Long-term all-cause mortality and hospitalizations after catheter ablation in patients with paroxysmal and persistent atrial fibrillation.
Bibliographic record
Abstract
AIMS: Persistent atrial fibrillation (AF) patients undergoing a catheter ablation are at risk for adverse outcomes, due to comorbidities and a more advanced arrhythmia substrate. There may be barriers to catheter ablation in patients with persistent AF, compared to those with paroxysmal AF. We compared long-term outcomes after ablation in patients with paroxysmal and persistent AF. METHODS AND RESULTS: Patients undergoing de novo AF catheter ablation from April 2012 to March 2022 in Ontario, Canada, were included. The primary outcome was a composite of all-cause mortality and all-cause hospitalization. Inverse probability of treatment weighting created balanced cohorts of paroxysmal and persistent AF patients. Cox proportional hazards models estimated the effect on persistent vs. paroxysmal AF. There were 10 788 patients who underwent an ablation. Persistent AF patients accounted for 25% of the population. In our weighted cohort, patients had similar age (standardized difference 0.027), female sex [standardized difference (SD) 0.018], and medical comorbidities (Charlson comorbidity score; 0.5% in both, SD 0.018). In the weighted cohort, the primary composite outcome occurred in 5.5% in paroxysmal AF and 6.3% in persistent AF at 30 days (HR 1.15, 95% CI 0.94-1.40, P = 0.168), 19.8% vs. 19.7% at 1 year (HR 1.00, 95% CI 0.90-1.11, P = 0.971), and 34.1% vs. 35.4% at 3 years (HR 1.05, 95% CI 0.97-1.13, P = 0.269). There was no increased risk of the individual components at 30 days, 1 year, or 3 years. CONCLUSION: The risk of all-cause mortality and hospitalization outcomes in persistent and paroxysmal AF patients undergoing ablation was similar at 30 days, 1 year, and 3 years post-ablation. The impact of persistent AF on long-term outcomes (i.e. all-cause mortality) is primarily attributable to comorbid conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".