Long-term outcomes of pace-and-ablate strategy in patients with atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: The pace-and-ablate strategy is second -line therapy to obtain rate control in patients with persistent symptomatic atrial fibrillation (AF) when other treatment options fail. This study aims to evaluate long-term effects on clinical outcomes following pace-and-ablate strategy in AF patients. METHODS: This retrospective study includes patients who underwent successful pacemaker implantation (right ventricular pacing (RVP) or cardiac re-synchronization therapy (CRT)) followed by atrioventricular node ablation (AVNA) between 2010 and 2020. Patients were treated according to the prevailing guidelines. The primary endpoint was a composite of all-cause mortality and heart failure hospitalization (HFH). Secondary endpoints were individual outcomes of all-cause mortality, HFH, and left-ventricular ejection fraction (LVEF) change. RESULTS: Two hundred ninety-eight patients were included, 162 undergoing RVP, and 136 receiving CRT, with a median follow-up of 5.8 years [4.1-8.0]. The primary endpoint occured in 47% of the RVP group and 49% of the CRT group (p = 0.206). All-cause mortality occurred in 36% of the RVP group and in 45% of the CRT group (p = 0.005). HFH occurred in 22% of the RVP group and in 15% of the CRT group (p = 0.328), with 17(10%) upgrades to CRT in the RVP group. Median LVEF in the RVP group remained stable (56% [49-60] to 53% [43-57]; p = 0.081), while it improved in the CRT group (31% [22-38] to 43% [32-51]; p < 0.001). CONCLUSION: Mortality and HFH in patients with AF managed through a pace-and-ablate strategy are high. Reassuringly, LVEF deterioration requiring upgrade to CRT is uncommon in patients undergoing RVP with normal baseline LVEF before AVNA. CRT improves LVEF in patients with reduced LVEF before AVNA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".