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Identification of clinical phenotypes in patients with and without COPD using cluster analysis

Miguel Divo, Ciro Casanova, Jose M. Marin, Bartolome Celli, Juan Pablo de Torres, Francesca Polverino, Rebeca Baz, Elizabeth Cordoba-Lanus, Victor Pinto-Plata
European Respiratory Journal 2016 48: PA4613; DOI: 10.1183/13993003.congress-2016.PA4613
Miguel Divo
1Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MAUnited States
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Ciro Casanova
2Pulmonary Medicine, Hospital Universitario La Candelaria, Tenerife, Canary IslandSpain
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Jose M. Marin
3Respiratory Service, Hospital Universitario Miguel Servet, Zaragoza, AragonSpain
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Bartolome Celli
1Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MAUnited States
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Juan Pablo de Torres
5Pulmonary Medicine, University Clinic of Navarra, Pamplona, Spain
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Francesca Polverino
1Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MAUnited States
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Rebeca Baz
2Pulmonary Medicine, Hospital Universitario La Candelaria, Tenerife, Canary IslandSpain
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Elizabeth Cordoba-Lanus
2Pulmonary Medicine, Hospital Universitario La Candelaria, Tenerife, Canary IslandSpain
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Victor Pinto-Plata
4Pulmonary and Critical Care, Baystate Medical Center, Springfield, MAUnited States
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Abstract

COPD is an heterogeneous disease better characterized by multidimensional phenotyping. Clustering is a technique used to identify discrete subgroups with similar combinations of traits. Except for pulmonary function variables, many other characteristics (6MWT, BMI, QOL) are not unique to COPD.

Aim: To compare how discrete clusters form in a mixed cohort of 120 individuals with COPD and controls.

Methods: Hundred and twenty patients matched by age and gender were selected, 90 with COPD of which 60 died at 3 years of follow-up. Hierarchical clustering was applied using pulmonary function, functional, anthropometric and QOL variables. Clusters were compared against a selected reference.

Results: Four clusters were identified and their composition is shown in A. Cluster 4 composed with 84% controls was used as reference and the comparison and descriptions are shown in B.

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Conclusion: Clustering is a useful tool to discriminate clinical meaningful phenotypes and by including “controls” we showed that >10% were assigned in COPD predominant clusters. This could be important when designing exploratory studies.

  • COPD - diagnosis
  • Epidemiology
  • Extrapulmonary impact
  • Copyright ©the authors 2016
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Identification of clinical phenotypes in patients with and without COPD using cluster analysis
Miguel Divo, Ciro Casanova, Jose M. Marin, Bartolome Celli, Juan Pablo de Torres, Francesca Polverino, Rebeca Baz, Elizabeth Cordoba-Lanus, Victor Pinto-Plata
European Respiratory Journal Sep 2016, 48 (suppl 60) PA4613; DOI: 10.1183/13993003.congress-2016.PA4613

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Identification of clinical phenotypes in patients with and without COPD using cluster analysis
Miguel Divo, Ciro Casanova, Jose M. Marin, Bartolome Celli, Juan Pablo de Torres, Francesca Polverino, Rebeca Baz, Elizabeth Cordoba-Lanus, Victor Pinto-Plata
European Respiratory Journal Sep 2016, 48 (suppl 60) PA4613; DOI: 10.1183/13993003.congress-2016.PA4613
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