Diagnosis-to-Ablation Time and Recurrence of Atrial Fibrillation Following Catheter Ablation
Bibliographic record
Abstract
Background: The optimal timing of catheter ablation for atrial fibrillation (AF) in reference to the time of diagnosis is unknown. We sought to assess the impact of the duration between first diagnosis of AF and ablation, or diagnosis-to-ablation time (DAT), on AF recurrence following catheter ablation. Methods: We conducted a systematic electronic search for observational studies reporting the outcomes associated with catheter ablation for atrial fibrillation stratified by diagnosis-to-ablation time. The primary meta-analysis using a random effects model assessed AF recurrence stratified by DAT ≤1 year versus >1 year. A secondary analysis assessed outcomes stratified by DAT ≤3 years versus >3 years. Results: Of the 632 screened studies, 6 studies met inclusion criteria for a total of 4950 participants undergoing AF ablation for symptomatic AF. A shorter DAT ≤1 year was associated with a lower relative risk of AF recurrence compared with DAT >1 year (relative risk, 0.73 [95% CI, 0.65–0.82]; P <0.001). Heterogeneity was moderate (I 2 =51%). When excluding the one study consisting of only patients with persistent AF, the heterogeneity improved substantially (I 2 =0%, Cochran’s Q P =0.55) with a similar estimate of effect (relative risk, 0.78 [95% CI, 0.71–0.85]; P <0.001). Conclusions: Shorter duration between time of first AF diagnosis and AF ablation is associated with an increased likelihood of ablation procedural success. Additional study is required to confirm these results and to explore implementation of earlier catheter AF ablation and patient outcomes within the current AF care pathway. Visual Overview A visual overview is available for this article.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".