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Eur Respir J. 2017 Nov 2;50(5). pii: 1701034. doi: 10.1183/13993003.01034-2017. Print 2017 Nov.

A simple algorithm for the identification of clinical COPD phenotypes.

Author information

1
University Paris Descartes (EA2511), Sorbonne Paris Cité, Paris, France pierre-regis.burgel@cch.aphp.fr.
2
Dept of Respiratory Medicine, Cochin Hospital, AP-HP, Paris, France.
3
Effi-Stat, Paris, France.
4
Respiratory Division, University Hospital Gasthuisberg, K.U. Leuven, Leuven, Belgium.
5
Dept of Respiratory Medicine, Le Raincy-Montfermeil Hospital, Montfermeil, France.
6
Dept General Practice - Academic Medical Center, Amsterdam, The Netherlands.
7
ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra (UPF), CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
8
Unidad de Investigación, Servicio de Neumología, Hospital Universitario Son Espases, Palma de Mallorca, Spain.
9
Dept of Clinical Science, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway.
10
Epidemiology, Biostatistics und Prevention Institute (EBPI), University of Zurich, Zurich, Switzerland.
11
Dept of Public Health and General Practice, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway.
12
Universidad de Sevilla, Seville, Spain.
13
Internal Medicine, Hospital Universitari Mutua de Terrassa, Universitat de Barcelona, Barcelona, Spain.
14
Pneumology Service, La Princesa Institute for Health Research (IP), Hospital Universitario de la Princesa, Madrid, Spain.
15
Brigham and Women's Hospital, Boston, MA, USA.
16
Hospital Nuestra Señora de la Candelaria, Tenerife, Spain.
17
Clınica Universidad de Navarra, Pamplona, Spain.
18
Servicio de Neumonología Hospital San Juan de Dios de La Plata, Buenos Aires, Argentina.
19
Hospital Galdakao-Usansolo, Galdakao, Spain.
20
Servicio de Neumología, Hospital Universitario La Princesa, Madrid, Spain.
21
University of Michigan, Ann Arbor, MI, USA.
22
Centre for Clinical Research, Haukeland University Hospital, Bergen, Norway.
23
Dept of Pulmonary Medicine, Paracelsus Medical University Hospital, Salzburg, Austria.
24
Dept of Pulmonary Medicine, General Hospital Linz (AKH), Linz, Austria.
25
Section of Social Medicine, Dept of Public Health, Copenhagen University, Copenhagen, Denmark.
26
Centre for Clinical Documentation and Evaluation, Northern Norway Regional Health Authority, Tromso, Norway.
27
Hospital Universitario Miguel Servet, Zaragoza, Spain.
28
Pneumologie, Centre Hospitalier de Compiègne, Compiègne, France.
29
Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.
30
Universidad Autónoma de Chile, San Miguel, Chile.
31
Pneumology Dept, Hospital Universitary Vall d'Hebron. CIBER de Enfermedades Respiratorias (CIBERES), Barcelona, Spain.
32
Dept of Respiratory Care and Sleep Control Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
33
Facultad de Medicina UASLP, San Luis Potosí, México.
34
James Hogg Research Centre, University of British Columbia; Division of Respiratory Medicine, Dept of Medicine, St Paul's Hospital, Vancouver, Canada.
35
Hospital Universitario Araba, Sede Txagorritxu, Vitoria, Spain.
36
Servicio de Neumología, Hospital Arnau de Vilanova, Valencia, Spain.
37
Queen Elizabeth Hospital Research Laboratories, Birmingham, UK.
38
H.U. Son Espases, Palma de Mallorca, Spain.
39
Instituto de Investigación Hospital Universitario de la Princesa (IISP), Universidad Autónoma de Madrid, Madrid, Spain.
40
University Paris Descartes (EA2511), Sorbonne Paris Cité, Paris, France.

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.

Comment in

PMID:
29097431
DOI:
10.1183/13993003.01034-2017
[Indexed for MEDLINE]
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