Effect of Left Atrial Wall Thickness on Radiofrequency Ablation Success
Bibliographic record
Abstract
INTRODUCTION: Radiofrequency (RF) ablation in thicker regions of the left atrium (LA) may require increased ablation energy in order to achieve effective transmural lesions. Consequently, many cases of recurrent atrial fibrillation (AF) postablation may be due to thicker-than-normal atrial tissue. The aim of this study was to test the hypotheses that patients with recurrent AF have thicker tissue overall and that electrical reconnection is more likely in regions of thicker tissue. METHODS AND RESULTS: Retrospective analysis was performed on 86 CT images acquired preoperatively from a cohort of 119 patients who had undergone RF ablation for AF. Of these, 33 patients experienced recurrence of AF within 1 year of initial treatment and 29 returned for a repeat ablation. For each patient, LA wall thickness (LAWT) was measured from the images in 12 anatomical regions using custom software. Patients with recurrent AF had larger LAWT compared to successfully treated patients (1.6 ± 0.6 mm vs. 1.5 ± 0.5 mm, P < 0.001) and reconnection was found to be at regions of thicker tissue (1.6 ± 0.6 mm, P = 0.038) compared to nonreconnected regions (1.5 ± 0.5 mm). The superior right posterior wall of the LA was significantly related to both recurrence (P = 0.048) and reconnection (P = 0.014). CONCLUSION: Increased LAWT has a small but significant effect on postablation recurrence and reconnection. Measures of LAWT may facilitate appropriate dosing of RF energy, but other factors will be critical in transmural lesion formation and ablation success.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".