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Record W4226374387 · doi:10.1113/jp283112

Cerebral haemodynamics during arrhythmia in health, ischaemic heart disease and heart failure with reduced ejection fraction, and in a preclinical swine model

2022· article· en· W4226374387 on OpenAlexafffundabout

Bibliographic record

VenueThe Journal of Physiology · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsQueen's UniversityWestern UniversityMcMaster UniversityUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research FoundationHeart and Stroke Foundation of Canada
KeywordsHeart failureEjection fractionCerebral perfusion pressureHemodynamicsCerebral blood flowPopulationHeart diseasePerfusionBlood pressure

Abstract

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Abstract Ventricular arrhythmias are associated with neurological impairment and could represent a source of cerebral hypoperfusion. In the present study, data from healthy individuals ( n = 11), patients with ischaemic heart disease (IHD; ejection fraction >40%; n = 9) and patients with heart failure with reduced ejection fraction (HFrEF; EF = 31 (5)%, n = 11), as well as data from swine surgeries, where spontaneous ventricular arrhythmias were observed during cerebrovascular examination (transcranial Doppler ultrasound in humans and laser Doppler in swine) were analysed retrospectively to investigate the effect of arrhythmia on cerebral microvascular haemodynamics. A subset of participants also completed the Montreal Cognitive Assessment (MoCA). Middle cerebral artery mean blood velocity (MCA Vmean ) decreased during premature ventricular contraction (PVC) in all groups, and data from swine indicate PVCs reduced cerebral microvascular perfusion. Overall MCA Vmean was decreased in the HFrEF vs . control group. Further, %∆MCA Vmean /%∆mean arterial pressure during the PVC was greater in the HFrEF vs . control group and was correlated with decreased MoCA scores. Subanalysis of HFrEF data revealed that during bigeminy MCA Vmean decreased owing to reductions during irregular beats only. During non‐sustained ventricular tachycardia, MCA Vmean decreased but recovered above baseline upon return to sinus rhythm. Also, haemodynamic perturbations during and following the PVC were greater in the brachial artery vs . the MCA. Therefore, ventricular arrhythmias decreased indices of cerebral perfusion irrespective of IHD or HFrEF. The relative magnitude of arrhythmia‐induced haemodynamic perturbations appears to be population specific and arrhythmia type and organ dependent. The cumulative burden of arrhythmia‐induced deficits may exacerbate existing cerebral hypoperfusion in HFrEF and contribute to neurological abnormalities in this population. Key points Irregular heartbeats are often considered benign in isolation, but individuals who experience them frequently have a higher prevalence of cerebrovascular and/or cognitive associated disorders. How irregular heartbeats affect blood pressure and cerebral haemodynamics in healthy and cardiovascular disease patients, those with and without reduced ejection fraction, remains unknown. Here it was found that in the absence of symptoms associated with irregular heartbeats, such as dizziness or hypotension, single, multiple non‐sustained and sustained irregular heartbeats influence cerebral haemodynamics in a population‐specific, arrhythmia‐type and organ‐dependent manner. Relative deficits in the index of cerebral blood flow normalized to relative deficits in blood pressure were greatest in patients with heart failure with reduced ejection and inversely related with cognitive performance. Chronic arrhythmias may exacerbate existing cerebral hypoperfusion in heart failure with reduced ejection fraction, thereby providing a mechanistic link between otherwise benign irregular heartbeats and cognitive dysfunction, independent of embolism.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.291
Teacher spread0.272 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations8
Published2022
Admission routes3
Has abstractyes

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