Catheter ablation for atrial fibrillation in heart failure with reduced ejection fraction: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Previous randomized controlled trials (RCT)s showed similar outcomes in patients with atrial fibrillation (AF) and heart failure with reduced ejection fraction (HFrEF) treated with anti-arrhythmic drugs (AAD) compared to rate control therapy. We sought to evaluate whether catheter ablation is superior to medical therapy in patients with AF and HFrEF. METHODS: We searched electronic databases for all RCTs that compared catheter ablation and medical therapy (with or without use of AAD). We used random-effects models to summarize the studies. The primary end-point was all-cause mortality. Secondary outcomes included heart failure-related hospitalizations and change in left ventricular ejection fraction (LVEF). RESULTS: We retrieved and summarized 7 randomized controlled trials, enrolling 856 patients (429 in the catheter ablation arm and 427 in the medical therapy arm). Compared with medical therapy (including use of AAD), AF catheter ablation was associated with a significant reduction in mortality (risk ratio 0.50; 95% confidence interval [CI]: 0.34 to 0.74; P = 0.0005) and heart failure-related hospitalizations (risk ratio 0.56; 95% CI: 0.44 to 0.71; P < 0.0001). Furthermore, catheter ablation led to significant improvements in LVEF (weighted mean difference, 7.48; 95% CI: 3.71 to 11.26; P < 0.0001). CONCLUSIONS: Compared to medical therapy, including use of AAD, catheter ablation for AF was associated with a significant reduction in mortality and heart failure-related hospitalizations as well as an improvement in LVEF in patients with HFrEF. Larger trials are needed to confirm whether rhythm control with ablation is superior to rate control in patients with AF and heart failure.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.027 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".