Modelling Growth, Remodelling and Damage of a Thick-walled Fibre-reinforced Artery with Active Response: Application to Cerebral Vasospasm and Treatment

Giulia Pederzani, Andrii Grytsan, Alfons G. Hoekstra, Anne M. Robertson, Paul N. Watton
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Abstract

Cerebral vasospasm, a prolonged constriction of cerebral arteries, is the first cause of morbidity and mortality for patients who survive hospitalisation after aneurysmal subarachnoid haemorrhage. The recent finding that stent-retrievers can successfully treat the disease has challenged the viewpoint that damage to the extracellular matrix is necessary. We apply a 3D finite element rate-based constrained mixture model (rb-CMM) to simulate vasospasm, remodelling and treatment with stents. The artery is modelled as a thick-walled fibre-reinforced constrained mixture subject to physiological pressure and axial stretch. The model accounts for distributions of collagen fibre homeostatic stretches, VSMC active response, remodelling and damage. After simulating vasospasm and subsequent remodelling of the artery to a new homeostatic state, we simulate treatment with commonly available stent-retrievers. We perform a parameter study to examine how arterial diameter and thickness affect the success of stent treatment. The model predictions on the pressure required to mechanically resolve the constriction are consistent with stent-retrievers. In agreement with clinical observations, our model predicts that stent-retrievers tend to be effective in arteries of up to 3mm diameter, but fail in larger ones. Variations in arterial wall thickness significantly affect stent pressure requirements. We have developed a novel rb-CMM that accounts for VSMC active response, remodelling and damage. Consistently with clinical observations, simulations predict that stent-retrievers can mechanically resolve vasospasm. Moreover, accounting for a patient's arterial properties is important for predicting likelihood of stent success. This in silico tool has the potential to support clinical decision-making and guide the development and evaluation of dedicated stents for personalised treatment of vasospasm.
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用主动响应模拟厚壁纤维增强动脉的生长、重塑和损伤:在脑血管痉挛和治疗中的应用
脑血管痉挛是指脑动脉长时间收缩,是动脉瘤性蛛网膜下腔出血后住院存活患者发病和死亡的首要原因。最近的研究发现,支架缓释器可以成功治疗这种疾病,这对细胞外基质必须受到破坏的观点提出了挑战。我们采用基于速率的三维有限元约束混合模型(rb-CMM)来模拟血管痉挛、重塑和支架治疗。动脉被模拟为受到生理压力和轴向拉伸的厚壁纤维增强约束混合物。在模拟了血管痉挛和随后的动脉重塑到新的平衡状态后,我们模拟了使用常见的支架治疗。我们进行了参数研究,以考察动脉直径和厚度如何影响支架治疗的成功率。模型对以机械方式解决收缩所需的压力的预测与支架取出器一致。与临床观察结果一致,我们的模型预测支架取回器在直径不超过 3 毫米的动脉中往往有效,但在更大的动脉中则会失效。动脉壁厚度的变化会显著影响支架压力要求。我们开发了一种新颖的rb-CMM,它考虑到了血管内皮细胞的主动反应、重塑和损伤。此外,考虑患者的动脉特性对于预测支架成功的可能性也很重要。这种硅学工具有望为临床决策提供支持,并指导开发和评估用于个性化治疗血管痉挛的专用支架。
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