Optimizing Patient Selection for Physiological Pacing in Bradyarrhythmia: Factors Associated With High Ventricular Pacing Burden
Bibliographic record
Abstract
Background: Right ventricular (RV) pacing is established as the most common ventricular pacing (VP) strategy for patients with symptomatic bradyarrhythmia. Some patients with high VP burden suffer deterioration of left ventricular (LV) function, termed pacing-induced cardiomyopathy (PICM). Patients who pace > 20% of the time from the RV apex are at increased risk of PICM, but independent predictors of increased RV pacing burden have not been elucidated in those who have a permanent pacemaker (PPM) inserted for bradyarrhythmia. Methods: We aimed to identify factors that are associated with increased VP burden > 20%, hence determining those at risk for resultant PICM. In this retrospective cohort study, we identified the most recent 300 consecutive cardiac implantable electronic device (CIED) implants in our center and collected past medical history, electrocardiogram (ECG), echo, medication and pacemaker check data. Results: A total of 236 individuals met inclusion criteria. Of the patients, 35% had RV pacing burden < 20%, while 65% had VP burden ≥ 20%; 96.2% of patients with complete heart block (CHB) paced > 20% (P = 0.002). Utilization of DDD or VVI (75.2% and 89.2% of patients, respectively) without mode switch algorithms was associated with VP > 20% (P < 0.001). Male or previous coronary artery bypass grafting (CABG) patients also statistically paced > 20%. Other factors trending towards significance included prolonged PR interval, atrial fibrillation or more advanced age. Conclusion: High-grade atrioventricular (AV) block was associated with an RV pacing burden > 20% over 3 years but this was not consistent in patients with only transient episodes of high-grade AV block. We found a significant association between high VP% and male sex, previous CABG and the absence of mode switching algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".