TY - JOUR
T1 - A simple algorithm for the identification of clinical COPD phenotypes
AU - Burgel, Pierre-Régis
AU - Paillasseur, Jean-Louis
AU - Janssens, Wim
AU - Piquet, Jacques
AU - Ter Riet, Gerben
AU - Garcia-Aymerich, Judith
AU - Cosio, Borja
AU - Bakke, Per
AU - Puhan, Milo A
AU - Langhammer, Arnulf
AU - Alfageme, Inmaculada
AU - Almagro, Pere
AU - Ancochea, Julio
AU - Celli, Bartolome R
AU - Casanova, Ciro
AU - de-Torres, Juan P
AU - Decramer, Marc
AU - Echazarreta, Andrés
AU - Esteban, Cristobal
AU - Gomez Punter, Rosa Mar
AU - Han, MeiLan K
AU - Johannessen, Ane
AU - Kaiser, Bernhard
AU - Lamprecht, Bernd
AU - Lange, Peter
AU - Leivseth, Linda
AU - Marin, Jose M
AU - Martin, Francis
AU - Martinez-Camblor, Pablo
AU - Miravitlles, Marc
AU - Oga, Toru
AU - Sofia Ramírez, Ana
AU - Sin, Don D
AU - Sobradillo, Patricia
AU - Soler-Cataluña, Juan J
AU - Turner, Alice M
AU - Verdu Rivera, Francisco Javier
AU - Soriano, Joan B
AU - Roche, Nicolas
N1 - With supplementary file.
PY - 2017
Y1 - 2017
N2 - 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.
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.
KW - Aged
KW - Aged, 80 and over
KW - Algorithms
KW - Belgium/epidemiology
KW - Body Mass Index
KW - Cluster Analysis
KW - Cohort Studies
KW - Comorbidity
KW - Female
KW - Forced Expiratory Volume
KW - France/epidemiology
KW - Humans
KW - International Cooperation
KW - Kaplan-Meier Estimate
KW - Male
KW - Middle Aged
KW - Phenotype
KW - Pulmonary Disease, Chronic Obstructive/classification
KW - Severity of Illness Index
KW - Time Factors
U2 - 10.1183/13993003.01034-2017
DO - 10.1183/13993003.01034-2017
M3 - Article
C2 - 29097431
VL - 50
JO - The European respiratory journal
JF - The European respiratory journal
SN - 0903-1936
IS - 5
M1 - 1701034
ER -