- Email: [email protected]
Predicting the outcome of transcatheter mitral valve implantation using image-based computational models
Journal Pre-proof Predicting the outcome of transcatheter mitral valve implantation using image-based computational models Yousef Alharbi, James Otton, David W.M. Muller, Peter Geelan-Small, Nigel H. Lovell, Amr Al Abed, Socrates Dokos PII:
S1934-5925(19)30339-9
DOI:
https://doi.org/10.1016/j.jcct.2019.11.016
Reference:
JCCT 1380
To appear in:
Journal of Cardiovascular Computed Tomograph
Received Date: 18 June 2019 Revised Date:
6 September 2019
Accepted Date: 27 November 2019
Please cite this article as: Alharbi Y, Otton J, Muller DWM, Geelan-Small P, Lovell NH, Al Abed A, Dokos S, Predicting the outcome of transcatheter mitral valve implantation using image-based computational models, Journal of Cardiovascular Computed Tomograph (2019), doi: https:// doi.org/10.1016/j.jcct.2019.11.016. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc. on behalf of Society of Cardiovascular Computed Tomography
Title Predicting the Outcome of Transcatheter Mitral Valve Implantation using Image-based Computational Models Brief title Predicting LVOT pressure gradients post TMVI Authors Yousef Alharbi, M.S. a, b, James Otton, MBBS, Ph.D.c,d, David W.M. Muller, MBBS, M.D. c,e, Peter Geelan-Small, Ph.D. f, Nigel H. Lovell, Ph.D. a, Amr Al Abed, Ph.D. a, Socrates Dokos, Ph.D. a
Authors' affiliations a: Graduate School of Biomedical Engineering, UNSW, Sydney, Australia; b: College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia; c: Victor Chang Cardiac Research Institute, Sydney, Australia; d: Department of Cardiology, Liverpool Hospital, Sydney, Australia; e: Department of Cardiology and Cardiothoracic Surgery, St Vincent’s Hospital, Sydney, Australia; f: Mark Wainwright Analytical Centre, UNSW, Sydney, Australia Authors' current e-mail addresses YA: [email protected] JO: [email protected] DM: [email protected] PG: [email protected] NL: [email protected] AA: [email protected] SD: [email protected] Financial support and disclosures YA is funded by Prince Sattam bin Abdulaziz University, Al-Kharj, Riyadh, Saudi Arabia. JO, PG, NL, AA, and SD: No conflicts of interests to declare. DM consultant/advisory board member to Abbott, Cephea and Medtronic. Address for correspondence Yousef Alharbi The Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia Telephone: +61293859406 Fax: +61296632108 Email: [email protected]
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Abstract
Background The appropriate placement and size selection of mitral prostheses in transcatheter mitral valve implantation (TMVI) is critical, as encroachment on the left ventricular outflow tract (LVOT) may lead to flow obstruction. Recent advances in computed tomography (CT) can be employed for preprocedural planning of mitral prosthetic valve placement. This study aims to develop patient-specific computational fluid dynamics models of the left ventricle (LV) in the presence of a mitral valve prosthesis to investigate blood flow and LVOT pressure gradients during systole.
Methods Patient-specific computational fluid dynamics simulations of TMVI with varied cardiac anatomy and insertion angles were performed (n = 30). Wide-volume full cycle cardiovascular CT images prior to TMVI were used as source anatomical data (n = 6 patients). Blood movement was governed by Navier-Stokes equations and the LV endocardial wall deformation was derived from each patient’s CT images.
Results The computed pressure gradients in the presence of the mitral prosthesis compared well with clinically measured gradients. Analysis of the effects of prosthetic valve angulation, aorto-mitral annular angle, ejection fraction, LV size and new LVOT area (neo-LVOT) after TMVI in silico revealed that the neo-LVOT area (p < 0.001) was the most significant factor affecting LVOT pressure gradient. Angulation of the mitral valve can substantially mitigate LVOT gradient.
Conclusions Computational fluid dynamics simulation is a promising method to aid in pre- TMVI planning and understanding the factors influencing LVOT gradients.
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Keywords Mitral regurgitation; mitral prosthetic valve; computational fluid dynamics; patient-specific simulation; pressure gradients; LVOT obstruction
Abbreviations Mitral Regurgitation (MR) Transcatheter Mitral Valve Implantation (TMVI) Computed Tomography (CT) Left Ventricular Outflow Tract (LVOT) New LVOT area after TMVI (neo-LVOT area) Aorto-Mitral-Annular (AMA) Computational Fluid Dynamics (CFD) Left Ventricle/Ventricular (LV) Left Ventricular Volume (LVV) Ejection Fraction (EF) New York Heart Association (NYHA) Computed Aided Design (CAD)
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1. Introduction Mitral regurgitation (MR) is one of the most common forms of valvular heart disease and has high prevalence in the elderly population (1,2). The rapidly aging population, multiple comorbidities and associated high risk result in the deferral of open surgical repair or replacement in a significant percentage of patients (3-6). For these patients, transcatheter mitral valve (MV) implantation (TMVI) is emerging as an alternative approach, as it eliminates the need for cardio-pulmonary bypass and may afford faster recovery (7). TMVI procedures are not without their challenges, which limit their widespread adaptation. A mismatch between patient anatomy and prosthetic size and positioning may lead to left ventricular outflow tract (LVOT) obstruction post-TMVI (7-9). Subsequent elevation of the LVOT pressure gradient may lead to significant morbidity or death (10) and necessitate prosthetic MV retrieval (11). Therefore, the TMVI procedure cannot be offered to patients who are at a high risk of developing LVOT obstruction. This cohort is significant and constitutes up to 60% of screened patients (12). There are multiple factors that may cause LVOT flow obstruction following TMVI. The aorto-mitralannular (AMA) angle between the planes of the two valves influences the direction and extent of protrusion of the prosthetic MV into the LVOT (13). A second risk factor is small ventricular dimensions, as a larger LV cavity is more likely to accommodate the prosthetic MV (14). Other risk factors that may compromise the LVOT include interventricular septal hypertrophy (8) and MV prosthesis characteristics including geometry and size (14). The current clinical approach to predicting LVOT obstruction is to visualize different placements of the prosthesis prior to TMVI. Computer Aided Design (CAD) based on patient CT images is employed to measure the new LVOT area in the presence of the prosthetic MV, the so-called neoLVOT area (15,16). There is no clinical consensus on the critical value of this area that can predict the risk of LVOT obstruction post-TMVI (17). However, recent studies in patients with hypertrophic cardiomyopathy have proposed a cutoff value of less than 200 mm2 (15,16). Another CT-based study, comparing pre-procedural CAD predicted neo-LVOT areas with post-TMVI measurements, found that LVOT obstruction can be empirically predicted if the predicted neo-LVOT area is ≤ 189.4 mm2
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(16). Additionally, a recent study conducted on 194 patients revealed that simulated neo-LVOT ≤ 170 mm2 predicted the LVOT obstruction with sensitivity and specificity greater than 90% (18). These proposed cutoffs for defining the risk of outflow tract obstruction are solely based on visualizing the LVOT in the presence of the mitral prosthesis and do not account for blood flow dynamics. By knowing the unique interaction between MV prostheses and individual subject anatomies, computational fluid dynamics (CFD) simulations may provide an accurate but accessible approach for exploring procedural outcomes that cannot be quantifiable by clinical estimation or current experimental techniques (19). The main goal of this retrospective pilot study was to develop image-based CFD models to assess LVOT pressure gradients in the presence of a MV prosthesis. The LV endocardial wall movement was reconstructed from pre-procedural CT images of patients admitted for TMVI and used as a basis for the three-dimensional (3D) simulations. We hypothesize that these models are capable of reproducing patient global baseline LV characteristics as well as predicting LVOT obstruction postTMVI, and are therefore a useful tool for investigation of the various factors (procedural, prosthesis, patient) underlying LVOT pressure gradients. 2. Methods 2.1. Study population The study consisted of a cohort of six patients (age 74.6 ± 2.2 years, range 72 to 78 years) enrolled in the Tendyne Global Feasibility Trial (20). Use of the data for this study was approved by the St Vincent’s Hospital Medical Ethics committee. Patient characteristics and MR severity are summarized in Table 1. All patients were diagnosed with a history of MR, and were admitted for TMVI at St Vincent’s Public Hospital, Sydney, Australia. Five patients had successful implantation of the Tendyne MV prosthesis (Abbott Laboratories, USA) with no residual MR or significant elevation of LVOT pressure gradient. In one patient, the attempted TMVI resulted in LVOT obstruction with an elevated pressure gradient leading to compromised hemodynamics. Furthermore, systolic anterior motion of the anterior MV leaflet occurred exacerbating the LVOT pressure gradient. Consequently, the procedure was abandoned, and the prosthetic MV was retrieved successfully through the delivery 5
sheath. The LVOT pressure gradient was measured in all patients during the procedure and immediately following placement of the MV.
2.2. Imaging modalities: Echocardiography and CT Prior to TMVI each patient underwent comprehensive 2- and 3-dimensional transthoracic and transesophageal echocardiography screening looking for any abnormalities in mitral apparatus or systolic motion of the anterior mitral leaflet (Figure 1A-B). CT scans were also conducted pre- and post-procedure for all patients. Images were acquired using 55-80 ml of intravenous iodinated contrast depending on the patient’s body mass (iopromide; Ultravist-350) with continuous acquisition throughout the cardiac cycle using a 320 detector-row CT scanner at 0.5 mm isotropic resolution. For each patient, twenty volumetric datasets were reconstructed at 5% phase increments throughout the cardiac cycle. Based on these scans, the mitral annular contour was defined and the annular area, septal-to-lateral and inter-commissural diameters measured.
2.3. Mitral Valve Prosthesis The Tendyne MV system consists of a self-expanding D-shaped outer housing, containing a tri-leaflet bio-prosthetic valve component. The valve housing sits opposed to the atrial surface of the mitral annulus and is fixed in place by tension from a tether placed at the ventricular apex. This artificial valve is delivered via a sheath inserted trans-apically into the ventricle (21). The successful implantation of the prosthetic MV results in minimal para-valvular leak due to apposition of the housing and the atrial surface of the mitral annulus (7). The central section of the housing sits within the LV cavity and splays the native mitral leaflets apart. Part of the device and the displaced anterior MV leaflet sit within the LVOT (22).
2.4. Valve Simulation Using standard commercially available software (Mimics Innovation Suite segmentation software, v19.0, Materialise, Belgium), the MV prosthesis was virtually implanted within the mitral annulus as
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shown in Figure 2. Left ventricular wall motion and flow through the LVOT was simulated during systole using an image-based computational fluid dynamics approach, generating a predicted LVOT gradient for each patient. Wall movement and fluid dynamics simulations were undertaken using COMSOL Multiphysics finite element solver software (version 5.2, COMSOL AB, Sweden). Each CFD model required approximately 2.5 hours to solve using a standard desktop PC. The simulation methodology is summarized in Figure 3, and more details can be found in the online Appendix. The investigators developing and performing the computer simulations were blinded to the clinically measured LVOT pressure gradients post-TMVI.
2.5. Data and Statistical Analysis In simulations, the LVOT pressure gradient was calculated by taking the peak differential pressure between a plane just below the aortic outlet and another plane 10 mm above the LV apex. Neo-LVOT area at the time of peak LVOT pressure gradient was measured by defining the minimal area of the plane orthogonal to the LVOT centerline (14) close to the tip of prosthetic MV. Clinical data and simulation results were analyzed using R version 3.4.3 statistical software (23). To identify significant factors that influence the LVOT pressure gradient, backwards stepwise variable elimination was conducted using a linear mixed-effects model (lme4 package, version 1.1.15) (24). The AMA, neo-LVOT area, EF, and LVV were defined as fixed effects and patient ID was defined as a random effect. Based on diagnostic residual plots from modelling, the outcome variable, LVOT pressure gradient, and the explanatory variable, neo-LVOT area, were log-transformed for modelling. AMA and LVV were converted into units of radians and liters, respectively, and the EF was scaled by 0.01. The effects package for R (version 4.0.0) was used to estimate the predicted values of the final model with 95% confidence band (25). For all statistical inferences, a p-value of 0.05 was used as the threshold for statistical significance. Following standard statistical practice, the model assumptions of normal distribution and constant variance of residuals were assessed using residual diagnostic plots.
3. Results
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3.1. Patient-Specific Simulations post-TMVI The image-based CFD LV models were able to simulate flow dynamics and intraventricular pressures relative to the outlet from end-diastole to end-systole. Figure 4 illustrates the deformation of the LV chamber for Patient 2 and concurrent blood flow velocity and LV pressure changes at various stages of systole (refer also to the supplementary video). Note that peak blood ejection velocity and intraventricular pressure relative to the outlet occurred at mid-systole. Simulated LVOT pressure gradients in 5 out of 6 patients were not greater than 13.0 mmHg (7.69 ± 3.03) with peak flow velocity at the LVOT ranging from 1 m.s-1 to 1.75 m.s-1 (1.358 ±0.254) (Figure 5). In Patient 6, simulated LVOT gradients were higher than Patients 1-5, exceeding 42 mmHg 142 ms after the start of systole and approximately 20.0 mmHg at late systole (Figure 6 and supplementary video). In addition, in silico results predicted that this patient exhibited higher flow velocity at the outlet compared to other patients. To validate our computational models, we compared clinical and in silico measures of the LVOT pressure gradient (p = 0.31 Wilcoxon test for paired data) for all six patients (Table 2, Figure 7A), also, there was no significant difference when excluding the patient with obstruction (p = 0.19 Wilcoxon test for paired data). The pressure gradients were taking at three clinical measurements of each patient clinical pressure traces during the TMVI attempt (12.3 ± 3.8).
3.2. Simulating the Effects of Insertion Angle For all patients (n = 6) the prosthetic MV orientation angle was varied in silico in the sagittal plane from +5° towards the posterior LV wall, to -15° towards the anterior LV wall in 5° step sizes (n = 30 simulations, Figure 2A and Figure 3C1). The consequent changes in neo-LVOT area and predicted peak LVOT pressure gradients are shown in Figure 7B-D. The simulations predicted a general trend of an increase in neo-LVOT area as the titling angle varied from -15° anterior to +5° posterior (n = 6, Figure 7C). The neo-LVOT area for Patient 6, who exhibited flow obstruction following TMV placement, was lower than patients 1-5 irrespective of the
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tilting angle. In Patient 1, representing the later subgroup of patients, the neo-LVOT area increased from 2.14 cm2 to 4.28 cm2 as the orientation angle was changed from -15° to +5°, and consequently the LVOT pressure gradient dropped from 33.5 mmHg to 11 mmHg (n = 6, Figure 7D). On the other hand, Patient 6 was more sensitive to a change in the prosthetic MV tilting angle in the 5° (anterior) to +5° (posterior) range. The neo-LVOT area increased from 1.62 cm2 at -5° to 2.4 cm2 at +5°. The relative change in neo-LVOT area was similar to the other patients at insertion angles of 5° and 0°, but the corresponding magnitude of change in LVOT pressure gradient was larger than the other patients. Tilting the prosthesis towards the LV posterior wall increased the neo-LVOT area giving more space for the blood to flow smoothly into the aorta, resulting in a peak LVOT pressure gradient drop below the critical gradient for obstruction (< 30 mmHg). The AMA angle in the patients of this study was 136.4° ± 16.5° (range 114.5°-150°). A more acute angle is associated with greater protrusion of the MV prosthesis into the LVOT, which may reduce the LVOT area and impede blood flow into the systemic circulation (13). Figure 8 shows an obvious difference in AMA angles of Patients 4 and 6, with less and higher chance of obstruction, respectively. Subsequently, we investigated the relationship between neo-LVOT area in the presence of the prosthetic MV to the AMA, LVV and EF. Our analysis indicates that the neo-LVOT area is significantly influenced by the AMA angle (p < 0.001), consistent with other recent reports (13).
3.3. Predicting Factors Affecting LVOT Pressure Gradients post-TMVI Aggregating in silico predictions from all patients for all prosthetic MV tilting angles, the LVOT pressure gradient and neo-LVOT area showed a non-linear relationship (Figure 7B). Modelling of log-transformed LVOT pressure gradient in a linear mixed model using backwards elimination led to the following explanatory variables being dropped as non-significant: LVV (p = 0.962), EF (p = 0.377) and AMA (p = 0.093). The final model contained only log-transformed neoLVOT area as significant (p < 0.001). The stroke volume, represented by the interaction between EF and LVV, was a non-significant factor. This suggests that the increased risk of LVOT obstruction associated with a small LV cavity is mediated through implications on neo-LVOT dimensions, rather
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than acting as an independent predictor. The final regression equation from the mixed effect model can be used to predict the LVOT pressure gradient:
PG = 73.72
.
(1)
where the units of the neo-LVOT area (A) and LVOT pressure gradient (PG) are cm2 and mmHg, respectively. This regression equation predicts that LVOT pressure gradient increases non-linearly with the reduction of neo-LVOT area (Figure 7B). Based on the above statistical model, the threshold neo-LVOT area at which obstruction (defined as 30 mmHg LVOT pressure gradient) is predicted to occur is 1.72 cm2 (95% confidence interval 1.49 cm2 to 2.02 cm2). It should be noted that the predicted gradient from the simplified equation and CFD simulation are discrepant at smaller and more clinically relevant neo-LVOT areas, less than 2 cm2.
4. Discussion Obstruction of the LVOT remains a major complication and concern of TMVI, precluding implantation in a large percentage of screened patients (12). In the setting of myocardial dysfunction associated with severe MR, even modestly elevated LVOT gradients may have a significantly deleterious outcome. LVOT obstruction can be caused by different factors (13,14,22). Typically, pre-procedural CT has been used to plan TMVI and assess the potential risk following MV replacement (8,13,14,26). One study applied CAD to generate a 3D printed heart model, which was used to simulate different angles of the mitral prosthetic valve placement and approximate the neo-LVOT dimensions (27). A recent study statistically analyzed post-TMVI LVOT pressure gradients and neo-LVOT area to define a safe cutoff neo-LVOT area for predicting obstruction (neo-LVOT area ≤ 189.4 mm2) (16). This empiric value closely approximates our simulation results. Nevertheless, empiric estimation of LVOT obstruction remains imperfect (8) due to the uncertain cardiac phase that is chosen for pre-procedural
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assessment. Improved applicability of TMVI to the clinical population requires both improved accuracy and precision. We present an algorithmic approach for developing image-based patient-specific simulations of LV fluid dynamics to predict pressure gradients in the presence of prosthetic mitral valves. For each patient, the deformation of the LV chamber was simulated based on pre-procedural patient CT images, and fluid dynamics simulations reproduced systolic blood flow towards the LVOT governed by the LV wall deformation. In clinical validation, our simulations correctly predicted significant LVOT obstruction. In addition to simple prediction of post-procedural LVOT gradient, CFD modeling can provide more in-depth understanding of procedural physiological outcome and mitigation strategies by providing time-varying visualization of blood flow behavior after TMVI in a variety of different anatomical settings. Our data indicates that the neo-LVOT area is the prime determinant of post procedural LVOT gradient, but strategies such as altering MV prosthesis angulation along the mitral annular trajectory line, when clinically feasible, may alter the resultant gradient. A simplified equation can predict LVOT gradient in most patients but the simplified prediction and full CFD model become discrepant at smaller neo-LVOT areas, < 2 cm2. Gradient estimation at these smaller neo-LVOT areas is of most critical clinical importance, and further research is required to examine if the full CFD model can be simplified while preserving accuracy. CFD may prove most clinically useful in these small and borderline neo-LOVT areas. Computational simulations are being more frequently applied to structural heart intervention with several studies demonstrating potential utility (28,29). The techniques demonstrated, while complex by the standards of clinical medicine, use standard commercially available or open source software with similar packages available from multiple vendors. Clinical image viewing software now frequently offers simplified CAD, for example allowing the virtual implantation of an aortic or mitral prosthesis, and overlaying the virtual device onto CT images (30). We have previously proposed the use of patient-specific CFD simulations to predict LVOT gradients post-TMVI (19). This study
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details our CFD approach and demonstrates the feasibility of its application to a whole patient cohort. We foresee that a simplified modelling framework based on our workflow (Figure 3) can be introduced into existing clinical software, moving the basis of structural heart planning from observational measurement to more complete physiological simulation and prediction. This study has several important limitations. First, it involved only a small patient cohort, and although a range of LV morphologies were used in the simulation, this represents only a small subset of possible patient anatomies. Further validation is required. Second, modelling, as is always the case, required certain simplifications. A simplified model of the MV prosthesis was used, and the wire struts were discarded to minimize the mesh complexity and reduce computing time. A preliminary simulation indicated that these had minimal effects on predictions. Likewise, motion of the anterior mitral valve leaflet was not modeled. Finally, a fundamental limitation of current modeling approaches is that the patient’s physiological response to the prosthesis is not captured. For example, changes in cardiac output due to the abolition of MR, or over the longer term, LV remodeling, are not represented in current simulations, and may be hard to predict. Clinical assessment will remain key for these considerations.
5. Conclusion The methodology presented provides a computational approach to generate 3D models of LV flow in the presence of mitral valve prostheses. The patient-specific simulations in this study were able to predict pressure gradients at the outflow tract, correctly identifying LVOT obstruction which is predominantly determined by neo-LVOT area. Statistical analysis suggests that a small LV cavity per se is not a risk factor for LVOT obstruction but is so through implications on neo-LVOT dimensions. Implantation strategies including valve angulation may mitigate LVOT obstruction in some patients. Computational modelling is a promising technique that may guide structural heart intervention and allow broader and more certain application of TMVI techniques.
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7. Figure Legends Figure 1: Echocardiography, Valve Placement Visualization, and CT Image post-TMVI from Patient 2. (A) 2D echocardiography (B) 3D echocardiography showing detailed structure of the MV region. Preprocedural visualization of the transcatheter valve placement is conducted using (C) a 2D fourchamber CT image and (D) volume view of the heart. Post-TMVI visualization of prosthesis reconstructed from CT images using in a (E) short-axis view of the LV viewed from the apex, and (F) a four-chamber view showing apicolateral placement of the tether, perpendicular to the true MA plane (31).
Figure 2: Mitral Artificial Valve Alignment. (A) The annular trajectory line oriented perpendicular to the mitral annular (MA) plane. (B) Top view of the transcatheter valve aligned with MA plane. (C) The reconstructed endocardial surfaces after valve registration (ID: 6).
Figure 3: Computational modelling workflow for the patient-specific simulations, including (A1) acquisition of CT images (A2) segmentation, reconstruction and final 3D model of acquired images (A3) valve alignment. Then (B1) calculation of wall movement distance and direction at each point of LV surface (B2) mesh generation (B3) the CFD simulation result. Finally, (C1) investigating the MV prosthetic valve angulation effect and (C2) predicting the neo-LVOT area LVOT gradient relationship using in silico simulation.
Figure 4: Image-based simulation of left ventricular blood dynamics of a representative patient (ID: 2) with no left ventricular outflow tract obstruction post TMVI. Snapshots at various time points during systole illustrating the endocardial surface deformation as well as (A) flow velocity streamlines, and (B) 16
intraventricular pressure. For this simulation, the prosthetic MV was aligned at the standard angle. More dense streamlines in (A) illustrate higher flow magnitudes.
Figure 5: In silico Predictions of Intraventricular Flow and Pressure for Five Patients. (A) Flow velocity and (B) pressure gradients at peak systole. These patients did not exhibit any LVOT pressure gradient abnormality after prosthetic mitral valve placement. For this simulation the transcatheter valve was aligned at the standard angle.
Figure 6: Simulated Fluid Dynamics for a Patient with Outflow Tract Obstruction. This Patient (ID: 6) exhibited LVOT obstruction after placement of the prosthetic mitral valve. Snapshots at various time points during systole illustrating the endocardial surface deformation as well as (A) flow velocity streamlines, and (B) intraventricular pressure. High pressure gradients are obvious in this patient compared to the patients of Figure 5. For this simulation the valve was aligned at the standard angle.
Figure 7: (A) The in silico LVOT pressure gradients agree with intraventricular measurements during the TMVI procedure. (B) A scatter plot of data aggregated from 30 computational simulations (n = 6 patients) at 5 different prosthetic mitral valve insertion angles illustrates the non-linear increase in left ventricular outflow tract (LVOT) with the drop in neo-LVOT area. Resulting fit of the regression equation is overlaid. Image-based computational fluid dynamics was used to predict changes in the (C) neo-LVOT and (D) peak LVOT pressure gradients after turning the prosthesis tip from +5° posterior to -15°.
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Figure 8: AMA Angles of Two Patients. Four-chamber views show the different AMA angles in (A) Patient 4 who did not exhibit LVOT obstruction with the prosthetic MV implanted and (B) Patient 6 who did.
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Table 1: Patient Clinical Details Age (years)
74.6 ± 2.2
Male
(5/6)
Comorbidities Atrial fibrillation
(2/6)
Prior myocardial infarction
(4/6)
NYHA II
(3/6)
III
(3/6)
Mitral Regurgitation Moderate
(1/6)
Severe
(5/6)
LVEF (%)
43.3 ± 9.7
AMA angle (degree)
136.4 ± 16.5
LVV at end-systole (mL)
144.8 ± 29.1
Values are average ± standard deviation. AMA = Aorto-mitral-annular; LVEF = Left ventricular ejection fraction; LVV = Left ventricular volume; NYHA = New York Heart Association.
Table 2: Patient Left Ventricular Characteristics
Patient
EF (%)
LVV (ml) at end-systole
Peak LVOT Pressure Gradient (mmHg)
1
42.1
160.3
In silico 12.9
Clinical
2
39.4
162.6
6.8
9.7 ± 3.8
3
41.3
161.0
5.2
2.3 ± 1.0
4
37.0
138.4
7.2
3.7 ± 3.2
5
32.2
178.9
4.5
3.5 ± 2.7
6
48.6
110.4
43.3
45.9 ± 9.8
8.9 ± 2.7
Validating computational predictions against intra-procedural measurements at the clinically adopted valve insertion angle. The peak LVOT pressure gradient was measured clinically by taking the difference between peak systolic LV pressure and the peak central aortic pressure, and in silico by calculating the peak pressure differential between a plane just below the aortic outlet and a plane 10 mm superior to the LV apex. EF = Left ventricular ejection fraction; LVOT = Left ventricular outflow tract; LVV = Left ventricular volume.
S1934-5925(19)30339-9
DOI:
https://doi.org/10.1016/j.jcct.2019.11.016
Reference:
JCCT 1380
To appear in:
Journal of Cardiovascular Computed Tomograph
Received Date: 18 June 2019 Revised Date:
6 September 2019
Accepted Date: 27 November 2019
Please cite this article as: Alharbi Y, Otton J, Muller DWM, Geelan-Small P, Lovell NH, Al Abed A, Dokos S, Predicting the outcome of transcatheter mitral valve implantation using image-based computational models, Journal of Cardiovascular Computed Tomograph (2019), doi: https:// doi.org/10.1016/j.jcct.2019.11.016. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc. on behalf of Society of Cardiovascular Computed Tomography
Title Predicting the Outcome of Transcatheter Mitral Valve Implantation using Image-based Computational Models Brief title Predicting LVOT pressure gradients post TMVI Authors Yousef Alharbi, M.S. a, b, James Otton, MBBS, Ph.D.c,d, David W.M. Muller, MBBS, M.D. c,e, Peter Geelan-Small, Ph.D. f, Nigel H. Lovell, Ph.D. a, Amr Al Abed, Ph.D. a, Socrates Dokos, Ph.D. a
Authors' affiliations a: Graduate School of Biomedical Engineering, UNSW, Sydney, Australia; b: College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia; c: Victor Chang Cardiac Research Institute, Sydney, Australia; d: Department of Cardiology, Liverpool Hospital, Sydney, Australia; e: Department of Cardiology and Cardiothoracic Surgery, St Vincent’s Hospital, Sydney, Australia; f: Mark Wainwright Analytical Centre, UNSW, Sydney, Australia Authors' current e-mail addresses YA: [email protected] JO: [email protected] DM: [email protected] PG: [email protected] NL: [email protected] AA: [email protected] SD: [email protected] Financial support and disclosures YA is funded by Prince Sattam bin Abdulaziz University, Al-Kharj, Riyadh, Saudi Arabia. JO, PG, NL, AA, and SD: No conflicts of interests to declare. DM consultant/advisory board member to Abbott, Cephea and Medtronic. Address for correspondence Yousef Alharbi The Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia Telephone: +61293859406 Fax: +61296632108 Email: [email protected]
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Abstract
Background The appropriate placement and size selection of mitral prostheses in transcatheter mitral valve implantation (TMVI) is critical, as encroachment on the left ventricular outflow tract (LVOT) may lead to flow obstruction. Recent advances in computed tomography (CT) can be employed for preprocedural planning of mitral prosthetic valve placement. This study aims to develop patient-specific computational fluid dynamics models of the left ventricle (LV) in the presence of a mitral valve prosthesis to investigate blood flow and LVOT pressure gradients during systole.
Methods Patient-specific computational fluid dynamics simulations of TMVI with varied cardiac anatomy and insertion angles were performed (n = 30). Wide-volume full cycle cardiovascular CT images prior to TMVI were used as source anatomical data (n = 6 patients). Blood movement was governed by Navier-Stokes equations and the LV endocardial wall deformation was derived from each patient’s CT images.
Results The computed pressure gradients in the presence of the mitral prosthesis compared well with clinically measured gradients. Analysis of the effects of prosthetic valve angulation, aorto-mitral annular angle, ejection fraction, LV size and new LVOT area (neo-LVOT) after TMVI in silico revealed that the neo-LVOT area (p < 0.001) was the most significant factor affecting LVOT pressure gradient. Angulation of the mitral valve can substantially mitigate LVOT gradient.
Conclusions Computational fluid dynamics simulation is a promising method to aid in pre- TMVI planning and understanding the factors influencing LVOT gradients.
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Keywords Mitral regurgitation; mitral prosthetic valve; computational fluid dynamics; patient-specific simulation; pressure gradients; LVOT obstruction
Abbreviations Mitral Regurgitation (MR) Transcatheter Mitral Valve Implantation (TMVI) Computed Tomography (CT) Left Ventricular Outflow Tract (LVOT) New LVOT area after TMVI (neo-LVOT area) Aorto-Mitral-Annular (AMA) Computational Fluid Dynamics (CFD) Left Ventricle/Ventricular (LV) Left Ventricular Volume (LVV) Ejection Fraction (EF) New York Heart Association (NYHA) Computed Aided Design (CAD)
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1. Introduction Mitral regurgitation (MR) is one of the most common forms of valvular heart disease and has high prevalence in the elderly population (1,2). The rapidly aging population, multiple comorbidities and associated high risk result in the deferral of open surgical repair or replacement in a significant percentage of patients (3-6). For these patients, transcatheter mitral valve (MV) implantation (TMVI) is emerging as an alternative approach, as it eliminates the need for cardio-pulmonary bypass and may afford faster recovery (7). TMVI procedures are not without their challenges, which limit their widespread adaptation. A mismatch between patient anatomy and prosthetic size and positioning may lead to left ventricular outflow tract (LVOT) obstruction post-TMVI (7-9). Subsequent elevation of the LVOT pressure gradient may lead to significant morbidity or death (10) and necessitate prosthetic MV retrieval (11). Therefore, the TMVI procedure cannot be offered to patients who are at a high risk of developing LVOT obstruction. This cohort is significant and constitutes up to 60% of screened patients (12). There are multiple factors that may cause LVOT flow obstruction following TMVI. The aorto-mitralannular (AMA) angle between the planes of the two valves influences the direction and extent of protrusion of the prosthetic MV into the LVOT (13). A second risk factor is small ventricular dimensions, as a larger LV cavity is more likely to accommodate the prosthetic MV (14). Other risk factors that may compromise the LVOT include interventricular septal hypertrophy (8) and MV prosthesis characteristics including geometry and size (14). The current clinical approach to predicting LVOT obstruction is to visualize different placements of the prosthesis prior to TMVI. Computer Aided Design (CAD) based on patient CT images is employed to measure the new LVOT area in the presence of the prosthetic MV, the so-called neoLVOT area (15,16). There is no clinical consensus on the critical value of this area that can predict the risk of LVOT obstruction post-TMVI (17). However, recent studies in patients with hypertrophic cardiomyopathy have proposed a cutoff value of less than 200 mm2 (15,16). Another CT-based study, comparing pre-procedural CAD predicted neo-LVOT areas with post-TMVI measurements, found that LVOT obstruction can be empirically predicted if the predicted neo-LVOT area is ≤ 189.4 mm2
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(16). Additionally, a recent study conducted on 194 patients revealed that simulated neo-LVOT ≤ 170 mm2 predicted the LVOT obstruction with sensitivity and specificity greater than 90% (18). These proposed cutoffs for defining the risk of outflow tract obstruction are solely based on visualizing the LVOT in the presence of the mitral prosthesis and do not account for blood flow dynamics. By knowing the unique interaction between MV prostheses and individual subject anatomies, computational fluid dynamics (CFD) simulations may provide an accurate but accessible approach for exploring procedural outcomes that cannot be quantifiable by clinical estimation or current experimental techniques (19). The main goal of this retrospective pilot study was to develop image-based CFD models to assess LVOT pressure gradients in the presence of a MV prosthesis. The LV endocardial wall movement was reconstructed from pre-procedural CT images of patients admitted for TMVI and used as a basis for the three-dimensional (3D) simulations. We hypothesize that these models are capable of reproducing patient global baseline LV characteristics as well as predicting LVOT obstruction postTMVI, and are therefore a useful tool for investigation of the various factors (procedural, prosthesis, patient) underlying LVOT pressure gradients. 2. Methods 2.1. Study population The study consisted of a cohort of six patients (age 74.6 ± 2.2 years, range 72 to 78 years) enrolled in the Tendyne Global Feasibility Trial (20). Use of the data for this study was approved by the St Vincent’s Hospital Medical Ethics committee. Patient characteristics and MR severity are summarized in Table 1. All patients were diagnosed with a history of MR, and were admitted for TMVI at St Vincent’s Public Hospital, Sydney, Australia. Five patients had successful implantation of the Tendyne MV prosthesis (Abbott Laboratories, USA) with no residual MR or significant elevation of LVOT pressure gradient. In one patient, the attempted TMVI resulted in LVOT obstruction with an elevated pressure gradient leading to compromised hemodynamics. Furthermore, systolic anterior motion of the anterior MV leaflet occurred exacerbating the LVOT pressure gradient. Consequently, the procedure was abandoned, and the prosthetic MV was retrieved successfully through the delivery 5
sheath. The LVOT pressure gradient was measured in all patients during the procedure and immediately following placement of the MV.
2.2. Imaging modalities: Echocardiography and CT Prior to TMVI each patient underwent comprehensive 2- and 3-dimensional transthoracic and transesophageal echocardiography screening looking for any abnormalities in mitral apparatus or systolic motion of the anterior mitral leaflet (Figure 1A-B). CT scans were also conducted pre- and post-procedure for all patients. Images were acquired using 55-80 ml of intravenous iodinated contrast depending on the patient’s body mass (iopromide; Ultravist-350) with continuous acquisition throughout the cardiac cycle using a 320 detector-row CT scanner at 0.5 mm isotropic resolution. For each patient, twenty volumetric datasets were reconstructed at 5% phase increments throughout the cardiac cycle. Based on these scans, the mitral annular contour was defined and the annular area, septal-to-lateral and inter-commissural diameters measured.
2.3. Mitral Valve Prosthesis The Tendyne MV system consists of a self-expanding D-shaped outer housing, containing a tri-leaflet bio-prosthetic valve component. The valve housing sits opposed to the atrial surface of the mitral annulus and is fixed in place by tension from a tether placed at the ventricular apex. This artificial valve is delivered via a sheath inserted trans-apically into the ventricle (21). The successful implantation of the prosthetic MV results in minimal para-valvular leak due to apposition of the housing and the atrial surface of the mitral annulus (7). The central section of the housing sits within the LV cavity and splays the native mitral leaflets apart. Part of the device and the displaced anterior MV leaflet sit within the LVOT (22).
2.4. Valve Simulation Using standard commercially available software (Mimics Innovation Suite segmentation software, v19.0, Materialise, Belgium), the MV prosthesis was virtually implanted within the mitral annulus as
6
shown in Figure 2. Left ventricular wall motion and flow through the LVOT was simulated during systole using an image-based computational fluid dynamics approach, generating a predicted LVOT gradient for each patient. Wall movement and fluid dynamics simulations were undertaken using COMSOL Multiphysics finite element solver software (version 5.2, COMSOL AB, Sweden). Each CFD model required approximately 2.5 hours to solve using a standard desktop PC. The simulation methodology is summarized in Figure 3, and more details can be found in the online Appendix. The investigators developing and performing the computer simulations were blinded to the clinically measured LVOT pressure gradients post-TMVI.
2.5. Data and Statistical Analysis In simulations, the LVOT pressure gradient was calculated by taking the peak differential pressure between a plane just below the aortic outlet and another plane 10 mm above the LV apex. Neo-LVOT area at the time of peak LVOT pressure gradient was measured by defining the minimal area of the plane orthogonal to the LVOT centerline (14) close to the tip of prosthetic MV. Clinical data and simulation results were analyzed using R version 3.4.3 statistical software (23). To identify significant factors that influence the LVOT pressure gradient, backwards stepwise variable elimination was conducted using a linear mixed-effects model (lme4 package, version 1.1.15) (24). The AMA, neo-LVOT area, EF, and LVV were defined as fixed effects and patient ID was defined as a random effect. Based on diagnostic residual plots from modelling, the outcome variable, LVOT pressure gradient, and the explanatory variable, neo-LVOT area, were log-transformed for modelling. AMA and LVV were converted into units of radians and liters, respectively, and the EF was scaled by 0.01. The effects package for R (version 4.0.0) was used to estimate the predicted values of the final model with 95% confidence band (25). For all statistical inferences, a p-value of 0.05 was used as the threshold for statistical significance. Following standard statistical practice, the model assumptions of normal distribution and constant variance of residuals were assessed using residual diagnostic plots.
3. Results
7
3.1. Patient-Specific Simulations post-TMVI The image-based CFD LV models were able to simulate flow dynamics and intraventricular pressures relative to the outlet from end-diastole to end-systole. Figure 4 illustrates the deformation of the LV chamber for Patient 2 and concurrent blood flow velocity and LV pressure changes at various stages of systole (refer also to the supplementary video). Note that peak blood ejection velocity and intraventricular pressure relative to the outlet occurred at mid-systole. Simulated LVOT pressure gradients in 5 out of 6 patients were not greater than 13.0 mmHg (7.69 ± 3.03) with peak flow velocity at the LVOT ranging from 1 m.s-1 to 1.75 m.s-1 (1.358 ±0.254) (Figure 5). In Patient 6, simulated LVOT gradients were higher than Patients 1-5, exceeding 42 mmHg 142 ms after the start of systole and approximately 20.0 mmHg at late systole (Figure 6 and supplementary video). In addition, in silico results predicted that this patient exhibited higher flow velocity at the outlet compared to other patients. To validate our computational models, we compared clinical and in silico measures of the LVOT pressure gradient (p = 0.31 Wilcoxon test for paired data) for all six patients (Table 2, Figure 7A), also, there was no significant difference when excluding the patient with obstruction (p = 0.19 Wilcoxon test for paired data). The pressure gradients were taking at three clinical measurements of each patient clinical pressure traces during the TMVI attempt (12.3 ± 3.8).
3.2. Simulating the Effects of Insertion Angle For all patients (n = 6) the prosthetic MV orientation angle was varied in silico in the sagittal plane from +5° towards the posterior LV wall, to -15° towards the anterior LV wall in 5° step sizes (n = 30 simulations, Figure 2A and Figure 3C1). The consequent changes in neo-LVOT area and predicted peak LVOT pressure gradients are shown in Figure 7B-D. The simulations predicted a general trend of an increase in neo-LVOT area as the titling angle varied from -15° anterior to +5° posterior (n = 6, Figure 7C). The neo-LVOT area for Patient 6, who exhibited flow obstruction following TMV placement, was lower than patients 1-5 irrespective of the
8
tilting angle. In Patient 1, representing the later subgroup of patients, the neo-LVOT area increased from 2.14 cm2 to 4.28 cm2 as the orientation angle was changed from -15° to +5°, and consequently the LVOT pressure gradient dropped from 33.5 mmHg to 11 mmHg (n = 6, Figure 7D). On the other hand, Patient 6 was more sensitive to a change in the prosthetic MV tilting angle in the 5° (anterior) to +5° (posterior) range. The neo-LVOT area increased from 1.62 cm2 at -5° to 2.4 cm2 at +5°. The relative change in neo-LVOT area was similar to the other patients at insertion angles of 5° and 0°, but the corresponding magnitude of change in LVOT pressure gradient was larger than the other patients. Tilting the prosthesis towards the LV posterior wall increased the neo-LVOT area giving more space for the blood to flow smoothly into the aorta, resulting in a peak LVOT pressure gradient drop below the critical gradient for obstruction (< 30 mmHg). The AMA angle in the patients of this study was 136.4° ± 16.5° (range 114.5°-150°). A more acute angle is associated with greater protrusion of the MV prosthesis into the LVOT, which may reduce the LVOT area and impede blood flow into the systemic circulation (13). Figure 8 shows an obvious difference in AMA angles of Patients 4 and 6, with less and higher chance of obstruction, respectively. Subsequently, we investigated the relationship between neo-LVOT area in the presence of the prosthetic MV to the AMA, LVV and EF. Our analysis indicates that the neo-LVOT area is significantly influenced by the AMA angle (p < 0.001), consistent with other recent reports (13).
3.3. Predicting Factors Affecting LVOT Pressure Gradients post-TMVI Aggregating in silico predictions from all patients for all prosthetic MV tilting angles, the LVOT pressure gradient and neo-LVOT area showed a non-linear relationship (Figure 7B). Modelling of log-transformed LVOT pressure gradient in a linear mixed model using backwards elimination led to the following explanatory variables being dropped as non-significant: LVV (p = 0.962), EF (p = 0.377) and AMA (p = 0.093). The final model contained only log-transformed neoLVOT area as significant (p < 0.001). The stroke volume, represented by the interaction between EF and LVV, was a non-significant factor. This suggests that the increased risk of LVOT obstruction associated with a small LV cavity is mediated through implications on neo-LVOT dimensions, rather
9
than acting as an independent predictor. The final regression equation from the mixed effect model can be used to predict the LVOT pressure gradient:
PG = 73.72
.
(1)
where the units of the neo-LVOT area (A) and LVOT pressure gradient (PG) are cm2 and mmHg, respectively. This regression equation predicts that LVOT pressure gradient increases non-linearly with the reduction of neo-LVOT area (Figure 7B). Based on the above statistical model, the threshold neo-LVOT area at which obstruction (defined as 30 mmHg LVOT pressure gradient) is predicted to occur is 1.72 cm2 (95% confidence interval 1.49 cm2 to 2.02 cm2). It should be noted that the predicted gradient from the simplified equation and CFD simulation are discrepant at smaller and more clinically relevant neo-LVOT areas, less than 2 cm2.
4. Discussion Obstruction of the LVOT remains a major complication and concern of TMVI, precluding implantation in a large percentage of screened patients (12). In the setting of myocardial dysfunction associated with severe MR, even modestly elevated LVOT gradients may have a significantly deleterious outcome. LVOT obstruction can be caused by different factors (13,14,22). Typically, pre-procedural CT has been used to plan TMVI and assess the potential risk following MV replacement (8,13,14,26). One study applied CAD to generate a 3D printed heart model, which was used to simulate different angles of the mitral prosthetic valve placement and approximate the neo-LVOT dimensions (27). A recent study statistically analyzed post-TMVI LVOT pressure gradients and neo-LVOT area to define a safe cutoff neo-LVOT area for predicting obstruction (neo-LVOT area ≤ 189.4 mm2) (16). This empiric value closely approximates our simulation results. Nevertheless, empiric estimation of LVOT obstruction remains imperfect (8) due to the uncertain cardiac phase that is chosen for pre-procedural
10
assessment. Improved applicability of TMVI to the clinical population requires both improved accuracy and precision. We present an algorithmic approach for developing image-based patient-specific simulations of LV fluid dynamics to predict pressure gradients in the presence of prosthetic mitral valves. For each patient, the deformation of the LV chamber was simulated based on pre-procedural patient CT images, and fluid dynamics simulations reproduced systolic blood flow towards the LVOT governed by the LV wall deformation. In clinical validation, our simulations correctly predicted significant LVOT obstruction. In addition to simple prediction of post-procedural LVOT gradient, CFD modeling can provide more in-depth understanding of procedural physiological outcome and mitigation strategies by providing time-varying visualization of blood flow behavior after TMVI in a variety of different anatomical settings. Our data indicates that the neo-LVOT area is the prime determinant of post procedural LVOT gradient, but strategies such as altering MV prosthesis angulation along the mitral annular trajectory line, when clinically feasible, may alter the resultant gradient. A simplified equation can predict LVOT gradient in most patients but the simplified prediction and full CFD model become discrepant at smaller neo-LVOT areas, < 2 cm2. Gradient estimation at these smaller neo-LVOT areas is of most critical clinical importance, and further research is required to examine if the full CFD model can be simplified while preserving accuracy. CFD may prove most clinically useful in these small and borderline neo-LOVT areas. Computational simulations are being more frequently applied to structural heart intervention with several studies demonstrating potential utility (28,29). The techniques demonstrated, while complex by the standards of clinical medicine, use standard commercially available or open source software with similar packages available from multiple vendors. Clinical image viewing software now frequently offers simplified CAD, for example allowing the virtual implantation of an aortic or mitral prosthesis, and overlaying the virtual device onto CT images (30). We have previously proposed the use of patient-specific CFD simulations to predict LVOT gradients post-TMVI (19). This study
11
details our CFD approach and demonstrates the feasibility of its application to a whole patient cohort. We foresee that a simplified modelling framework based on our workflow (Figure 3) can be introduced into existing clinical software, moving the basis of structural heart planning from observational measurement to more complete physiological simulation and prediction. This study has several important limitations. First, it involved only a small patient cohort, and although a range of LV morphologies were used in the simulation, this represents only a small subset of possible patient anatomies. Further validation is required. Second, modelling, as is always the case, required certain simplifications. A simplified model of the MV prosthesis was used, and the wire struts were discarded to minimize the mesh complexity and reduce computing time. A preliminary simulation indicated that these had minimal effects on predictions. Likewise, motion of the anterior mitral valve leaflet was not modeled. Finally, a fundamental limitation of current modeling approaches is that the patient’s physiological response to the prosthesis is not captured. For example, changes in cardiac output due to the abolition of MR, or over the longer term, LV remodeling, are not represented in current simulations, and may be hard to predict. Clinical assessment will remain key for these considerations.
5. Conclusion The methodology presented provides a computational approach to generate 3D models of LV flow in the presence of mitral valve prostheses. The patient-specific simulations in this study were able to predict pressure gradients at the outflow tract, correctly identifying LVOT obstruction which is predominantly determined by neo-LVOT area. Statistical analysis suggests that a small LV cavity per se is not a risk factor for LVOT obstruction but is so through implications on neo-LVOT dimensions. Implantation strategies including valve angulation may mitigate LVOT obstruction in some patients. Computational modelling is a promising technique that may guide structural heart intervention and allow broader and more certain application of TMVI techniques.
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7. Figure Legends Figure 1: Echocardiography, Valve Placement Visualization, and CT Image post-TMVI from Patient 2. (A) 2D echocardiography (B) 3D echocardiography showing detailed structure of the MV region. Preprocedural visualization of the transcatheter valve placement is conducted using (C) a 2D fourchamber CT image and (D) volume view of the heart. Post-TMVI visualization of prosthesis reconstructed from CT images using in a (E) short-axis view of the LV viewed from the apex, and (F) a four-chamber view showing apicolateral placement of the tether, perpendicular to the true MA plane (31).
Figure 2: Mitral Artificial Valve Alignment. (A) The annular trajectory line oriented perpendicular to the mitral annular (MA) plane. (B) Top view of the transcatheter valve aligned with MA plane. (C) The reconstructed endocardial surfaces after valve registration (ID: 6).
Figure 3: Computational modelling workflow for the patient-specific simulations, including (A1) acquisition of CT images (A2) segmentation, reconstruction and final 3D model of acquired images (A3) valve alignment. Then (B1) calculation of wall movement distance and direction at each point of LV surface (B2) mesh generation (B3) the CFD simulation result. Finally, (C1) investigating the MV prosthetic valve angulation effect and (C2) predicting the neo-LVOT area LVOT gradient relationship using in silico simulation.
Figure 4: Image-based simulation of left ventricular blood dynamics of a representative patient (ID: 2) with no left ventricular outflow tract obstruction post TMVI. Snapshots at various time points during systole illustrating the endocardial surface deformation as well as (A) flow velocity streamlines, and (B) 16
intraventricular pressure. For this simulation, the prosthetic MV was aligned at the standard angle. More dense streamlines in (A) illustrate higher flow magnitudes.
Figure 5: In silico Predictions of Intraventricular Flow and Pressure for Five Patients. (A) Flow velocity and (B) pressure gradients at peak systole. These patients did not exhibit any LVOT pressure gradient abnormality after prosthetic mitral valve placement. For this simulation the transcatheter valve was aligned at the standard angle.
Figure 6: Simulated Fluid Dynamics for a Patient with Outflow Tract Obstruction. This Patient (ID: 6) exhibited LVOT obstruction after placement of the prosthetic mitral valve. Snapshots at various time points during systole illustrating the endocardial surface deformation as well as (A) flow velocity streamlines, and (B) intraventricular pressure. High pressure gradients are obvious in this patient compared to the patients of Figure 5. For this simulation the valve was aligned at the standard angle.
Figure 7: (A) The in silico LVOT pressure gradients agree with intraventricular measurements during the TMVI procedure. (B) A scatter plot of data aggregated from 30 computational simulations (n = 6 patients) at 5 different prosthetic mitral valve insertion angles illustrates the non-linear increase in left ventricular outflow tract (LVOT) with the drop in neo-LVOT area. Resulting fit of the regression equation is overlaid. Image-based computational fluid dynamics was used to predict changes in the (C) neo-LVOT and (D) peak LVOT pressure gradients after turning the prosthesis tip from +5° posterior to -15°.
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Figure 8: AMA Angles of Two Patients. Four-chamber views show the different AMA angles in (A) Patient 4 who did not exhibit LVOT obstruction with the prosthetic MV implanted and (B) Patient 6 who did.
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Table 1: Patient Clinical Details Age (years)
74.6 ± 2.2
Male
(5/6)
Comorbidities Atrial fibrillation
(2/6)
Prior myocardial infarction
(4/6)
NYHA II
(3/6)
III
(3/6)
Mitral Regurgitation Moderate
(1/6)
Severe
(5/6)
LVEF (%)
43.3 ± 9.7
AMA angle (degree)
136.4 ± 16.5
LVV at end-systole (mL)
144.8 ± 29.1
Values are average ± standard deviation. AMA = Aorto-mitral-annular; LVEF = Left ventricular ejection fraction; LVV = Left ventricular volume; NYHA = New York Heart Association.
Table 2: Patient Left Ventricular Characteristics
Patient
EF (%)
LVV (ml) at end-systole
Peak LVOT Pressure Gradient (mmHg)
1
42.1
160.3
In silico 12.9
Clinical
2
39.4
162.6
6.8
9.7 ± 3.8
3
41.3
161.0
5.2
2.3 ± 1.0
4
37.0
138.4
7.2
3.7 ± 3.2
5
32.2
178.9
4.5
3.5 ± 2.7
6
48.6
110.4
43.3
45.9 ± 9.8
8.9 ± 2.7
Validating computational predictions against intra-procedural measurements at the clinically adopted valve insertion angle. The peak LVOT pressure gradient was measured clinically by taking the difference between peak systolic LV pressure and the peak central aortic pressure, and in silico by calculating the peak pressure differential between a plane just below the aortic outlet and a plane 10 mm superior to the LV apex. EF = Left ventricular ejection fraction; LVOT = Left ventricular outflow tract; LVV = Left ventricular volume.