Nollo G, Porta A, Faes L, Del Greco M, Disertori M, Ravelli F
Dipartimento di Fisica, Università di Trento, and Istituto Trentino di Cultura-irst, 38050 Povo-Trento, Italy.
Am J Physiol Heart Circ Physiol. 2001 Apr;280(4):H1830-9. doi: 10.1152/ajpheart.2001.280.4.H1830.
Spectral and cross-spectral analysis of R-R interval and systolic arterial pressure (SAP) spontaneous fluctuations have been proposed for noninvasive evaluation of baroreflex sensitivity (BRS). However, results are not in good agreement with clinical measurements. In this study, a bivariate parametric autoregressive model with exogenous input (ARXAR model), able to divide the R-R variability into SAP-related and -unrelated parts, was used to quantify the gain (alpha(ARXAR)) of the baroreflex regulatory mechanism. For performance assessing, two traditional noninvasive methods based on frequency domain analysis [spectral, baroreflex gain by autogressive model (alpha(AR)); cross-spectral, baroreflex gain by bivariate autoregressive model (alpha(2AR))] and one based on the time domain [baroreflex gain by sequence analysis (alpha(SEQ))] were considered and compared with the baroreflex gain by phenylephrine test (alpha(PHE)). The BRS evaluation was performed on 30 patients (61 +/- 10 yr) with recent (10 +/- 3 days) myocardial infarction. The ARXAR model allowed dividing the R-R variability (950 +/- 1,099 ms(2)) into SAP-related (256 +/- 418 ms(2)) and SAP-unrelated (694 +/- 728 ms(2)) parts. alpha(AR) (12.2 +/- 6.1 ms/mmHg) and alpha(2AR) (8.9 +/- 5.6 ms/mmHg) as well as alpha(SEQ) (12.6 +/- 7.1 ms/mmHg) overestimated BRS assessed by alpha(PHE) (6.4 +/- 4.7 ms/mmHg), whereas the ARXAR index gave a comparable value (alpha(ARXAR) = 5.4 +/- 3.3 ms/mmHg). All noninvasive methods were significantly correlated to alpha(PHE) (alpha(ARXAR) and alpha(SEQ) were more correlated than the other indexes). Thus the baroreflex gain obtained describing the causal dependence of R-R interval on SAP showed a good agreement with alpha(PHE) and may provide additional information regarding the gain estimation in the frequency domain.
有人提出通过对R-R间期和收缩期动脉压(SAP)自发波动进行频谱和互谱分析,来对压力反射敏感性(BRS)进行无创评估。然而,结果与临床测量结果并不十分吻合。在本研究中,采用具有外部输入的双变量参数自回归模型(ARXAR模型),该模型能够将R-R变异性分为与SAP相关和不相关的部分,以量化压力反射调节机制的增益(α(ARXAR))。为了进行性能评估,考虑了两种基于频域分析的传统无创方法[频谱分析,自回归模型的压力反射增益(α(AR));互谱分析,双变量自回归模型的压力反射增益(α(2AR))]以及一种基于时域的方法[序列分析的压力反射增益(α(SEQ))],并将它们与苯肾上腺素试验的压力反射增益(α(PHE))进行比较。对30例近期(10±3天)发生心肌梗死的患者(61±10岁)进行了BRS评估。ARXAR模型能够将R-R变异性(950±1,099 ms²)分为与SAP相关的部分(256±418 ms²)和与SAP不相关的部分(694±728 ms²)。α(AR)(12.2±6.1 ms/mmHg)、α(2AR)(8.9±5.6 ms/mmHg)以及α(SEQ)(12.6±7.1 ms/mmHg)均高估了通过α(PHE)评估的BRS(6.4±4.7 ms/mmHg),而ARXAR指数给出了一个相近的值(α(ARXAR)=5.4±3.3 ms/mmHg)。所有无创方法与α(PHE)均显著相关(α(ARXAR)和α(SEQ)的相关性比其他指标更高)。因此,通过描述R-R间期对SAP的因果依赖性所获得的压力反射增益与α(PHE)显示出良好的一致性,并且可能提供有关频域增益估计的额外信息。