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Updated: Jul 23, 2026
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The WATCHMAN Left Atrial Appendage Closure Device for Atrial Fibrillation
Published on: February 28, 2012
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FEops HEARTguide Patient-Specific Computational Simulations for WATCHMAN FLX Left Atrial Appendage Closure: A
Lauren S Ranard1, Torsten P Vahl1, Robert Sommer1
1Structural Heart and Valve Center, NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, New York, USA.
JACC. Advances
|June 28, 2024
Summary
Patient-specific computational models accurately predict WATCHMAN FLX device size for left atrial appendage closure. This FEops HEARTguide platform shows promise in guiding device selection and implantation, potentially improving procedural outcomes.
Area of Science:
- Cardiovascular Imaging
- Medical Device Technology
- Computational Modeling
Background:
- Three-dimensional transesophageal echocardiography (3D-TEE) is standard for left atrial appendage closure planning.
- The role of cardiac computed tomography angiography (CCTA) and patient-specific computational models remains unclear.
Purpose of the Study:
- To assess the accuracy of FEops HEARTguide computational modeling in predicting WATCHMAN FLX device size, location, and compression.
- To compare computational model predictions with intraprocedural 3D-TEE findings.
Main Methods:
- Twenty-two patients with pre- and postprocedural CCTA and 3D-TEE undergoing WATCHMAN FLX implantation were studied.
- The FEops HEARTguide platform used CCTA to predict device size, position, and dimensions.
- Blinded and unblinded simulations were performed and compared to actual implantation data.
Main Results:
- The model accurately predicted the final implanted device size in 72.7% of patients (16/22).
- In these cases, 3D-TEE measurements showed excellent correlation (r ≥ 0.90) with no peridevice leaks.
- Unblinded model predictions demonstrated excellent correlation with 3D-TEE measurements.
Conclusions:
- This study is the first to show FEops HEARTguide accurately predicts WATCHMAN FLX implantation characteristics.
- Further research is needed to determine if computational modeling enhances confidence in device sizing, positioning, and compression without affecting success rates.
