Tonelli Adriano R, Wang Xiao-Feng, Abbay Anara, Zhang Qi, Ramos José, McCarthy Kevin
Department of Pulmonary, Allergy and Critical Care Medicine, Respiratory Institute, Cleveland Clinic
Respiratory Institute Biostatistics Core, Quantitative Health Sciences, Cleveland Clinic.
Respir Care. 2015 Apr;60(4):517-25. doi: 10.4187/respcare.03555. Epub 2014 Dec 16.
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.
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.
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.
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.
我们假设,有呼吸道症状患者的耗氧量(V̇o2)估计不准确,而通过考虑动脉血气或肺量计变量可改善这一情况。
在这项回顾性研究中,我们纳入了连续接受心肺运动试验的受试者。静息V̇o2采用逐次呼吸测试方法测定。我们利用一个训练队列(n = 336)开发了3个预测V̇o2的模型。在一个验证组(n = 114)中,我们将我们的模型与7个可用公式进行了比较。
我们的第一个模型(V̇o2 = -184.99 + 189.64 × 体表面积[BSA,m²] + 1.49 × 心率[次/分钟] + 51.51 × 吸入氧分数[21% = 0;30% = 1] + 30.62 × 性别[男性 = 1;女性 = 0])的R²为0.5。我们的第二个模型(V̇o2 = -208.06 + 188.67 × BSA + 1.38 × 心率 + 35.6 × 性别 + 2.06 × 呼吸频率[次/分钟])的R²为0.49。我们的最后一个模型纳入了分钟通气量,其R²最佳(0.68)(V̇o2 = -142.92 + 0.52 × 心率 + 126.84 × BSA + 14.68 × 分钟通气量[L])。在验证队列中,这3个模型的表现优于其他可用方程,但一致性界限较宽,尤其是在身材较矮、心率较高和最大自主通气量较低的老年个体中。
我们开发了更准确的公式来预测有呼吸道症状受试者的静息V̇o2;然而,方程的一致性界限较宽,尤其是在某些受试者群体中。动脉血气和肺量计变量并未显著改善预测方程。