RT Journal Article
SR Electronic
T1 Can We Better Estimate Resting Oxygen Consumption by Incorporating Arterial Blood Gases and Spirometric Determinations?
JF Respiratory Care
FD American Association for Respiratory Care
SP respcare.03555
DO 10.4187/respcare.03555
A1 Adriano R Tonelli
A1 Xiao-Feng Wang
A1 Anara Abbay
A1 Qi Zhang
A1 José Ramos
A1 Kevin McCarthy
YR 2014
UL http://rc.rcjournal.com/content/early/2014/12/16/respcare.03555.abstract
AB BACKGROUND: We hypothesize that oxygen consumption (V̇o2) estimation in patients with respiratory symptoms is inaccurate and can be improved by considering arterial blood gases or spirometric variables. METHODS: For this retrospective study, we included consecutive subjects who underwent cardiopulmonary exercise testing. Resting V̇o2 was determined using breath-by-breath testing methodology. Using a training cohort (n = 336), we developed 3 models to predict V̇o2. In a validation group (n = 114), we compared our models with 7 available formulae. RESULTS: Our first model (V̇o2 = −184.99 + 189.64 × body surface area [BSA, m2] + 1.49 × heart rate [beats/min] + 51.51 × FIO2 [21% = 0; 30% = 1] + 30.62 × gender [male = 1; female = 0]) showed an R2 of 0.5. Our second model (V̇o2 = −208.06 + 188.67 × BSA + 1.38 × heart rate + 35.6 × gender + 2.06 × breathing frequency [breaths/min]) showed an R2 of 0.49. The best R2 (0.68) was obtained with our last model, which included minute ventilation (V̇o2 = −142.92 + 0.52 × heart rate + 126.84 × BSA + 14.68 × minute ventilation [L]). In the validation cohort, these 3 models performed better than other available equations, but had wide limits of agreement, particularly in older individuals with shorter stature, higher heart rate, and lower maximum voluntary ventilation. CONCLUSIONS: We developed more accurate formulae to predict resting V̇o2 in subjects with respiratory symptoms; however, equations had wide limits of agreement, particularly in certain groups of subjects. Arterial blood gases and spirometric variables did not significantly improve the predictive equations.