Mean apneic steady-state values for all variables were used to define baseline values for each step of the protocol. The maximum decrease in SAP after a positive-pressure breath, as compared to apneic SAP, was defined as A down. The maximum increase in SAP during the positive-pressure breath as compared to apneic SAP was defined as A up. Changes in SAP from the apneic baseline were analyzed in relation to the maximum variation in LV area before and after the positive-pressure breath during each of the four steps. SAP variation, mean LV area measurements, and their variations were compared within each condition by using analysis of variance. Statistical significance was defined as p < 0.05.
In an attempt to quantify any additional effect that the positive-pressure breath may have had on subsequent decreases in SAP, we estimated the cumulative SA deficit throughout the breath relative to mean apneic values and correlated this SA deficit with A down. The mean apneic SA was taken as the mean SA of the three cardiac cycles preceding the breath, whereas the SA deficit was taken as the sum of each SA minus the mean SA for each cardiac cycle from the start of the breath until the lowest systolic BP occurred. This volume is referred to in the text as the cumulative SA deficit. Data are expressed as mean (± SD). Click Here
Because the phase relation between positive-pressure ventilation and both SAP and LV area data would be different, depending on which process described above determined the response, we further analyzed the relations between airway pressure, arterial pressure, and LV area by Fourier analysis using the ventilatory cycle as the primary harmonic for three sequential breaths. Furthermore, we examined the effects of positive-pressure inspiration and expiration on the matching cardiac cycles to ascertain if single beat changes in EDA, ESA, or SA occurred relative to the ventilatory cycle.
In the validation group, TEE-ABD-derived SA and electromagnetic flow probe-derived stroke volume were compared over one complete ventilatory cycle by using simple correlation statistics and the method of least squares.