Department of Critical Care Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.
Respir Care. 2012 Jul;57(7):1106-14. doi: 10.4187/respcare.01320. Epub 2012 Jan 23.
P(aCO(2)) as measured during exercise in patients with COPD is poorly predicted (predicted P(aCO(2))) from lung function testing and some noninvasive measurements, such as end-tidal P(CO(2)) (P(ETCO(2))).
We performed a number of statistical techniques on P(ETCO(2)) and its interaction with other physiologic variables during exercise testing, in order to improve our ability to predict P(aCO(2)). The estimated P(aCO(2)) as determined from these techniques may therefore be used to contrast the P(ETCO(2)) readings that are measured during an incremental exercise test on a breath-by-breath basis (ie, P(aCO(2)) - P(ETCO(2))), and to identify exercise-induced hypercapnia.
Forty-seven men with COPD underwent both pulmonary function testing and incremental exercise testing until limited by symptoms. Arterial blood gases and exercise physiological measurements were performed during maximal exercise testing. The prediction equations for P(aCO(2)) were generated using regression techniques with the leave-one-out cross-validation technique.
Forty-one patients were included in the final analysis after 6 patients were excluded due to inadequate data collection. The best prediction equation we found was: predicted P(aCO(2)) = 23.71 + P(ETCO(2)) × (0.9-0.01 × D(LCO) -0.04 × V(T)) - 2.61 × SVC - 0.04 × MEP, where D(LCO) is diffusing capacity for carbon monoxide in mL/min/mm Hg, V(T) is tidal volume in L, SVC is slow vital capacity in L, and MEP is maximum expiratory pressure in cm H(2)O. The difference between the measured and predicted P(aCO(2)) at each time point was not statistically significant (all P > .05). The standard errors of the estimated P(aCO(2)) at each time point were 0.91-1.12 mm Hg.
A validated mixed-model regression derived equation yields a predicted P(aCO(2)) trend during exercise that can be helpful when interpreting exercise testing to determine P(aCO(2)) - P(ETCO(2)) and exercise-induced hypercapnia.
在 COPD 患者的运动过程中测量的 P(aCO(2)) （实测 P(aCO(2))) 无法通过肺功能测试和一些无创测量（如呼气末 P(CO(2)))（P(ETCO(2))) 进行很好的预测。
我们对运动测试期间的 P(ETCO(2)) 及其与其他生理变量的相互作用进行了多项统计技术分析，以便提高预测 P(aCO(2)) 的能力。因此，可以使用这些技术确定的估计的 P(aCO(2)) 与在递增运动测试期间逐口气测量的 P(ETCO(2)) 读数进行对比（即，P(aCO(2))-P(ETCO(2)))，并识别运动引起的高碳酸血症。
47 名男性 COPD 患者均接受了肺功能测试和递增运动测试，直至症状受限。在最大运动测试期间进行了动脉血气和运动生理测量。使用回归技术和留一交叉验证技术生成 P(aCO(2))的预测方程。
在排除了 6 名因数据收集不足的患者后，41 名患者被纳入最终分析。我们发现的最佳预测方程是：预测 P(aCO(2))=23.71+P(ETCO(2))×(0.9-0.01×D(LCO)-0.04×V(T))-2.61×SVC-0.04×MEP，其中 D(LCO) 是一氧化碳弥散量，单位为毫升/分钟/毫米汞柱，V(T) 是潮气量，单位为升，SVC 是慢肺活量，单位为升，MEP 是最大呼气压力，单位为厘米水柱。在每个时间点实测和预测 P(aCO(2))之间的差异无统计学意义（均 P＞.05）。每个时间点估计的 P(aCO(2)) 的标准误差为 0.91-1.12 毫米汞柱。
验证后的混合模型回归方程可产生运动过程中的预测 P(aCO(2)) 趋势，有助于解释运动测试以确定 P(aCO(2))-P(ETCO(2)) 和运动引起的高碳酸血症。
