Reduction in unnecessary ventricular pacing fails to affect hard clinical outcomes in patients with preserved left ventricular function: a meta-analysis
Bibliographic record
Abstract
Aims: Several pacing modalities across multiple manufacturers have been introduced to minimize unnecessary right ventricular pacing. We conducted a meta-analysis to assess whether ventricular pacing reduction modalities (VPRM) influence hard clinical outcomes in comparison to standard dual-chamber pacing (DDD). Methods and Results: An electronic search was performed using Cochrane Central Register, PubMed, Embase, and Scopus. Only randomized controlled trials (RCT) were included in this analysis. Outcomes of interest included: frequency of ventricular pacing (VP), incident persistent/permanent atrial fibrillation (PerAF), all-cause hospitalization and all-cause mortality. Odds ratios (OR) were reported for dichotomous variables. Seven RCTs involving 4119 adult patients were identified. Ventricular pacing reduction modalities were employed in 2069 patients: (MVP, Medtronic Inc.) in 1423 and (SafeR, Sorin CRM, Clamart) in 646 patients. Baseline demographics and clinical characteristics were similar between VPRM and DDD groups. The mean follow-up period was 2.5 ± 0.9 years. Ventricular pacing reduction modalities showed uniform reduction in VP in comparison to DDD groups among all individual studies. The incidence of PerAF was similar between both groups {8 vs. 10%, OR 0.84 [95% confidence interval (CI) 0.57; 1.24], P = 0.38}. Ventricular pacing reduction modalities showed no significant differences in comparison to DDD for all-cause hospitalization or all-cause mortality [9 vs. 11%, OR 0.82 (95% CI 0.65; 1.03), P= 0.09; 6 vs. 6%, OR 0.97 (95% CI 0.74; 1.28), P = 0.84, respectively]. Conclusion: Novel VPRM measures effectively reduce VP in comparison to standard DDD. When actively programmed, VPRM did not improve clinical outcomes and were not superior to standard DDD programming in reducing incidence of PerAF, all-cause hospitalization, or all-cause 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.052 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".