A simple algorithm for the identification of clinical COPD phenotypes

Research output: Contribution to journalArticlepeer-review

Authors

  • Pierre-Régis Burgel
  • Jean-Louis Paillasseur
  • Wim Janssens
  • Jacques Piquet
  • Gerben Ter Riet
  • Judith Garcia-Aymerich
  • Borja Cosio
  • Per Bakke
  • Milo A Puhan
  • Arnulf Langhammer
  • Inmaculada Alfageme
  • Pere Almagro
  • Julio Ancochea
  • Bartolome R Celli
  • Ciro Casanova
  • Juan P de-Torres
  • Marc Decramer
  • Andrés Echazarreta
  • Cristobal Esteban
  • Rosa Mar Gomez Punter
  • MeiLan K Han
  • Ane Johannessen
  • Bernhard Kaiser
  • Bernd Lamprecht
  • Peter Lange
  • Linda Leivseth
  • Jose M Marin
  • Francis Martin
  • Pablo Martinez-Camblor
  • Marc Miravitlles
  • Toru Oga
  • Ana Sofia Ramírez
  • Don D Sin
  • Patricia Sobradillo
  • Juan J Soler-Cataluña
  • Alice Turner
  • Francisco Javier Verdu Rivera
  • Joan B Soriano
  • Nicolas Roche
  • Initiatives BPCO cohort
  • EABPCO cohort
  • Leuven Cohort
  • 3CIA initiative

Colleges, School and Institutes

External organisations

  • AP-HP Hôpital Saint Antoine
  • Effi-Stat, Paris, France.
  • University Hospital Gasthuisberg
  • Le Raincy-Montfermeil Hospital
  • Academic Medical Center
  • Universitat Pompeu Fabra
  • Unidad de Investigación, Servicio de Neumología, Hospital Universitario Son Espases, Palma de Mallorca, Spain.
  • University of Bergen
  • University of Zurich
  • Norwegian University of Science and Technology
  • Universidad de Sevilla
  • Universitat de Barcelona
  • Pneumology Service, La Princesa Institute for Health Research (IP), Hospital Universitario de la Princesa, Madrid, Spain.
  • Brigham and Women's Hospital
  • Hospital Nuestra Señora de la Candelaria, Tenerife, Spain.
  • Clınica Universidad de Navarra, Pamplona, Spain.
  • Servicio de Neumonología Hospital San Juan de Dios de La Plata
  • Hospital Galdakao-Usansolo, Galdakao, Spain.
  • Servicio de Neumología, Hospital Universitario La Princesa, Madrid, Spain.
  • University of Michigan
  • Haukeland University Hospital
  • Paracelsus Medical University
  • General Hospital Linz (AKH)
  • University of Copenhagen
  • Centre for Clinical Documentation and Evaluation, Northern Norway Regional Health Authority, Tromso, Norway.
  • Oncología Pediátricas,Hospital Infantil Universitario Miguel Servet, Zaragoza, Spain.
  • Pneumologie, Centre Hospitalier de Compiègne, Compiègne, France.
  • Universidad Autónoma de Chile
  • Pneumology Dept, Hospital Universitary Vall d'Hebron. CIBER de Enfermedades Respiratorias (CIBERES), Barcelona, Spain.
  • Kyoto University
  • Facultad de Medicina UASLP
  • University of British Columbia
  • Hospital Universitario Araba, Sede Txagorritxu, Vitoria, Spain.
  • Servicio de Neumología, Hospital Arnau de Vilanova, Valencia, Spain.
  • H.U. Son Espases, Palma de Mallorca, Spain.
  • Universidad Autónoma de Madrid

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.

Details

Original languageEnglish
JournalThe European respiratory journal
Volume50
Issue number5
Publication statusPublished - Nov 2017

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