Towards improving the accuracy of aortic transvalvular pressure gradients: rethinking Bernoulli

Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography

BACKGROUND AND OBJECTIVE

Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value.

METHODS

Twenty patients underwent echocardiography, cardiac CTA, and invasive catheterization for pressure measurements. Cardiac CTA employed retrospective electrocardiographic gating to capture multi-phase data throughout the cardiac cycle. We segmented the region of interest based on mid-systolic phase cardiac CTA images. Then, we computed the average flow velocity into the aorta as the inlet boundary condition, using variations in end-diastolic and end-systolic left ventricular volume. Finally, we conducted CFD simulations using a steady-state model to obtain pressure distribution within the computational domain, allowing for the derivation of MPG.

RESULTS

The mean value of MPG, measured via invasive catheterization (MPGInv), echocardiography (MPGEcho), and cardiac CTA (MPGCT), were 51.3 ± 28.4 mmHg, 44.8 ± 19.5 mmHg, and 55.8 ± 25.6 mmHg, respectively. In comparison to MPGInv, MPGCT exhibited a higher correlation of 0.91, surpassing that of MPGEcho, which was 0.82. Moreover, the limits of agreement for MPGCT ranged from -27.7 to 18.7, outperforming MPGEcho, which ranged from -40.1 to 18.0.

CONCLUSIONS

The proposed method based on cardiac CTA enables the evaluation of MPG for aortic valve stenosis patients. In future clinical practice, a single cardiac CTA examination can comprehensively assess both the anatomical and functional hemodynamic aspects of aortic valve disease.

A parametric geometry model of the aortic valve for subject-specific blood flow simulations using a resistive approach

Cardiac valves simulation is one of the most complex tasks in cardiovascular modeling. Fluid-structure interaction is not only highly computationally demanding but also requires knowledge of the mechanical properties of the tissue. Therefore, an alternative is to include valves as resistive flow obstacles, prescribing the geometry (and its possible changes) in a simple way, but, at the same time, with a geometry complex enough to reproduce both healthy and pathological configurations. In this work, we present a generalized parametric model of the aortic valve to obtain patient-specific geometries that can be included into blood flow simulations using a resistive immersed implicit surface (RIIS) approach. Numerical tests are presented for geometry generation and flow simulations in aortic stenosis patients whose parameters are extracted from ECG-gated CT images.

Comparison of hemodynamics in biological surgical aortic valve replacement and transcatheter aortic valve implantation: An in-silico study

OBJECTIVES

In aortic valve replacement (AVR), the treatment strategy as well as the model and size of the implanted prosthesis have a major impact on the postoperative hemodynamics and thus on the clinical outcome. Preinterventional prediction of the hemodynamics could support the treatment decision. Therefore, we performed paired virtual treatment with transcatheter AVR (TAVI) and biological surgical AVR (SAVR) and compared hemodynamic outcomes using numerical simulations.

METHODS

10 patients with severe aortic stenosis (AS) undergoing TAVI were virtually treated with both biological SAVR and TAVI to compare post-interventional hemodynamics using numerical simulations of peak-systolic flow. Virtual treatment procedure was done using an in-house developed tool based on position-based dynamics methodology, which was applied to the patient's anatomy including LVOT, aortic root and aorta. Geometries were automatically segmented from dynamic CT-scans and patient-specific flow rates were calculated by volumetric analysis of the left ventricle. Hemodynamics were assessed using the STAR CCM+ software by solving the RANS equations.

RESULTS

Virtual treatment with TAVI resulted in realistic hemodynamics comparable to echocardiographic measurements (median difference in transvalvular pressure gradient [TPG]: -0.33 mm Hg). Virtual TAVI and SAVR showed similar hemodynamic functions with a mean TPG with standard deviation of 8.45 ± 4.60 mm Hg in TAVI and 6.66 ± 3.79 mm Hg in SAVR (p = 0.03) while max. Wall shear stress being 12.6 ± 4.59 vs. 10.2 ± 4.42 Pa (p = 0.001).

CONCLUSIONS

Using the presented method for virtual treatment of AS, we were able to reliably predict post-interventional hemodynamics. TAVI and SAVR show similar hemodynamics in a pairwise comparison.

Relative pressure estimation from 4D flow MRI using generalized Bernoulli equation in a phantom model of arterial stenosis

OBJECTIVE

Arterial stenosis is a significant cardiovascular disease requiring accurate estimation of the pressure gradients for determining hemodynamic significance. In this paper, we propose Generalized Bernoulli Equation (GBE) utilizing interpolated-based method to estimate relative pressures using streamlines and pathlines from 4D Flow MRI.

METHODS

4D Flow MRI data in a stenotic phantom model and computational fluid dynamics simulated velocities generated under identical flow conditions were processed by Generalized Bernoulli Equation (GBE), Reduced Bernoulli Equations (RBE), as well as the Simple Bernoulli Equation (SBE) which is clinically prevalent. Pressures derived from 4D flow MRI and noise corrupted CFD velocities were compared with pressures generated directly with CFD as well as pressures obtained using Millar catheters under identical flow conditions.

RESULTS

It was found that SBE and RBE methods underestimated the relative pressure for lower flow rates while overestimating the relative pressure at higher flow rates. Specifically, compared to the reference pressure, SBE underestimated the maximum relative pressure by 22[Formula: see text] for a pulsatile flow data with peak flow rate [Formula: see text] and overestimated by around 40[Formula: see text] when [Formula: see text]. In contrast, for GBE method the relative pressure values were overestimated by 15[Formula: see text] with [Formula: see text]and around 10[Formula: see text] with [Formula: see text].

CONCLUSION

GBE methods showed robust performance to additive image noise compared to other methods. Our findings indicate that GBE pressure estimation over pathlines attains the highest level of accuracy compared to GBE over streamlines, and the SBE and RBE methods.

Treatment Algorithm for Patients With von Willebrand Syndrome Type 2A and Congenital Heart Disease-A Treatment Algorithm May Reduce Perioperative Blood Loss in Children With Congenital Heart Disease

OBJECTIVES

In children with congenital heart disease (CHD), excessive perioperative bleeding is associated with increased morbidity and mortality, thus making adequate perioperative hemostasis crucial. We investigate the prevalence of acquired von Willebrand syndrome type 2A (aVWS) in CHD and develop a treatment algorithm for patients with aVWS and CHD (TAPAC) to reduce perioperative blood loss.

DESIGN

Retrospective cohort study.

SETTING

Single-center study.

PATIENTS

A total of 627 patients with CHD, undergoing corrective cardiac surgery between January 2008 and May 2017.

INTERVENTIONS

The evaluation of perioperative bleeding risk was based on the laboratory parameters von Willebrand factor (VWF) antigen, ristocetin cofactor activity, platelet function analyzer (PFA) closure time adenosine diphosphate, and PFA epinephrine. According to the bleeding risk, treatment was performed with desmopressin or VWF.

MEASUREMENTS AND MAIN RESULTS

aVWS was confirmed in 63.3 %, with a prevalence of 45.5% in the moderate and 66.3 % in the high-risk group. In addition, prevalence increased with ascending peak velocity above the stenosis (v max ) from 40.0% at less than or equal to 3 m/s to 83.3% at greater than 5 m/s. TAPAC reduced mean blood loss by 36.3% in comparison with a historical control cohort ( p < 0.001), without increasing the number of thrombotic or thromboembolic events during the hospital stay. With ascending v max , there was an increase in perioperative blood loss in the historical cohort ( p < 0.001), which was not evident in the TAPAC cohort ( p = 0.230).

CONCLUSIONS

The prevalence of aVWS in CHD seems to be higher than assumed and leads to significantly higher perioperative blood loss, especially at high v max . Identifying these patients through appropriate laboratory analytics and adequate treatment could reduce blood loss effectively.

Computed Tomography-Based Assessment of Transvalvular Pressure Gradient in Aortic Stenosis

Background: In patients with aortic stenosis, computed tomography (CT) provides important information about cardiovascular anatomy for treatment planning but is limited in determining relevant hemodynamic parameters such as the transvalvular pressure gradient (TPG).

Purpose: In the present study, we aimed to validate a reduced-order model method for assessing TPG in aortic stenosis using CT data.

Methods: TPG_CT was calculated using a reduced-order model requiring the patient-specific peak-systolic aortic flow rate (Q) and the aortic valve area (AVA). AVA was determined by segmentation of the aortic valve leaflets, whereas Q was quantified based on volumetric assessment of the left ventricle. For validation, invasively measured TPG_catheter was calculated from pressure measurements in the left ventricle and the ascending aorta. Altogether, 84 data sets of patients with aortic stenosis were used to compare TPG_CT against TPG_catheter.

Results: TPG_catheter and TPG_CT were 50.6 ± 28.0 and 48.0 ± 26 mmHg, respectively (p = 0.56). A Bland–Altman analysis revealed good agreement between both methods with a mean difference in TPG of 2.6 mmHg and a standard deviation of 19.3 mmHg. Both methods showed good correlation with r = 0.72 (p < 0.001).

Conclusions: The presented CT-based method allows assessment of TPG in patients with aortic stenosis, extending the current capabilities of cardiac CT for diagnosis and treatment planning.

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.

Synthetic Database of Aortic Morphometry and Hemodynamics: Overcoming Medical Imaging Data Availability

Modeling of hemodynamics and artificial intelligence have great potential to support clinical diagnosis and decision making. While hemodynamics modeling is extremely time- and resource-consuming, machine learning (ML) typically requires large training data that are often unavailable. The aim of this study was to develop and evaluate a novel methodology generating a large database of synthetic cases with characteristics similar to clinical cohorts of patients with coarctation of the aorta (CoA), a congenital heart disease associated with abnormal hemodynamics. Synthetic data allows use of ML approaches to investigate aortic morphometric pathology and its influence on hemodynamics. Magnetic resonance imaging data (154 patients as well as of healthy subjects) of aortic shape and flow were used to statistically characterize the clinical cohort. The methodology generating the synthetic cohort combined statistical shape modeling of aortic morphometry and aorta inlet flow fields and numerical flow simulations. Hierarchical clustering and non-linear regression analysis were successfully used to investigate the relationship between morphometry and hemodynamics and to demonstrate credibility of the synthetic cohort by comparison with a clinical cohort. A database of 2652 synthetic cases with realistic shape and hemodynamic properties was generated. Three shape clusters and respective differences in hemodynamics were identified. The novel model predicts the CoA pressure gradient with a root mean square error of 4.6 mmHg. In conclusion, synthetic data for anatomy and hemodynamics is a suitable means to address the lack of large datasets and provide a powerful basis for ML to gain new insights into cardiovascular diseases.