Arduini Mattia, Pham Jonathan, Marsden Alison L, Chen Ian Y, Ennis Daniel B, Dual Seraina A
Department of Radiology, Stanford University, Palo Alto, CA, United States.
Mechanical Engineering, Stanford University, Palo Alto, CA, United States.
Front Cardiovasc Med. 2022 Aug 1;9:895291. doi: 10.3389/fcvm.2022.895291. eCollection 2022.
Despite being responsible for half of heart failure-related hospitalizations, heart failure with preserved ejection fraction (HFpEF) has limited evidence-based treatment options. Currently, a substantial clinical issue is that the disease etiology is very heterogenous with no patient-specific treatment options. Modeling can provide a framework for evaluating alternative treatment strategies. Counterpulsation strategies have the capacity to improve left ventricular diastolic filling by reducing systolic blood pressure and augmenting the diastolic pressure that drives coronary perfusion. Here, we propose a framework for testing the effectiveness of a soft robotic extra-aortic counterpulsation strategy using a patient-specific closed-loop hemodynamic lumped parameter model of a patient with HFpEF. The soft robotic device prototype was characterized experimentally in a physiologically pressurized (50-150 mmHg) soft silicone vessel and modeled as a combination of a pressure source and a capacitance. The patient-specific model was created using open-source software and validated against hemodynamics obtained by imaging of a patient (male, 87 years, HR = 60 bpm) with HFpEF. The impact of actuation timing on the flows and pressures as well as systolic function was analyzed. Good agreement between the patient-specific model and patient data was achieved with relative errors below 5% in all categories except for the diastolic aortic root pressure and the end systolic volume. The most effective reduction in systolic pressure compared to baseline (147 vs. 141 mmHg) was achieved when actuating 350 ms before systole. In this case, flow splits were preserved, and cardiac output was increased (5.17 vs. 5.34 L/min), resulting in increased blood flow to the coronaries (0.15 vs. 0.16 L/min). Both arterial elastance (0.77 vs. 0.74 mmHg/mL) and stroke work (11.8 vs. 10.6 kJ) were decreased compared to baseline, however left atrial pressure increased (11.2 vs. 11.5 mmHg). A higher actuation pressure is associated with higher systolic pressure reduction and slightly higher coronary flow. The soft robotic device prototype achieves reduced systolic pressure, reduced stroke work, slightly increased coronary perfusion, but increased left atrial pressures in HFpEF patients. In future work, the framework could include additional physiological mechanisms, a larger patient cohort with HFpEF, and testing against clinically used devices.
尽管射血分数保留的心力衰竭（HFpEF）导致了一半与心力衰竭相关的住院病例，但基于证据的治疗选择却很有限。目前，一个重大的临床问题是该疾病的病因非常异质性，且没有针对特定患者的治疗方案。建模可以为评估替代治疗策略提供一个框架。反搏策略有能力通过降低收缩压和增加驱动冠状动脉灌注的舒张压来改善左心室舒张期充盈。在此，我们提出一个框架，用于使用HFpEF患者的特定患者闭环血流动力学集总参数模型来测试软机器人主动脉外反搏策略的有效性。软机器人设备原型在生理压力（50 - 150 mmHg）的软硅胶血管中进行了实验表征，并被建模为压力源和电容的组合。使用开源软件创建了特定患者模型，并根据一名HFpEF患者（男性，87岁，心率 = 60次/分钟）成像获得的血流动力学进行了验证。分析了驱动时机对流量、压力以及收缩功能的影响。特定患者模型与患者数据之间达成了良好的一致性，除舒张期主动脉根部压力和收缩末期容积外，所有类别中的相对误差均低于5%。与基线相比（147 vs. 141 mmHg），在收缩期前350毫秒驱动时实现了最有效的收缩压降低。在这种情况下，血流分流得以保留，心输出量增加（5.17 vs. 5.34 L/分钟），导致冠状动脉血流量增加（0.15 vs. 0.16 L/分钟）。与基线相比，动脉弹性（0.77 vs. 0.74 mmHg/mL）和每搏功（11.8 vs. 10.6 kJ）均降低，然而左心房压力升高（11.2 vs. 11.5 mmHg）。较高的驱动压力与较高的收缩压降低和略高的冠状动脉血流量相关。软机器人设备原型在HFpEF患者中实现了收缩压降低、每搏功降低、冠状动脉灌注略有增加，但左心房压力升高。在未来的工作中，该框架可纳入更多生理机制、更大的HFpEF患者队列，并与临床使用的设备进行对比测试。
