BACKGROUND: 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))). OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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))). OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.