Department of Aerospace Engineering, Texas A&M University, College Station, Texas.
Department of Health and Kinesiology, Texas A&M University, College Station, Texas.
J Appl Physiol (1985). 2021 Jun 1;130(6):1983-2001. doi: 10.1152/japplphysiol.00727.2020. Epub 2021 Apr 29.
The human cardiovascular (CV) system elicits a physiological response to gravitational environments, with significant variation between different individuals. Computational modeling can predict CV response, however model complexity and variation of physiological parameters in a normal population makes it challenging to capture individual responses. We conducted a sensitivity analysis on an existing 21-compartment lumped-parameter hemodynamic model in a range of gravitational conditions to ) investigate the influence of model parameters on a tilt test CV response and ) to determine the subset of those parameters with the most influence on systemic physiological outcomes. A supine virtual subject was tilted to upright under the influence of a constant gravitational field ranging from 0 g to 1 g. The sensitivity analysis was conducted using a Latin hypercube sampling/partial rank correlation coefficient methodology with subsets of model parameters varied across a normal physiological range. Sensitivity was determined by variation in outcome measures including heart rate, stroke volume, central venous pressure, systemic blood pressures, and cardiac output. Results showed that model parameters related to the length, resistance, and compliance of the large veins and parameters related to right ventricular function have the most influence on model outcomes. For most outcome measures considered, parameters related to the heart are dominant. Results highlight which model parameters to accurately value in simulations of individual subjects' CV response to gravitational stress, improving the accuracy of predictions. Influential parameters remain largely similar across gravity levels, highlighting that accurate model fitting in 1 g can increase the accuracy of predictive responses in reduced gravity. Computational modeling is used to predict cardiovascular responses to altered gravitational environments. However, considerable variation between subjects and model complexity makes accurate parameter assignment for individuals challenging. This computational effort studies sensitivity in cardiovascular model outcomes due to varying parameters across a normal physiological range. This allows determination of which parameters have the largest influence on outcomes, i.e., which parameters must be most carefully selected to give accurate predictions of individual responses.
人体心血管（CV）系统对重力环境会产生生理反应，不同个体之间存在显著差异。计算建模可以预测 CV 反应，但是由于生理参数的复杂性和变异性，在正常人群中捕捉个体反应具有挑战性。我们在一系列重力条件下对现有的 21 个隔室集总参数血液动力学模型进行了敏感性分析，以）研究模型参数对倾斜测试 CV 反应的影响，和）确定对全身生理结果影响最大的参数子集。在从 0g 到 1g 的恒定重力场的影响下，仰卧虚拟受试者倾斜至直立。敏感性分析使用拉丁超立方抽样/偏秩相关系数方法进行，模型参数子集在正常生理范围内变化。敏感性通过包括心率、心搏量、中心静脉压、全身血压和心输出量在内的结果测量值的变化来确定。结果表明，与大静脉长度、阻力和顺应性相关的模型参数以及与右心室功能相关的参数对模型结果的影响最大。对于大多数考虑的结果测量值，与心脏相关的参数是主要的。结果突出了在模拟个体对重力应激的 CV 反应时需要准确评估的模型参数，从而提高预测的准确性。在不同的重力水平下，有影响力的参数仍然大致相似，这突出表明在 1g 中进行准确的模型拟合可以提高在低重力下预测响应的准确性。计算建模用于预测改变的重力环境对心血管的反应。然而，由于个体之间的差异和模型的复杂性，准确地为个体分配参数具有挑战性。这项计算研究研究了心血管模型结果由于正常生理范围内参数的变化而产生的敏感性。这可以确定哪些参数对结果的影响最大，即哪些参数必须仔细选择才能准确预测个体反应。
