2H2) and helium (He) were acquired at 100 Hz using custom data acquisition software. Data files were saved to disk for later analysis. CardOut data were analyzed using the technique described and validated previously (9). Because calculated CardOut is critically dependent on correct time alignment of gas flow and gas concentration signals, we optimized the time delay by comparing calculated inspired dead space (mass balance of He gas) with known dead space of the breathing valve. This time-delay parameter was relatively constant (±4 ms) among subjects. At each stage of exercise, the arterial to mixed venous O2 content difference, or O2 extraction, was calculated from O2 extraction = V? o 2 (ml/min)/CardOut (l/min)/10.
Data for exercise intensity, heart rate (HR), V? o 2, V? co 2, CardOut, respiratory exchange ratio (RER = V? co 2/V? o 2, where V? co 2 is CO2 production), respiratory rate, minute ventilation, and tidal volume were tabulated for each exercise intensity by subject. The CardOut vs. V? o 2, HR vs. V? o 2, and SV vs. V? o 2 relationships were represented in each subject by fitting the quadratic relationship y = a + b·V? o 2 + c·(V? o 2) 2 using multiple regression and collecting the three parameter estimates as well as the associated t statistics and P values. Subjects were then separated into two groups, those with a statistically significant (P < 0.05) c < 0 for the CardOut vs. V? o 2 relationship (“negative curvature group”; n = 25) and those with either nonsignificant or significantly positive value for c (“nonnegative curvature group”; n = 47). Only two subjects had a statistically significant c > 0. Differences in all parameters between these two groups were tested using the Wilcoxon’s rank sum test. For all subjects, we derived initial and final slope from the first derivative of the quadratic equation using the measured othersing and V? o 2max for the subject, respectively: initial slope = b + 2·c·(V? o 2rest); final slope = b + 2·c·(V? o dos maximum).
To quantify curvature of the CardOut vs. V? o 2 relationships, we explored several parameters, including 1) the ratio of final to initial slope (slope ratio = final slope/initial slope), 2) the maximal difference in CardOut between that predicted by the quadratic relationship and that predicted by a linear relationship derived from the first and last observation, and 3) the quadratic term from the fitted curve. Because there was a high degree of correlation among these parameters (R 2 from 0.69 to 0.90) and because the slope ratio was dimensionless and conceptually independent of the magnitude of CardOut, we focused on slope ratio as a measure of curvature. Although only two subjects had statistically significant positive curvature to the CardOut vs. V? o 2 relationship, 15 subjects had a slope ratio of >1.0, which can only occur with positive curvature. To minimize the effect of these data, slope ratios of >1.0 were replaced with a value of 1.0 (Winsorized) in regression analysis.
To help sort out determinants of exercise capacity, the maximal/resting ratios of the three components of the Fick equation (V? o 2, CardOut, extraction) were calculated. Maximal/resting ratios of SV and HR were also analyzed.
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A total of 73 subjects were studied, although one subject’s data were excluded from analysis because of aberrantly low CardOut values and maximal O2 extraction near 30 ml/100 ml. Subject characteristics for the remaining 72 subjects are listed in Table 1. Overall, subjects were of average fitness level for their age (range 20–40 yr; V? o dos maximum/kg ranged from 22 to 55 ml·min ?1 ·kg ?1 ). The subjects were studied on 2 separate days using identical work intensity increases; however, the maximal HRs were well matched between the 2 days [mean for all subjects on day 1 was 184 beats/min (SD 10) and day 2 was 183.8 beats/min (SD 10.5)]. We thus believe these two exercise tests were equivalent, so matching V? o 2 values from day 1 with CardOut from day 2 was legitimate.