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COPD classification models and mortality prediction capacity

Authors Aramburu A, Arostegui I, Moraza J, Barrio I, Aburto M, García-Loizaga A, Uranga A, Zabala T, Quintana JM, Esteban C

Received 21 August 2018

Accepted for publication 6 November 2018

Published 7 March 2019 Volume 2019:14 Pages 605—613

DOI https://doi.org/10.2147/COPD.S184695

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Justinn Cochran

Peer reviewer comments 4

Editor who approved publication: Dr Richard Russell


Amaia Aramburu,1 Inmaculada Arostegui,2–4 Javier Moraza,1 Irantzu Barrio,2 Myriam Aburto,1 Amaia García-Loizaga,1 Ane Uranga,1 Txomin Zabala,1 José María Quintana,3,5 Cristóbal Esteban1,3

1Respiratory Department, Hospital Galdakao-Usansolo, Galdakao, Bizkaia, Spain; 2Department of Applied Mathematics, Statistics and Operative Research, University of the Basque Country (UPV/EHU), Basque Country, Spain; 3Health Services Research on Chronic Patients Network (REDISSEC), Galdakao-Usansolo Hospital, Bizkaia, Spain; 4Basque Center for Applied Mathematics (BCAM), University of Basque Country, Leioa, Bizkaia, Spain; 5Research Unit, Hospital Galdakao-Usansolo, Galdakao, Bizkaia, Spain

Objective: Our aim was to assess the impact of comorbidities on existing COPD prognosis scores.
Patients and methods: A total of 543 patients with COPD (FEV1 <80% and FEV1/FVC <70%) were included between January 2003 and January 2004. Patients were stable for at least 6 weeks before inclusion and were followed for 5 years without any intervention by the research team. Comorbidities and causes of death were established from medical reports or information from primary care medical records. The GOLD system and the body mass index, obstruction, dyspnea and exercise (BODE) index were used for COPD classification. Patients were also classified into four clusters depending on the respiratory disease and comorbidities. Cluster analysis was performed by combining multiple correspondence analyses and automatic classification. Receiver operating characteristic curves and the area under the curve (AUC) were calculated for each model, and the DeLong test was used to evaluate differences between AUCs. Improvement in prediction ability was analyzed by the DeLong test, category-free net reclassification improvement and the integrated discrimination index.
Results: Among the 543 patients enrolled, 521 (96%) were male, with a mean age of 68 years, mean body mass index 28.3 and mean FEV1% 55%. A total of 167 patients died during the study follow-up. Comorbidities were prevalent in our cohort, with a mean Charlson index of 2.4. The most prevalent comorbidities were hypertension, diabetes mellitus and cardiovascular diseases. On comparing the BODE index, GOLDABCD, GOLD2017 and cluster analysis for predicting mortality, cluster system was found to be superior compared with GOLD2017 (0.654 vs 0.722, P=0.006), without significant differences between other classification models. When cardiovascular comorbidities and chronic renal failure were added to the existing scores, their prognostic capacity was statistically superior (P<0.001).
Conclusion: Comorbidities should be taken into account in COPD management scores due to their prevalence and impact on mortality.

Keywords: comorbidities, COPD, mortality, GOLD, cluster analysis, BODE index

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