Computationally guided personalized targeted ablation of persistent atrial fibrillation

Area of Science:

• Cardiovascular Medicine
• Biomedical Engineering
• Computational Biology

Background:

• Atrial fibrillation (AF) is a common arrhythmia increasing stroke and heart failure risk.
• Catheter ablation can restore normal heart rhythms but has lower success rates in persistent AF with atrial fibrosis.
• Patients with persistent AF and fibrosis often require multiple, risky ablation procedures due to failed treatments.

Purpose of the Study:

• To develop and validate personalized computational modeling for identifying reliable atrial ablation targets in patients with persistent AF and atrial fibrosis.
• To integrate computational predictions into a clinical mapping system for guiding ablation procedures.
• To assess the feasibility and potential benefits of this computational approach in improving AF ablation efficacy.

Main Methods:

• Creating personalized computational models of the atria to identify fibrotic tissue.
• Determining ablation targets by simulating the effect of ablating identified fibrotic regions on AF maintenance.
• Integrating predicted ablation targets into a clinical mapping system.
• Testing the feasibility of the integrated system in ten patients with persistent AF.

Main Results:

• Computational models successfully identified fibrotic tissue that, if ablated, would not sustain AF.
• Integration of predicted ablation targets into a clinical mapping system was feasible.
• The approach has the potential to avoid lengthy electrical mapping procedures.
• The method may improve the accuracy and efficacy of AF ablation, reducing the need for repeat procedures.

Conclusions:

• Personalized computational modeling offers a reliable method for predetermining atrial ablation targets in persistent AF with fibrosis.
• This approach can guide ablation procedures, potentially enhancing success rates and patient safety.
• The computational prediction of ablation targets may streamline the ablation process and reduce healthcare costs associated with repeat interventions.