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<p>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.</p>

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Improved prediction of permanent pacemaker implantation in TAVR for aortic regurgitation: integrating Self-expanding valve implants with 3D printed Patient-specific aortic root models and sensors

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Yuanting Yang,
  • Hongning Song,
  • Ji Zhang,
  • Sheng Cao,
  • Tuantuan Tan,
  • Shixin Tao,
  • Bing Huang,
  • Changwu Xu,
  • Zheng Hu,
  • Jing Chen,
  • Qing Zhou

摘要

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.