Samsel R W, Schumacker P T
Section of Pulmonary and Critical Care Medicine, University of Chicago, Illinois 60637.
J Appl Physiol (1985). 1988 May;64(5):2074-82. doi: 10.1152/jappl.1988.64.5.2074.
Normally, metabolic need determines tissue O2 consumption (VO2). In states of reduced supply, VO2 declines sharply below a critical level of O2 delivery (QO2 = 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 QO2, we used a Monte Carlo technique, repeatedly perturbing the experimental data to simulate experimental error. We then calculated the variance of the critical QO2 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.
正常情况下，代谢需求决定组织耗氧量（VO2）。在供应减少的状态下，VO2会在氧输送（QO2 = 血流量×动脉血氧含量）低于临界水平时急剧下降。尽管有几位研究者测量了全动物或离体组织中的临界氧输送，但对于如何从一组实际数据中确定临界点尚无普遍共识。在本研究中，我们比较了三种从一组实验数据中寻找临界氧输送的算法。我们还提出了一种技术，用于估计实验误差对这些算法精度的影响。使用在正常犬身上收集的16个数据集，我们比较了用于识别临界氧输送的单线、双线和多项式回归算法。双线和多项式回归技术对数据的拟合效果（平均剩余平方偏差分别为0.024和0.031）优于单回归线方法（0.110）。为了研究实验误差对导出的临界QO2的影响，我们使用了蒙特卡罗技术，反复扰动实验数据以模拟实验误差。然后，我们计算了将三种算法应用于扰动数据时获得的临界QO2频率分布的方差。通过该分析，双线回归技术对实验误差的敏感性低于多项式技术。
