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COPD assessment test and FEV(1): do they predict oxygen uptake in COPD?

BACKGROUND: Chronic obstructive pulmonary disease (COPD) manifests itself in complex ways, with local and systemic effects; because of this, a multifactorial approach is needed for disease evaluation, in order to understand its severity and impact on each individual. Thus, our objective was to study...

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Detalles Bibliográficos
Autores principales: Carvalho-Jr, Luiz Carlos S, Trimer, Renata, Arêas, Guilherme PT, Caruso, Flávia CR, Zangrando, Katiany TL, Jürgensen, Soraia Pilon, Bonjorno, José C, de Oliveira, Cláudio Ricardo, Cabiddu, Ramona, Mendes, Renata G, Borghi-Silva, Audrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6183695/
https://www.ncbi.nlm.nih.gov/pubmed/30349223
http://dx.doi.org/10.2147/COPD.S167369
Descripción
Sumario:BACKGROUND: Chronic obstructive pulmonary disease (COPD) manifests itself in complex ways, with local and systemic effects; because of this, a multifactorial approach is needed for disease evaluation, in order to understand its severity and impact on each individual. Thus, our objective was to study the correlation between easily accessible variables, usually available in clinical practice, and maximum aerobic capacity, and to determine models for peak oxygen uptake (VO(2)peak) estimation in COPD patients. SUBJECTS AND METHODS: Individuals with COPD were selected for the study. At the first visit, clinical evaluation was performed. During the second visit, the volunteers were subjected to the cardiopulmonary exercise test. To determine the correlation coefficient of VO(2)peak with forced expiratory volume in 1 second (FEV(1)) (% pred.) and the COPD Assessment Test score (CATs), Pearson or Spearman tests were performed. VO(2) at the peak of the exercise was estimated from the clinical variables by simple and multiple linear regression analyses. RESULTS: A total of 249 subjects were selected, 27 of whom were included after screening (gender: 21M/5F; age: 65.0±7.3 years; body mass index: 26.6±5.0 kg/m(2); FEV(1) (% pred.): 56.4±15.7, CAT: 12.4±7.4). Mean VO(2) peak was 12.8±3.0 mL⋅kg(−1)⋅min(−1) and VO(2)peak (% pred.) was 62.1%±14.9%. VO(2)peak presented a strong positive correlation with FEV(1) (% pred.), r: 0.70, and a moderate negative correlation with the CATs, r: -0.54. In the VO(2)peak estimation model based on the CAT (estimated VO(2)peak =15.148− [0.185× CATs]), the index explained 20% of the variance, with estimated error of 2.826 mL⋅kg(−1)⋅min(−1). In the VO(2)peak estimation model based on FEV(1) (estimated VO(2)peak =6.490+ [0.113× FEV(1)]), the variable explained 50% of the variance, with an estimated error of 2.231 mL⋅kg(−1)⋅min(−1). In the VO(2)peak estimation model based on CATs and FEV(1) (estimated VO(2)peak =8.441− [0.0999× CAT] + [0.1000× FEV(1)]), the variables explained 55% of the variance, with an estimated error of 2.156 mL⋅kg(−1)⋅min(−1). CONCLUSION: COPD patients’ maximum aerobic capacity has a significant correlation with easily accessible and widely used clinical variables, such as the CATs and FEV(1), which can be used to estimate peak VO(2).