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Updated: May 14, 2026

Robotic Ablation of Atrial Fibrillation
Published on: May 29, 2015
Individualized dynamic ablation strategy for persistent atrial fibrillation based on ibutilide response
1Jiangxi Provincial People's Hospital,The First Affiliated Hospital of Nanchang Medical College, Nanchang, China. hujing2015jx@126.com.
Background:
Persistent atrial fibrillation (PsAF) ablation faces a dilemma between insufficient pulmonary vein isolation (PVI) leading to high recurrence and excessive ablation increasing complications without clear benefit. Ibutilide may help identify critical ablation targets by modifying the arrhythmia substrate.
Methods:
This retrospective single-center study analyzed 367 PsAF patients undergoing ibutilide-guided ablation. After PVI, patients converting directly to sinus rhythm (SR) (PVI group, n = 86) received no further ablation. Non-responders (PVI+Linear group, n = 281) underwent linear ablation and were categorized by acute termination pattern: Directly to SR (n = 109), AFL to SR (n = 87), DC to SR (n = 75), or Failure (n = 10). Subgroups based on low-voltage areas (LVZs) and AF duration were analyzed. AF recurrence (> 30s post 3-month blanking period) was assessed.
Results:
Over a mean follow-up, AF-free survival rate was non-significantly higher in the PVI group vs. PVI+Linear group ((73.9% vs. 60.7%, P = 0.070). In the PVI+Linear group, success rates differed significantly by acute termination pattern (Direct to SR:67.8%, AFL to SR:72.6%, DC to SR:26.4%, Failure to SR:30.0%, P < 0.001), LVZs presence (Absent:63.8% vs. Present:55.8%, P < 0.001), and AF duration (≤ 1 year:73.9%, 1-5 year:55.3%, ≥ 5 year:44.6%, P < 0.001). Female, larger LA diameter, longer AF duration, and DC/Failure termination patterns independently predicted recurrence.
Conclusion:
The use of ibutilide may help identify "PVI‑sensitive" atrial fibrillation, reducing the extent of ablation without compromising efficacy.Female sex, LA enlargement, prolonged AF duration, and requiring DC/failing to terminate acutely predict recurrence.
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