Effective evaluation of arterial pulse waveform analysis by two-dimensional stroke volume variation-stroke volume index plots
Effective evaluation of arterial pulse waveform analysis by two-dimensional stroke volume variation-stroke volume index plots
复制标题
通过二维每搏输出量变化-每搏输出量指数图有效评估动脉脉搏波形分析
DOI：
10.1007/s10877-016-9916-7
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发表时间：
2016
影响因子：
2.2
通讯作者：
Shigemi K
中科院分区：
文献类型：
--
作者：
Sawa T;Kinoshita M;Kainuma A;Akiyama K;Naito Y;Kato H;Amaya F;Shigemi K
动脉搏动波形分析(APWA)结合半侵入性心输出量监测装置在围手术期血流动力学和液体管理中得到广泛应用。然而，在APWA中，血流动力学数据的评估并没有得到很好的讨论。在这项研究中，我们分析了我们如何直观地解释血流动力学数据，包括APWA得出的每搏量变化(SVV)和每搏量(SV)。我们对SVV-SV关系进行算术估计，并将测量值应用于该估计。然后，我们收集了六个麻醉病例的测量值，包括三个肝移植和三个其他类型的手术，以将它们应用到这个SVV-SVI(每搏量变异指数)图中。算术分析表明，SVV与SV之间的关系可绘制成双曲线。在半对数范围内绘制SVV-SV值呈线性相关，线性回归线的斜率理论上代表平均心肌收缩能力。在APWA的临床测量中，在线性尺度和半对数尺度上绘制SVV和SVI值，显示出双曲线和线性回归直线表示的相关性。根据失血量和输血量的不同，曲线图大致在矩形双曲线上移动。算术估计更接近于双曲线中SVV-SV相互作用的真实测量值。在APWA中，使用SVV作为前负荷指数，以及根据动脉压心输出量得出的心脏指数或SVI作为心功能指数，可能适合于作为Forrester亚型的替代来划分血流动力学分期。
Arterial pulse waveform analysis (APWA) with a semi-invasive cardiac output monitoring device is popular in perioperative hemodynamic and fluid management. However, in APWA, evaluation of hemodynamic data is not well discussed. In this study, we analyzed how we visually interpret hemodynamic data, including stroke volume variation (SVV) and stroke volume (SV) derived from APWA. We performed arithmetic estimation of the SVV–SV relationship and applied measured values to this estimation. We then collected measured values in six anesthesia cases, including three liver transplantations and three other types of surgeries, to apply them to this SVV–SVI (stroke volume variation index) plot. Arithmetic analysis showed that the relationship between SVV and SV can be drawn as hyperbolic curves. Plotting SVV-SV values in the semi-logarithmic scale showed linear correlations, and the slopes of the linear regression lines theoretically represented average mean cardiac contractility. In clinical measurements in APWA, plotting SVV and SVI values in the linear scale and the semi-logarithmic scale showed the correlations represented by hyperbolic curves and linear regression lines. The plots approximately shifted on the rectangular hyperbolic curves, depending on blood loss and blood transfusion. Arithmetic estimation is close to real measurement of the SVV–SV interaction in hyperbolic curves. In APWA, using SVV as an index of preload and the cardiac index or SVI derived from arterial pressure-based cardiac output as an index of cardiac function, is likely to be appropriate for categorizing hemodynamic stages as a substitute for Forrester subsets.