ArticleJACC. Advances2022
FEops HEARTguide Patient-Specific Computational Simulations for WATCHMAN FLX Left Atrial Appendage Closure: A Retrospective Study.
Article in JACC. Advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Cardiac CT Roadmap for Left Atrial Appendage Occlusion: From Planning to Strategy to Follow-Up.Journal of clinical medicine · 2026Review
- An integrated fluid-dynamic and structural pipeline to assess atrial hemodynamics before and after left atrial appendage occluder deployment in a patient-specific anatomy.Medical & biological engineering & computing · 2026Article
- Digital twins and simulations in transcatheter coronary and structural heart interventions.European heart journal. Digital health · 2026Review
- Advanced Computer Simulation Based on Cardiac Imaging in Planning of Structural Heart Disease Interventions.Journal of clinical medicine · 2025Review
- Simulation for Procedural Planning and Intracardiac Echo for Enhancing Precision in Cardiac Interventions With a Focus on Left Atrial Appendage Closure.Structural heart : the journal of the Heart Team · 2025Review
- The Next Chapter in TAVR: Innovations and the Road Ahead.Journal of clinical medicine · 2025Review
- Left atrial appendage occlusion in atrial fibrillation: shaping the future of stroke prevention.Future cardiology · 2025Review
- Simulation for Procedural Planning and Intracardiac Echo for Enhancing Precision in Cardiac Interventions With a Focus on Left Atrial Appendage Closure.Structural heart : the journal of the Heart Team · 2025Review
- Probing the limits and capabilities of diffusion models for the anatomic editing of digital twins.NPJ digital medicine · 2024Article
- The Art of Planning a Left Atrial Appendage Closure: Will Computational Modeling Simplify the Intervention?JACC. Advances · 2022Article
- A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring.Digital healthArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Three-dimensional transesophageal echocardiography (3D-TEE) is the primary imaging tool for left atrial appendage closure planning. The utility of cardiac computed tomography angiography (CCTA) and patient-specific computational models is unknown. Objectives: The purpose of this study was to evaluate the accuracy of the FEops HEARTguide patient-specific computational modeling in predicting appropriate device size, location, and compression of the WATCHMAN FLX compared to intraprocedural 3D-TEE. Methods: Patients with both preprocedural and postprocedural CCTA and 3D-TEE imaging of the LAA who received a WATCHMAN FLX left atrial appendage closure device were studied (n = 22). The FEops HEARTguide platform used baseline CCTA imaging to generate a prediction of device size(s), device position(s), and device dimensions. Blinded (without knowledge of implanted device size/position) and unblinded (implant device size/position disclosed) simulations were evaluated. Results: In 16 (72.7%) patients, the blind simulation predicted the final implanted device size. In these patients, the 3D-TEE measurements were not significantly different and had excellent correlation (Pearson correlation coefficient (r) ≥ 0.90). No patients had peridevice leak after device implant. In the 6 patients for whom the model did not predict the implanted device size, a larger device size was ultimately implanted as per operator preference. The model measurements of the unblinded patients demonstrated excellent correlation with 3D-TEE. Conclusions: This is the first study to demonstrate that the FEops HEARTguide model accurately predicts WATCHMAN FLX device implantation characteristics. Future studies are needed to evaluate if computational modeling can improve confidence in sizing, positioning, and compression of the device without compromising technical success.
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