Mines Saint-Étienne, Univ Lyon, Univ Jean Monnet, INSERM, Saint-Étienne, France.
Mechanics & High Performance Computing Group, Department of Mechanical Engineering, Technical University of Munich, Garching, Germany.
Int J Numer Method Biomed Eng. 2021 Dec;37(12):e3529. doi: 10.1002/cnm.3529. Epub 2021 Oct 11.
Endovascular aortic repair (EVAR) has become the preferred intervention option for aortic aneurysms and dissections. This is because EVAR is much less invasive than the alternative open surgery repair. While in-hospital mortality rates are smaller for EVAR than open repair (1%-2% vs. 3%-5%), the early benefits of EVAR are lost after 3 years due to larger rates of complications in the EVAR group. Clinicians follow instructions for use (IFU) when possible, but are left with personal experience on how to best proceed and what choices to make with respect to stent-graft (SG) model choice, sizing, procedural options, and their implications on long-term outcomes. Computational modeling of SG deployment in EVAR and tissue remodeling after intervention offers an alternative way of testing SG designs in silico, in a personalized way before intervention, to ultimately select the strategies leading to better outcomes. Further, computational modeling can be used in the optimal design of SGs in cases of complex geometries. In this review, we address some of the difficulties and successes associated with computational modeling of EVAR procedures. There is still work to be done in all areas of EVAR in silico modeling, including model validation, before models can be applied in the clinic, but much progress has already been made. Critical to clinical implementation are current efforts focusing on developing fast algorithms that can achieve (near) real-time solutions, as well as ways of dealing with inherent uncertainties related to patient aortic wall degradation on an individualized basis. We are optimistic that EVAR modeling in the clinic will soon become a reality to help clinicians optimize EVAR interventions and ultimately reduce EVAR-associated complications.
血管内主动脉修复术（EVAR）已成为治疗主动脉瘤和夹层的首选介入治疗方法。这是因为 EVAR 比传统的开放性手术修复创伤小得多。虽然 EVAR 的住院死亡率比开放性修复低（1%-2%比 3%-5%），但在 3 年后，EVAR 组的并发症发生率更高，导致早期获益丧失。临床医生在可能的情况下遵循使用说明（IFU），但在如何最好地进行操作以及在支架移植物（SG）模型选择、尺寸、手术选择及其对长期结果的影响方面，仍需要依靠个人经验。EVAR 中 SG 部署和干预后组织重塑的计算建模提供了一种替代方法，可以在干预前个性化地在计算机上测试 SG 设计，最终选择导致更好结果的策略。此外，计算建模可用于复杂几何形状的 SG 最佳设计。在这篇综述中，我们讨论了与 EVAR 手术计算建模相关的一些困难和成功。在计算机上进行 EVAR 模拟的所有领域，包括模型验证，都需要进一步研究，然后才能将模型应用于临床，但已经取得了很大进展。将计算模型应用于临床的关键是目前专注于开发能够实现（接近）实时解决方案的快速算法，以及针对个体化患者主动脉壁降解相关固有不确定性的处理方法。我们乐观地认为，EVAR 临床模型很快将成为现实，以帮助临床医生优化 EVAR 干预，并最终减少 EVAR 相关并发症。
