Beneficial effects of right ventricular non-apical vs. apical pacing: a systematic review and meta-analysis of randomized-controlled trials
Bibliographic record
Abstract
AIMS: Previous studies have suggested that right ventricular apical (RVA) pacing may have deleterious effects on left ventricular function. Whether right ventricular non-apical (RVNA) pacing offers a better alternative to RVA pacing is unclear. We aimed to conduct a systematic review and meta-analysis of randomized-controlled trials (RCTs) in order to compare the mid- and long-term effects of RVA and RVNA pacing. METHODS AND RESULTS: We systematically searched the Cochrane library, EMBASE, and MEDLINE databases for RCTs comparing RVA with RVNA pacing over >2 months follow-up. Data were pooled using random-effects models. Fourteen RCTs met our inclusion criteria involving 754 patients. Compared with subjects randomized to RVA pacing, those randomized to RVNA pacing had greater left ventricular ejection fractions (LVEF) at the end of follow-up [13 RCTs: weighted mean difference (WMD) 4.27%, 95% confidence interval (CI) 1.15%, 7.40%]. RVNA had a better LVEF at the end of follow-up in RCTs with follow-up ≥12 months (WMD 7.53%, 95% CI 2.79%, 12.27%), those with <12 months of follow-up (WMD 1.95%, 95% CI 0.17%, 3.72%), and those conducted in patients with baseline LVEF ≤40-45% (WMD 3.71%, 95% CI 0.72%, 6.70%); no significant difference was observed in RCTs of patients whose baseline LVEF was preserved. Randomized-controlled trials provided inconclusive results with respect to exercise capacity, functional class, quality of life, and survival. CONCLUSIONS: While RCTs suggest that LVEF is higher with RVNA than with RVA pacing, there remains a need for large RCTs to compare the safety and efficacy of RVNA and RVA 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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".