RT Journal Article SR Electronic T1 A simple algorithm for the identification of clinical COPD phenotypes JF European Respiratory Journal JO Eur Respir J FD European Respiratory Society SP 1701034 DO 10.1183/13993003.01034-2017 VO 50 IS 5 A1 Pierre-Régis Burgel A1 Jean-Louis Paillasseur A1 Wim Janssens A1 Jacques Piquet A1 Gerben ter Riet A1 Judith Garcia-Aymerich A1 Borja Cosio A1 Per Bakke A1 Milo A. Puhan A1 Arnulf Langhammer A1 Inmaculada Alfageme A1 Pere Almagro A1 Julio Ancochea A1 Bartolome R. Celli A1 Ciro Casanova A1 Juan P. de-Torres A1 Marc Decramer A1 Andrés Echazarreta A1 Cristobal Esteban A1 Rosa Mar Gomez Punter A1 MeiLan K. Han A1 Ane Johannessen A1 Bernhard Kaiser A1 Bernd Lamprecht A1 Peter Lange A1 Linda Leivseth A1 Jose M. Marin A1 Francis Martin A1 Pablo Martinez-Camblor A1 Marc Miravitlles A1 Toru Oga A1 Ana Sofia Ramírez A1 Don D. Sin A1 Patricia Sobradillo A1 Juan J. Soler-Cataluña A1 Alice M. Turner A1 Francisco Javier Verdu Rivera A1 Joan B. Soriano A1 Nicolas Roche A1 , YR 2017 UL http://erj.ersjournals.com/content/50/5/1701034.abstract 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