Beneficial effects of statin therapy for prevention of atrial fibrillation following DDDR pacemaker implantation
Bibliographic record
Abstract
AIMS: Data suggest that atrial pacing, statins, angiotensin-converting enzyme-inhibitors and angiotensin receptor blocking drugs prevent atrial tachycardia/atrial fibrillation (AT/AF) in some patients. The clinical predictors of at/af recurrence following dual-chamber pacemaker insertion were examined in 185 consecutive patients with paroxysmal AF. METHODS AND RESULTS: Predictors of AT/AF recurrence were evaluated in this observational cohort study. The time to first AT/AF recurrence and AT/AF burden (h/day) was retrieved at each follow-up visit by interrogating the pacemaker. AT/AF recurred following pacemaker implantation in 157 (85%) patients. At 1 year of follow-up, patients without recurrence were more likely to be on statin therapy (54%) when compared with patients without statin therapy (25%, chi = 12.31, P = 0.0004). Statin therapy was the only significant predictor of AT/AF recurrence in a multivariate logistic regression model (adjusted odds ratio 0.33, 95% confidence interval 0.14-0.74, P = 0.007). AT/AF burden was significantly lower in the group on statin therapy (median 0.10 h/day) when compared with the group not on statin therapy (median 0.39 h/day, P = 0.0059). CONCLUSION: AT/AF recurs frequently following pacemaker implantation in patients with sinus node disease. The progression to permanent AF remains low over time. Statin therapy was significantly associated with AT/AF suppression.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".