Cardiovascular Fluid Mechanics Laboratory

Predictive Modeling of Transcatheter Heart Valve Replacements

Predictive Modeling of Transcatheter Heart Valve Replacements

Transcatheter aortic valve replacement (TAVR) is a minimally invasive procedure designed to replace stenotic aortic valves and is currently approved for patients at all levels of surgical risk. TAVR is rapidly increasing in its use due to randomized clinical trials showing equivalence to the more traditional highly invasive surgical aortic valve replacement (SAVR). U.S. Food and Drug Administration approval of low surgical risk patients enables treatment of most aortic valve diseased patients, setting the stage for TAVR to become the standard of care. Recent data comparing TAVR to SAVR in low-risk patients showed a decrease in rehospitalization and mortality after 1 ​year, however, these differences were not seen after 2 ​years, with higher incidence of valve thrombosis in the TAVR arm. Valve thrombosis is just one of many adverse outcomes associated with TAVR, with others including but not limited to aortic root rupture, permanent pacemaker implantation (PPI), coronary obstruction (CO), and paravalvular leak (PVL).

Structural and fluid computational modeling have grown in accuracy and ability to model a wide range of valvular diseased states and clinical procedures. Previous studies range from basic native valve mechanics, finite element (FE) simulation of transcatheter heart valve (THV) deployment, computational fluid dynamics (CFD) and fluid-structure interaction (FSI), and use of highly intricate biological material models. These studies have improved our insight into the biomechanics and hemodynamics of various diseased states. However, these studies have not described predictive models for assessing risk of complications in transcatheter heart valve replacement procedures in large cohorts for application in a patient-specific setting.

As a clinician, it is extremely valuable to have knowledge of the outcomes of computational studies involving TAVR to have a greater understanding of its mechanisms from a biomechanics standpoint and to see the usefulness in modeling capabilities toward future discovery and guideline implementation. This is especially true in specific patient morphologies such as severe calcification, bicuspid aortic valve disease, a failed bioprosthesis, and low coronary ostia. The goal of our computational modeling research is to develop validated patient-specific predictive models that can help guide clinical decision making for achieving optimal implantation of THVs while minimizing the risk of complications.

Coronary Obstruction after Transcatheter Aortic Valve Replacement

Coronary obstruction (CO) is a procedural complication of TAVR that has been observed in 0.7% of all TAVR cases. Anatomical predictors of CO include low coronary height, small sinus of Valsalva, female sex, and presence of previous surgical aortic bioprosthesis. According to Ribeiro et al., (2013) even though reported cases of post-TAVR symptomatic CO were rare, life-threatening complications in balloon-expandable valve recipients, women, and patients with previous surgical bioprosthesis were more frequent. Acute and late mortality is reported to be high even after successful treatment in patients with a lower coronary ostium and shallow sinus of Valsalva.

The role of computational modeling in predicting adverse outcomes such as annular rupture, conduction abnormalities, and PVL have been mentioned in the literature. However, predictive computational models on CO have not been described until recently. Computational models account for certain anatomic factors such as calcific lesion size/location, leaflet length, sinus width at coronary ostium, etc. that are not considered in current clinical guidelines. Heitkemper et al. published an article on computational modeling for CO based on computational modeling of TAVR with FE methods. The objective of the model is to predict the closest distance between the coronary ostia and corresponding leaflet cusp after deployment of a TAV device. The model simulates the expansion of an idealized stent (cylinder) to the diameter of a TAV inside a patient-specific geometry that includes the calcification deposits on the leaflets. The closest distance between the native aortic valve leaflet and the tip of the coronary artery is then measured, and the ratio of this distance to the diameter of the corresponding coronary artery ostium is considered a representative measure of the fraction of obstruction. Clinically, coronary artery height and sinus of Valsalva diameter (measured on preoperative CT imaging) are used to predict the risk of CO. Based on the sensitivity/specificity plots obtained from 28 cases retrospectively, a distance from leaflet cusp to coronary ostium (DLC) normalized with coronary artery diameter (DLC/d) parameter value of 0.7 was determined to be the cutoff for risk of CO. The three-dimensional computational model was 38% more effective (based on sensitivity and specificity analyses) at predicting CO than coronary artery height and 58% more effective than sinus of Valsalva diameter.

Candidacy for TAVR is often dependent on the assessment of risk of CO during procedural planning. Therefore, a less sensitive/specific predictor may lead to low CO risk patients not receiving TAVR and high CO risk patients undergoing TAVR. Computational patient specific modeling can help minimize risk of adverse complications and lead to better patient selection for TAVR through accurate assessment of risk of CO.

TAVR in Bicuspid Aortic Valves

Bicuspid aortic valves (BAVs) are known to have increased short-term complications with increased calcium severity, but calcium impact on long-term outcomes is unknown. In the short-term, this has been shown to increase the incidence of aortic root rupture and PVL, but the outcomes have dramatically improved using newer generation valves. Predicting these risk factors from routine clinical imaging is very difficult due to the complex nature of TAVR deployment mechanics and patient specific anatomy.

TAVR in BAVs have observed elliptical stent outcomes, potentially caused by the heavy and asymmetrical calcium distributions. It is unclear how the stent eccentricity impacts long-term leaflet function. The goal of the following study was to investigate the correlation of calcium severity on eccentricity and impact on long-term leaflet durability parameters after TAVR in BAV patients.

23 computed tomography (CT) images of patients with BAV disease evaluated for TAVR were collected and the anatomy was reconstructed in Materialise Mimics and classified based on commissural calcium severity of mild, moderate or severe. Simulation of the SAPIEN 3 TAV deployment (Edwards Lifesciences, Irvine, CA) was performed in Abaqus 2020 corresponding to manufacturer-recommended valve size derived from aortic annulus area using a validated computational technique. Eccentricity of the stent (ratio of maximum to minimum diameter) at the waist region was recorded. The results of the modeling were compared to post-TAVR CT images in 7 cases. The bioprosthetic leaflets of the TAV were pressurized under diastolic pressure conditions and the pinwheeling index of the TAV leaflet was calculated. 

Calcium volume correlated positively with stent eccentricity at the waist region (p<0.01). Mean difference in waist diameter and eccentricity between BAV modeling prediction and post-TAVR CT was 9.2 ± 3.2% and 3.8 ± 5.2% respectively. Significant correlation was found between both eccentricity and leaflet pinwheeling and leaflet stress (p<0.01).

Transcatheter Mitral Valve Replacement

Patients with a failed native or bioprosthetic heart valve in the mitral position who are considered high risk for surgical valve replacement undergo transcatheter mitral valve replacement (TMVR). Left ventricular outflow tract (LVOT) obstruction is a potentially fatal complication after TMVR caused by the protrusion of the native anterior mitral leaflet in the LVOT. LVOT obstruction can be characterized by elevated outflow velocities and increased pressure gradient. Patients who are at high risk of obstruction of LVOT undergo anterior mitral leaflet laceration (LAMPOON technique) to mitigate the risk of obstruction, enabling additional blood flow into the LVOT. Accurate modeling of the risk of LVOT obstruction is critical for appropriately selecting patients who can safely undergo TMVR. Currently, the only way of estimating LVOT obstruction is by using CT imaging which does not account for the displacement of the anterior mitral leaflet and calcification after THV implantation. Computational modeling of TMVR implantation and subsequent investigation of hemodynamics with and without LAMPOON can be used to optimize the technique and achieve excellent clinical outcomes.

Preprocedural cardiac computed tomography (CT) of a patient with severe mitral annular calcification (MAC) who was considered for THV implantation in the mitral position was obtained for analysis under IRB approved protocols. The mitral valve, left ventricle, left atrium, aortic root, and calcium were segmented. The LAMPOON procedure was simulated by removing a line of elements from the middle free edge along the belly of the cusp to the large calcium nodule at the base. Neo-LVOT area was estimated by creating a spline through the neo-LVOT and measuring the minimum area.  This was compared with the simplified method of neo-LVOT obstruction prediction which consists of overlaying a cylinder in the mitral position.  For the 29 mm THV case, a large difference in area of the neo-LVOT (73 mm2 compared to 282 mm2) was observed between the complex finite elemental simulation and the simplified stent deployment, the area was increased to 104 mm2 after LAMPOON however, it is still ~2.7 times smaller than the simplified deployment method.  This is likely due to the simplified deployment not factoring the structural deformation of the anterior cusp, incomplete splaying of the cusp after LAMPOON, and irregular THV expansion caused from severe annular and cusp calcification.  The effect of LAMPOON was further investigated using computation fluid dynamics (CFD) on the post simulation results following methods detailed in a previous study.  The 29 mm THV implantation with and without LAMPOON cases were analyzed. Without LAMPOON the velocities through the neo-LVOT were higher. The pressure gradient between the left ventricle and the sinotubular junction without LAMPOON and with LAMPOON were 156 mmHg and 86 mmHg respectively. These high pressure gradients are consistent with reported clinical measurements in patients with neo-­LVOT areas < 100 mm2. In this case LAMPOON was not sufficient in preventing LVOT obstruction (pressure gradient> 10 mmHg) and there was a significant discrepancy in the neo-LVOT area between the simulated and simplified models with the simulated model predicting a high risk of LVOT obstruction and the simplified model predicting a low risk of LVOT obstruction.

High accuracy modeling can be used in this manner to better predict neo-LVOT area and analyze the patient hemodynamics prior to the clinical procedure. Validation of this methodology through its use in more patients with post operation CT and echocardiography will be extremely valuable. CFD can implement the flow across the cardiac cycle and the geometry of the native or bioprosthetic aortic valve to give temporal information on how LVOT obstruction effects the pressure gradient across the aortic valve. This will give information on what levels of LVOT obstruction may be acceptable in high-surgical risk patients which only have transcatheter treatment options.

Publications & Presentations:

  1. Keshav Kohli, Zhenglun Alan Wei, Vahid Sadri, Andrew Siefert, Philipp Blanke, Emily Perdoncin, Adam Greenbaum, Jaffar M Khan, Robert Lederman, Vasilis Babaliaros, Ajit Yoganathan, John Oshinski, “Assessing the Hemodynamic Impact of Anterior Leaflet Laceration in Transcatheter Mitral Valve Replacement: An in silico Study“, Frontiers in Cardiovascular Medicine, Pages 1225, (2022)
  2. Fateme Esmailie, Atefeh Razavi, Breandan Yeats, Sri Krishna Sivakumar, Huang Chen, Milad Samaee, Imran A. Shah, Alessandro Veneziani, Pradeep Yadav, Vinod H. Thourani, Lakshmi Prasad Dasi, “Biomechanics of Transcatheter Aortic Valve Replacement Complications and Computational Predictive Modeling”, Structural Heart, Volume 6, Issue 2, 2022, 100032, ISSN 2474-8706,
  3. Chen, H., Yeats, B., Swamy, K., Samaee M., Sivakumar S., Esmailie F., Razavi A., Yadav P., Thourani V., Polsani V., Dasi LP, “Image Registration-Based Method for Reconstructing Transcatheter Heart Valve Geometry from Patient-Specific CT scans”. Ann Biomed Eng 50, 805–815 (2022).
  4. Baig, I., Lee, A. J., Brinkman, W., Gopal, A., Dasi, L. P., & Al-Azizi, K. (2022). “Simultaneous Kissing Balloon Inflation of the Transcatheter Aortic Valve Replacement Valve and an Ostial Coronary Stent—A Novel Coronary Protection Technique”. Structural Heart.
  5. Yeats, B.B., Yadav, P.K., Dasi, L.P. et al. “Treatment of Bicuspid Aortic Valve Stenosis with TAVR: Filling Knowledge Gaps Towards Reducing Complications”. Curr Cardiol Rep 24, 33–41 (2022). https://doi.org/10.1007/s11886-021-01617-w
  6. Sivakumar S, Yadav P, Polsani V, et al. “Computational modeling of Coronary Obstruction in valve-in-valve TAVR: Choosing the right virtual valve to coronary distance”. J Am Coll Cardiol. 2022 Mar, 79 (9_Supplement) 778
  7. Yeats, B., Polsani, V., Yadav, P., Thourani, V., & Dasi, L. (2022). “TCT-485 Calcium Severity Increases Stent Eccentricity and Bioprosthetic Leaflet Pinwheeling and Stress Following Simulated TAVR for Bicuspid Aortic Valves”. Journal of the American College of Cardiology, 80(12), B196-B197
  8. Sivakumar, S. K., Akodad, M., Blanke, P., Sellers, S., Leipsic, J., Webb, J., … & Sathananthan, J. (2022). “TCT-405 Predictive Computational Modeling of THV Under-Expansion and Late Balloon Aortic Valvuloplasty to Treat THV Dysfunction”. Journal of the American College of Cardiology, 80(12), B164.
  9. Keshav Kohli, Zhenglun Alan Wei, Vahid Sadri, Tiffany Netto, John Lisko, Adam Greenbaum, Vasilis Babaliaros, John N. Oshinski, Ajit P. Yoganathan “A Simplified In Silico Model of Left Ventricular Outflow in Patients After Transcatheter Mitral Valve Replacement with Anterior Leaflet Laceration” Ann Biomed Eng 49, pages1449–1461 (2021)
  10. Keshav Kohli, Zhenglun Alan Wei, Vahid Sadri, Jaffar M Khan, John C Lisko, Tiffany Netto, Adam B Greenbaum, Philipp Blanke, John N Oshinski, Robert J Lederman, Ajit P Yoganathan, Vasilis C Babaliaros “Dynamic nature of the LVOT following transcatheter mitral valve replacement with LAMPOON: new insights from post-procedure imaging“, European Heart Journal – Cardiovascular Imaging, Volume 23, Issue 5, May 2022, Pages 650–662 (2021)
  11. M. Heitkemper, H. Hatoum, A. Azimian, B. Yeats, J. Dollery, B. Whitson, et al. “Modeling risk of coronary obstruction during transcatheter aortic valve replacement” J Thorac Cardiovasc Surg, 159 (2020), pp. 829-838.e3
  12. Megan Heitkemper, Sri Krishna Sivakumar, Hoda Hatoum, Jennifer Dollery, Scott M Lilly, Lakshmi Prasad Dasi, “Simple 2D anatomical model to predict risk of coronary obstruction during transcatheter aortic valve replacement”, Journal of Thoracic and Cardiovascular Surgery, Feb 2020