Real-world outcomes, complications, and cost of catheter-based ablation for atrial fibrillation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Catheter-based ablation for atrial fibrillation is a useful and effective form of rhythm-control therapy for symptomatic patients. This article reviews the 'real-world' experience on the outcomes, complications, and costs of atrial fibrillation ablation. RECENT FINDINGS: Currently, real-world outcomes of atrial fibrillation ablation are derived from retrospective analysis of administrative databases or prospective registries from selected centers and patients. The rate of atrial fibrillation recurrence was reported to be as high as 60% and the rate of repeat ablation ranged from ≈10 to 18% within 1 year after ablation. All-cause hospitalizations after atrial fibrillation ablation were frequent, at up to ≈30% within 1 year and with up to half of them related to atrial fibrillation recurrence or repeat procedures. Rates of periprocedural complications were relatively low (≈3%). Female sex was associated with higher risk of complications such as bleeding, vascular injury, and tamponade. Markov models examining the cost-effectiveness of ablation yielded favorable results when success rates of more than 70% were assumed with long time horizons (>5 years). SUMMARY: The real-world outcomes of atrial fibrillation ablation are sobering. Confirmation of these findings with prospective, population-based, minimally biased studies is needed. There is a critical need to delineate the downstream economic impact of atrial fibrillation ablation on society to justify its continued delivery and growth.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".