Variation and clinical consequences of wait-times for atrial fibrillation ablation: population level study in Ontario, Canada
Bibliographic record
Abstract
AIMS: Atrial fibrillation (AF) is the most common cardiac rhythm disorder. Emerging evidence supporting the efficacy of catheter ablation in managing AF has led to increased demand for this therapy, potentially outpacing the capacity to perform this procedure. Mismatch between demand and capacity for AF ablation results in wait-times which have not been comprehensively evaluated at a population level. Additionally, the consequences of such delays in AF ablation, namely the risk of hospitalization or adverse events, have not been studied. METHODS AND RESULTS: This observational cohort study included adults referred for catheter ablation to treat AF in Ontario, Canada, between 1 April 2016 and 31 March 2020. Wait-time was defined from referral to the earliest of ablation, death, off-list, or the study endpoint of 31 March 2022. The outcomes of interest included a composite of death, hospitalization for AF/heart failure, and emergency department visit for AF/heart failure. Our study cohort included 6253 patients referred for de novo AF ablation. The median wait-time for patients who received and who did not receive ablation was 218 days (IQR: 112-363) and 520 days (IQR: 270-763), respectively. Wait-time increased consistently for patients referred between October 2017 and March 2020. Mortality was rare, but significant morbidity was observed, affecting 19.2% of patients on the waitlist for AF ablation. Paroxysmal AF was associated with a statistically significant greater risk for adverse outcomes on the waitlist (HR 1.51, 95% CI 1.18-1.93). CONCLUSION: Wait-times for AF ablation are increasing and are associated with significant morbidity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".