Differences in Healthcare Use Between Patients With Persistent and Paroxysmal Atrial Fibrillation Undergoing Catheter‐Based Atrial Fibrillation Ablation: A Population‐Based Cohort Study From Ontario, Canada
Bibliographic record
Abstract
Background Patients with persistent atrial fibrillation (AF) undergoing catheter-based AF ablation have lower success rates than those with paroxysmal AF. We compared healthcare use and clinical outcomes between patients according to their AF subtypes. Methods and Results Consecutive patients undergoing AF ablation were prospectively identified from a population-based registry in Ontario, Canada. Via linkage with administrative databases, we performed a retrospective analysis comparing the following outcomes between patients with persistent and paroxysmal AF: healthcare use (defined as AF-related hospitalizations/emergency room visits), periprocedural complications, and mortality. Multivariable Poisson modeling was performed to compare the rates of AF-related and all-cause hospitalizations/emergency room visits in the year before versus after ablation. Between April 2012 and March 2016, there were 3768 consecutive patients who underwent first-time AF ablation, of whom 1040 (27.6%) had persistent AF. The mean follow-up was 1329 days. Patients with persistent AF had higher risk of AF-related hospitalization/emergency room visits (hazard ratio [HR], 1.21; 95% CI, 1.09-1.34), mortality (HR, 1.74; 95% CI, 1.15-2.63), and periprocedural complications (odds ratio, 1.36; 95% CI, 1.02-1.75) than those with paroxysmal AF. In the overall cohort, there was a 48% reduction in the rate of AF-related hospitalization/emergency room visits in the year after versus before ablation (rate ratio [RR], 0.52; 95% CI, 0.48-0.56). This reduction was observed for patients with paroxysmal (RR, 0.45; 95% CI, 0.41-0.50) and persistent (RR, 0.74; 95% CI, 0.63-0.87) AF. Conclusions Although patients with persistent AF had higher risk of adverse outcomes than those with paroxysmal AF, ablation was associated with a favorable reduction in downstream AF-related healthcare use, irrespective of AF type.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".