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Abstract 4136812: Optimal Pacing for Cerebral Perfusion: Elucidating Relationship Between Resting Pacemaker Heart Rate and Pulse Pressure

2024· article· en· W4404302186 on OpenAlexaff
Ahmed Moustafa, M. Firoz Mian, Habib Khan

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCardiologyPulse pressureInternal medicineCerebral perfusion pressureHeart ratePulse (music)PerfusionArtificial cardiac pacemakerBlood pressureAnesthesia

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.060
GPT teacher head0.354
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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