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Cardiovascular risk prediction using physical performance measures in COPD: results from a multicentre observational study.

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Bolton, Charlotte E  ORCID logo
MacNee, William 
Cockcroft, John R 


OBJECTIVES: Although cardiovascular disease (CVD) is a common comorbidity associated with chronic obstructive pulmonary disease (COPD), it is unknown how to improve prediction of cardiovascular (CV) risk in individuals with COPD. Traditional CV risk scores have been tested in different populations but not uniquely in COPD. The potential of alternative markers to improve CV risk prediction in individuals with COPD is unknown. We aimed to determine the predictive value of conventional CVD risk factors in COPD and to determine if additional markers improve prediction beyond conventional factors. DESIGN: Data from the Evaluation of the Role of Inflammation in Chronic Airways disease cohort, which enrolled 729 individuals with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage II-IV COPD were used. Linked hospital episode statistics and survival data were prospectively collected for a median 4.6 years of follow-up. SETTING: Five UK centres interested in COPD. PARTICIPANTS: Population-based sample including 714 individuals with spirometry-defined COPD, smoked at least 10 pack years and who were clinically stable for >4 weeks. INTERVENTIONS: Baseline measurements included aortic pulse wave velocity (aPWV), carotid intima-media thickness (CIMT), C reactive protein (CRP), fibrinogen, spirometry and Body mass index, airflow Obstruction, Dyspnoea and Exercise capacity (BODE) Index, 6 min walk test (6MWT) and 4 m gait speed (4MGS) test. PRIMARY AND SECONDARY OUTCOME MEASURES: New occurrence (first event) of fatal or non-fatal hospitalised CVD, and all-cause and cause-specific mortality. RESULTS: Out of 714 participants, 192 (27%) had CV hospitalisation and 6 died due to CVD. The overall CV risk model C-statistic was 0.689 (95% CI 0.688 to 0.691). aPWV and CIMT neither had an association with study outcome nor improved model prediction. CRP, fibrinogen, GOLD stage, BODE Index, 4MGS and 6MWT were associated with the outcome, independently of conventional risk factors (p<0.05 for all). However, only 6MWT improved model discrimination (C=0.727, 95% CI 0.726 to 0.728). CONCLUSION: Poor physical performance defined by the 6MWT improves prediction of CV hospitalisation in individuals with COPD. TRIAL REGISTRATION NUMBER: ID 11101.



cardiac epidemiology, chronic airways disease, epidemiology, primary care, respiratory medicine (see thoracic medicine), Cardiovascular Diseases, Carotid Intima-Media Thickness, Heart Disease Risk Factors, Humans, Physical Functional Performance, Pulmonary Disease, Chronic Obstructive, Pulse Wave Analysis, Risk Factors

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BMJ Open

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Medical Research Council (MR/L003120/1)
British Heart Foundation (None)
British Heart Foundation (RG/18/13/33946)
Cambridge University Hospitals NHS Foundation Trust (CUH) (146281)
This work was supported by a grant (9157-61188) from Innovate UK (formerly known as Technology Strategy Board) with contributory funding in kind (e.g. scientific expertise and meeting rooms) from GSK, a consortium partner, who also funded the corresponding author’s PhD. As a consortium partner, GSK was involved in the study design, data analysis, decision to publish, and preparation of the manuscript. The specific roles of all authors are articulated in the ‘author contributions’ section. RTS was a co-investigator on the grant and as a consortium member was involved in the decision to publish, and preparation of the manuscript. IBW, JC and CMM acknowledge funding support from the NIHR Cambridge Comprehensive Biomedical Research Centre. CEB is supported by the NIHR Nottingham BRC respiratory theme. This work was supported by core funding from: the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*]. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. *The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.