Diagnosis-to-ablation time predicts recurrent atrial fibrillation and rehospitalization following catheter ablation
Bibliographic record
Abstract
BACKGROUND: Wait times for catheter ablation in patients with symptomatic atrial fibrillation (AF) may influence clinical outcomes. OBJECTIVE: This study examined the relationship between the duration from AF diagnosis to ablation, or diagnosis-to-ablation time (DAT), on the clinical response to catheter ablation in a large nationwide cohort of patients. METHODS: We identified patients with new AF who underwent catheter ablation between January 2014 and December 2017 using the IBM MarketScan databases. Cox proportional hazard models were used to estimate the strength of the association between DAT and the outcomes of AF recurrence and hospitalization at 1 year postablation. RESULTS: -VASc score was 2. Median DAT was 5.5 (2.6, 13.1) months. At 1 year postablation, 10.0% (n = 1116) developed recurrent AF. For each year increase in DAT, the risk of AF recurrence increased by 20% after adjustment for baseline comorbidities and medications (hazard ratio [HR] 1.20, 95% confidence interval [CI] 1.11-1.30). A longer DAT was associated with an increased risk of hospitalization (HR 1.08 per DAT year, 95% CI 1.02-1.15). DAT was a stronger predictor of AF recurrence postablation than traditional clinical risk factors, including age, prior heart failure, or renal failure. CONCLUSION: Increasing duration between AF diagnosis and catheter ablation is associated with higher AF recurrence rates and all-cause hospitalization. Our findings are consistent with a growing body of evidence supporting the benefits of prioritizing early restoration of sinus rhythm.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".