MOX, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.
LaBS, Dipartimento di Chimica, Materiali e Ingegneria Chimica, Politecnico di Milano, Milan, Italy.
Int J Numer Method Biomed Eng. 2023 Jun;39(6):e3704. doi: 10.1002/cnm.3704. Epub 2023 Apr 5.
Transcatheter aortic valve implantation (TAVI) is a minimally invasive intervention for the treatment of severe aortic valve stenosis. The main cause of failure is the structural deterioration of the implanted prosthetic leaflets, possibly inducing a valvular re-stenosis 5-10 years after the implantation. Based solely on pre-implantation data, the aim of this work is to identify fluid-dynamics and structural indices that may predict the possible valvular deterioration, in order to assist the clinicians in the decision-making phase and in the intervention design. Patient-specific, pre-implantation geometries of the aortic root, the ascending aorta, and the native valvular calcifications were reconstructed from computed tomography images. The stent of the prosthesis was modeled as a hollow cylinder and virtually implanted in the reconstructed domain. The fluid-structure interaction between the blood flow, the stent, and the residual native tissue surrounding the prosthesis was simulated by a computational solver with suitable boundary conditions. Hemodynamical and structural indicators were analyzed for five different patients that underwent TAVI - three with prosthetic valve degeneration and two without degeneration - and the comparison of the results showed a correlation between the leaflets' structural degeneration and the wall shear stress distribution on the proximal aortic wall. This investigation represents a first step towards computational predictive analysis of TAVI degeneration, based on pre-implantation data and without requiring additional peri-operative or follow-up information. Indeed, being able to identify patients more likely to experience degeneration after TAVI may help to schedule a patient-specific timing of follow-up.
经导管主动脉瓣植入术(TAVI)是一种治疗严重主动脉瓣狭窄的微创介入方法。植入人工瓣叶的结构恶化是导致其失败的主要原因,可能会在植入后 5-10 年内导致瓣口再次狭窄。本工作仅基于植入前的数据,旨在确定可能预测瓣膜恶化的流体动力学和结构指标,以协助临床医生进行决策阶段和干预设计。从计算机断层扫描图像中重建了患者特定的主动脉根部、升主动脉和原生瓣叶钙化的术前几何形状。将假体的支架建模为空心圆柱体,并在重建的区域中虚拟植入。通过具有适当边界条件的计算求解器模拟血流、支架和假体周围残留的原生组织之间的流固耦合。对 5 名不同的患者进行了 TAVI 手术,其中 3 名患者的人工瓣膜发生了退行性变,2 名患者没有退行性变,对结果进行了分析,并比较了结果,发现瓣叶结构退行性变与近端主动脉壁上壁面切应力分布之间存在相关性。这项研究是基于植入前数据并无需额外的围手术期或随访信息,对 TAVI 退行性变进行计算预测分析的第一步。事实上,能够识别出 TAVI 后更有可能发生退行性变的患者,可能有助于安排特定患者的随访时间。