Predictive Model for Thrombus Formation After Transcatheter Valve Replacement

Physical and Computational Modeling for Transcatheter Structural Heart Interventions

Structural heart disease interventions rely heavily on preprocedural planning and simulation to improve procedural outcomes and predict and prevent potential procedural complications. Modeling technologies, namely 3-dimensional (3D) printing and computational modeling, are nowadays increasingly used to predict the interaction between cardiac anatomy and implantable devices. Such models play a role in patient education, operator training, procedural simulation, and appropriate device selection. However, current modeling is often limited by the replication of a single static configuration within a dynamic cardiac cycle. Recognizing that health systems may face technical and economic limitations to the creation of "in-house" 3D-printed models, structural heart teams are pivoting to the use of computational software for modeling purposes.

Effect of Sinotubular Junction Size on TAVR Leaflet Thrombosis: A Fluid-Structure Interaction Analysis

TAVR has emerged as a standard approach for treating severe aortic stenosis patients. However, it is associated with several clinical complications, including subclinical leaflet thrombosis characterized by Hypoattenuated Leaflet Thickening (HALT). A rigorous analysis of TAVR device thrombogenicity considering anatomical variations is essential for estimating this risk. Clinicians use the Sinotubular Junction (STJ) diameter for TAVR sizing, but there is a paucity of research on its influence on TAVR devices thrombogenicity. A Medtronic Evolut® TAVR device was deployed in three patient models with varying STJ diameters (26, 30, and 34 mm) to evaluate its impact on post-deployment hemodynamics and thrombogenicity, employing a novel computational framework combining prosthesis deployment and fluid-structure interaction analysis. The 30 mm STJ patient case exhibited the best hemodynamic performance: 5.94 mmHg mean transvalvular pressure gradient (TPG), 2.64 cm2 mean geometric orifice area (GOA), and the lowest mean residence time (TR)-indicating a reduced thrombogenic risk; 26 mm STJ exhibited a 10 % reduction in GOA and a 35% increase in mean TPG compared to the 30 mm STJ; 34 mm STJ depicted hemodynamics comparable to the 30 mm STJ, but with a 6% increase in TR and elevated platelet stress accumulation. A smaller STJ size impairs adequate expansion of the TAVR stent, which may lead to suboptimal hemodynamic performance. Conversely, a larger STJ size marginally enhances the hemodynamic performance but increases the risk of TAVR leaflet thrombosis. Such analysis can aid pre-procedural planning and minimize the risk of TAVR leaflet thrombosis.

Effect of Ascending Aortic Curvature on Flow in the Sinus and Neo-sinus Following TAVR: A Patient-Specific Study

Patient-specific aortic geometry and its influence on the flow in the vicinity of Transcatheter Aortic Valve (TAV) has been highlighted in numerous studies using both in silico and in vitro experiments. However, there has not yet been a detailed Particle Image Velocimetry (PIV) experiment conducted to quantify the relationship between the geometry, flow downstream of TAV, and the flow in the sinus and the neo-sinus. We tested six different patient-specific aorta models with a 26-mm SAPIEN 3 valve (Edwards Lifesciences, Irvine, CA, USA) in a left heart simulator with coronary flow. Velocities in all three cusps and circulation downstream of TAV were computed to evaluate the influence of the ascending aorta curvature on the flow field. The in vitro analysis showed that the patient-specific aortic curvature had positive correlation to the circulation in the ascending aorta (p = 0.036) and circulation had negative correlation to the particle washout time in the cusps (p = 0.011). These results showed that distinct vortical flow patterns in the ascending aorta as the main jet impinges on the aortic wall causes a recirculation region that facilitates the flow back into the sinus and the neo-sinus, thus reducing the risk of flow stagnation and washout time.

Decoding thrombosis through code: a review of computational models

From the molecular level up to a blood vessel, thrombosis and hemostasis involves many interconnected biochemical and biophysical processes over a wide range of length and time scales. Computational modeling has gained eminence in offering insights into these processes beyond what can be obtained from in vitro or in vivo experiments, or clinical measurements. The multiscale and multiphysics nature of thrombosis has inspired a wide range of modeling approaches that aim to address how a thrombus forms and dismantles. Here, we review recent advances in computational modeling with a focus on platelet-based thrombosis. We attempt to summarize the diverse range of modeling efforts straddling the wide-spectrum of physical phenomena, length scales, and time scales; highlighting key advancements and insights from existing studies. Potential information gleaned from models is discussed, ranging from identification of thrombus-prone regions in patient-specific vasculature to modeling thrombus deformation and embolization in response to fluid forces. Furthermore, we highlight several limitations of current models, future directions in the field, and opportunities for clinical translation, to illustrate the state-of-the-art. There are a plethora of opportunity areas for which models can be expanded, ranging from topics of thromboinflammation to platelet production and clearance. Through successes demonstrated in existing studies described here, as well as continued advancements in computational methodologies and computer processing speeds and memory, in silico investigations in thrombosis are poised to bring about significant knowledge growth in the years to come.

Effect of Sinotubular Junction Size on TAVR Leaflet Thrombosis: A Fluid-structure Interaction Analysis

Purpose

TAVR has emerged as a standard approach for treating severe aortic stenosis patients. However, it is associated with several clinical complications, including subclinical leaflet thrombosis characterized by Hypoattenuated Leaflet Thickening (HALT). A rigorous analysis of TAVR device thrombogenicity considering anatomical variations is essential for estimating this risk. Clinicians use the Sinotubular Junction (STJ) diameter for TAVR sizing, but there is a paucity of research on its influence on TAVR devices thrombogenicity.

Methods

A Medtronic Evolut® TAVR device was deployed in three patient models with varying STJ diameters (26, 30, and 34mm) to evaluate its impact on post-deployment hemodynamics and thrombogenicity, employing a novel computational framework combining prosthesis deployment and fluid- structure interaction analysis.

Results

The 30 mm STJ patient case exhibited the best hemodynamic performance: 5.94 mmHg mean transvalvular pressure gradient (TPG), 2.64 cm 2 mean geometric orifice area (GOA), and the lowest mean residence time (T R ) - indicating a reduced thrombogenic risk; 26 mm STJ exhibited a 10 % reduction in GOA and a 35% increase in mean TPG compared to the 30 mm STJ; 34 mm STJ depicted hemodynamics comparable to the 30 mm STJ, but with a 6% increase in T R and elevated platelet stress accumulation.

Conclusion

A smaller STJ size impairs adequate expansion of the TAVR stent, which may lead to suboptimal hemodynamic performance. Conversely, a larger STJ size marginally enhances the hemodynamic performance but increases the risk of TAVR leaflet thrombosis. Such analysis can aid pre- procedural planning and minimize the risk of TAVR leaflet thrombosis.

A model of fluid-structure and biochemical interactions for applications to subclinical leaflet thrombosis

Subclinical leaflet thrombosis (SLT) is a potentially serious complication of aortic valve replacement with a bioprosthetic valve in which blood clots form on the replacement valve. SLT is associated with increased risk of transient ischemic attacks and strokes and can progress to clinical leaflet thrombosis. SLT following aortic valve replacement also may be related to subsequent structural valve deterioration, which can impair the durability of the valve replacement. Because of the difficulty in clinical imaging of SLT, models are needed to determine the mechanisms of SLT and could eventually predict which patients will develop SLT. To this end, we develop methods to simulate leaflet thrombosis that combine fluid-structure interaction and a simplified thrombosis model that allows for deposition along the moving leaflets. Additionally, this model can be adapted to model deposition or absorption along other moving boundaries. We present convergence results and quantify the model's ability to realize changes in valve opening and pressures. These new approaches are an important advancement in our tools for modeling thrombosis because they incorporate both adhesion to the surface of the moving leaflets and feedback to the fluid-structure interaction.

Long-term prognostic impact of paravalvular leakage on coronary artery disease requires patient-specific quantification of hemodynamics

Transcatheter aortic valve replacement (TAVR) is a frequently used minimally invasive intervention for patient with aortic stenosis across a broad risk spectrum. While coronary artery disease (CAD) is present in approximately half of TAVR candidates, correlation of post-TAVR complications such as paravalvular leakage (PVL) or misalignment with CAD are not fully understood. For this purpose, we developed a multiscale computational framework based on a patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-structure interaction model to quantify metrics of global circulatory function, metrics of global cardiac function and local cardiac fluid dynamics in 6 patients. Based on our findings, PVL limits the benefits of TAVR and restricts coronary perfusion due to the lack of sufficient coronary blood flow during diastole phase (e.g., maximum coronary flow rate reduced by 21.73%, 21.43% and 21.43% in the left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) respectively (N = 6)). Moreover, PVL may increase the LV load (e.g., LV load increased by 17.57% (N = 6)) and decrease the coronary wall shear stress (e.g., maximum wall shear stress reduced by 20.62%, 21.92%, 22.28% and 25.66% in the left main coronary artery (LMCA), left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) respectively (N = 6)), which could promote atherosclerosis development through loss of the physiological flow-oriented alignment of endothelial cells. This study demonstrated that a rigorously developed personalized image-based computational framework can provide vital insights into underlying mechanics of TAVR and CAD interactions and assist in treatment planning and patient risk stratification in patients.

Biomechanics of Transcatheter Aortic Valve Replacement Complications and Computational Predictive Modeling

Transcatheter aortic valve replacement (TAVR) is a rapidly growing field enabling replacement of diseased aortic valves without the need for open heart surgery. However, due to the nature of the procedure and nonremoval of the diseased tissue, there are rates of complications ranging from tissue rupture and coronary obstruction to paravalvular leak, valve thrombosis, and permanent pacemaker implantation. In recent years, computational modeling has shown a great deal of promise in its capabilities to understand the biomechanical implications of TAVR as well as help preoperatively predict risks inherent to device-patient-specific anatomy biomechanical interaction. This includes intricate replication of stent and leaflet designs and tested and validated simulated deployments with structural and fluid mechanical simulations. This review outlines current biomechanical understanding of device-related complications from TAVR and related predictive strategies using computational modeling. An outlook on future modeling strategies highlighting reduced order modeling which could significantly reduce the high time and cost that are required for computational prediction of TAVR outcomes is presented in this review paper. A summary of current commercial/in-development software is presented in the final section.