二叶式主动脉瓣人群经导管主动脉瓣置换术后冠状动脉窦口阻塞的生物力学机理研究

Coronary ostia obstruction is a life-threatening complication of transcatheter aortic valve replacement (TAVR)，and have a higher incidence among patients with the bicuspid aortic valve (BAV). However, the complex aortic root structures of BAV population and the lack of clinical data makes it very difficult to perform accurate preoperative CO risk assessment. In view of the above issues, this study intends to first generate a database of BAV aortic root models and their corresponding postoperative CO risks through data-driven parametric modelling and numerical simulation processes. Then the machine learning based on Probabilistic Neural Network (PNN) will be utilized to analyze the database to reveal the relationship between the morphology of the aortic root and the high incidence of CO, clarify the underlying biomechanical mechanism, and establish a risk assessment model. Finally, an in-vitro TAVR test platform will be built to carry out the in-vitro experimental validation of the simulation results and risk assessment model. The successful implementation of this project will not only help to identify the risk factors that cause the high incidence of CO among BAV population after TAVR but also provides a scientific basis for further improving the risk assessment for TAVR. At the same time, the results from this study are of great significance to further improve the TAVR preoperative risk assessment system and promote the apply of precision medicine strategies in the relevant clinical diagnosis and treatment activities.

冠状动脉窦口阻塞（CO）是二叶式主动脉瓣（BAV）人群经导管主动脉瓣置换（TAVR）术后高发的一种严重并发症，然而BAV人群主动脉根部复杂的解剖学形态以及临床数据的缺乏使得对该人群CO风险的术前评估极为困难。针对上述问题，本研究拟首先通过数据驱动的BAV人群TAVR参数化数值仿真流程，形成包括大量不同形态BAV主动脉根模型及相应术后CO风险数据的样本库，克服数据不足的限制；其后借助概率神经网络（PNN）对上述样本进行机器学习，明确BAV人群CO高发的生物力学机理，形成基于主动脉根形态参数的风险评估模型；最后建立针对BAV人群的TAVR模拟实验平台，对上述仿真结果及风险预测模型开展实验验证。本项目的顺利实施不仅将有助于明确BAV人群TAVR术后CO高发的生物力学机理及风险因素，为该人群CO术前风险评估提供科学依据；亦对进一步完善TAVR术前风险评估体系，实现相关疾病的精准医疗具有重要意义。

受人工瓣膜置入的影响，经导管主动脉瓣置换（TAVR）可能造成冠状动脉血流动力学环境发生改变，甚至导致冠状动脉窦口阻塞（CO）等围手术期并发症。然而主动脉瓣疾病患者主动脉根部复杂的解剖学形态使得对相关并发症风险的术前评估极为困难。本项目围绕上述问题，针对TAVR人工瓣膜置入对冠状动脉血流动力学的影响机制开展了研究。主要研究内容包括：基于患者影像数据实现主动脉根的自动分割、三维重建并形成个体化主动脉根模型数据库；在个体化主动脉根模型数据库的基础上开展数值仿真研究，阐明人工瓣膜置入对于冠状动脉血流动力学的影响机制；构建TAVR体外流动实验平台并对仿真结果开展实验验证。在本项目资助下，发展了主动脉根自动分割、三维重建及关键参数测量方法，实现了基于CT影像的高效、精准的主动脉根自动分割与三维重建，并在此基础上实现了瓣环直径、瓣环面积及冠状动脉开口高度等关键参数的自动测量分析；开展了人工心脏瓣膜对于下游血流动力学影响的数值仿真研究，明确了人工瓣膜置入对下游升主动脉及冠状动脉内血流影响的关键因素；建成了TAVR脉动流体外实验平台，为本研究相关仿真结果的验证提供了基础，亦为相关临床疾病的手术训练、术前评估和规划以及器械测试提供了平台保障；形成了主动脉狭窄患者的主动脉根三维模型数据库，包含超过100例个体化主动脉根三维模型，为进一步开展主动脉瓣膜疾病的相关研究提供了数据基础。本项目的研究将加深对于TAVR对于冠状动脉血流动力学影响机制的认识，促进TAVR术前风险评估体系的进一步完善。

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