Systems and methods for predicting thrombosis for heart valve replacements
Methods for determining likelihood of thrombosis based on patient-specific anatomic, valve, and flow parameters are disclosed herein. Such methods are used to select a transcatheter aortic valve that decreases likelihood of thrombosis after TAVR procedures. The methods correlate a number of fluid flow and geometric parameters such as stasis volume, neo-sinus volume, kinematic viscosity, dynamic viscosity, heart rate, the circulation, ejection time, velocity of the main jet, wall shear stress, total kinetic energy in the neo sinus volume, width of the neo-sinus, height or depth of the neo-sinus, the angle between the velocity direction and the stent of the transcatheter valve, the distance from the tip of the leaflet perpendicular to the leaflet edge and intersecting the sinotubular junction, and the cross-sectional area of the neo-sinus taken from a longitudinal or axial perspective. Such parameters are used to derive empirical or semi-empirical mathematical models to determine the likelihood of thrombosis.
1. A method for predicting the development of thrombosis for a patient planning on receiving a heart valve replacement, the method comprising:
executing, by at least a processor, program code stored in a non-transitory computer-readable-medium to perform a simulation, comprising:
gathering image data representing one or more anatomical parameters of a region of a heart of a patient, wherein the image data comprises three-dimensional shapes of the region of the patient's heart;
obtaining one or more flow parameters based on fluid dynamic simulations of blood flow through the region of the patient's heart comprising a neo-sinus area;
and
using a computational model to quantify a risk that patient will develop thrombosis upon implantation of the heart valve replacement in the patient, wherein the computational model comprises a mathematical equation r norm ˜V·d·cosθ·Ratio˜V·d·cosθ·A NS /A separated representing a fraction of circulation influx into the neo-sinus area based on the one or more anatomical and flow parameters, wherein V is a velocity of a main jet, d is a distance from a tip of a leaflet perpendicular to a leaflet edge and intersecting a sinotubular junction, θ is an angle between a velocity direction and a stent of the heart valve, A NS is a ratio of an area of a neo-sinus opening, T ej is an ejection time, A separated is a separated area, and A c is a cross-sectional area of the neo-sinus taken from a longitudinal or axial perspective.
2. The method of claim 1 , wherein the computational model further comprises numerical simulation comprising computational fluid dynamics, fluid structure interaction models.
3. The method of claim 1 , wherein the computational model further comprises a trained artificial intelligence neural network to determine a likelihood of thrombosis based on the fraction of circulation influx into the neo-sinus area based on the one or more anatomical and flow parameters.
4. The method of claim 1 , wherein the computational model further comprises a machine learning algorithm to obtain the one or more anatomical parameters and the one or more flow parameters.
5. A computer implemented method to evaluate a risk of development of thrombosis for a patient planning on receiving a heart valve implantation, the method comprising:
executing, by at least a processor, program code stored in a non-transitory computer-readable-medium to perform a simulation, comprising:
gathering image data representing one or more anatomical and hemodynamic parameters of a region of a heart of the patient prior to receiving the heart valve implantation, wherein the image data comprises three-dimensional shapes of the region of the patient's heart;
simulating a post-deployment anatomy of the patient's heart corresponding to a selection of the heart valve implantation, based on the image data; and
using a mathematical model to calculate one or more quantities that correlate to the risk of the development of thrombosis post-deployment of the heart valve with specified sensitivity and specificity, wherein the mathematical model requires inputs comprising geometrical parameters obtained based on simulated post-deployment anatomy and the image data, and wherein the one or more quantities comprise percent stasis volumes during systole and during diastole.
6. The computer-implemented method of claim 5 , wherein the image data comprise X-ray imaging data, computed tomography imaging data, magnetic resonance imaging data, and/or ultrasound imaging data.
7. The computer-implemented method of claim 5 , wherein the region of the heart comprises a neo-sinus area.
8. The computer-implemented method of claim 5 , wherein the selection of the heart valve implantation comprises one or more of: size of the heart valve implantation, type of the heart valve implantation, positioning of the heart valve implantation comprising a pitch angle, a yaw angle, and/or a depth.
9. The computer-implemented method of claim 5 , wherein the one or more quantities comprise a stasis volume (SV).
10. The computer-implemented method of claim 5 , wherein the one or more quantities comprise a fluid circulation (Γ).
11. The computer-implemented method of claim 5 , wherein the one or more quantities comprise a total kinetic energy (KE) defined over a neo-sinus volume.
12. The computer-implemented method of claim 5 , wherein the one or more quantities comprise an average wall shear stress (WSS) for near wall stagnation defined over a neo-sinus volume.
13. The computer-implemented method of claim 5 , wherein the one or more quantities comprise a normalized fluid circulation parameter (Γ norm ).
14. The computer-implemented method of claim 5 , wherein the mathematical model is an empirical or a semi-empirical mathematical model.
15. The computer-implemented method of claim 5 , wherein the inputs comprise a neo-sinus volume (NSV), a kinematic viscosity (γ), a dynamic viscosity (μ), a heart rate (HR); an ejection time (T ej ), a velocity of a main jet (V), a width of each of a neo-sinus (w), a height or depth of each neo-sinus (h), an angle between a velocity direction and a stent of a transcatheter valve (Θ), a distance from a tip of a leaflet perpendicular to a leaflet edge and intersecting a sinotubular (STJ) junction (d), a cross-sectional area (A c ), and each of a neo-sinus taken from a longitudinal or axial perspective.
16. The computer-implemented method of claim 15 , further comprising using a trained artificial intelligence model to determine a risk of the development of thrombosis post-deployment of the heart valve based on the inputs.
17. The computer-implemented method of claim 15 , further comprising using a trained machine learning model to determine a risk of the development of thrombosis post-deployment of the heart valve based on the inputs.
18. The computer-implemented method of claim 5 , further comprising using the mathematical model to calculate one or more quantities that correlate to the risk of the development of thrombosis post-deployment of the heart valve with a specified uncertainty.
19. The computer-implemented method of claim 5 , further comprising determining a high risk of the development of thrombosis post-deployment of the heart valve, in response to determining that the calculated one or more quantities are below a cutoff value.
20. A computer implemented method to evaluate a risk of development of thrombosis for a patient planning on receiving a heart valve implantation, the method comprising:
executing, by at least a processor, program code stored in a non-transitory computer-readable-medium to perform a simulation, comprising:
gathering image data representing one or more anatomical and hemodynamic parameters of a region of a heart of the patient prior to receiving the heart valve implantation, wherein the image data comprises three-dimensional shapes of the region of the patient's heart;
simulating a post-deployment anatomy of the patient's heart corresponding to a selection of the heart valve implantation, based on the image data; and
using a mathematical model to calculate one or more quantities that correlate to the risk of the development of thrombosis post-deployment of the heart valve with specified sensitivity and specificity, wherein the mathematical model requires inputs comprising geometrical parameters obtained based on simulated post-deployment anatomy and the image data, and wherein the inputs comprise a neo-sinus volume (NSV), a kinematic viscosity (γ), a dynamic viscosity (μ), a heart rate (HR); an ejection time (T ej ), a velocity of a main jet (V), a width of each of a neo-sinus (w), a height or depth of each neo-sinus (h), an angle between a velocity direction and a stent of a transcatheter valve (Θ), a distance from a tip of a leaflet perpendicular to a leaflet edge and intersecting a sinotubular (STJ) junction (d), a cross-sectional area (A c ), and each of a neo-sinus taken from a longitudinal or axial perspective.
21. The computer-implemented method of claim 20 , further comprising using a trained artificial intelligence model to determine a risk of the development of thrombosis post-deployment of the heart valve based on the inputs.
22. The computer-implemented method of claim 20 , further comprising using a trained machine learning model to determine a risk of the development of thrombosis post-deployment of the heart valve based on the inputs.
23. The computer-implemented method of claim 20 , wherein the image data comprise X-ray imaging data, computed tomography imaging data, magnetic resonance imaging data, and/or ultrasound imaging data.
24. The computer-implemented method of claim 20 , wherein the selection of the heart valve implantation comprises one or more of: size of the heart valve implantation, type of the heart valve implantation, positioning of the heart valve implantation comprising a pitch angle, a yaw angle, and/or a depth.
25. The computer-implemented method of claim 20 , wherein the one or more quantities comprise a stasis volume (SV).
26. The computer-implemented method of claim 20 , wherein the one or more quantities comprise a fluid circulation (Γ).
27. The computer-implemented method of claim 20 , wherein the one or more quantities comprise a total kinetic energy (KE) defined over a neo-sinus volume.
28. The computer-implemented method of claim 20 , wherein the one or more quantities comprise an average wall shear stress (WSS) for near wall stagnation defined over a neo-sinus volume.
29. The computer-implemented method of claim 20 , wherein the one or more quantities comprise a normalized fluid circulation parameter (Γ norm ).
30. The computer-implemented method of claim 20 , wherein the one or more quantities comprise percent stasis volumes during systole and during diastole.
31. The computer-implemented method of claim 20 , wherein the mathematical model is an empirical or a semi-empirical mathematical model.
32. The computer-implemented method of claim 20 , further comprising using the mathematical model to calculate one or more quantities that correlate to the risk of the development of thrombosis post-deployment of the heart valve with a specified uncertainty.
33. The computer-implemented method of claim 20 , further comprising determining a high risk of the development of thrombosis post-deployment of the heart valve, in response to determining that the calculated one or more quantities are below a cutoff value.