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Image-Based Flow Simulations of Pre- and Post-left Atrial Appendage Closure in the Left Atrium.

Dongjie Jia1, Byunghwan Jeon2,3, Hyung-Bok Park3,4

  • 1Department of Mechanical, Aerospace & Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.

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Summary

This study introduces a computational fluid dynamic (CFD) framework to predict left atrial appendage (LAA) closure outcomes in atrial fibrillation patients. The simulation accurately predicted reduced thrombus formation risk, aiding procedural planning.

Keywords:
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Area of Science:

  • Cardiovascular Imaging and Simulation
  • Medical Device Technology
  • Computational Fluid Dynamics

Background:

  • Atrial fibrillation (AF) patients are at high risk for stroke due to thrombus formation in the left atrial appendage (LAA).
  • Percutaneous LAA occlusion reduces stroke risk, but an 11% failure rate persists due to anatomical variabilities.
  • Accurate patient-specific assessment is crucial for effective LAA occlusion procedures.

Purpose of the Study:

  • To develop an image-based, patient-specific computational fluid dynamic (CFD) simulation framework.
  • To predict treatment outcomes for LAA closure procedures in atrial fibrillation patients.
  • To utilize automated image segmentation for reduced image processing time.

Main Methods:

  • A novel, fully-automated image segmentation procedure to identify left atrium (LA) and LAA geometries from CT scans.
  • Finite-element mesh generation to create a 3D volume mesh with defined boundary planes.
  • CFD simulations incorporating AF flow boundary conditions and analysis of pre- and post-LAA closure flow characteristics (velocity, streamlines, vortices).

Main Results:

  • CFD analysis on a patient with successful LAA closure showed a significant reduction in flow velocity (max factor 2.21).
  • Mitral outflow became more organized, and vortex size, number, intensity, and duration were dramatically reduced.
  • Predicted flow characteristics indicated a reduced risk of thrombus formation and stroke, consistent with the actual procedural outcome.

Conclusions:

  • The developed framework successfully predicted patient-specific LAA closure outcomes for an AF patient.
  • The framework holds potential as a tool for pre-procedural planning and patient selection.
  • Further validation with more patient data is required to establish broader clinical utility.