Does Minimizing Ventricular Pacing Reduce the Incidence of Atrial Fibrillation? A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Right ventricular pacing disrupts atrioventricular synchrony and increases the risk of atrial fibrillation (AF). However, whether algorithms for minimizing ventricular pacing reduce the incidence of AF remains controversial. Therefore, we conducted a systematic review and meta-analysis to compare the incidence of AF between minimizing ventricular pacing and conventional pacing protocols in patients with pacemakers implanted. Methods: The PubMed, Embase, and Cochrane Library databases were searched up to August 1, 2017, for randomized controlled trials that reported the incidence of AF in patients with and without the use of algorithms for minimizing ventricular pacing. Results: Eleven studies comprising 5705 participants (61% males, mean age 71 years [standard deviation 11 years]) were finally included in the analysis. The mean follow-up duration was 24 months. Use of algorithms for minimizing ventricular pacing significantly reduced the incidence of AF, with an odds ratio of 0.74 (95% confidence interval 0.55–1.00; P<0.05). There was moderate heterogeneity among studies (I 2=63%). Conclusions: The incidence of AF was reduced by 26% with use of algorithms for minimizing ventricular pacing. The incorporation of such algorithms in routine clinical practice should in theory lead to a decrease in AF-related morbidity and 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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".