Determination of the critical O₂ delivery from experimental data: Sensitivity to error

Normally, metabolic need determines tissue O₂ consumption (V̇O₂). In states of reduced supply, V̇O₂ declines sharply below a critical level of O₂ delivery (Q̇O₂ = blood flow x arterial O₂ content). Although several investigators have measured a critical O₂ delivery in whole animals or in isolated tissues, there is no general agreement over how to determine the critical point from a collection of real data. In this study, we compare three algorithms for finding the critical O₂ delivery from a set of experimental data. We also present a technique for estimating the effect of experimental error on the precision of these algorithms. Using 16 data sets collected in normal dogs, we compare single-line, dual-line, and polynomial regression algorithms for identifying the critical O₂ delivery. The dual-line and polynomial regression techniques fit the data better (mean residual square deviation 0.024 and 0.031, respectively) than the single-regression line approach (0.110). To investigate the influence of experimental error on the derived critical Q̇O₂, we used a Monte Carlo technique, repeatedly perturbing the experimental data to simulate experimental error. We then calculated the variance of the critical Q̇O₂ frequency distribution obtained when the three algorithms were applied to the perturbed data. By this analysis, the dual-line regression technique was less sensitive to experimental error than the polynomial technique.