Arterial pressure transfer characteristics: effects of travel time

Abstract

We investigated the quantitative contribution of all local conduit arterial, blood, and distal load properties to the pressure transfer function from brachial artery to aorta. The model was based on anatomical data, Young's modulus, wall viscosity, blood viscosity, and blood density. A three-element windkessel represented the distal arterial tree. Sensitivity analysis was performed in terms of frequency and magnitude of the peak of the transfer function and in terms of systolic, diastolic, and pulse pressure in the aorta. The root mean square error (RMSE) described the accuracy in wave-shape prediction. The percent change of these variables for a 25% alteration of each of the model parameters was calculated. Vessel length and diameter are found to be the most important parameters determining pressure transfer. Systolic and diastolic pressure changed Ͻ3% and RMSE Ͻ1.8 mmHg for a 25% change in vessel length and diameter. To investigate how arterial tapering influences the pressure transfer, a single uniform lossless tube was modeled. This simplification introduced only small errors in systolic and diastolic pressures (1% and 0%, respectively), and wave shape was less well described (RMSE, ϳ2.1 mmHg). Local (arm) vasodilation affects the transfer function little, because it has limited effect on the reflection coefficient. Since vessel length and diameter translate into travel time, this parameter can describe the transfer accurately. We suggest that with a, preferably, noninvasively measured travel time, an accurate individualized description of pressure transfer can be obtained. blood pressure; transfer function; personalization; brachial artery; aorta AORTIC PRESSURE appears a better indicator of cardiovascular morbidity and mortality than peripheral, e.g., brachial or radial, pressure (5,15,16,37,38). Also, ascending aortic pressure defines the systolic load on the heart through ventricular wall stress. Effects of therapy are preferably studied using aortic pressure (19). However, whereas peripheral pressures can be obtained noninvasively, measurement of aortic pressure requires invasive techniques. Transfer functions that calculate aortic pressure from peripheral pressure circumvent this invasive measurement and provide an opportunity to noninvasively obtain this cardiovascular information.Several approaches have been taken to arrive at transfer functions. Chen et al. (4) and Fetics et al. (7) used a special mathematical transformation to obtain a transfer function from human data, and the averaged transfer function of a group of patients was used as "standard" transfer. Karamanoglu et al.(18) used a segmented model of the arterial tree to derive the transfer function. Gizdulich et al. (9, 10) proposed a method to obtain brachial pressure from finger pressure by fitting a second order filter to averaged data. Stergiopulos et al. (27) showed that splitting the brachial pressure in its backward and forward waves and by shifting these waves with respect to each other over the travel time between aorta and brach...

Comparison of arterial waves derived by classical wave separation and wave intensity analysis in a model of aortic coarctation

“…For the development of a segmented model of the arterial circulation of the adult, the previous model of Westerhof et al [22,23] was used and extended using data of Stergiopulos et al [18]. These data have formed the basis for previous studies of wave propagation, for example by Wang and Parker [21].…”

“…In total, the model contained 121 arterial segments and 30 three-element Windkessels. Details on the distributed model can be found elsewhere [18,22]. Propagation of a pressure and flow pulse through the arterial tree was calculated firstly by decomposing the flow and pressure waves into frequency components by standard Fourier analysis.…”

Continuous and less invasive central hemodynamic monitoring by blood pressure waveform analysis

“…We (109) developed another technique for minimally invasive or noninvasive monitoring of the central ABP waveform, but this time by analysis of one peripheral ABP waveform. The idea is to derive central ABP in a fully adaptive manner by defining the transfer function relating peripheral ABP to central ABP through a distributed model of arterial pressure and flow to account for the confounding wave reflections (105,116) and then estimating all model parameters by exploiting the preknowledge of aortic flow.…”

“…10A). The ith tube represents the path between the aorta and the ith peripheral (105,116). Waves traveling along each tube are reflected at the terminal load in a frequency-dependent manner so as to mimic the progressive distortion that actual arterial pressure and flow waveforms undergo with increasing distance from the heart (see Fig.…”

“…10C). For comparison, the analogous errors of totally or partially generalized transfer function techniques (35,105,116) were, on average, 63% higher than the technique (see Fig. 10C), even though they had the advantage of being developed with a subset of the reference central ABP waveforms.…”