Risk assessment of patient-specific cardiac conduction abnormality (CCA) after transcatheter aortic valve replacement (TAVR) and dynamic insights from a beating heart model

Transcatheter aortic valve replacement (TAVR), a minimally invasive heart procedure has emerged as a lifesaver for people who are at intermediate or high risk of complications from open-heart surgery. Despite its early success, the TAVR procedure is associated with several post-procedural clinical complications. Cardiac conduction abnormalities (CCA) are one of the major persistent complications which may lead to permanent pacemaker (PPM) implantation. Localized mechanical compression exerted by the prosthetic valve frame in proximity to the atrioventricular (A-V) node may disturb the cardiac conduction and cause the resultant CCA. In this study, a patient-specific finite element-based computational framework is developed to assess post-TAVR CCA risk by analyzing logarithmic strain during the TAVR deployment, and their time history during the TAVR deployment. Area-weighted average maximum principal logarithmic strain (AMPLS) was significantly elevated in the PPM patient- as compared to the control patient who did not require PPM. In conclusion, elevated stresses generated by TAVR devices during deployment appear to correlate with CCA risk, with AMPLS in the MS region emerging as a strong predictor. Subsequently, the study was extended to analyze the effect of heart motion in developing new CCA after TAVR, using an electro-mechanically coupled beating heart model. In this study, a two-fold increase of cyclic maximum logarithmic strain in the A-V node was observed following TAVR in comparison to a normal heartbeat which may also contribute to the development of new CCA following TAVR. Such computational studies could further be used for preprocedural planning.