernando J Martinez; Robert A Wise; Wanda K O'Neal; R Graham Barr; Nadia N Hansel Journal: Ann Am Thorac Soc Date: 2018-06
Authors: Christine M Lusk; Angela S Wenzlaff; Donovan Watza; Jessica C Sieren; Natasha Robinette; Garrett Walworth; Michael Petrich; Christine Neslund-Dudas; Michael J Flynn; Thomas Song; David Spizarny; Michael J Simoff; Ayman O Soubani; Shirish Gadgeel; Ann G Schwartz Journal: Cancer Epidemiol Biomarkers Prev Date: 2019-01-14 Impact factor: 4.254
Authors: Melissa J McDonnell; Stefano Aliberti; Pieter C Goeminne; Marcos I Restrepo; Simon Finch; Alberto Pesci; Lieven J Dupont; Thomas C Fardon; Robert Wilson; Michael R Loebinger; Dusan Skrbic; Dusanka Obradovic; Anthony De Soyza; Chris Ward; John G Laffey; Robert M Rutherford; James D Chalmers Journal: Lancet Respir Med Date: 2016-11-16 Impact factor: 30.700