Abstract
BACKGROUND:
PaCO2 as measured during exercise in patients with COPD is poorly predicted (predicted PaCO2 ) from lung function testing and some noninvasive measurements, such as end-tidal PCO2 (PETCO2 ).
OBJECTIVE:
We performed a number of statistical techniques on PETCO2 and its interaction with other physiologic variables during exercise testing, in order to improve our ability to predict PaCO2 . The estimated PaCO2 as determined from these techniques may therefore be used to contrast the PETCO2 readings that are measured during an incremental exercise test on a breath-by-breath basis (ie, PaCO2 – PETCO2 ), 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 PaCO2 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 PaCO2 = 23.71 + PETCO2 × (0.9–0.01 × DLCO –0.04 × VT) – 2.61 × SVC – 0.04 × MEP, where DLCO is diffusing capacity for carbon monoxide in mL/min/mm Hg, VT is tidal volume in L, SVC is slow vital capacity in L, and MEP is maximum expiratory pressure in cm H2O. The difference between the measured and predicted PaCO2 at each time point was not statistically significant (all P > .05). The standard errors of the estimated PaCO2 at each time point were 0.91–1.12 mm Hg.
CONCLUSIONS:
A validated mixed-model regression derived equation yields a predicted PaCO2 trend during exercise that can be helpful when interpreting exercise testing to determine PaCO2 – PETCO2 and exercise-induced hypercapnia.
Keywords
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