PT - JOURNAL ARTICLE AU - Pierre-Régis Burgel AU - Jean-Louis Paillasseur AU - Wim Janssens AU - Jacques Piquet AU - Gerben ter Riet AU - Judith Garcia-Aymerich AU - Borja Cosio AU - Per Bakke AU - Milo A. Puhan AU - Arnulf Langhammer AU - Inmaculada Alfageme AU - Pere Almagro AU - Julio Ancochea AU - Bartolome R. Celli AU - Ciro Casanova AU - Juan P. de-Torres AU - Marc Decramer AU - Andrés Echazarreta AU - Cristobal Esteban AU - Rosa Mar Gomez Punter AU - MeiLan K. Han AU - Ane Johannessen AU - Bernhard Kaiser AU - Bernd Lamprecht AU - Peter Lange AU - Linda Leivseth AU - Jose M. Marin AU - Francis Martin AU - Pablo Martinez-Camblor AU - Marc Miravitlles AU - Toru Oga AU - Ana Sofia Ramírez AU - Don D. Sin AU - Patricia Sobradillo AU - Juan J. Soler-Cataluña AU - Alice M. Turner AU - Francisco Javier Verdu Rivera AU - Joan B. Soriano AU - Nicolas Roche ED - , TI - A simple algorithm for the identification of clinical COPD phenotypes AID - 10.1183/13993003.01034-2017 DP - 2017 Nov 01 TA - European Respiratory Journal PG - 1701034 VI - 50 IP - 5 4099 - http://erj.ersjournals.com/content/50/5/1701034.short 4100 - http://erj.ersjournals.com/content/50/5/1701034.full SO - Eur Respir J2017 Nov 01; 50 AB - This study aimed to identify simple rules for allocating chronic obstructive pulmonary disease (COPD) patients to clinical phenotypes identified by cluster analyses.Data from 2409 COPD patients of French/Belgian COPD cohorts were analysed using cluster analysis resulting in the identification of subgroups, for which clinical relevance was determined by comparing 3-year all-cause mortality. Classification and regression trees (CARTs) were used to develop an algorithm for allocating patients to these subgroups. This algorithm was tested in 3651 patients from the COPD Cohorts Collaborative International Assessment (3CIA) initiative.Cluster analysis identified five subgroups of COPD patients with different clinical characteristics (especially regarding severity of respiratory disease and the presence of cardiovascular comorbidities and diabetes). The CART-based algorithm indicated that the variables relevant for patient grouping differed markedly between patients with isolated respiratory disease (FEV1, dyspnoea grade) and those with multi-morbidity (dyspnoea grade, age, FEV1 and body mass index). Application of this algorithm to the 3CIA cohorts confirmed that it identified subgroups of patients with different clinical characteristics, mortality rates (median, from 4% to 27%) and age at death (median, from 68 to 76 years).A simple algorithm, integrating respiratory characteristics and comorbidities, allowed the identification of clinically relevant COPD phenotypes.An algorithm integrating respiratory characteristics and comorbidities identifies clinical COPD phenotypes http://ow.ly/eSRp30fJPG5