Respiratory care · 2014
Can we better estimate resting oxygen consumption by incorporating arterial blood gases and spirometric determinations?
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Tonelli Adriano R, Wang Xiao-Feng, Abbay Anara, Zhang Qi, Ramos José, McCarthy Kevin
Аннотация
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, m(2)] + 1.49 × heart rate [beats/min] + 51.51 × FIO2 [21% = 0; 30% = 1] + 30.62 × gender [male = 1; female = 0]) showed an R(2) 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 R(2) of 0.49. The best R(2) (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.
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, m(2)] + 1.49 × heart rate [beats/min] + 51.51 × FIO2 [21% = 0; 30% = 1] + 30.62 × gender [male = 1; female = 0]) showed an R(2) 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 R(2) of 0.49. The best R(2) (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.