Left Atrial Slow‐Conduction Channels Predict Pulmonary Vein Isolation Outcomes in Paroxysmal Atrial Fibrillation
Bibliographic record
Abstract
INTRODUCTION: The mechanisms underlying recurrent atrial fibrillation (AF) despite durable pulmonary vein isolation (PVI) in patients with paroxysmal AF (PAF) remain unclear. This study investigates whether left atrial (LA) slow-conduction channels act as a substrate for recurrence following durable PVI. METHODS: High-density LA mapping was performed in 120 PAF patients. LA conduction velocities were calculated using the Local Velocity Vector algorithm. Channels were defined as narrow regions with slow conduction (< 0.5 m/s) and fragmented electrograms. All patients underwent PVI using radiofrequency energy. One-year freedom from recurrent AF was compared based on baseline LA channel presence. A control group of five patients with atrioventricular reentrant tachycardia was included. RESULTS: Among 120 patients, 42 (35%) had 54 LA channels identified. Channels represented ~1% of total LA surface area, exhibited slow conduction (~0.23 m/s), were bordered by lines of block, displayed abnormal electrograms, and demonstrated decremental conduction, suggesting a potential role in reentry. Half were in low-voltage areas while the remainder were in regions with preserved voltage. At 1 year, 3 of 78 patients without channels (4%) had PAF recurrence, all due to PV reconnections, compared to 7 of 42 patients with channels (17%) who experienced recurrence (p = 0.02), 6 of whom had durably isolated PVs on repeat study. No channels were identified in controls. CONCLUSION: The absence of LA channels may indicate a PAF phenotype with high PVI success. Conversely, patients with channels are less likely to respond to PVI. It remains unclear whether channels serve as risk indicators or targets for ablation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".