Ciocanel Maria-Veronica, Docken Steffen S, Gasper Rebecca E, Dean Caron, Carlson Brian E, Olufsen Mette S
The Ohio State University, Columbus, USA.
University of California, Davis, Davis, USA.
Biol Cybern. 2019 Apr;113(1-2):105-120. doi: 10.1007/s00422-018-0781-y. Epub 2018 Sep 12.
Mathematical models can provide useful insights explaining behavior observed in experimental data; however, rigorous analysis is needed to select a subset of model parameters that can be informed by available data. Here we present a method to estimate an identifiable set of parameters based on baseline left ventricular pressure and volume time series data. From this identifiable subset, we then select, based on current understanding of cardiovascular control, parameters that vary in time in response to blood withdrawal, and estimate these parameters over a series of blood withdrawals. These time-varying parameters are first estimated using piecewise linear splines minimizing the mean squared error between measured and computed left ventricular pressure and volume data over four consecutive blood withdrawals. As a final step, the trends in these splines are fit with empirical functional expressions selected to describe cardiovascular regulation during blood withdrawal. Our analysis at baseline found parameters representing timing of cardiac contraction, systemic vascular resistance, and cardiac contractility to be identifiable. Of these parameters, vascular resistance and cardiac contractility were varied in time. Data used for this study were measured in a control Sprague-Dawley rat. To our knowledge, this is the first study to analyze the response to multiple blood withdrawals both experimentally and theoretically, as most previous studies focus on analyzing the response to one large blood withdrawal. Results show that during each blood withdrawal both systemic vascular resistance and contractility decrease acutely and partially recover, and they decrease chronically across the series of blood withdrawals.
数学模型可以提供有用的见解来解释实验数据中观察到的行为;然而,需要进行严格的分析来选择一组可以由现有数据提供信息的模型参数。在这里,我们提出了一种基于基线左心室压力和容积时间序列数据来估计一组可识别参数的方法。然后,根据目前对心血管控制的理解,从这个可识别的子集中选择随时间变化以响应失血的参数,并在一系列失血过程中估计这些参数。这些随时间变化的参数首先使用分段线性样条进行估计,以最小化连续四次失血过程中测量的和计算得到的左心室压力和容积数据之间的均方误差。作为最后一步,这些样条的趋势与为描述失血期间心血管调节而选择的经验函数表达式进行拟合。我们在基线时的分析发现,代表心脏收缩时间、全身血管阻力和心脏收缩力的参数是可识别的。在这些参数中,血管阻力和心脏收缩力随时间变化。本研究使用的数据是在对照的斯普拉格-道利大鼠中测量的。据我们所知,这是第一项从实验和理论上分析对多次失血反应的研究,因为大多数先前的研究都集中在分析对一次大量失血的反应。结果表明,在每次失血过程中,全身血管阻力和收缩力都会急剧下降并部分恢复,并且在一系列失血过程中会长期下降。