A simple algorithm for the identification of clinical COPD phenotypes

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 M. Turner; Francisco Javier Verdu Rivera; Joan B.  Soriano; Nicolas Roche; Initiatives BPCO, EABPCO, Leuven and 3CIA study groups

doi:10.1183/13993003.01034-2017

A simple algorithm for the identification of clinical COPD phenotypes

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 PunterMeiLan 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 M. Turner, Francisco Javier Verdu Rivera, Joan B. Soriano, Nicolas Roche, Initiatives BPCO, EABPCO, Leuven and 3CIA study groups

Nutrition and Exercise

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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.

Original language	English
Article number	1701034
Pages (from-to)	1-11
Journal	European Respiratory Journal
Volume	50
Issue number	5
DOIs	https://doi.org/10.1183/13993003.01034-2017
Publication status	Published - 1 Nov 2017

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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., ... Initiatives BPCO, EABPCO, Leuven and 3CIA study groups (2017). A simple algorithm for the identification of clinical COPD phenotypes. European Respiratory Journal, 50(5), 1-11. Article 1701034. https://doi.org/10.1183/13993003.01034-2017

@article{7d8d715f1e9148b1901a45ec11757b01,

title = "A simple algorithm for the identification of clinical COPD phenotypes",

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.",

author = "Pierre-R{\'e}gis Burgel and Jean-Louis Paillasseur and Wim Janssens and Jacques Piquet and {ter Riet}, Gerben and Judith Garcia-Aymerich and Borja Cosio and Per Bakke and Puhan, {Milo A.} and Arnulf Langhammer and Inmaculada Alfageme and Pere Almagro and Julio Ancochea and Celli, {Bartolome R.} and Ciro Casanova and de-Torres, {Juan P.} and Marc Decramer and Andr{\'e}s Echazarreta and Cristobal Esteban and {Gomez Punter}, {Rosa Mar} and Han, {MeiLan K.} and Ane Johannessen and Bernhard Kaiser and Bernd Lamprecht and Peter Lange and Linda Leivseth and Marin, {Jose M.} and Francis Martin and Pablo Martinez-Camblor and Marc Miravitlles and Toru Oga and {Sofia Ram{\'i}rez}, Ana and Sin, {Don D.} and Patricia Sobradillo and Soler-Catalu{\~n}a, {Juan J.} and Turner, {Alice M.} and {Verdu Rivera}, {Francisco Javier} and Soriano, {Joan B.} and Nicolas Roche and {Initiatives BPCO, EABPCO, Leuven and 3CIA study groups}",

note = "With supplementary file. ",

year = "2017",

month = nov,

day = "1",

doi = "10.1183/13993003.01034-2017",

language = "English",

volume = "50",

pages = "1--11",

journal = "European Respiratory Journal",

issn = "0903-1936",

publisher = "European Respiratory Society",

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Burgel, P-R, Paillasseur, J-L, Janssens, W, Piquet, J, ter Riet, G, Garcia-Aymerich, J, Cosio, B, Bakke, P, Puhan, MA, Langhammer, A, Alfageme, I, Almagro, P, Ancochea, J, Celli, BR, Casanova, C, de-Torres, JP, Decramer, M, Echazarreta, A, Esteban, C, Gomez Punter, RM, Han, MK, Johannessen, A, Kaiser, B, Lamprecht, B, Lange, P, Leivseth, L, Marin, JM, Martin, F, Martinez-Camblor, P, Miravitlles, M, Oga, T, Sofia Ramírez, A, Sin, DD, Sobradillo, P, Soler-Cataluña, JJ, Turner, AM, Verdu Rivera, FJ, Soriano, JB, Roche, N & Initiatives BPCO, EABPCO, Leuven and 3CIA study groups 2017, 'A simple algorithm for the identification of clinical COPD phenotypes', European Respiratory Journal, vol. 50, no. 5, 1701034, pp. 1-11. https://doi.org/10.1183/13993003.01034-2017

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

AU - Initiatives BPCO, EABPCO, Leuven and 3CIA study groups

N1 - With supplementary file.

PY - 2017/11/1

Y1 - 2017/11/1

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.

U2 - 10.1183/13993003.01034-2017

DO - 10.1183/13993003.01034-2017

M3 - Article

C2 - 29097431

SN - 0903-1936

VL - 50

SP - 1

EP - 11

JO - European Respiratory Journal

JF - European Respiratory Journal

IS - 5

M1 - 1701034

ER -

A simple algorithm for the identification of clinical COPD phenotypes

Abstract

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