Elucidating the Relationship Between Pacemaker Heart Rates and Pulse Pressure: A Clue to Optimal Programming in Elderly Patients with Cardiovascular Risk
Bibliographic record
Abstract
Background: Low diastolic blood pressure (DBP) and high pulse pressure (PP), markers of vascular stiffness, are linked to higher stroke risk and cognitive impairment. Increasing resting heart rate (HR) in pacemaker patients could lower PP and enhance cerebral perfusion. This study examines how raising resting HR affects PP in patients with higher vascular risk. Methods: A cross-sectional interventional study involved 21 patients (mean age, 77 years; 9 male, 12 female) over age 65 years with dual-chamber permanent pacemakers and normal ventricular function. Hemodynamic parameters were measured at 5-minute intervals at baseline and every 10 beats per minute (bpm) up to 100 bpm using the Finapres NOVA plethysmograph. The effects of higher pacing rates on PP, systolic BP, DBP, and cardiac output were analyzed. Results: = 0.25) among different HRs. All patients remained asymptomatic, and no adverse events were observed during the study. The findings indicated variability in response, with most patients showing decreased PP at higher HRs. Conclusions: An increased HR to 70 bpm reduced PP in most patients with dual-chamber permanent pacemakers and sinus rhythm, suggesting a way to improve hemodynamics in patients with poor vascular compliance. Customized HR settings may optimize PP and reduce the risk of cognitive decline or stroke. Further research is needed to evaluate for risks of pacemaker-induced cardiomyopathy in individuals requiring frequent ventricular 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".