Impact of cardiac patch alignment on restoring post-infarct ventricular function.

Acute myocardial infarction (MI) leads to a loss of cardiac function which, following adverse ventricular remodeling (AVR), can ultimately result in heart failure. Tissue-engineered contractile patches placed over the infarct offer potential for restoring cardiac function and reducing AVR. In this computational study, we investigate how improvement of pump function depends on the orientation of the cardiac patch and the fibers therein relative to the left ventricle (LV). Additionally, we examine how model outcome depends on the choice of material properties for healthy and infarct tissue. In a finite element model of LV mechanics, an infarction was induced by eliminating active stress generation and increasing passive tissue stiffness in a region comprising 15% of the LV wall volume. The cardiac patch was modeled as a rectangular piece of healthy myocardium with a volume of 25% of the infarcted tissue. The orientation of the patch was varied from 0 to 150 ∘ relative to the circumferential plane. The infarct reduced stroke work by 34% compared to the healthy heart. Optimal patch support was achieved when the patch was oriented parallel to the subepicardial fiber direction, restoring 9% of lost functionality. Typically, about one-third of the total recovery was attributed to the patch, while the remainder resulted from restored functionality in native myocardium adjacent to the infarct. The patch contributes to cardiac function through two mechanisms. A contribution of tissue in the patch and an increased contribution of native tissue, due to favorable changes in mechanical boundary conditions.

Strain-controlled electrophysiological wave propagation alters in silico scar-based substrate for ventricular tachycardia.

Introduction: Assessing a patient's risk of scar-based ventricular tachycardia (VT) after myocardial infarction is a challenging task. It can take months to years after infarction for VT to occur. Also, if selected for ablation therapy, success rates are low. Methods: Computational ventricular models have been presented previously to support VT risk assessment and to provide ablation guidance. In this study, an extension to such virtual-heart models is proposed to phenomenologically incorporate tissue remodeling driven by mechanical load. Strain amplitudes in the heart muscle are obtained from simulations of mechanics and are used to adjust the electrical conductivity. Results: The mechanics-driven adaptation of electrophysiology resulted in a more heterogeneous distribution of propagation velocities than that of standard models, which adapt electrophysiology in the structural substrate from medical images only. Moreover, conduction slowing was not only present in such a structural substrate, but extended in the adjacent functional border zone with impaired mechanics. This enlarged the volumes with high repolarization time gradients (≥10 ms/mm). However, maximum gradient values were not significantly affected. The enlarged volumes were localized along the structural substrate border, which lengthened the line of conduction block. The prolonged reentry pathways together with conduction slowing in functional regions increased VT cycle time, such that VT was easier to induce, and the number of recommended ablation sites increased from 3 to 5 locations. Discussion: Sensitivity testing showed an accurate model of strain-dependency to be critical for low ranges of conductivity. The model extension with mechanics-driven tissue remodeling is a potential approach to capture the evolution of the functional substrate and may offer insight into the progression of VT risk over time.

3D-printed stenotic aortic valve model to simulate physiology before, during, and after transcatheter aortic valve implantation.

AIMS: Pressure loss versus transvalvular flow analysis challenges physiologic models of current aortic valve stenosis. New conceptual frameworks are needed to explain these real-world observations. METHODS AND RESULTS: A patient-specific, 3D-printed, silicon model of a stenotic valve was placed inside an in-vitro haemodynamic model of the circulatory system. Instantaneous pressure and flow in the aorta and left ventricle were simulated according to measured patient specific parameters. Thereafter, a realistic transcatheter aortic valve was implanted (TAVI) in the model. Simulated post-TAVI mean pressure gradients resembled patient observations (3.7 ± 0.7 mmHg vs 6.7 ± 2.3 mmHg), but pre-TAVI measurements underestimated the pressure gradient (35.1 ± 0.6 mmHg vs 45.3 ± 1.5 mmHg). CONCLUSION: Patient-specific 3D-printed stenotic aortic valve models could simulate baseline haemodynamics. A TAVI procedure was successfully performed on the 3D silicone rubber valve in a physiologic in-vitro model. Pre-TAVI haemodynamics in the model underestimated in-patient mean pressure gradient, whereas post TAVI pressure gradient was predicted correctly with the TAVI valve inside the 3D printed model. This study shows that these types of models could be used to study AS hemodynamics with the TAVI valve inside the 3D printed model. Improvements in the 3D-printed model, like addition of calcification and fine-tuning of the haemodynamic model, could further enhance accuracy of the simulation.