Advanced Computer Simulation Based on Cardiac Imaging in Planning of Structural Heart Disease Interventions
Abstract
1. Introduction
2. Rationale for Advanced Computer Simulation
Clinical Translation and Validation Evidence
3. Types of Computer Simulations and Platforms
3.1. Basic Computer Modeling
3.2. Advanced Computer Simulation
3.3. Platform–Device Compatibility
4. Computer Simulation for Specific Structural Heart Interventions
4.1. TAVI
4.1.1. Optimal Sizing
4.1.2. Anatomical Rupture
4.1.3. Paravalvular Leak
4.1.4. Percutaneous Alternatives for Coronary Artery Obstruction
4.1.5. Leaflet Thrombosis
4.1.6. Prediction of Conduction Disturbances
4.1.7. TAVI in Bicuspid Aortic Valves
4.2. TMVR
4.2.1. Mitral Valve-in-Valve TAVI
4.2.2. Mitral Valve in Ring and Valve in Mitral Annular Calcification
4.2.3. Native TMVR
4.2.4. Mitral Edge-to-Edge Repair
4.2.5. Limitations in Transcatheter Edge-to-Edge Repair Simulation
4.3. Left Atrial Appendage Occlusion
4.3.1. Methods of Analysis for CT-Based Computational Modeling of LAAO
4.3.2. Virtual Implantation and Device Selection in Left Atrial Appendages (VIDAA) Platform (Universitat Pompeu Fabra, Barcelona, Spain)
4.3.3. Computational Fluid Dynamics to Predict Device-Related Thrombosis
4.3.4. Artificial Intelligence (AI) Integration and Enhanced Simulation
4.3.5. Implementation Barriers and Clinical Adoption Challenges
5. Future Directions
5.1. TAVI
5.2. TMVR
5.3. Tricuspid Interventions
5.4. LAAO
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CFD | Computational fluid dynamics |
FSI | Fluid–structure interaction |
ECAP | Endothelial cell activation potential |
VIDAA | Virtual implantation and device selection in left atrial appendages |
ARR | Aortic root rupture |
FEA | Finite element analysis |
FEopsTM | Finite element-optimized patient-specific |
DASI | Direct analytical surgical individualization |
ViR | Valve in ring |
THV | Transcatheter heart valve |
CO | Coronary obstruction |
ViV | Valve in valve |
ViMAC | Valve in mitral annular calcification |
LAAO | Left atrial appendage occlusion |
CPI | Contact pressure index |
PDL | Peri-device leak |
DRT | Device-related thrombosis |
LVOT | Left ventricular outflow tract |
SSR | Shear strain rate |
MAC | Mitral annular calcification |
m-TEER | Mitral transcatheter edge-to-edge repair |
PVL | Paravalvular leak |
TAVI | Transcatheter aortic valve implantation |
TAVR | Transcatheter aortic valve replacement |
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Procedure Type | Platform | Study Design | Sample Size | Key Outcomes | Clinical Impact |
---|---|---|---|---|---|
LAAO | FEops HEARTguide™ | RCT (PREDICT-LAA) [25] | 200 patients | Primary composite outcomes: 28.9% vs. 41.8% (p = 0.08, trend favoring simulation) | First AI-enabled RCT proving procedural improvement |
LAAO | FEops HEARTguide™ | Multicenter (PRECISE LAAO) [28] | ~100 patients | Complete LAA closure: 59.8% with standard planning vs. 90.2% with simulation planning | Demonstrates improved procedural planning accuracy |
TAVI | FEops HEARTguide™ | Prospective (PRECISE-TAVI) [26] | 77 patients | PVL prediction accuracy AUC: 0.69, PPM prediction AUC: 0.83 | Validated predictive modeling for challenging anatomies |
Publication | Study Design | Methodology | Key Findings |
---|---|---|---|
Buysschaert et al. [64] (2021) | Prospective study N = 15 Patients receiving LAAO with AmuletTM | Proceduralists first made sizing assessments based on standard pre-procedural planning and rated their confidence on a scale from 0 to 10. Then, they were provided the FEopsTM assessment and asked to make a sizing decision and re-rate their confidence in the decision. |
|
PREDICT-LAA [25,65] (2023) | Prospective, multicenter, randomized study N = 200 Patients receiving LAAO with AmuletTM | Randomization (1:1) to computational simulation arm and standard arm Primary endpoint: Incomplete LAAC and DRT assessed at 3 months using post-procedure CT |
|
Bavo et al. [63] (2020) | Retrospective validation study N = 30 Patients with pre- and post-procedural CT scans who received LAAO with either AmuletTM (n = 15) or WatchmanTM devices (n = 15) | Comparison of device frame deformation parameters and LAA wall apposition in virtually implanted devices and acute implants |
|
Ranard et al. [66] (2022) | Retrospective study N = 22 Patients who received LAAO with Watchman FLXTM and had pre- and post-CT scans, as well as TEE imaging, available | Comparison of blinded and unblinded FEopsTM simulation results based on 3D-TEE measures of device deformation |
|
Publication | Study Design | Methodology | Key Findings |
---|---|---|---|
Mill et al. [73] (2021) | N = 6 (3 with DRT and 3 without DRT) Patients with Amplatzer AmuletTM LAAO | CT scans were acquired twice—once between months 1 and 3 and once between months 3 and 6 after LAAC. Slicer 4.10.1 software was used to construct a 3D model. CFD simulations were performed using AnsysTM Fluent 19 R3. Post-processing and visualization of the simulation results were performed using ParaView 5.4.1. |
|
Aguado et al. [74] (2019) | N = 4 Patients with WatchmanTM or AmuletTM LAAO (3 WatchmanTM, 1 AmuletTM) | Surface meshes of the LA were reconstructed from 3D CT images or rotational angiography images. The VIDAA platform was used to explore multiple WatchmanTM and AmuletTM configurations. The VIDAA data was then exported for CFD simulation using AnsysTM Fluent 18.2. |
|
Vogl et al. [75] (2022) | Retrospective, N = 4 Patients with WatchmanTM LAAO (2 patients with DRT and 2 without DRT) | Three-dimensional models were created based on CT imaging. FEopsTM was used to deploy the device. CFD was performed on patient-specific models before and after device implantation. |
|
D’Alessandro et al. [76] (2023) | N = 5 Simulated pre-LAAO morphology, LAAO with “pacifier device”, and LAAO with “plug device” | LA models were obtained from CT imaging. |
|
Planas et al. [77] (2022) | N = 6 Watchman FLXTM and AmuletTM LAAO simulations | Three-dimensional surface meshes were created from pre-procedural CT scans using Meshmixer. VIDAA simulations were performed with the pulmonary ridge covered and uncovered. CFD was performed for all simulations using AnsysTM Fluent 19.2. |
|
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Agarwal, A.; Ranard, L.; Vahl, T.; Khalique, O. Advanced Computer Simulation Based on Cardiac Imaging in Planning of Structural Heart Disease Interventions. J. Clin. Med. 2025, 14, 6885. https://doi.org/10.3390/jcm14196885
Agarwal A, Ranard L, Vahl T, Khalique O. Advanced Computer Simulation Based on Cardiac Imaging in Planning of Structural Heart Disease Interventions. Journal of Clinical Medicine. 2025; 14(19):6885. https://doi.org/10.3390/jcm14196885
Chicago/Turabian StyleAgarwal, Alaukika, Lauren Ranard, Torsten Vahl, and Omar Khalique. 2025. "Advanced Computer Simulation Based on Cardiac Imaging in Planning of Structural Heart Disease Interventions" Journal of Clinical Medicine 14, no. 19: 6885. https://doi.org/10.3390/jcm14196885
APA StyleAgarwal, A., Ranard, L., Vahl, T., & Khalique, O. (2025). Advanced Computer Simulation Based on Cardiac Imaging in Planning of Structural Heart Disease Interventions. Journal of Clinical Medicine, 14(19), 6885. https://doi.org/10.3390/jcm14196885