Biomechanical assessment of aortic valve stenosis: Advantages and limitations

Numerical analysis of stenoses severity and aortic wall mechanics in patients with supravalvular aortic stenosis

Supravalvular aortic stenosis (SVAS) is an aortic malformation characterized by a narrowing of the ascending aorta, resulting in abnormal hemodynamics and pressure drop across the stenosed region. It has been observed that the pressure drops measured from Doppler ultrasound exams often tend to be higher than those obtained from invasive cardiac catheterization. These misleadingly elevated pressure measurements may drive the decision to refer patients for surgical treatment prematurely. Considering this strong clinical association, the purpose of this work is to develop a computational modeling approach using a two-way coupled fluid-structure interaction methodology to determine an accurate prediction of trans-stenotic pressure drop and to further highlight the discrepancy between the SVAS assessment methods. Blood is modeled using Navier-Stokes equations while the aortic wall is simulated by a composite poroelastic structure to represent the three main layers of the arterial wall. The relationship between aortic wall elasticity and the blood flow conditions is examined in varying levels of stenosis, ranging from mild to severe degrees of vessel diameter narrowing. A substantial overestimation of the traditional Doppler pressure drop measurement is observed, especially for severe stenosis levels. The simulation results indicate that elasticity of the aortic wall has a relatively little effect on trans-stenotic pressure drop for the range of mild to moderate SVAS cases, but predicted to have a profound effect for severe SVAS cases. Moreover, significant sensitivity to the pressure drop across the SVAS region from stenosis severity is observed.

Personalized intervention cardiology with transcatheter aortic valve replacement made possible with a non-invasive monitoring and diagnostic framework

One of the most common acute and chronic cardiovascular disease conditions is aortic stenosis, a disease in which the aortic valve is damaged and can no longer function properly. Moreover, aortic stenosis commonly exists in combination with other conditions causing so many patients suffer from the most general and fundamentally challenging condition: complex valvular, ventricular and vascular disease (C3VD). Transcatheter aortic valve replacement (TAVR) is a new less invasive intervention and is a growing alternative for patients with aortic stenosis. Although blood flow quantification is critical for accurate and early diagnosis of C3VD in both pre and post-TAVR, proper diagnostic methods are still lacking because the fluid-dynamics methods that can be used as engines of new diagnostic tools are not well developed yet. Despite remarkable advances in medical imaging, imaging on its own is not enough to quantify the blood flow effectively. Moreover, understanding of C3VD in both pre and post-TAVR and its progression has been hindered by the absence of a proper non-invasive tool for the assessment of the cardiovascular function. To enable the development of new non-invasive diagnostic methods, we developed an innovative image-based patient-specific computational fluid dynamics framework for patients with C3VD who undergo TAVR to quantify metrics of: (1) global circulatory function; (2) global cardiac function as well as (3) local cardiac fluid dynamics. This framework is based on an innovative non-invasive Doppler-based patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-solid interaction. We validated the framework against clinical cardiac catheterization and Doppler echocardiographic measurements and demonstrated its diagnostic utility by providing novel analyses and interpretations of clinical data in eleven C3VD patients in pre and post-TAVR status. Our findings position this framework as a promising new non-invasive diagnostic tool that can provide blood flow metrics while posing no risk to the patient. The diagnostic information, that the framework can provide, is vitally needed to improve clinical outcomes, to assess patient risk and to plan treatment.

Modeling of aortic valve stenosis using fluid-structure interaction method

BACKGROUND AND OBJECTIVES

Fluid structure interaction (FSI) is defined as interaction of the structures with contacting fluids. The aortic valve experiences the interaction with blood flow in systolic phase. In this study, we have tried to predict the hemodynamics of blood flow through a normal and stenotic aortic valve in two relaxation and exercise conditions using a three-dimensional FSI method.

METHODS

The aorta valve was modeled as a three-dimensional geometry including a normal model and two others with 25% and 50% stenosis. The geometry of the aortic valve was extracted from CT images and the models were generated by MMIMCS software and then they were implemented in ANSYS software. The pulsatile flow rate was used for all cases and the numerical simulations were conducted based on a time-dependent domain.

RESULTS

The obtained results including the velocity, pressure, and shear stress contours in different systolic time sequences were explained and discussed. The maximum blood flow velocity in relaxation phase was obtained 1.62 m/s (normal valve), 3.78 m/s (25% stenosed valve), and 4.73 m/s (50% stenosed valve). In exercise condition, the maximum velocities are 2.86, 4.32, and 5.42 m/s respectively. The maximum blood pressure in relaxation phase was calculated 111.45 mmHg (normal), 148.66 mmHg (25% stenosed), and 164.21 mmHg (50% stenosed). However, the calculated values in exercise situation were 129.57, 163.58, and 191.26 mmHg.The validation of the predicted results was also conducted using existing literature.

CONCLUSIONS

We believe that such model are useful tools for biomechanical experts. The further studies should be done using experimental data and the data are implemented on the boundary conditions for better comparison of the results.