Skip to main content
Research Article
Originally Published 26 May 2023
Open Access

Reducing Long‐Term Mortality Post Transcatheter Aortic Valve Replacement Requires Systemic Differentiation of Patient‐Specific Coronary Hemodynamics

Journal of the American Heart Association

Abstract

Background

Despite the proven benefits of transcatheter aortic valve replacement (TAVR) and its recent expansion toward the whole risk spectrum, coronary artery disease is present in more than half of the candidates for TAVR. Many previous studies do not focus on the longer‐term impact of TAVR on coronary arteries, and hemodynamic changes to the circulatory system in response to the anatomical changes caused by TAVR are not fully understood.

Methods and Results

We developed a multiscale patient‐specific computational framework to examine the effect of TAVR on coronary and cardiac hemodynamics noninvasively. Based on our findings, TAVR might have an adverse impact on coronary hemodynamics due to the lack of sufficient coronary blood flow during diastole phase (eg, maximum coronary flow rate reduced by 8.98%, 16.83%, and 22.73% in the left anterior descending, left circumflex coronary artery, and right coronary artery, respectively [N=31]). Moreover, TAVR may increase the left ventricle workload (eg, left ventricle workload increased by 2.52% [N=31]) and decrease the coronary wall shear stress (eg, maximum time averaged wall shear stress reduced by 9.47%, 7.75%, 6.94%, 8.07%, and 6.28% for bifurcation, left main coronary artery, left anterior descending, left circumflex coronary artery, and right coronary artery branches, respectively).

Conclusions

The transvalvular pressure gradient relief after TAVR might not result in coronary flow improvement and reduced cardiac load. Optimal revascularization strategy pre‐TAVR and progression of coronary artery disease after TAVR could be determined by noninvasive personalized computational modeling.

Nonstandard Abbreviations and Acronyms

AS
aortic stenosis
FSI
fluid–structure interaction
LMCA
left main coronary artery
TAVR
transcatheter aortic valve replacement
TAWSS
time averaged wall shear stress

Clinical Perspective

What Is New?

Coronary artery disease is the most common concomitant complication in patients with aortic stenosis, and the adverse effects of transcatheter aortic valve replacement (TAVR) on coronary arteries could have serious longer‐term consequences.
We developed a noninvasive personalized computational framework based on clinical images to describe the effect of (1) TAVR deployment characteristics and anatomical characteristics of post‐TAVR environment on aortic root and coronary hemodynamics and (2) the circulatory function adaptation to post‐TAVR condition on left ventricle workload and coronary flow.

What Are the Clinical Implications?

Diastolic flow might not improve after TAVR: the restriction of blood flow pathways in the aortic root after TAVR limits the optimal delivery of blood flow to the coronary arteries and promotes future coronary artery disease.
Patients with lower coronary ostium height or smaller aortic root size might be at higher risk of reduced coronary flow after TAVR.
Our in‐silico analysis could be used to inform the interventional cardiologist of TAVR deployment details on coronary arteries hemodynamics; the hemodynamic parameters extracted from the computational model can provide crucial insights for optimizing TAVR planning and minimizing the likelihood of coronary artery disease progress.
Transcatheter aortic valve replacement (TAVR) is becoming popular for patients who are of a high, intermediate, or low risk, indicating that it may become the superior treatment modality for aortic stenosis (AS) for the whole risk spectrum and younger populations.1, 2 Although coronary artery disease (CAD) is present in up to two‐thirds of the TAVR population,3 CAD severity assessment is challenging, and the decision for coronary lesion revascularization is currently mainly based on invasive procedures.4 However, invasive coronary access is limited in significant proportion of patients who undergo TAVR, due to anatomical restrictions such as overlap between transcatheter heart valve and the coronary ostia.5 Consequently, this presents a dilemma among patients with post‐TAVR complications, regarding the relative contribution of CAD versus the valve insufficiencies on long‐term symptoms and prognosis, and more studies are needed to understand how TAVR affects coronary hemodynamics.3
After TAVR, immediate increase in coronary flow is expected as a result of reduced afterload and subendocardial compression. However, there is uncertainty regarding coronary flow reserve post‐TAVR, with some studies suggesting that flow recovery occurs instantly after TAVR6 and others suggesting that it is a long‐term event.7 Currently, the decision for revascularization of CAD after TAVR is being made readily through methods that solely focus on obstructed coronary arteries, such as angiography assessment, fractional flow reserve, and instantaneous wave free ratio.3 However, due to the challenges and nonreliability of conventional CAD assessment methods (such as fractional flow reserve and instantaneous wave free ratio) in the population with AS,8, 9 the optimal method and timing of the intervention remain undefined, especially in patients with multivessel CAD, and some studies suggest that incomplete revascularization is associated with increased cardiovascular events.10 In addition, areas of contention and uncertainty remain for the patients with signs of ischemic heart disease because of reduced coronary flow and with no obstructed coronary arteries.11 Therefore, due to the complexity of dual pathology (AS and CAD) and the effect of the TAVR procedure on coronary hemodynamics, it is recommended that the decision for revascularization must be made on a case‐by‐case basis until further trial data.3
Computational simulations are a powerful tool for accurate and early diagnosis of long‐term hemodynamic complications that are difficult or impossible to infer in clinical practice.12, 13 Some technical challenges and concerns of the effect of TAVR on both short‐term and long‐term coronary hemodynamics can be predicted through numerical simulation.3 However, such simulations should be personalized for each patient and be able to capture the effect of global and local hemodynamics on a case‐by‐case basis to provide a clinically meaningful tool based on patient anatomy and valve implantation parameters.14, 15
In this study, we propose a novel strategy to quantitatively investigate the impact of TAVR on coronary artery hemodynamics. We developed a personalized lumped parameter model and computational fluid dynamics framework based on patient‐specific hemodynamic and anatomical parameters to examine the effect of factors such as valve to coronary distance, aortic root diameter, coronary ostium height, and commissural misalignment on coronary artery hemodynamics (Figure 1). We showed that coronary flow improvement depends on post‐TAVR aortic root remodeling and ventricular‐vascular coupling hemodynamics. We quantified the effect of TAVR on coronary flow and cardiac function and investigated the correlation of hemodynamic parameters with the metrics currently used in clinical practice. We used the clinically measured hemodynamic metrics of 31 patients in both pre‐ and post‐TAVR conditions to provide novel hemodynamic analysis and interpretations of clinical data (Figures 2, 3, 4, 5, 6, 7).
image
Figure 1. Cardiac and circulatory function and hemodynamics.
A, Data acquisition. A multiscale computational framework was developed based on noninvasive clinically measured hemodynamic metrics (brachial blood pressure and Doppler echocardiography measurements) and computed tomography imaging to estimate local and global hemodynamics. B, Schematic diagram of patient‐specific lumped parameter model to calculate global hemodynamics. C, Schematic diagram of 3‐dimensional flow model to calculate local hemodynamics. Three‐dimensional reconstructions of the aortic roots and coronary arteries were acquired from CT images of patients, allowing us to study hemodynamics using computational fluid dynamics. CT indicates computed tomography; LAD, left anterior descending coronary artery; LV, left ventricle; and TAVR, transcatheter aortic valve replacement.
image
Figure 2. Flow modeling in patient #26.
A, Blood flow vortical structure in the sinus and coronary ostium during diastole in pre‐ and post‐TAVR states. B, Left and right coronary branches TAWSS during diastole. In patient #26, the restricted gap between stent and the coronary ostium as well as anatomical alterations of aortic root impede the flow reaching coronary arteries. CT indicates computed tomography; LCC, left coronary cusp; NCC, noncoronary cusp; RCC, right coronary cusp; TAVR, transcatheter aortic valve replacement; and TAWSS, time averaged wall shear stress.
image
Figure 3. Example of changes in predicted global hemodynamics before intervention and after TAVR in patient #25.
A, LV workload LV and ascending aorta pressures. B, Changes in predicted coronary flowrate for left anterior descending coronary artery, left circumflex coronary artery, and right coronary artery branches before intervention and after TAVR for both patients. LV indicates left ventricle; and TAVR, transcatheter aortic valve replacement.
image
Figure 4. Changes in coronary circulatory hemodynamics in patients between pre‐ and post‐TAVR (N=31).
A, LAD peak diastolic flow. B, LAD peak systolic flow. C, LCX peak diastolic flow. D, LCX peak systolic flow. E, RCA peak diastolic flow. F, RCA peak systolic flow. LAD indicates left anterior descending coronary artery; LCX, left circumflex coronary artery; RCA, right coronary artery; and TAVR, transcatheter aortic valve replacement.
image
Figure 5. Changes in local hemodynamics in patients between pre‐ and post‐TAVR (N=31).
A, Bifurcation maximum TAWSS. B, Left main coronary maximum TAWSS. C, Left anterior descending coronary artery maximum TAWSS. D, Left circumflex coronary artery maximum TAWSS. E, Right coronary artery maximum TAWSS. TAVR indicates transcatheter aortic valve replacement; and TAWSS, time averaged wall shear stress.
image
Figure 6. Changes in LV workload and clinical assessments of LV in patients between baseline and post‐TAVR (N=31).
A, LV workload. B, Aortic valve mean pressure gradient. C, Ejection fraction. D, Box plots comparing LV workload and clinical parameters pre‐ and post‐TAVR. LV indicates left ventricle; and TAVR, transcatheter aortic valve replacement.
image
Figure 7. Statistics and data plots for parameters (N=31).
A, Scatter plot of LV workload vs mean coronary flow (pre‐TAVR). B, Scatter plot of LV workload vs mean coronary flow (post‐TAVR). C, Scatter plot of valve to left coronary distance vs mean coronary flow (post‐TAVR). D, Scatter plot of valve to right coronary distance vs mean coronary flow (post‐TAVR). E, Scatter plot of left coronary ostium height vs mean coronary flow (post‐TAVR). F, Scatter plot of aortic root diameter vs mean coronary flow (post‐TAVR). G, Scatter plot of LV workload vs pulse pressure. H, Scatter plot of LV workload vs systole pressure. I, Box plots comparing coronary branches TAWSS changes after TAVR for all patients. LAD indicates left anterior descending coronary artery; LCX, left circumflex coronary artery; LMCA, left main coronary artery; LV, left ventricle; RCA, right coronary artery; TAVR, transcatheter aortic valve replacement; and TAWSS, time averaged wall shear stress.

Methods

Patient Selection and Study Design

Among a cohort of 201 patients undergoing TAVR during the study period, 31 patients with severe AS who received TAVR (Table: patients' characteristics) and underwent a follow‐up echo and computed tomography (CT) post‐TAVR were retrospectively selected from anonymized databases between 2020 and 2022 at the Hamilton General Hospital in Hamilton, Ontario, Canada. Patients' selections for post‐TAVR CT were done by the heart team blinded to the study objectives and was based on choosing patients with higher risks of future adverse events, excluding those with either medical (eg, substantial possibility of contrast nephropathy) or social (eg, unable to go or refused to take part) impediments to executing the follow‐up CT. In addition, those treated for surgical aortic bioprostheses degeneration (ie, valve‐in‐valve) and those with unsuccessful devices as per Valve Academic Research Consortium criteria were excluded.16 Other exclusion criteria were any patient data with missing required inputs for the computational modeling. We did not exclude any data based on image quality. All 31 patients had normal or mild CAD (<50% obstruction) interpreted with a >50% threshold to define obstructive lesions, per guidelines.17 All discrepancies were sorted out by communicating with the local investigators. The protocols were reviewed and approved by the Hamilton Integrated Research Ethics Board, and informed consents were collected from all participants. Measurements were obtained according to guidelines including American Heart Association, American College of Cardiology, and American Society of Echocardiography. Data were collected at both pre‐procedure and post‐procedure time points. Results were expressed as mean±SD.
Table . Patients' Characteristics
Patient descriptionPre‐TAVR (n=31, mean±SD)Post‐TAVR (n=31, mean±SD)
Mean age, y77.8±7.7878.3±7.23
Sex(Male: 17; female: 14)(Male: 17; female: 14)
Mean weight, kg88±29.888±28.8
Mean height, cm167.4±11.11167.4±11.11
Society of Thoracic Surgeons score (%)2.8±1.84N/A
Body surface area1.91±0.271.91±0.27
Smokern=6n=6
Prior AFn=4N/A
Previous stroken=3N/A
Arterial hemodynamics
Systolic arterial pressure, mm Hg131±21.35138.7±23.5
Diastolic arterial pressure, mm Hg69.7±9.872±16.48
Coronary artery diseasen=14n=14
Hypertensionn=24n=24
Dyslipidemian=14n=14
Prior coronary angiogramn=5N/A
Coronary angiogram during TAVRn=13n=13
Prior percutaneous coronary interventionn=4N/A
Hemoglobin level, g/dL (day of procedure)132±17132±17
Creatinine, mg/dL (day of procedure)82.71±19.2582.71±19.25
Chronic kidney diseasen=4n=4
Chronic obstructive pulmonary diseasen=4n=4
Anticoagulationn=5n=5
Aortic valve hemodynamics
Aortic valve area, cm2Pre‐TAVR: 0.81±0.19Post‐TAVR: 1.67±0.52
Aortic valve area indexPre‐TAVR: 0.43±0.09Post‐TAVR: 0.88±0.28
Stenotic aortic valve typeTricuspid: 25; Bicuspid: 6N/A
Prosthetic size, mmN/A25±2.5
Prosthetic type
Edwards SAPIEN 3N/An=31
Maximum aortic valve pressure gradient, mm HgPre‐TAVR: 81.37±21.85Post‐TAVR: 28.9±14.9
Mean aortic valve pressure gradient,mm HgPre‐TAVR: 46.1±13.2Post‐TAVR: 16.6±8.18
Maximum aortic valve velocity, m/sPre‐TAVR: 4.47±0.59Post‐TAVR: 2.62±0.65
Doppler velocity indexPre‐TAVR: 0.25±0.05Post‐TAVR: 0.48±0.11
Pre‐dilationn=5N/A
Post‐dilationN/An=4
Left ventricle and atrial hemodynamics
Ejection fraction (%)Pre‐TAVR: 56.8±13Post‐TAVR: 59.8±13
Heart rate, bpmPre‐TAVR: 71±14Post‐TAVR: 72±13
Left ventricle mass indexPre‐TAVR: 94.9±18.55Post‐TAVR: 93.4±26.2
New‐onset AFN/An=4
New left bundle‐branch blockN/An=6
AF indicates atrial fibrillation; and TAVR, transcatheter aortic valve replacement.

Doppler Echocardiography

Doppler echocardiography data included the measurements and reports that were collected at baseline and post‐procedure at different time points. Transthoracic echocardiography or transesophageal echocardiography was performed at 2 time points to collect the stenotic valve and transcatheter valve hemodynamics data: baseline (pre‐TAVR) and 1 year post‐TAVR. This research made use of the same date follow‐up echocardiogram data from the 31 patients who had undergone post‐procedure CT scans at the same time. Echocardiograms and reports were reviewed and analyzed in a blinded fashion by senior cardiologists using OsiriX imaging software (OsiriX version 8.0.2; Pixmeo, Bernex, Switzerland). The following metrics were collected for clinical assessment and computational model: aortic valve area, mean pressure gradient, maximum aortic valve velocity, ejection fraction, Doppler velocity index, forward left ventricular outflow tract stroke volume, left ventricle (LV) outflow tract velocity time integral, LV outflow tract diameter, cardiac cycle time, ejection time, aortic valve effective orifice area, mitral valve effective orifice area, ascending aorta cross‐sectional area, LV outflow tract area, grading of aortic and mitral valves regurgitation severity measured by Doppler echocardiography, as well as brachial systolic and diastolic pressures measured by a sphygmomanometer.

CT Acquisition and Analysis

CT imaging was performed on a GE 320‐detector Revolution scanner (General Electric, Milwaukee, WI) or a Siemens 64‐detector dual‐energy scanner (Somatom Definition Flash; Siemens, Erlangen, Germany) with a retrospective gated acquisition protocol, with 0.625 mm slice thickness, 0.35‐second tube rotation, 0.24:1 pitch, 100 or 120 kV tube voltage based on patient size and iterative reconstruction at 30%. The image acquisitions were chosen at the best diastolic phase of the cardiac cycle (70%–75%). CT images of the patients at the baseline and follow‐up post‐TAVR were used to segment and reconstruct the 3‐dimensional (3D) geometries. Detailed information regarding geometry reconstruction is outlined in Data S1. The CT was performed at 2 time points at the same time of echo data collection: baseline (pre‐TAVR) and around 1 year post‐TAVR. The following parameters were collected from the CT images and reconstructed geometries: left main coronary artery (LMCA) average diameter, left anterior descending (LAD) coronary artery average diameter, left circumflex coronary artery (LCX) coronary artery average diameter, right coronary artery (RCA) average diameter, bifurcation angle, aorta angle, commissural misalignment, aortic root diameter, ascending aorta diameter, coronary height, and valve to coronary distance.18

TAVR Procedure

The TAVR procedure was performed through the transfemoral access as per the common practice with Edwards SAPIEN valve system.19 In certain cases, the native aortic valve was predilated according to the operator's discretion. The choice of transcatheter heart valve size was based on the pre‐TAVR CT measurements of the effective aortic annular diameter and area as well as leaflet calcium volume.20, 21 The present research includes patients who underwent TAVR with balloon expandable Sapien 3 (Edwards Lifesciences Inc., Irvine, CA). The used valve sizes were 23, 26, and 29. Depending on the operator's discretion, balloon post‐dilatation was conducted in chosen circumstances.

Statistical Analysis

Appropriate statistical tests were performed using Jamovi (v.1.8.1.0). Normality of each continuous variable was assessed using the Shapiro–Wilk test (Figure 7; Table). Continuous variables were expressed as mean ± SD, categorical variables were expressed as counts. Correlations, and comparisons between the variables was performed using Pearson's r, Spearman's rho. Paired samples t tests were performed on pre‐ and post‐TAVR variables using Mann–Whitney U or Wilcoxon W depending on normality. Statistical significance was considered when the P value was <0.05. Detailed information regarding normalization of relevant parameters is outlined in Data S1.

Numerical Study

Global Hemodynamics

We developed a noninvasive diagnostic computational‐mechanics framework for complex and mixed valvular, vascular, and ventricle diseases (Figure 1)14 to quantify local and global hemodynamics. The model requires limited input parameters from Doppler echocardiography and blood pressures measured using a sphygmomanometer (Figure S1, Table S1). It is important to note that our proposed method does not need cardiac catheter data to estimate hemodynamics. The developed framework was validated against cardiac catheterization data with a considerable inter‐ and intrapatient variability with a broad range of diseases in a population of 49 patients (with AS).14 Moreover, some of the submodels of the patient‐specific lumped parameter algorithm have been used and validated previously,14, 15, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 with validation against in vivo cardiac catheterization35, 36 in patients with vascular diseases, in vivo magnetic resonance imaging data32 in patients with AS, and in vivo magnetic resonance imaging data37, 38, 39 in patients with mixed valvular diseases and coarctation.

Local Hemodynamic

A computational fluid dynamics and lumped parameter modeling framework was developed to calculate 3D blood flow dynamics in all main coronary artery branches (LMCA, LAD, LCX, and RCA), aortic root, ascending aorta, sinus, and neo‐sinus regions for both pre‐ and post‐TAVR (Figure 1). We used computed tomography images to reconstruct the geometries. This framework is based on lumped parameter modeling and 3D FSI modeling as implemented in open‐source foam‐extend library and validated against in vivo Doppler echocardiography data as explained in Khodaei et al15, 40, 41 and Keshavarz‐Motamed et al.24 Please refer to Figure S1 and Table S1 for all details about our numerical study.

Results

At the baseline (n=31), the peak systole flow was 1.25 mL/s, 1.14 mL/s, and 1.48 mL/s for LAD, LCX, and RCA, respectively, whereas the peak diastole flow in the same arteries was 1.67 mL/s, 1.01 mL/s, and 0.88 mL/s (as shown in Figure 4). Also, maximum time averaged wall shear stress (TAWSS) was 0.95 pa, 1.16 pa, 2.16 pa, 1.61 pa, and 1.75 pa for bifurcation, LMCA, LAD, LCX, and RCA branches, respectively (Figure 5). Furthermore, the average aortic valve mean pressure gradient, ejection fraction, and LV workload were found to be 46.1 mm Hg, 56.8%, and 1.59 J, respectively.

Coronary Blood Flow

As shown in Figure 4, on average, the peak systole flow increased 12.8% (1.25–1.41 mL/s) (W=129; P=0.242), 4.39% (1.14–1.19 mL/s) (W=159.5; P=0.694) and 25.67% (1.48–1.86 mL/s) (t=2.258; P<0.05) for LAD, LCX, and RCA, respectively, whereas the peak diastole flow decreased by 8.98% (1.67–1.52 mL/s) (W=244; P=0.084), 16.83% (1.01–0.84 mL/s) (t=2.193; P<0.05), and 22.73% (0.88–0.68 mL/s) (W=250; P=0.059) for LAD, LCX, and RCA, respectively. Looking at each patient individually (Figures 3, 4), we found distinctive patterns among patients in terms of coronary flow improvements after TAVR.

Coronary Wall Shear Stress

We calculated the TAWSS over the diastole for all patients in pre‐ and post‐TAVR states. On average, TAWSS decreased for 84% of the patients for different coronary branches. As shown in Figures 5 and 7I, maximum TAWSS decreased 9.47% (0.95–0.86 pa) (t=2.3; P<0.05), 7.75% (1.16–1.07 pa) (W=343; P=0.06), 6.94% (2.16–2.01 pa) (t=1.7; P=0.1), 8.07% (1.61–1.48 pa) (t=1.7; P=0.1), and 6.28% (1.75–1.64 pa) (t=1.5; P=0.2) for bifurcation, LMCA, LAD, LCX, and RCA branches, respectively.
We also observed that the distribution of TAWSS and its maximum is very different for each patient and is highly dependent on the geometrical characterization of the coronary walls. For patient #26 as a sample (Figure 2B), the TAWSS ranges from 0.12 pa to 1.5 pa across the coronary arteries with considerable reduction (to a range of 0.1–1.2 pa) followed by TAVR.

Left Ventricle Workload

Despite the significant decrease in aortic valve pressure gradient for all patients after TAVR (Figure 6), the LV workload did not reduce for 54.84% of the patients. As shown in Figure 6, although on average, the aortic valve mean pressure gradient dropped by 64% (46.1–16.6 mm Hg) (t=12.4; P<0.001) after TAVR, the LV workload burden was not removed (2.52% increase [1.59–1.63 J] [W=213; P=0.221] in LV workload) after TAVR. In addition, in 24 patients (77.42%) the LV workload remained above 1 J after TAVR, which is an indicator of overloaded ventricle.14

Correlation Analysis

We examined the correlation between coronary flow and other hemodynamics and geometrical parameters (please see Data S1 for the comprehensive statistical analysis and correlation matrix). As shown in Figure 7A and 7B, we observed a strong correlation between LV workload and mean coronary flow for both pre‐ and post‐TAVR cases (r=0.75; P<0.001 pre‐TAVR and r=0.52; P<0.003 post‐TAVR). Moreover, LV workload was strongly correlated with pulse pressure (r=0.59; P<0.001) and systole blood pressure (r=0.65; P<0.001), as shown in Figures 7G and 7H. This is an interesting finding, as LV workload could be an effective noninvasive metric to predict the oxygen demand of myocardium after TAVR without a need to perform invasive coronary hemodynamic measurements.
Among different geometrical parameters, strong correlations were observed between mean coronary flow and valve to coronary distance, left coronary ostium height, and aortic root diameter with mean coronary flow. As shown in Figure 7E and 7F, mean coronary flow was strongly correlated with coronary ostium height (r=0.79; P<0.001) and aortic root diameter (r=0.58; P<0.001). Moreover, a strong correlation was observed between mean coronary flow and valve to left coronary distance (Figure 7C) (r=0.78; P<0.001). This correlation was less significant for valve to right coronary distance (Figure 7D) (r=0.5; P<0.05). In summary, our findings showed that the restricted gap between the expanded valve and coronary inlet might impede the coronary blood flow. We also found that patients with lower coronary ostium height are at higher risk of reduced coronary flow after TAVR.
Based on the preliminary correlation analysis with all patient‐specific variables against total mean coronary flow, we identified key parameters for multivariate regression as left coronary height, LV workload, aortic root diameter, valve‐to‐left‐coronary distance, and valve‐to‐right‐coronary distance. The overall regression for total mean coronary flow pre‐TAVR was significant (R2=0.67; F(3, 22)=14.6; P<0.001) (Table S2) with the only significant predictor being the LV workload. On the other hand, after TAVR, the fitted model that also included valve‐to‐coronary distances was found to be a better predictor for total mean coronary flow (R2=0.88; F(5, 20)=30; P<0.001) (Table S3) with the strongest predictors being the valve‐to‐left‐coronary distance (F=11.4; P<0.01), LV workload (F=11.4; P<0.01), and the aortic root diameter (F=4.5; P<0.05). The valve‐to‐right‐coronary distance along with the left coronary height did not significantly predict the total mean coronary flow.

Discussion

CAD is present in more than 50% of the patients who undergo TAVR, and the question of whether CAD should be treated in severity before TAVR procedure is still up for debate.42 After TAVR, the risk of death is closely related to the complications and severity of CAD, with a significant increase in all‐cause mortality at 1 year.43 However, there is still no clear evidence‐based recommendation for the risk–benefit ratio of PCI in TAVR population.43 Moreover, the variability in definition of CAD or CAD severity with syntax score across studies makes the clinical end point very subjective.42 Furthermore, most of the studies in literature had <2 years follow‐up and therefore, longer‐term effect of CAD on clinical outcomes of TAVR remains unknown.42
Coronary arteries are supplied with blood mainly during diastole, and recent studies have shown that coronary flow does not change during the wave‐free period of diastole after TAVR.4, 42 The coronary flow has direct impact on plaque progression, and the majority of coronary events post‐TAVR are related to atherosclerotic plaque formation and CAD progression42 (Figure 8). TAVR prosthetic and its influence on natural flow pathways in the sinus of Valsalva is attributed to disturbance of the blood entering the coronary circulation. The complications resulting from this are relatively unknown, and more research is needed. Indeed, quantification of global and local flow is important as it helps to correctly identify complications that exclusive anatomical examinations can overlook. These changes can lead to ischemia in the future and may have gone undetected if a hemodynamic assessment was not done.44 In the present work, there are several findings that should be individually discussed.
image
Figure 8. TAVR and the challenge of reducing the risk of coronary plaque progression over time.
CAD indicates coronary artery disease; and TAVR, transcatheter aortic valve replacement.

Coronary Diastolic Flow Might Not Recover After TAVR

Induced extravascular compressive forces caused by AS reduces the coronary perfusion pressure that is associated with reduced systemic arterial compliance.45 After TAVR, immediate increase of pressure and flow downstream of aortic valve (ascending aorta) is expected as results of AS removal.45 Our results confirm that the expected enhancement in pressure and coronary flow occurs during systole. However, the diastolic flow in coronaries might not improve or may even decrease for many patients. Such decrease in coronary blood flow is associated with reduced capacity to augment myocardial oxygenation, leading to LV dysfunction, increased apoptosis (which is linked to myocardial fibrosis and is an independent indicator of mortality), and sudden death.45, 46

Patients With Lower Valve to Coronary Distance Are at Higher Risk of Reduced Coronary Flow After TAVR

We observed that in addition to the global hemodynamics and aorta pressure, the geometrical details including the valve to aorta distance, valve to coronary distance, implantation depth, and aortic root shape drastically affect the local hemodynamics before reaching coronary ostium (Figure 7). Although the focus of recent studies has been on the risk assessment for coronary obstruction,47 our findings suggest that if the distance between stent and coronary ostium is restricted (even if not fully obstructed) after TAVR, the coronary flow will be impeded. On the other hand, although the number of patients who need a second TAVR is increasing as the procedure expands toward younger patients, the true risk of coronary obstruction is not known yet.48 This means the proper coronary filling mechanism will be more complicated in these populations with higher risk of mortality.

Coronary Arteries Might Be at Higher Risk of Plaque Progression After TAVR

Another interesting finding of this study was that TAVR might reduce the wall shear stress for the coronary arteries during diastole due to the reduced blood flow. This makes the coronary arteries more susceptible to atherosclerosis, due to the low wall shear stress‐induced inflammatory activation of endothelium mainly at the inner bend of curved arteries, ostia of branches, and lateral walls of bifurcations.49
The progression mechanism of AS is very similar to that of atherosclerosis in CAD, and there is a high cooccurrence of both diseases in the same patient.50 AS and CAD share several important cellular mechanisms including lipid deposition, inflammatory cell infiltration, cytokine release, and calcification,51, 52 which explains why CAD is present in ≈50% of the patients with AS.42 Our study revealed that the reduction of TAWSS for majority of patients during diastole might increase the current existing risk of CAD through enlargement of plaque area, increased plaque eccentricity, and reduced vessel area.53

Left Coronary Bifurcation Could Be at Higher Risk of Plaque Progression After TAVR

Coronary artery bifurcation has a unique local flow and wall shear stress condition because of patient‐specific anatomy of main and side branches.54 The unfavorable hemodynamic alterations at the branch points of bifurcation followed by reduced shear stress could promote endothelial cell dysfunction, which is attributed to the progression of atherosclerosis.54 It has been established that there is a strong association between biomarkers for vascular disease and flow patterns.55 Interestingly, our results showed that for all patients, regardless of flow alterations after TAVR, coronary bifurcation is exposed to a considerably lower wall shear stress region in comparison to right coronaries or other branches (which might contribute to plaque progression). Previous studies noticed that within the coronary vascular system, plaque was mainly found in areas of flow divergence (such as bifurcation) and speculated that distinctive wall shear stress profiles may be the cause of this plaque concentration.56 This is a critical concern for TAVR patients as there is a high risk of restenosis for bifurcation lesions after percutaneous coronary intervention.

In Some Patients: No Improvement in Left Ventricular Hemodynamics After TAVR

LV workload is an effective metric for LV functional assessment that is closely dependent to the coronary perfusion and the coronary flow.14 Increased LV workload promoted cardiac remodeling with an increase in myocardial oxygen demand, which makes myocardium vulnerable to ischemia.57 AS increases the LV workload in order to compensate for the reduced ejected flow, and after TAVR, a reduced LV workload is expected.14 However, our results showed that the reduction in aortic valve pressure gradient post‐TAVR was not associated with reduced LV workload for a large number of patients. Indeed, either a single pathology or an amalgamation of pathologies following TAVR procedure could contribute to an increased LV workload, including worsening or emerging of mitral valve regurgitation, paravalvular leakage, hypertension, and heart failure as well as increased forward LV stroke volume. Among 31 patients in this study, the LV workload increased post‐TAVR for 17 patients. In 82% of these patients, worsening mitral valve regurgitation (from mild to moderate) and increased forward LV stroke volume or increased blood pressure (systole and diastole) or presence of paravalvular leakage were responsible for a rise in LV workload. As 1 example, in patient #4, presence of moderate mitral regurgitation and paravalvular leakage, increased systolic blood pressure, and increased forward stroke volume caused a rise in LV workload from 1.23 J (pre‐TAVR) to 2.23 J (post‐TAVR).
Another interesting finding of our study was that the coronary flow was correlated with LV workload for both pre‐ and post‐TAVR states. Although we have previously shown the usefulness of LV workload metric for patients undergoing TAVR,14 this study demonstrated the significance of the interplay between LV workload and coronary flow. Our findings are indeed in line with the critical role of effective coronary circulation for cardio protection, as the reduction in coronary blood flow will lead to myocardial infarction over time.58

Conclusions

An optimal TAVR procedure strategy is subject specific, and there are several factors that affect the coronary hemodynamics, including the global hemodynamic and circulatory system alterations, aortic root and aortic valve anatomical specifications, coronary height, valve to coronary distance, and prosthetic overexpansion. The confined space between the valve stent and coronary ostium could obstruct the optimal flow movement toward coronary arteries and is associated with increased myocardium LV workload. The findings of this study suggest that special consideration should be paid during TAVR to the patients with lower coronary height or smaller aortic root, as these patients are at higher risk of reduced coronary flow and long‐term CAD. Personalized computational simulations can provide virtual experiments to guide the interventional cardiologist for optimal decision planning. The developed framework in this work is just such a tool to improve the clinical outcomes and guiding interventions for patients who receive TAVR and might be at risk of CAD over the course of time.

Limitations

This study was performed on 31 patients who underwent TAVR in both pre‐ and post‐intervention states. Future studies must be conducted on a larger population of such patients to further confirm the clinical findings of this study. Another limitation of this study was that it focused only on the Edwards Sapien 3 balloon‐expandable valve and did not consider other valve types. This means that the investigation of design factors was limited to only 1 type of valve. In the future, research will be conducted to broaden the scope of the study by including a wider range of self‐expandable and balloon‐expandable valves. This will enable a more thorough examination of the effect of design factors on hemodynamic outcomes. Although we have reported all the relevant periprocedural complications, our study did not focus specifically on follow‐up clinical outcomes such as mortality or myocardial infarction. Future study should investigate whether the simulated coronary blood flow (considering different geometries96) translates into impact on clinical outcomes such as mortality or other adverse events.

Sources of Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN‐2017‐05349). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.

Disclosures

None.

Acknowledgments

The authors are highly thankful for the great comments of the 2 anonymous reviewers that helped us improve the quality of this article.

Footnotes

This article was sent to Amgad Mentias, MD, Associate Editor, for review by expert referees, editorial decision, and final disposition.
Supplemental Material is available at Supplemental Material
For Sources of Funding and Disclosures, see page 14.

Supplemental Material

File (jah38462-sup-0001-supinfo.pdf)
Tables S1–S3
Data S1
Figure S1
References14, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96

REFERENCES

1.
Leon MB, Smith CR, Mack MJ, Makkar RR, Svensson LG, Kodali SK, Thourani VH, Tuzcu EM, Miller DC, Herrmann HC. Transcatheter or surgical aortic‐valve replacement in intermediate‐risk patients. N Engl J Med. 2016;374:1609–1620.
2.
Popma JJ, Deeb GM, Yakubov SJ, Mumtaz M, Gada H, O'Hair D, Bajwa T, Heiser JC, Merhi W, Kleiman NS, et al. Transcatheter aortic‐valve replacement with a self‐expanding valve in low‐risk patients. N Engl J Med. 2019;380:1706–1715.
3.
Patel KP, Michail M, Treibel TA, Rathod K, Jones DA, Ozkor M, Kennon S, Forrest JK, Mathur A, Mullen MJ, et al. Coronary revascularization in patients undergoing aortic valve replacement for severe aortic stenosis. J Am Coll Cardiol Intv. 2021;14:2083–2096.
4.
Vendrik J, Ahmad Y, Eftekhari A, Howard JP, Wijntjens GWM, Stegehuis VE, Cook C, Terkelsen CJ, Christiansen EH, Koch KT, et al. Long‐term effects of transcatheter aortic valve implantation on coronary hemodynamics in patients with concomitant coronary artery disease and severe aortic stenosis. J Am Heart Assoc. 2020;9:e015133.
5.
Ochiai T, Chakravarty T, Yoon S‐H, Kaewkes D, Flint N, Patel V, Mahani S, Tiwana R, Sekhon N, Nakamura M, et al. Coronary access after TAVR. J Am Coll Cardiol Intv. 2020;13:693–705.
6.
Wiegerinck EMA, van de Hoef TP, Rolandi MC, Yong Z, van Kesteren F, Koch KT, Vis MM, de Mol BAJM, Piek JJ, Baan J. Impact of aortic valve stenosis on coronary hemodynamics and the instantaneous effect of transcatheter aortic valve implantation. Circ: Cardiovasc Interv. 2015;8:e002443.
7.
Rolandi MC, Wiegerinck EMA, Casadonte L, Yong Z‐Y, Koch KT, Vis M, Piek JJ, Baan J, Spaan JAE, Siebes M. Transcatheter replacement of stenotic aortic valve normalizes cardiac–coronary interaction by restoration of systolic coronary flow dynamics as assessed by wave intensity analysis. Circ Cardiovasc Interv. 2016;9:e002356.
8.
Pesarini G, Scarsini R, Zivelonghi C, Piccoli A, Gambaro A, Gottin L, Rossi A, Ferrero V, Vassanelli C, Ribichini F. Functional assessment of coronary artery disease in patients undergoing transcatheter aortic valve implantation. Circ Cardiovasc Interv. 2016;9:e004088.
9.
Scarsini R, Pesarini G, Zivelonghi C, Piccoli A, Ferrero V, Lunardi M, Barbierato M, Caprioglio F, Vassanelli C, Ribichini F. Coronary physiology in patients with severe aortic stenosis: comparison between fractional flow reserve and instantaneous wave‐free ratio. Int J Cardiol. 2017;243:40–46.
10.
Faroux L, Campelo‐Parada F, Munoz‐Garcia E, Nombela‐Franco L, Fischer Q, Donaint P, Serra V, Veiga G, Gutiérrez E, Vilalta V, et al. Procedural characteristics and late outcomes of percutaneous coronary intervention in the workup pre‐TAVR. J Am Coll Cardiol Intv. 2020;13:2601–2613.
11.
Bairey Merz CN, Pepine CJ, Walsh MN, Fleg JL, Camici PG, Chilian WM, Clayton JA, Cooper LS, Crea F, Di Carli M, et al. Ischemia and no obstructive coronary artery disease (INOCA). Circulation. 2017;135:1075–1092.
12.
Keshavarz‐Motamed Z, Del Alamo JC, Bluestein D, Edelman ER, Wentzel JJ. Editorial: novel methods to advance diagnostic and treatment value of medical imaging for cardiovascular disease. Front Bioeng Biotechnol. 2022;10:987326. Accessed March 15, 2023. https://www.frontiersin.org/articles/10.3389/fbioe.2022.987326
13.
Kadem M, Garber L, Abdelkhalek M, Al‐Khazraji BK, Keshavarz‐Motamed Z. Hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. IEEE Rev Biomed Eng. 2023;16:403–423.
14.
Keshavarz‐Motamed Z. A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases. Sci Rep. 2020;10:1–19.
15.
Khodaei S, Henstock A, Sadeghi R, Sellers S, Blanke P, Leipsic J, Emadi A, Keshavarz‐Motamed Z. Personalized intervention cardiology with transcatheter aortic valve replacement made possible with a non‐invasive monitoring and diagnostic framework. Sci Rep. 2021;11:10888.
16.
Kappetein AP, Head SJ, Généreux P, Piazza N, van Mieghem NM, Blackstone EH, Brott TG, Cohen DJ, Cutlip DE, van Es G‐A, et al. Updated standardized endpoint definitions for transcatheter aortic valve implantation: the Valve Academic Research Consortium‐2 consensus document††the Valve Academic Research Consortium (VARC) consists of representatives from several independent academic research organization, several surgery and cardiology societies, members of the U.S. Food and Drug Administration (FDA), and several independents experts. However, it is not a society document. Neither the societies nor the FDA have been asked to endorse the document. J Am Coll Cardiol. 2012;60:1438–1454.
17.
Mancini GBJ, Leipsic J, Budoff MJ, Hague CJ, Min JK, Stevens SR, Reynolds HR, O'Brien SM, Shaw LJ, Manjunath CN, et al. CT angiography followed by invasive angiography in patients with moderate or severe ischemia‐insights from the ISCHEMIA trial. JACC Cardiovasc Imaging. 2021;14:1384–1393.
18.
Blanke P, Soon J, Dvir D, Park JK, Naoum C, Kueh S‐H, Wood DA, Norgaard BL, Selvakumar K, Ye J, et al. Computed tomography assessment for transcatheter aortic valve in valve implantation: the Vancouver approach to predict anatomical risk for coronary obstruction and other considerations. J Cardiovasc Comput Tomogr. 2016;10:491–499.
19.
Stortecky S, Buellesfeld L, Wenaweser P, Windecker S. Transcatheter aortic valve implantation: the procedure. Heart. 2012;98:iv44–iv51.
20.
Binder RK, Webb JG, Willson AB, Urena M, Hansson NC, Norgaard BL, Pibarot P, Barbanti M, Larose E, Freeman M, et al. The impact of integration of a multidetector computed tomography annulus area sizing algorithm on outcomes of transcatheter aortic valve replacement. J Am Coll Cardiol. 2013;62:431–438.
21.
Achenbach S, Delgado V, Hausleiter J, Schoenhagen P, Min JK, Leipsic JA. SCCT expert consensus document on computed tomography imaging before transcatheter aortic valve implantation (TAVI)/transcatheter aortic valve replacement (TAVR). J Cardiovasc Comput Tomogr. 2012;6:366–380.
22.
Ben‐Assa E, Brown J, Keshavarz‐Motamed Z, de la Torre Hernandez JM, Leiden B, Olender M, Kallel F, Palacios IF, Inglessis I, Passeri JJ, et al. Ventricular stroke work and vascular impedance refine the characterization of patients with aortic stenosis. Sci Transl Med. 2019;11:11.
23.
Garber L, Khodaei S, Keshavarz‐Motamed Z. The critical role of lumped parameter models in patient‐specific cardiovascular simulations. Arch Computat Methods Eng. 2021;29:2977–3000.
24.
Keshavarz‐Motamed Z, Khodaei S, Rikhtegar Nezami F, Amrute JM, Lee SJ, Brown J, Ben‐Assa E, Camarero TG, Calvo JR, Sellers S, et al. Mixed Valvular disease following transcatheter aortic valve replacement: quantification and systematic differentiation using clinical measurements and image‐based patient‐specific In Silico modeling. J Am Heart Assoc. 2020;9:e015063.
25.
Bahadormanesh N, Tomka B, Kadem M, Khodaei S, Keshavarz‐Motamed Z. An ultrasound‐exclusive non‐invasive computational diagnostic framework for personalized cardiology of aortic valve stenosis. Med Image Anal. 2023;87:102795.
26.
Bahadormanesh N, Tomka B, Khodaei S, Abdelkhalek M, Keshavarz‐Motamed Z. A Doppler‐exclusive non‐invasive computational diagnostic framework for personalized transcatheter aortic valve replacement. Sci Rep. 2023.
27.
Garber L, Khodaei S, Maftoon N, Keshavarz‐Motamed Z. Impact of TAVR on coronary artery hemodynamics using clinical measurements and image‐based patient‐specific in silico modeling. Sci Rep. 2023.
28.
Khodaei S, Abdelkhalek M, Maftoon N, Emadi A, Keshavarz‐Motamed Z. Early detection of risk of neo‐sinus blood stasis post‐transcatheter aortic valve replacement using personalized hemodynamic analysis. Structural Heart. 2023:100180.
29.
Baiocchi M, Barsoum S, Khodaei S, de la Torre Hernandez JM, Valentino SE, Dunford EC, MacDonald MJ, Keshavarz‐Motamed Z. Effects of choice of medical imaging modalities on a non‐invasive diagnostic and monitoring computational framework for patients with complex valvular, vascular, and ventricular diseases who undergo transcatheter aortic valve replacement. Front Bioeng Biotechnol. 2021;9:389.
30.
Asaadi M, Mawad W, Djebbari A, Keshavardz‐Motamed Z, Dahdah N, Kadem L. On left ventricle stroke work efficiency in children with moderate aortic valve regurgitation or moderate aortic valve stenosis. Pediatr Cardiol. 2022;43:45–53.
31.
Benevento E, Djebbari A, Keshavarz‐Motamed Z, Cecere R, Kadem L. Hemodynamic changes following aortic valve bypass: a mathematical approach. PLoS One. 2015;10:e0123000.
32.
Keshavarz‐Motamed Z, Garcia J, Gaillard E, Capoulade R, Ven FL, Cloutier G, Kadem L, Pibarot P. Non‐invasive determination of left ventricular workload in patients with aortic stenosis using magnetic resonance imaging and Doppler echocardiography. PLoS One. 2014;9:e86793.
33.
Keshavarz‐Motamed Z, Garcia J, Pibarot P, Larose E, Kadem L. Modeling the impact of concomitant aortic stenosis and coarctation of the aorta on left ventricular workload. J Biomech. 2011;44:2817–2825.
34.
Keshavarz‐Motamed Z, Edelman ER, Motamed PK, Garcia J, Dahdah N, Kadem L. The role of aortic compliance in determination of coarctation severity: lumped parameter modeling, in vitro study and clinical evaluation. J Biomech. 2015;48:4229–4237.
35.
Keshavarz‐Motamed Z, Nezami FR, Partida RA, Nakamura K, Staziaki PV, Ben‐Assa E, Ghoshhajra B, Bhatt AB, Edelman ER. Elimination of transcoarctation pressure gradients has no impact on left ventricular function or aortic shear stress after intervention in patients with mild coarctation. J Am Coll Cardiol Intv. 2016;9:1953–1965.
36.
Sadeghi R, Khodaei S, Ganame J, Keshavarz‐Motamed Z. Towards non‐invasive computational‐mechanics and imaging‐based diagnostic framework for personalized cardiology for coarctation. Sci Rep. 2020;10:9048.
37.
Sadeghi R, Gasner N, Khodaei S, Garcia J, Keshavarz‐Motamed Z. Impact of mixed valvular disease on coarctation hemodynamics using patient‐specific lumped parameter and lattice Boltzmann modeling. Int J Mech Sci. 2022;217:107038.
38.
Sadeghi R, Tomka B, Khodaei S, Garcia J, Ganame J, Keshavarz‐Motamed Z. Reducing morbidity and mortality in patients with coarctation requires systematic differentiation of impacts of mixed valvular disease on coarctation hemodynamics. J Am Heart Assoc. 2022;11:e022664.
39.
Sadeghi R, Tomka B, Khodaei S, Daeian M, Gandhi K, Garcia J, Keshavarz‐Motamed Z. Impact of extra‐anatomical bypass on coarctation fluid dynamics using patient‐specific lumped parameter and lattice Boltzmann modeling. Sci Rep. 2022;12:9718.
40.
Khodaei S, Sadeghi R, Blanke P, Leipsic J, Emadi A, Keshavarz‐Motamed Z. Towards a non‐invasive computational diagnostic framework for personalized cardiology of transcatheter aortic valve replacement in interactions with complex valvular, ventricular and vascular disease. Int J Mech Sci. 2021;202:106506.
41.
Khodaei S, Garber L, Bauer J, Emadi A, Keshavarz‐Motamed Z. Long‐term prognostic impact of paravalvular leakage on coronary artery disease requires patient‐specific quantification of hemodynamics. Sci Rep. 2022;12:21357.
42.
Faroux L, Guimaraes L, Wintzer‐Wehekind J, Junquera L, Ferreira‐Neto AN, del Val D, Muntané‐Carol G, Mohammadi S, Paradis J‐M, Rodés‐Cabau J. Coronary artery disease and transcatheter aortic valve replacement: JACC state‐of‐the‐art review. J Am Coll Cardiol. 2019;74:362–372.
43.
D'Ascenzo F, Verardi R, Visconti M, Conrotto F, Scacciatella P, Dziewierz A, Stefanini GG, Paradis J‐M, Omedè P, Kodali S, et al. Independent impact of extent of coronary artery disease and percutaneous revascularisation on 30‐day and one‐year mortality after TAVI: a meta‐analysis of adjusted observational results. EuroIntervention. 2018;14:e1169–e1177.
44.
Farag ES, Vendrik J, van Ooij P, Poortvliet QL, van Kesteren F, Wollersheim LW, Kaya A, Driessen AHG, Piek JJ, Koch KT, et al. Transcatheter aortic valve replacement alters ascending aortic blood flow and wall shear stress patterns: a 4D flow MRI comparison with age‐matched, elderly controls. Eur Radiol. 2019;29:1444–1451.
45.
McConkey HZR, Marber M, Chiribiri A, Pibarot P, Redwood SR, Prendergast BD. Coronary microcirculation in aortic stenosis. Circ Cardiovasc Interv. 2019;12:e007547.
46.
Heusch G. Myocardial ischemia: lack of coronary blood flow, myocardial oxygen supply‐demand imbalance, or what? Am J Phys Heart Circ Phys. 2019;316:H1439–H1446.
47.
Oh J‐H, Kobayashi Y, Kang G, Nishi T, Willemink MJ, Fearon WF, Fischbein M, Fleischmann D, Yeung AC, Kim JB. Distance between valvular leaflet and coronary ostium predicting risk of coronary obstruction during TAVR. IJC Heart Vasc. 2021;37:100917.
48.
Okuno T. Risk of “future” coronary obstruction. J Am Coll Cardiol Intv. 2022;15:725–727.
49.
Cameron JN, Mehta OH, Michail M, Chan J, Nicholls SJ, Bennett MR, Brown AJ. Exploring the relationship between biomechanical stresses and coronary atherosclerosis. Atherosclerosis. 2020;302:43–51.
50.
Carabello BA, Paulus WJ. Aortic stenosis. Lancet. 2009;373:956–966.
51.
Milin AC, Vorobiof G, Aksoy O, Ardehali R. Insights into aortic sclerosis and its relationship with coronary artery disease. J Am Heart Assoc. 2014;3:e001111.
52.
Abdelkhalek M, Daeian M, Chavarria J, Sellers S, Gulsin G, Leipsic J, Sheth T, Keshavarz‐Motamed Z. Patterns and structure of calcification in aortic stenosis. JACC Cardiovasc Imaging. 2023;S1936‐878X(23)00108‐0.
53.
Eshtehardi P, McDaniel MC, Suo J, Dhawan SS, Timmins LH, Binongo JNG, Golub LJ, Corban MT, Finn AV, Oshinsk JN, et al. Association of coronary wall shear stress with atherosclerotic plaque burden, composition, and distribution in patients with coronary artery disease. J Am Heart Assoc. 2012;1:e002543.
54.
Nakazawa G, Yazdani SK, Finn AV, Vorpahl M, Kolodgie FD, Virmani R. Pathological findings at bifurcation lesions: the impact of flow distribution on atherosclerosis and arterial healing after stent implantation. J Am Coll Cardiol. 2010;55:1679–1687.
55.
Methe H, Balcells M, del Carmen AM, Santacana M, Molins B, Hamik A, Jain MK, Edelman ER. Vascular bed origin dictates flow pattern regulation of endothelial adhesion molecule expression. Am J Phys Heart Circ Phys. 2007;292:H2167–H2175.
56.
Martorell J, Santomá P, Kolandaivelu K, Kolachalama VB, Melgar‐Lesmes P, Molins JJ, Garcia L, Edelman ER, Balcells M. Extent of flow recirculation governs expression of atherosclerotic and thrombotic biomarkers in arterial bifurcations. Cardiovasc Res. 2014;103:37–46.
57.
Banerjee P. Heart failure: a story of damage, fatigue and injury? Open Heart. 2017;4:e000684.
58.
Heusch G. The coronary circulation as a target of cardioprotection. Circ Res. 2016;118:1643–1658.
59.
Kim HJ, Vignon‐Clementel IE, Coogan JS, Figueroa CA, Jansen KE, Taylor CA. Patient‐specific modeling of blood flow and pressure in human coronary arteries. Ann Biomed Eng. 2010;38:3195–3209.
60.
Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013;61:2233–2241.
61.
Yin M, Yazdani A, Karniadakis GE. One‐dimensional modeling of fractional flow reserve in coronary artery disease: uncertainty quantification and Bayesian optimization. Comput Methods Appl Mech Eng. 2019;353:66–85.
62.
Li B, Wang W, Mao B, Liu Y. A method to personalize the lumped parameter model of coronary artery. Int J Comput Methods. 2019;16:1842004.
63.
Sankaran S, Esmaily Moghadam M, Kahn AM, Tseng EE, Guccione JM, Marsden AL. Patient‐specific multiscale modeling of blood flow for coronary artery bypass graft surgery. Ann Biomed Eng. 2012;40:2228–2242.
64.
Coogan JS, Humphrey JD, Figueroa CA. Computational simulations of hemodynamic changes within thoracic, coronary, and cerebral arteries following early wall remodeling in response to distal aortic coarctation. Biomech Model Mechanobiol. 2013;12:79–93.
65.
Fossan FE, Sturdy J, Müller LO, Strand A, Bråten AT, Jørgensen A, Wiseth R, Hellevik LR. Uncertainty quantification and sensitivity analysis for computational FFR estimation in stable coronary artery disease. Cardiovasc Eng Technol. 2018;9:597–622.
66.
Tajeddini F, Nikmaneshi MR, Firoozabadi B, Pakravan HA, Tafti SHA, Afshin H. High precision invasive FFR, low‐cost invasive iFR, or non‐invasive CFR?: optimum assessment of coronary artery stenosis based on the patient‐specific computational models. Int J Numer Method Biomed Eng. 2020;36:e3382.
67.
Koo B‐K, Erglis A, Doh J‐H, Daniels DV, Jegere S, Kim H‐S, Dunning A, DeFrance T, Lansky A, Leipsic J, et al. Diagnosis of ischemia‐causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER‐FLOW (Diagnosis Of Ischemia‐Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol. 2011;58:1989–1997.
68.
Mantero S, Pietrabissa R, Fumero R. The coronary bed and its role in the cardiovascular system: a review and an introductory single‐branch model. J Biomed Eng. 1992;14:109–116.
69.
Razminia M, Trivedi A, Molnar J, Elbzour M, Guerrero M, Salem Y, Ahmed A, Khosla S, Lubell DL. Validation of a new formula for mean arterial pressure calculation: the new formula is superior to the standard formula. Catheter Cardiovasc Interv. 2004;63:419–425.
70.
Zhou Y, Kassab GS, Molloi S. On the design of the coronary arterial tree: a generalization of Murray's law. Phys Med Biol. 1999;44:2929–2945.
71.
Duanmu Z, Yin M, Fan X, Yang X, Luo X. A patient‐specific lumped‐parameter model of coronary circulation. Sci Rep. 2018;8:874.
72.
Garcia D, Camici PG, Durand L‐G, Rajappan K, Gaillard E, Rimoldi OE, Pibarot P. Impairment of coronary flow reserve in aortic stenosis. J Appl Physiol. 2009;106:113–121.
73.
Ofili EO, Kern MJ, St Vrain JA, Donohue TJ, Bach R, al‐Joundi B, Aguirre FV, Castello R, Labovitz AJ. Differential characterization of blood flow, velocity, and vascular resistance between proximal and distal normal epicardial human coronary arteries: analysis by intracoronary Doppler spectral flow velocity. Am Heart J. 1995;130:37–46.
74.
Weller HG, Tabor G, Jasak H, Fureby C. A tensorial approach to computational continuum mechanics using object‐oriented techniques. Comput Phys. 1998;12:620–631.
75.
Khodaei S, Fatouraee N, Nabaei M. Numerical simulation of mitral valve prolapse considering the effect of left ventricle. Math Biosci. 2017;285:75–80.
76.
Pakravan HA, Saidi MS, Firoozabadi B. A multiscale approach for determining the morphology of endothelial cells at a coronary artery. Int J Numer Method Biomed Eng. 2017;33:e2891.
77.
Pandey R, Kumar M, Majdoubi J, Rahimi‐Gorji M, Srivastav VK. A review study on blood in human coronary artery: numerical approach. Comput Methods Prog Biomed. 2020;187:105243.
78.
Jasak H, Tuković Z. Automatic Mesh Motion for the Unstructured Finite Volume Method. 2004.
79.
Maldonado N, Kelly‐Arnold A, Vengrenyuk Y, Laudier D, Fallon JT, Virmani R, Cardoso L, Weinbaum S. A mechanistic analysis of the role of microcalcifications in atherosclerotic plaque stability: potential implications for plaque rupture. Am J Phys Heart Circ Phys. 2012;303:H619–H628.
80.
Cardoso L, Kelly‐Arnold A, Maldonado N, Laudier D, Weinbaum S. Effect of tissue properties, shape and orientation of microcalcifications on vulnerable cap stability using different hyperelastic constitutive models. J Biomech. 2014;47:870–877.
81.
Akyildiz AC, Speelman L, van Brummelen H, Gutiérrez MA, Virmani R, van der Lugt A, van der Steen AF, Wentzel JJ, Gijse FJ. Effects of intima stiffness and plaque morphology on peak cap stress. Biomed Eng Online. 2011;10:25.
82.
Maldonado N, Kelly‐Arnold A, Cardoso L, Weinbaum S. The explosive growth of small voids in vulnerable cap rupture; cavitation and interfacial debonding. J Biomech. 2013;46:396–401.
83.
Karimi A, Navidbakhsh M, Shojaei A, Faghihi S. Measurement of the uniaxial mechanical properties of healthy and atherosclerotic human coronary arteries. Mater Sci Eng C. 2013;33:2550–2554.
84.
Kohn JC, Lampi MC, Reinhart‐King CA. Age‐related vascular stiffening: causes and consequences. Front Genet. 2015;06:6.
85.
Morović S, Demarin V. Arterial stiffness and aging. In: Demarin V, ed Mind and Brain: Bridging Neurology and Psychiatry. Springer International Publishing; 2020:129–135.
86.
Shouling W, Cheng J, Shanshan L, Xiaoming Z, Xinyuan Z, Liufu C, Xiang G. Aging, arterial stiffness, and blood pressure association in Chinese adults. Hypertension. 2019;73:893–899.
87.
Tuković Ž, Karač A, Cardiff P, Jasak H, Ivanković A. OpenFOAM finite volume solver for fluid‐solid interaction. Transactions of FAMENA. 2018;42:1–31.
88.
Cardiff P, Demirdžić I. Thirty years of the finite volume method for solid mechanics. Arch Computat Methods Eng. 2021.
89.
Cardiff P, Karač A, De Jaeger P, Jasak H, Nagy J, Ivanković A, Tuković Ž. An open‐source finite volume toolbox for solid mechanics and fluid‐solid interaction simulations. arXiv:180810736 [Physics]. 2018. Accessed March 7, 2019. http://arxiv.org/abs/1808.10736
90.
Simo JC, Hughes TJR. Computational Inelasticity. Springer Science & Business Media; 2006:1–405.
91.
Cardiff P, Demirdžić I. Thirty years of the finite volume method for solid mechanics. Arch Computat Methods Eng. 2021;28:3721–3780.
92.
Degroote J, Bathe K‐J, Vierendeels J. Performance of a new partitioned procedure versus a monolithic procedure in fluid–structure interaction. Comput Struct. 2009;87:793–801.
93.
Tanné D, Kadem L, Rieu R, Pibarot P. Hemodynamic impact of mitral prosthesis‐patient mismatch on pulmonary hypertension: an in silico study. J Appl Physiol (1985). 2008;105:1916–1926.
94.
Stergiopulos N, Meister JJ, Westerhof N. Determinants of stroke volume and systolic and diastolic aortic pressure. Am J Phys Heart Circ Phys. 1996;270:H2050–H2059.
95.
Mynard JP, Davidson MR, Penny DJ, Smolich JJ. A simple, versatile valve model for use in lumped parameter and one‐dimensional cardiovascular models. Int J Numer Method Biomed Eng. 2012;28:626–641.
96.
Shen J, Faruqi AH, Jiang Y, Maftoon N. Mathematical reconstruction of patient‐specific vascular networks based on clinical images and global optimization. IEEE Access. 2021;9:20648–20661.

eLetters(0)

eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.

Information & Authors

Information

Published In

Go to Journal of the American Heart Association
Journal of the American Heart Association
PubMed: 37232234

Versions

You are viewing the most recent version of this article.

History

Received: 4 January 2023
Accepted: 7 April 2023
Published online: 26 May 2023
Published in print: 6 June 2023

Permissions

Request permissions for this article.

Keywords

  1. cardiac fluid dynamics
  2. coronary hemodynamics
  3. global hemodynamics
  4. local fluid dynamics
  5. patient‐specific lumped parameter model
  6. transcatheter aortic valve replacement

Subjects

Authors

Affiliations

Department of Mechanical Engineering McMaster University Hamilton Ontario Canada
School of Biomedical Engineering McMaster University Hamilton Ontario Canada
Mohamed Abdelkhalek, MSc https://orcid.org/0000-0003-3981-9040
School of Biomedical Engineering McMaster University Hamilton Ontario Canada
Nima Maftoon, PhD
Department of Systems Design Engineering University of Waterloo Ontario Canada
Centre for Bioengineering and Biotechnology University of Waterloo Ontario Canada
Department of Mechanical Engineering McMaster University Hamilton Ontario Canada
Department of Electrical and Computer Engineering McMaster University Hamilton Ontario Canada
Department of Mechanical Engineering McMaster University Hamilton Ontario Canada
School of Biomedical Engineering McMaster University Hamilton Ontario Canada
School of Computational Science and Engineering McMaster University Hamilton Ontario Canada

Notes

*
Correspondence to: Zahra Keshavarz‐Motamed, PhD, McMaster University, Department of Mechanical Engineering (Mail to JHE‐310), 1280 Main Street West, Hamilton L8S 4L7, Ontario, Canada. Email: [email protected]

Funding Information

Metrics & Citations

Metrics

Citations

Download Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Select your manager software from the list below and click Download.

  1. A multi-domain lattice Boltzmann method for mesh refinement with curved boundary interfaces, Computer Physics Communications, 313, (109637), (2025).https://doi.org/10.1016/j.cpc.2025.109637
    Crossref
  2. Comparison of blood viscosity models in different degrees of carotid artery stenosis, PeerJ, 13, (e19336), (2025).https://doi.org/10.7717/peerj.19336
    Crossref
  3. A multi-domain lattice Boltzmann mesh refinement method for non-Newtonian blood flow modeling, Physics of Fluids, 37, 1, (2025).https://doi.org/10.1063/5.0241118
    Crossref
  4. Enhancing Medical Imaging with Computational Modeling for Aortic Valve Disease Intervention Planning, Current and Future Trends in Health and Medical Informatics, (19-46), (2023).https://doi.org/10.1007/978-3-031-42112-9_2
    Crossref
  5. Incremental prognostic value of intensity-weighted regional calcification scoring using contrast CT imaging in TAVR, European Heart Journal - Imaging Methods and Practice, 1, 2, (2023).https://doi.org/10.1093/ehjimp/qyad027
    Crossref
  6. Impact of TAVR on coronary artery hemodynamics using clinical measurements and image‐based patient‐specific in silico modeling, Scientific Reports, 13, 1, (2023).https://doi.org/10.1038/s41598-023-31987-w
    Crossref
  7. A Doppler-exclusive non-invasive computational diagnostic framework for personalized transcatheter aortic valve replacement, Scientific Reports, 13, 1, (2023).https://doi.org/10.1038/s41598-023-33511-6
    Crossref
Loading...

View Options

View options

PDF and All Supplements

Download PDF and All Supplements

PDF/EPUB

View PDF/EPUB
Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Personal login Institutional Login
Purchase Options

Purchase this article to access the full text.

Purchase access to this article for 24 hours

Restore your content access

Enter your email address to restore your content access:

Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

Figures

Tables

Media

Share

Share

Share article link

Share

Comment Response