The Heart Valve Society

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A Novel Computational Model for Predicting Thrombosis Risk in Bioprosthetic Transcatheter Aortic Valves
Rajat Mittal1, Jung Hee Seo1, Kourosh Shoele1, Jon Resar2.
1Johns Hopkins University, Baltimore, MD, USA, 2Johns Hopkins Hospital, Baltimore, MD, USA.

Background: Transcatheter aortic valve replacement (TAVR) has emerged as an alternative to surgical aortic valve replacement in patients with severe aortic stenosis who are otherwise considered inoperable or at high operative risk. One significant advantage of bioprosthetic valves (including TAVs) over mechanical valves is the much lower incidence of valve thrombosis. This is because bioprosthetic valves mimic the morphology of natural valves and generate transvalvular hemodynamics with low shear stress, stagnation and pressure gradients, which are similar to those corresponding to natural valves. However, recent CT based studies have found higher than expected incidence of reduced leaflet motion(RLM) in multiple bioprostheses types including Portico, Edwards Sapien XT, Sapien 3 and CoreValve. Therapeutic anticoagulation resolves the condition, suggesting that the underlying cause of RLM is leaflet thrombosis. Valve thrombosis is likely initiated by specific factors associated with the implantation and subsequent functioning of TAVRs, and amplified by the coexisting prothrombotic medical conditions of the patient. However, the above studies are too underpowered for any differential analysis and have therefore provided little insight into the possible causal mechanisms.
Methods:
A novel multiphysics computational modeling approach that couples hemodynamics, leaflet dynamics and coagulation biochemistry is used to investigate the effect of valve positioning and orientation on the leaflet dynamics, transvalvular hemodynamics, and thrombogenesis. In this method, the BPV dynamics and transvalvular hemodynamics are resolved by flow-structure interaction (FSI) simulations and these are coupled to a biochemical model of thrombosis which consists of the coagulation cascade, platelet activation, and fibrin polymerization. Simulations have been employed to assess the effect of valve orientation on thrombosis risk. Patient-specific models are derived from the CT-scan data.
Results: The simulation results show that the orientation of the valve with respect to the aortic sinuses noticeably changes the residence time of blood in the sinuses and thus affects the thrombosis risk.
Conclusions:
The multiphysics computational model developed here provides a powerful means of assessing the valve thrombosis risk associated with TAVs.


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