PVI-only is not enough for all patients with persistent AF: A FLOW-AF subgroup analysis
Bibliographic record
Abstract
BACKGROUND: Since the Substrate and Trigger Ablation for Reduction of Atrial Fibrillation Trial Part II (STAR-AF II), there has been a trend toward pulmonary vein isolation (PVI)-only ablation strategies for persistent atrial fibrillation (PeAF). Electrographic flow (EGF) mapping can identify active sources of atrial fibrillation (AF) and estimate the electrographic flow consistency (EGFC) of wavefront propagation through substrate, revealing functional AF mechanisms. OBJECTIVE: We sought to examine the success of a PVI-only ablation strategy for a redo PeAF/longstanding PeAF population. METHODS: Electrographic Flow-Guided Ablation in Redo Patients With Persistent Atrial Fibrillation (FLOW-AF [NCT04473963]) prospectively enrolled patients with nonparoxysmal AF undergoing redo ablation at 4 centers. One-minute EGF recordings using 64-pole basket catheters were obtained both pre-PVI and post-PVI following a 20-minute wait and confirmation of electrical isolation of veins. Patients with EGF-identified sources were randomized 1:1 to EGF-guided source ablation vs PVI-only. Patients with no sources were not randomized and mostly received PVI only. RESULTS: -VASc scores compared with those with no sources (Group 1). After PVI only, Group 1 had 70% (16 of 23) freedom from recurrent AF (FFAF) within 1 year vs Group 2 with 35% (8 of 23), P = .018. In addition, patients with high electrographic flow consistency (EGFC) indicative of healthy or normal substrate had 67% (10 of 15) FFAF vs 45% (14 of 31) in those with low EGFC suggestive of abnormal substrate, P = .011. CONCLUSION: Success rates in no-sources patients receiving PVI only are better than in those with sources randomized to PVI only. For the clinically heterogenous population of patients with PeAF, the presence of EGF-identified sources matters clinically, and PVI only will not be enough for all patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".