Chen Xiaoxiao, Kim Jong-Kyung, Sala-Mercado Javier A, Hammond Robert L, Elahi Rafat I, Scislo Tadeusz J, Swamy Gokul, O'Leary Donal S, Mukkamala Ramakrishna
Department of Electrical and Computer Engineering, Michigan State University, 2120 Engineering Bldg., East Lansing, MI 48824, USA.
Am J Physiol Heart Circ Physiol. 2008 Jan;294(1):H293-301. doi: 10.1152/ajpheart.00852.2007. Epub 2007 Nov 2.
We previously developed a mathematical analysis technique for estimating the static gain values of the arterial total peripheral resistance (TPR) baroreflex (G(A)) and the cardiopulmonary TPR baroreflex (G(C)) from small, spontaneous beat-to-beat fluctuations in arterial blood pressure, cardiac output, and stroke volume. Here, we extended the mathematical analysis so as to also estimate the entire arterial TPR baroreflex impulse response [h(A)(t)] as well as the lumped arterial compliance (AC). The extended technique may therefore provide a linear dynamic characterization of TPR baroreflex systems during normal physiological conditions from potentially noninvasive measurements. We theoretically evaluated the technique with respect to realistic spontaneous hemodynamic variability generated by a cardiovascular simulator with known system properties. Our results showed that the technique reliably estimated h(A)(t) [error = 30.2 +/- 2.6% for the square root of energy (E(A)), 19.7 +/- 1.6% for absolute peak amplitude (P(A)), 37.3 +/- 2.5% for G(A), and 33.1 +/- 4.9% for the overall time constant] and AC (error = 17.6 +/- 4.2%) under various simulator parameter values and reliably tracked changes in G(C). We also experimentally evaluated the technique with respect to spontaneous hemodynamic variability measured from seven conscious dogs before and after chronic arterial baroreceptor denervation. Our results showed that the technique correctly predicted the abolishment of h(A)(t) [E(A) = 1.0 +/- 0.2 to 0.3 +/- 0.1, P(A) = 0.3 +/- 0.1 to 0.1 +/- 0.0 s(-1), and G(A) = -2.1 +/- 0.6 to 0.3 +/- 0.2 (P < 0.05)] and the enhancement of G(C) [-0.7 +/- 0.44 to -1.8 +/- 0.2 (P < 0.05)] following the chronic intervention. Moreover, the technique yielded estimates whose values were consistent with those reported with more invasive and/or experimentally difficult methods.
我们之前开发了一种数学分析技术，可根据动脉血压、心输出量和每搏输出量中微小的、自发的逐搏波动来估计动脉总外周阻力（TPR）压力反射（G(A)）和心肺TPR压力反射（G(C)）的静态增益值。在此，我们扩展了该数学分析，以便还能估计整个动脉TPR压力反射的脉冲响应[h(A)(t)]以及总动脉顺应性（AC）。因此，这种扩展技术可能通过潜在的非侵入性测量，在正常生理条件下提供TPR压力反射系统的线性动态特征。我们从理论上针对具有已知系统特性的心血管模拟器所产生的现实自发血流动力学变异性，对该技术进行了评估。我们的结果表明，在各种模拟器参数值下，该技术能够可靠地估计h(A)(t)[能量平方根（E(A)）的误差为30.2±2.6%，绝对峰值幅度（P(A)）的误差为19.7±1.6%，G(A)的误差为37.3±2.5%，总时间常数的误差为33.1±4.9%]以及AC（误差为17.6±4.2%），并且能够可靠地跟踪G(C)的变化。我们还针对从7只清醒犬在慢性动脉压力感受器去神经支配前后测量的自发血流动力学变异性，对该技术进行了实验评估。我们的结果表明，该技术正确地预测了慢性干预后h(A)(t)的消失[E(A)从1.0±0.2变为0.3±0.1，P(A)从0.3±0.1变为0.1±0.0 s(-1)，G(A)从 -2.1±0.6变为0.3±0.2（P<0.05）]以及G(C)的增强[-0.7±0.44变为 -1.8±0.2（P<0.05）]。此外，该技术得出的估计值与采用更具侵入性和/或实验难度更大的方法所报告的值一致。
