Aguado-Sierra J, Alastruey J, Wang J-J, Hadjiloizou N, Davies J, Parker K H
Department of Bioengineering, Imperial College, London, UK.
Proc Inst Mech Eng H. 2008 May;222(4):403-16. doi: 10.1243/09544119JEIM315.
Previous studies based on measurements made in the ascending aorta have demonstrated that it can be useful to separate the arterial pressure P into a reservoir pressure P* generated by the windkessel effect and a wave pressure p generated by the arterial waves: P = P*+p. The separation in these studies was relatively straightforward since the flow into the arterial system was measured. In this study the idea is extended to measurements of pressure and velocity at sites distal to the aortic root where flow into the arterial system is not known. P* is calculated from P at an arbitrary location in a large artery by fitting the pressure fall-off in diastole to an exponential function and assuming that p is proportional to the flow into the arterial system. A local reservoir velocity U* that is proportional to P* is also defined. The separation algorithm is applied to in vivo human and canine data and to numerical data generated using a one-dimensional model of pulse wave propagation in the larger conduit arteries. The results show that the proposed algorithm is reasonably robust, allowing for the separation of the measured pressure and velocity into reservoir and wave pressures and velocities. Application to data measured simultaneously in the aorta of the dog shows that the reservoir pressure is fairly uniform along the aorta, a test of self-consistency of the assumptions leading to the algorithm. Application to data generated with a validated numerical model indicates that the parameters derived by fitting the pressure data are close to the known values which were used to generate the numerical data. Finally, application to data measured in the human thoracic aorta indicates the potential usefulness of the separation.
以往基于升主动脉测量结果的研究表明，将动脉压P分为由风箱效应产生的储备压P和由动脉波产生的波动压p是有用的：P = P+p。由于测量了进入动脉系统的血流，这些研究中的分离相对简单。在本研究中，这一想法扩展到了主动脉根部远端部位的压力和速度测量，在这些部位进入动脉系统的血流是未知的。通过将舒张期压力下降拟合为指数函数，并假设p与进入动脉系统的血流成正比，从大动脉中任意位置的P计算出P*。还定义了与P成正比的局部储备速度U。该分离算法应用于体内人体和犬类数据以及使用大血管中脉搏波传播的一维模型生成的数值数据。结果表明，所提出算法相当稳健，能够将测量的压力和速度分离为储备压力和波动压力以及速度。应用于在犬主动脉中同时测量的数据表明，储备压沿主动脉相当均匀，这是对导致该算法的假设自洽性检验之一。应用于经过验证的数值模型生成的数据表明，通过拟合压力数据得出的参数接近用于生成数值数据的已知值。最后，应用于在人体胸主动脉中测量的数据表明了这种分离的潜在用途。
