Figure 7 - available via license: Creative Commons Attribution 4.0 International
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Map of flow velocity for Patient #2 with no sign of PVL; flow velocity shown from acceleration, to peak systole, early diastole, ending with late diastole, at three analysis planes.
Source publication
Bicuspid aortic valve (BAV) patients are conventionally not treated by transcathether aortic valve implantation (TAVI) because of anatomic constraint with unfavorable outcome. Patient-specific numerical simulation of TAVI in BAV may predict important clinical insights to assess the conformability of the transcathether heart valves (THV) implanted o...
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Background. Total sternotomy for aortic valve replacement has been superseded by less invasive approaches such as mini-sternotomy or transcatheter procedures. There has been an exponential uptake in transcatheter aortic valve implantation (TAVI) in younger and lower risk patients following recent randomized trials. This study aims to compare the ou...
Introduction:
Aortic stenosis (AS) is sometimes associated with gastrointestinal bleeding, and this phenomenon is known as Heyde's syndrome. Such bleeding is most often considered to originate from gastrointestinal angiodysplasias, but the frequency and endoscopic features of such bleeding remain unclear. This study aimed to determine the frequenc...
Objective
Periinterventional stroke is one of the most feared potential complication, among patients treated with transcatheter aortic valve implantation (TAVI). The purpose of this review was to investigate the incidence of cerebrovascular events and the influence of postinterventional neurologic check-up in patients undergoing TAVI.
Methods
A sy...
![Commissural alignment with the Evolut system. Tang et al.[20]...](publication/360679470/figure/fig5/AS:1159225015304212@1653392189736/Commissural-alignment-with-the-Evolut-system-Tang-et-al20-Permission-for-publication_Q320.jpg)
As transcatheter aortic valve implantation (TAVI) expands to younger and low-risk patients with longer life expectancies, the need for post-TAVI coronary access and reintervention for failing transcatheter heart valve (THV) is expected to increase. Commissural alignment in TAVI may facilitate subsequent coronary access, avoiding severe overlap with...
Purpose
Transcatheter aortic valve implantation (TAVI) has grown in popularity as an alternative to surgical aortic valve replacement (SAVR) for the management of aortic stenosis. In this review, we perform a critical appraisal of the key studies comparing TAVI and SAVR in aortic stenosis patients with intermediate and low risk of operative mortali...
Citations
... FEA has been employed in the crimping procedure for virtual pre-deployment and re-coiling during the virtual deployment process to assess mechanical stress on a BHV's stent structure [10,[30][31][32][33][34][35][36][37][38][39]. FEA studies have also focused on determining the stent contact areas on walls for assessment of anchoring [31,40]. ...
... FEA studies have also focused on determining the stent contact areas on walls for assessment of anchoring [31,40]. FEA also has been used to determine the contact pressures on aortic roots by the stent, and the degree of apposition between the prosthesis stent and aortic root to assess the risk of conduction problems [31,32,38,39,[41][42][43][44][45][46][47]. Moreover, FEA has been utilized to assess the risk of tissue degeneration by computing the structural stresses on leaflets for different valve designs and different implant depth positioning [31,33,[37][38][39]45,46,48]. ...
... While FEA and CFD studies provide limited information on structural stresses or flow hemodynamics, FSI analysis works on a comprehensive assessment of mechanical stresses on the BHV stent structure, aortic root, and the native and prosthetic leaflets caused by changing blood flow dynamics owing to valve motion [30,[32][33][34]36,38,39,47,[51][52][53]. An end-to-end conventional computational modeling approach for assessing the TAVR procedure is shown in Figure 2. Computational approaches are very powerful in assessing different valves for specific patients prior to TAVR, but their models suffer from long computational times and are not practical or readily available to clinicians. ...
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.
... 10,11,16,21,39,40 We are only aware of three prior studies that provide detailed descriptions of the entire TAVR device and its full interaction with the native aortic valve. Luraghi et al. 19,20 present an FSI model of TAVR in a patientspecific aortic root including a calcified native valve, and Pasta et al. 29 conduct an FSI study of TAVR in bicuspid aortic valve patients. However, all of these studies utilize an idealized linear elastic material model for the device's porcine pericardial leaflets and directly apply pressure waveforms as boundary conditions, which can generate nonphysical flow oscillations during valve closure. ...
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.
... The immersed boundary formulation presents the solid subdomain completely immersed in the fluid subdomain, resulting in a more suitable model for large structural deformations, thin elastic structures, and transient contact between structures [24]. Another numerical method is smoothed particle hydrodynamics (SPH), which uses a meshless approach to model the fluid domain [25][26][27]. The simplicity of SPH modelling as compared to that of the FSI technique is the use of the general contact algorithm to consider the interaction of the fluid with the structural domain. ...
... The simplicity of SPH modelling as compared to that of the FSI technique is the use of the general contact algorithm to consider the interaction of the fluid with the structural domain. For the assessment of paravalvular leakage, the small gap between the bioprosthesis and the aortic wall may require a considerable amount of smoothed particles to obtain an accurate estimation of the flow velocity as reported by Pasta et al. [26]. ...
... Most of the computational analyses deal with the assessment of PVL from a structural and fluid dynamics perspective. The typical computational workflow first simulates the TAVR procedure and then performs the CFD or FSI to quantify PVL [14,[16][17][18][19][20]23,[25][26][27]35,36]. Among these, Mao et al. [17] found a good agreement between the predicted and echocardiographic-based measurements of PVL when simulating the CoreValve in patient-specific models. ...
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.
The feasibility of transcatheter aortic valve implantation (TAVI) has been investigated by in-silico modeling to assist clinicians in the pre-operative planning of the device implantation. However, a rigorous standardization is needed to improve the reliability and effectiveness of the in-silico model. This study aims to verify and validate a structural finite-element model of TAVI using the ASME V&V40 protocol. After defining the context of use (COU) and model risk, verification activities were carried out for the computational model of the human aortic root as obtained from a cohort of twenty patients with TAVI. Validation involved comparing numerical predictions with post-procedural CT imaging measurements of both the aortic strain and valve orifice area at systole. Results demonstrated that the relative error between simulations and actual CT measurements ranged from 8.6% to 13.6% for the systolic strain whilst the calcific aortic valve presented differences in the range of 0.08% to 11.86%. This study presents a flexible and customizable framework for assessing the credibility of patient-specific modeling in TAVI patients, emphasizing the critical role of establishing credibility in the response of in-silico models.
The complicated interaction between a fluid flow and a deformable structure is referred to as fluid–structure interaction (FSI). FSI plays a crucial role in the functioning of the aortic valve. Blood exerts stresses on the leaflets as it passes through the opening or shutting valve, causing them to distort and vibrate. The pressure, velocity, and turbulence of the fluid flow have an impact on these deformations and vibrations. Designing artificial valves, diagnosing and predicting valve failure, and improving surgical and interventional treatments all require the understanding and modeling of FSI in aortic valve dynamics. The most popular techniques for simulating and analyzing FSI in aortic valves are computational fluid dynamics (CFD) and finite element analysis (FEA). By studying the relationship between fluid flow and valve deformations, researchers and doctors can gain knowledge about the functioning of valves and possible pathological diseases. Overall, FSI is a complicated phenomenon that has a great impact on how well the aortic valve works. Aortic valve diseases and disorders can be better identified, treated, and managed by comprehending and mimicking this relationship. This article provides a literature review that compiles valve reconstruction methods from 1952 to the present, as well as FSI modeling techniques that can help advance valve reconstruction. The Scopus, PubMed, and ScienceDirect databases were used in the literature search and were structured into several categories. By utilizing FSI modeling, surgeons, researchers, and engineers can predict the behavior of the aortic valve before, during, and after surgery. This predictive capability can contribute to improved surgical planning, as it provides valuable insights into hemodynamic parameters such as blood flow patterns, pressure distributions, and stress analysis. Additionally, FSI modeling can aid in the evaluation of different treatment options and surgical techniques, allowing for the assessment of potential complications and the optimization of surgical outcomes. It can also provide valuable information on the long-term durability and functionality of prosthetic valves. In summary, fluid–structure interaction modeling is an effective tool for predicting the outcomes of aortic valve surgery. It can provide valuable insights into hemodynamic parameters and aid in surgical planning, treatment evaluation, and the optimization of surgical outcomes.
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.
Background:
Recent trials indicated a further expansion of clinical indication of transcatheter aortic valve replacement to younger and low-risk patients. Factors related to longer-term complications are becoming more important for use in these patients. Accumulating evidence indicates that numerical simulation plays a significant role in improving the outcome of transcatheter aortic valve replacement. Understanding mechanical features' magnitude, pattern, and duration is a topic of ongoing relevance.
Methods:
We searched the PubMed database using keywords such as "transcatheter aortic valve replacement" and "numerical simulation" and reviewed and summarized relevant literature.
Findings:
This review integrated recently published evidence into three subtopics: 1) prediction of transcatheter aortic valve replacement outcomes through numerical simulation, 2) implications for surgeons, and 3) trends in transcatheter aortic valve replacement numerical simulation.
Interpretations:
Our study offers a comprehensive overview of the utilization of numerical simulation in the context of transcatheter aortic valve replacement, and highlights the advantages, potential challenges from a clinical standpoint. The convergence of medicine and engineering plays a pivotal role in enhancing the outcomes of transcatheter aortic valve replacement. Numerical simulation has provided evidence of potential utility for tailored treatments.
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.
