Yang Yuanting, Song Hongning, Zhang Ji, Cao Sheng, Tan Tuantuan, Tao Shixin, Huang Bing, Xu Changwu, Hu Zheng, Chen Jing, Zhou Qing
Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.
Department of Cardiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.
Int J Cardiovasc Imaging. 2025 Jul 31. doi: 10.1007/s10554-025-03461-3.
The aim of this study is to develop a comprehensive predictive model for PPMI after transcatheter aortic valve replacement (TAVR) in patients with aortic regurgitation by reflecting pressure changes in the critical region post-TAVR using 3D-printed root models equipped with pressure sensors, in conjunction with clinical baseline characteristics, MDCT findings, procedural factors. A retrospective analysis was performed on seventy-two patients with aortic regurgitation who performed pre-TAVR CT evaluation using self-expandable valves. The study excluded patients with a pre-existing PPMI or those who underwent surgical aortic valve replacement. The primary endpoint was in-hospital PPMI following TAVR. Pressure sensors integrated into 3D printed models of aortic root were utilized to visualize pressure in critical regions during TAVR simulation.Additionally, Baseline data, MDCT, and procedural outcomes were collected and analyzed according to established criteria. Multivariable logistic regression models were employed to identify the relationship between variables and the risk of PPMI. The new PPMI rate was 17.9%.The study validated the efficacy of using 3D-printed aortic root models with pressure sensors in predicting the risk of PPMI. On multivariate analysis, the maximum contact pressure, LVOT/annulus area ratio,△MSID and pre-existing RBBB were independent predictors of PPMI. A combination of these factors significantly increased the risk of PPMI post-TAVR. The 3D-printed model of aortic root with pressure sensors provides a valuable tool for visualizing pressure in critical regions and enhancing risk assessment in TAVR procedures.The study highlights the significance of integrating various clinical, anatomical, and procedural factors to predict PPMI risk accurately.
本研究的目的是通过使用配备压力传感器的3D打印根部模型反映经导管主动脉瓣置换术（TAVR）后关键区域的压力变化，并结合临床基线特征、MDCT检查结果和手术因素，为主动脉瓣反流患者开发一种全面的TAVR术后永久性起搏器植入（PPMI）预测模型。对72例使用自膨胀瓣膜进行TAVR术前CT评估的主动脉瓣反流患者进行了回顾性分析。该研究排除了已有PPMI的患者或接受过外科主动脉瓣置换术的患者。主要终点是TAVR术后的院内PPMI。在TAVR模拟过程中，利用集成到主动脉根部3D打印模型中的压力传感器来可视化关键区域的压力。此外，根据既定标准收集并分析基线数据、MDCT检查结果和手术结果。采用多变量逻辑回归模型来确定变量与PPMI风险之间的关系。新的PPMI发生率为17.9%。该研究验证了使用带有压力传感器的3D打印主动脉根部模型预测PPMI风险的有效性。多因素分析显示，最大接触压力、左心室流出道/瓣环面积比、△MSID和既往右束支传导阻滞是PPMI的独立预测因素。这些因素的组合显著增加了TAVR术后PPMI的风险。带有压力传感器的主动脉根部3D打印模型为可视化关键区域的压力和加强TAVR手术中的风险评估提供了一个有价值的工具。该研究强调了整合各种临床、解剖和手术因素以准确预测PPMI风险的重要性。
