Transcatheter Heart Valve Implantation in Bicuspid Patients with Self-Expanding Device

Material modeling and recent findings in transcatheter aortic valve implantation simulations

BACKGROUND AND OBJECTIVE

Transcatheter aortic valve implantation (TAVI) has significantly transformed the management of aortic valve (AV) diseases, presenting a minimally invasive option compared to traditional surgical valve replacement. Computational simulations of TAVI become more popular and offer a detailed investigation by employing patient-specific models. On the other hand, employing accurate material modeling procedures and applying basic modeling steps are crucial to determining reliable numerical results. Therefore, this review aims to outline the basic modeling approaches for TAVI, focusing on material modeling and geometry extraction, as well as summarizing the important findings from recent computational studies to guide future research in the field.

METHODS

This paper explains the basic steps and important points in setting up and running TAVI simulations. The material properties of the leaflets, valves, stents, and tissues utilized in TAVI simulations are provided, along with a comprehensive explanation of the geometric extraction methods employed. The differences between the finite element analysis, computational fluid dynamics, and fluid-structure interaction approaches are pointed out and the important aspects of TAVI modeling are described by elucidating the recent computational studies.

RESULTS

The results of the recent findings on TAVI simulations are summarized to demonstrate its powerful potential. It is observed that the material properties of aortic tissues and components of implanted valves should be modeled realistically to determine accurate results. For patient-specific AV geometries, incorporating calcific deposits on the leaflets is essential for ensuring the accuracy of computational findings. The results of numerical TAVI simulations indicate the significance of the selection of optimal valves and precise deployment within the appropriate anatomical position. These factors collectively contribute to the effective functionality of the implanted valve.

CONCLUSIONS

Recent studies in the literature have revealed the critical importance of patient-specific modeling, the selection of accurate material models, and bio-prosthetic valve diameters. Additionally, these studies emphasize the necessity of precise positioning of bio-prosthetic valves to achieve optimal performance in TAVI, characterized by an increased effective orifice area and minimal paravalvular leakage.

Parametric analysis of transcatheter aortic valve replacement in transcatheter aortic valve replacement: evaluation of coronary flow obstruction

Transcatheter aortic valve replacement (TAVR) is increasingly being considered for use in younger patients having longer life expectancy than those who were initially treated. The TAVR-in-TAVR procedure represents an appealing strategy to treat failed transcatheter heart valves (THV) likely occurring in young patients. However, the permanent displacement of first THV can potentially compromise the coronary access and ultimately inhibit the blood flow circulation. The objective of this study was to use finite-element analysis (FEA) to quantify coronary flow in a patient who underwent TAVR-in-TAVR. A parametric investigation was carried out to determine the impact of both the implantation depth and device size on coronary flow for several deployment configurations. The FEAs consisted of first delivering the SAPIEN 3 Ultra THV and then positioning the Evolut PRO device. Findings indicates that high implantation depth and device undersize of the second THV could significantly reduce coronary flow to 20% of its estimated level before TAVR. Additionally, a positive correlation was observed between coronary flow and the valve-to-coronary distance (R = 0.86 and p = 0.032 for the left coronary artery, and R = 0.93 and p = 0.014 for the right coronary artery). This study demonstrated that computational modeling can provide valuable insights to improve the pre-procedural planning of TAVR-in-TAVR.

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.

Abnormal mechanical stress on bicuspid aortic valve induces valvular calcification and inhibits Notch1/NICD/Runx2 signal

Background

Bicuspid aortic valve (BAV) is a congenital cardiac deformity, increasing the risk of developing calcific aortic valve disease (CAVD). The disturbance of hemodynamics can induce valvular calcification, but the mechanism has not been fully identified.

Methods

We constructed a finite element model (FEM) of the aortic valve based on the computed tomography angiography (CTA) data from BAV patients and tricuspid aortic valve (TAV) individuals. We analyzed the hemodynamic properties based on our model and investigated the characteristics of mechanical stimuli on BAV. Further, we detected the expression of Notch, NICD and Runx2 in valve samples and identified the association between mechanical stress and the Notch1 signaling pathway.

Results

Finite element analysis showed that at diastole phase, the equivalent stress on the root of BAV was significantly higher than that on the TAV leaflet. Correspondingly, the expression of Notch1 and NICH decreased and the expression of Runx2 elevated significantly on large BAV leaflet belly, which is associated with equivalent stress on leaflet. Our findings indicated that the root of BAV suffered higher mechanical stress due to the abnormal hemodynamic environment, and the disturbance of the Notch1/NICD/Runx2 signaling pathway caused by mechanical stimuli contributed to valvular calcification.

Patient-Specific Immersed Finite Element-Difference Model of Transcatheter Aortic Valve Replacement

Transcatheter aortic valve replacement (TAVR) first received FDA approval for high-risk surgical patients in 2011 and has been approved for low-risk surgical patients since 2019. It is now the most common type of aortic valve replacement, and its use continues to accelerate. Computer modeling and simulation (CM&S) is a tool to aid in TAVR device design, regulatory approval, and indication in patient-specific care. This study introduces a computational fluid-structure interaction (FSI) model of TAVR with Medtronic's CoreValve Evolut R device using the immersed finite element-difference (IFED) method. We perform dynamic simulations of crimping and deployment of the Evolut R, as well as device behavior across the cardiac cycle in a patient-specific aortic root anatomy reconstructed from computed tomography (CT) image data. These IFED simulations, which incorporate biomechanics models fit to experimental tensile test data, automatically capture the contact within the device and between the self-expanding stent and native anatomy. Further, we apply realistic driving and loading conditions based on clinical measurements of human ventricular and aortic pressures and flow rates to demonstrate that our Evolut R model supports a physiological diastolic pressure load and provides informative clinical performance predictions.

On the Modeling of Transcatheter Therapies for the Aortic and Mitral Valves: A Review

Transcatheter aortic valve replacement (TAVR) has become a milestone for the management of aortic stenosis in a growing number of patients who are unfavorable candidates for surgery. With the new generation of transcatheter heart valves (THV), the feasibility of transcatheter mitral valve replacement (TMVR) for degenerated mitral bioprostheses and failed annuloplasty rings has been demonstrated. In this setting, computational simulations are modernizing the preoperative planning of transcatheter heart valve interventions by predicting the outcome of the bioprosthesis interaction with the human host in a patient-specific fashion. However, computational modeling needs to carry out increasingly challenging levels including the verification and validation to obtain accurate and realistic predictions. This review aims to provide an overall assessment of the recent advances in computational modeling for TAVR and TMVR as well as gaps in the knowledge limiting model credibility and reliability.