Relationship Between Pacemaker Dependency and the Effect of Pacing Mode on Cardiovascular Outcomes
Bibliographic record
Abstract
BACKGROUND: A recently completed trial, the Canadian Trial of Physiological Pacing (CTOPP), showed that physiological pacing did not significantly reduce mortality, stroke, or heart failure hospitalization, but it did show that atrial fibrillation occurred less frequently in patients with physiological pacing. Many pacemaker patients experience only transient bradyarrhythmias with an adequate unpaced heart rate (UHR) and are not pacemaker-dependent. The purpose of the present analysis was to determine if pacemaker-dependent patients have an increased benefit from physiological pacing compared with non-pacemaker-dependent patients. METHODS AND RESULTS: Of 2568 patients included in the CTOPP trial, 2244 patients had a pacemaker dependency test performed at the first follow-up visit. The yearly event rate of cardiovascular death or stroke steadily increased with decreasing UHR in the ventricular pacing group, but it remained constant in the physiological pacing group. When the patients were subdivided to UHR </=60 bpm or >60 bpm, there was an interaction between pacing mode treatment and UHR subgroup. The Kaplan-Meier plot confirmed a physiological pacing advantage only in the UHR </=60 bpm subgroup. This differential effect was also present for the outcomes of cardiovascular death and total mortality. CONCLUSIONS: This study demonstrated that UHR at first follow-up has an important influence on how pacing mode selection affects cardiovascular death and total mortality. Pacemaker-dependent patients with low UHR will probably be paced frequently and will likely benefit from physiological pacing. In contrast, non-pacemaker-dependent patients will likely be paced infrequently and may not benefit from physiological 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".