Feasibility of using patient-specific models and the "minimum cut" algorithm to predict optimal ablation targets for...

Area of Science:

• Computational modeling in electrophysiology
• Cardiac arrhythmias research
• Medical imaging and simulation

Background:

• Left atrial flutter (LAFL) is a common complication following atrial fibrillation ablation.
• Identifying effective ablation targets for LAFL remains a significant clinical challenge.

Purpose of the Study:

• To develop patient-specific computational models for simulating LAFL.
• To predict optimal ablation targets using flow network theory.

Main Methods:

• Constructed atrial models from LGE-CMR scans of 10 LAFL patients, incorporating fibrosis.
• Simulated LAFL using in silico rapid pacing and analyzed reentrant wave propagation as an electric flow network.
• Identified minimum cut (MC) as the critical tissue separating flow and simulated ablation at MC sites.

Main Results:

• Successfully generated patient-specific atrial models and induced LAFL in 70% of simulations.
• Ablation at MCs terminated LAFL in 4 models; emergent LAFLs in others were also terminated after re-analysis.
• Simulated MC-based ablation targets closely matched clinical ablation sites in terminated cases.

Conclusions:

• Personalized atrial simulations accurately predict ablation targets for LAFL.
• This modeling approach offers a powerful tool for planning ablation procedures, potentially reducing time and complications.