Conventional versus 3‐D Echocardiography to Predict Arrhythmia Recurrence After Atrial Fibrillation Ablation
Bibliographic record
Abstract
Echocardiography to Predict AF Recurrence Background Arrhythmia recurrence after atrial fibrillation (AF) ablation remains high and requires repeat interventions in a substantial number of patients. We assessed the value of conventional and 3‐D echocardiography to predict AF recurrence. Methods and Results Consecutive patients undergoing AF ablation by means of pulmonary vein isolation were included in a prospective registry. Echocardiograms were obtained prior to the ablation procedure, and analyzed offline in a standardized manner, including 3‐D left atrial (LA) volumetry and determination of LA function and sphericity. The primary endpoint, AF recurrence (>30 seconds) between 3 to 12 months after AF ablation, was independently adjudicated. We included 276 patients (73% male, mean age 59.9 ± 9.9 years). Paroxysmal and persistent AF were present in 178 (64%) and 98 (36%) patients, respectively. Mean left ventricular ejection fraction and indexed LA volume in 3‐D (LAVI) were 52 ± 12% and 42 ± 13 mL/m2, respectively. AF recurrence was observed in 110 (40%) patients after a single procedure. Median (interquartile range) time to AF recurrence was 123 (92; 236) days. In multivariable Cox regression models, the only predictors for AF recurrence were the minimal, maximal, and indexed 3‐D LA volumes, P = 0.024, P = 0.016, and P = 0.014, respectively. Quartile specific analysis of 3‐D LAVI showed an HR of 1.885 (95%CI 1.066–3.334; P for trend = 0.015) for the highest compared to the lowest quartile. Conclusion Our results show the important role of LA volume for the long‐term freedom from arrhythmia after AF ablation. These data also highlight the potential of 3‐D echocardiography in this context and may facilitate patient selection for AF 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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".