Impact of atrial antitachycardia pacing and atrial pace prevention therapies on atrial fibrillation burden over long-term follow-up
Bibliographic record
Abstract
AIMS: Selective atrial pacing algorithms have been developed for prevention of atrial tachycardia/atrial fibrillation (AT/AF). Although short-term studies have shown modest to minimal incremental benefit of these algorithms compared with conventional dual-chamber (DDD/R) pacing for prevention of AT/AF, the long-term effects of these algorithms are unknown. Accordingly, we compared atrial antitachycardia pacing (ATP) therapy and combined atrial ATP and atrial pace prevention (ATP + Prevention) algorithms to conventional DDD/R pacing for prevention of AT/AF over long-term follow-up. METHODS AND RESULTS: Seventy-one patients with AT/AF following pacemaker insertion were randomized to DDD/R pacing, DDD/R plus ATP pacing, or DDD/R plus ATP and prevention pacing and followed for 3 years. Atrial tachycardia/AF burden and an AF symptom scale were compared over time between groups. Atrial tachycardia/AF burden remained stable over 3 years in the DDD/R and ATP + Prevention groups. Atrial tachycardia/AF burden increased significantly over time in the ATP group. Patients not on class I or III antiarrhythmic drug therapy were more likely to experience an increase in AT/AF burden over time. CONCLUSION: Atrial ATP and atrial ATP in combination with atrial pace prevention algorithms do not suppress AT/AF over long-term follow-up compared with DDD/R pacing.
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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 | 0.004 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".