BRIGHT EVIDENCE PACK / Demonstrated

Personalizing an ablation for persistent AF

A multicenter randomized, controlled, double-blind superiority trial tested whether adding AI-detected spatio-temporal electrogram-dispersion targets to pulmonary-vein isolation improves treatment of drug-refractory persistent atrial fibrillation.

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Dates and assessment

Source published: 2025-02-14
Bright published: 2026-09-07
Substantive update: None recorded
Evidence state: Demonstrated
Independent verification: Not established by this source review
Last source review: 2026-09-07

The claim in context

The human problem

Persistent and long-standing persistent atrial fibrillation can remain difficult to treat with a standard anatomical ablation procedure.

The prior constraint

Clinicians need reproducible ways to identify patient-specific electrical patterns that might guide additional ablation targets.

AI’s actual role

An AI algorithm detected areas of spatio-temporal electrogram dispersion for the tailored ablation arm, in addition to pulmonary-vein isolation.

The documented result

At 12 months after a single ablation, 88% of the tailored arm (n=187) versus 70% of the PVI-only arm (n=183) met the primary efficacy endpoint: freedom from documented atrial fibrillation with or without antiarrhythmic drugs (P<0.0001 for superiority).

Why it may matter

The study tests AI-guided targeting against a clinical comparator with a one-year patient outcome, rather than only a retrospective diagnostic metric.

Limitations

There was no significant between-arm difference in freedom from any atrial arrhythmia after one ablation.
Procedure and ablation times were twice as long in the tailored arm.
The results apply to the trial's selected persistent-AF population and procedure context.

Original evidence

Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial · paper

Attribution

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