Safety outcomes of catheter ablation versus antiarrhythmic drugs in atrial fibrillation: a comprehensive systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background and Aims Catheter ablation (CA) is superior to antiarrhythmic drugs (AADs) for maintaining sinus rhythm in atrial fibrillation (AF). However, most randomised controlled trials (RCTs) lack the power to assess CA safety. Hence, we sought to evaluate complication rates and relative risks of CA compared with AADs for the rhythm management of AF. Methods We searched MEDLINE, Embase, and Cochrane CENTRAL (inception–Oct 24, 2024) for RCTs comparing CA vs. AADs in AF management. The primary endpoint was a composite of serious adverse events (SAE), including death, additional intervention, prolonged or unplanned hospitalisation, or disability. Secondary endpoints included SAE components. A random-effects meta-analysis estimated pooled risk ratios (RR) with 95% confidence intervals (CI). Results 24 randomised trials comprising 6,665 participants (53.2% in the CA group) met the inclusion criteria. On follow-up (ranging from 6 to 60 months), 673 (10.1%) patients in either group developed SAEs. CA was associated with a 20% lower risk of serious adverse events compared with AAD (RR 0.80, 95% CI 0.69-0.93, I2 0%, p<0.01) and 47% reduction in risk unplanned hospitalisation (RR 0.53, 95% CI 0.38-0.72, I2 73%, p<0.01). Additionally, compared to AAD, CA was associated with a 37% lower risk of adverse cardiovascular events related to therapy (RR 0.63, 95% CI 0.44-0.90, I2 42%, p=0.01). Conclusions Catheter ablation for AF rhythm management reduced serious adverse events, unplanned hospitalisation, and adverse cardiovascular events compared to AADs. Across the spectrum of AF, rhythm control with antiarrhythmic drugs should not be preferred to catheter ablation on the grounds of patient safety.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".