Department of Mathematics, Virginia Commonwealth University, Richmond, VA, USA.
Math Biosci. 2013 Jan;241(1):56-74. doi: 10.1016/j.mbs.2012.09.003. Epub 2012 Oct 6.
This study develops a lumped cardiovascular-respiratory system-level model that incorporates patient-specific data to predict cardiorespiratory response to hypercapnia (increased CO(2) partial pressure) for a patient with congestive heart failure (CHF). In particular, the study focuses on predicting cerebral CO(2) reactivity, which can be defined as the ability of vessels in the cerebral vasculature to expand or contract in response CO(2) induced challenges. It is difficult to characterize cerebral CO(2) reactivity directly from measurements, since no methods exist to dynamically measure vasomotion of vessels in the cerebral vasculature. In this study we show how mathematical modeling can be combined with available data to predict cerebral CO(2) reactivity via dynamic predictions of cerebral vascular resistance, which can be directly related to vasomotion of vessels in the cerebral vasculature. To this end we have developed a coupled cardiovascular and respiratory model that predicts blood pressure, flow, and concentration of gasses (CO(2) and O(2)) in the systemic, cerebral, and pulmonary arteries and veins. Cerebral vascular resistance is incorporated via a model parameter separating cerebral arteries and veins. The model was adapted to a specific patient using parameter estimation combined with sensitivity analysis and subset selection. These techniques allowed estimation of cerebral vascular resistance along with other cardiovascular and respiratory parameters. Parameter estimation was carried out during eucapnia (breathing room air), first for the cardiovascular model and then for the respiratory model. Then, hypercapnia was introduced by increasing inspired CO(2) partial pressure. During eucapnia, seven cardiovascular parameters and four respiratory parameters was be identified and estimated, including cerebral and systemic resistance. During the transition from eucapnia to hypercapnia, the model predicted a drop in cerebral vascular resistance consistent with cerebral vasodilation.
本研究开发了一个集中式心血管-呼吸系统水平模型,该模型结合了患者特定的数据,以预测充血性心力衰竭(CHF)患者对高碳酸血症(CO2 分压增加)的心肺反应。特别是,该研究侧重于预测脑 CO2 反应性,这可以定义为脑脉管系统中的血管对 CO2 诱导的挑战扩张或收缩的能力。由于不存在动态测量脑脉管系统中血管舒缩的方法,因此很难直接从测量中描述脑 CO2 反应性。在本研究中,我们展示了如何将数学建模与可用数据相结合,通过预测脑血管阻力的动态变化来预测脑 CO2 反应性,脑血管阻力与脑脉管系统中血管的舒缩直接相关。为此,我们开发了一个耦合的心血管和呼吸系统模型,该模型可以预测系统、脑和肺动静脉中的血压、流量和气体(CO2 和 O2)浓度。脑血管阻力通过分离脑动脉和静脉的模型参数来纳入。该模型使用参数估计、敏感性分析和子集选择相结合的方法进行了特定患者的适应性调整。这些技术允许估算脑血管阻力以及其他心血管和呼吸参数。在 eucapnia(呼吸室内空气)期间进行参数估计,首先进行心血管模型的参数估计,然后进行呼吸模型的参数估计。然后,通过增加吸入 CO2 分压来引入高碳酸血症。在 eucapnia 期间,鉴定和估计了七个心血管参数和四个呼吸参数,包括脑和全身阻力。在从 eucapnia 过渡到高碳酸血症期间,模型预测脑血管阻力下降,与脑血管舒张一致。