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Author (up) Burgel, P.-R.; Paillasseur, J.-L.; Janssens, W.; Piquet, J.; Ter Riet, G.; Garcia-Aymerich, J.; Cosio, B.; Bakke, P.; Puhan, M.A.; Langhammer, A.; Alfageme, I.; Almagro, P.; Ancochea, J.; Celli, B.R.; Casanova, C.; de-Torres, J.P.; Decramer, M.; Echazarreta, A.; Esteban, C.; Gomez Punter, R.M.; Han, M.L.K.; Johannessen, A.; Kaiser, B.; Lamprecht, B.; Lange, P.; Leivseth, L.; Marin, J.M.; Martin, F.; Martinez-Camblor, P.; Miravitlles, M.; Oga, T.; Sofia Ramirez, A.; Sin, D.D.; Sobradillo, P.; Soler-Cataluna, J.J.; Turner, A.M.; Verdu Rivera, F.J.; Soriano, J.B.; Roche, N. url  doi
  Title A simple algorithm for the identification of clinical COPD phenotypes Type Journal Article
  Year 2017 Publication The European Respiratory Journal Abbreviated Journal Eur Respir J  
  Volume 50 Issue 5 Pages  
  Abstract 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.  
  Address Dept of Respiratory Medicine, Cochin Hospital, AP-HP, Paris, France  
  Corporate Author Initiatives BPCO, EABPCO, Leuven and 3CIA study groups Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0903-1936 ISBN Medium  
  Area Expedition Conference  
  Notes PMID:29097431 Approved no  
  Call Number HUNT @ maria.stuifbergen @ Serial 1894  
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