Abstract 4136812: Optimal Pacing for Cerebral Perfusion: Elucidating Relationship Between Resting Pacemaker Heart Rate and Pulse Pressure
Bibliographic record
Abstract
Background: Cognitive decline and dementia are significant global health concerns, with hypertension being a major risk factor. Increasingly, the elderly population is receiving pacemakers, and the pacing rates are set as low as 50bpm. Moreover, the relationship between blood pressure (BP) components, particularly diastolic BP (DBP) and pulse pressure (PP), and the risk of cognitive decline or dementia remains complex. The highest risk is observed in patients with low DBP and concurrently increased PP, likely indicating vascular stiffness. To our knowledge, no studies have assessed the programming of pacemakers to increase heart rates to target DBP and PP improving cerebral perfusion. Objectives: In patients with permanent pacemakers, resting heart rate (HR) can be artificially increased, and its consequence on hemodynamics, including PP, can be easily measured. This study aims to investigate the impact of increasing resting HR on PP in patients with poor vascular compliance. Secondary outcomes include evaluating changes in DBP, SBP, and cardiac output, as well as assessing patient tolerance to higher-paced heart rates through recording subjective symptoms. Methods: The study will employ a prospective cross-sectional interventional design. Digital plethysmography will be utilized to measure hemodynamic parameters, including systolic blood pressure (SBP), DBP, PP, and cardiac output, at baseline resting-paced heart rates. Subsequently, the pacemaker resting heart rate will be incrementally increased to a final rate of 100 beats per minute (bpm), with measurements taken 2-5 minutes after each change. Results: Ten out of twenty patients were recruited, with a mean age of 75.3 years old. The median change in PP from baseline HR of 70 to 100 was -8.7% (IQR -18.8 - +2.8, p= 0.114), SBP 4.2% (IQR 0 – 11.9, p=0.059), and DBP 20.1% (IQR 12.9 -23, p=0.005). There were no adverse outcomes and no patient-reported symptoms during higher pacing rates. Conclusion: This study provides the basis for proof of concept. Since an increased HR decreases PP, this will be subsequently tested against improvement in cerebral perfusion by cerebral Doppler studies, and its impact on cognitive function will be tested. This has the potential to change the standard of practice for patients with pacemakers and allow for collaboration across multiple specialties such as neurology, geriatric medicine and cardiology.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".