Atrial Fibrillation Recurrence Post-Ablation Across Heart Failure Categories: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Previous studies have provided evidence of reduced recurrence of atrial fibrillation (AF), all-cause mortality, and heart failure (HF) hospitalizations after catheter ablation (CA) in both HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). Aggregate data comparing the efficacy of AF ablation and clinical endpoints in HF with mildly reduced ejection fraction (HFmrEF) to HFrEF and HFpEF are lacking. Methods: We conducted a systematic review and meta-analysis aimed at determining any differences in AF recurrence rate, all-cause mortality, and HF hospitalizations among patients with HFrEF, HFmrEF, and HFpEF who underwent AF ablation. A systematic search of PubMed/MEDLINE, Embase, and Cochrane Library databases was performed until October 31, 2023. Results: A total of seven studies comprising 3,795 patients were retained: HFrEF 1,281 (33.8%), HFmrEF 870 (22.9%), and HFpEF 1,644 (43.3%). After median follow-up of 24 months, there was no significant difference in rate of AF recurrence between the three HF categories: HFrEF 40% (30-49%), HFmrEF 35% (28-43%); and HFpEF 35% (25-45%). Only two studies which included outcomes in the three HF categories were identified. Pooled hazard ratio (HR) of all-cause mortality and HF hospitalization combined after ablation or other rhythm control compared to other conservative management were: HFrEF 0.77 (0.63 - 0.94); HFmrEF 0.81 (0.55 - 1.20); and HFpEF 0.74 (0.55 - 1.00). Conclusions: CA has similar efficacy in the long-term resolution of AF among patients with HFrEF, HFmrEF, and HFpEF. Further studies are needed to provide a robust analysis on the potential impact of CA on all-cause mortality.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".