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Record W2118975313 · doi:10.1161/circep.111.965095

Investigating the Role of the Coronary Vasculature in the Mechanisms of Defibrillation

2011· article· en· W2118975313 on OpenAlexaff
Martin J. Bishop, Gernot Plank, Edward J. Vigmond

Bibliographic record

VenueCirculation Arrhythmia and Electrophysiology · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of Calgary
FundersNational Heart, Lung, and Blood InstituteWellcome Trust
KeywordsDefibrillationVentricular fibrillationReentryVentricleShock (circulatory)CardiologyFibrillationInternal medicineBidomain modelMedicineBiomedical engineeringNeuroscienceBiologyAtrial fibrillation

Abstract

fetched live from OpenAlex

BACKGROUND: The direct role of coronary vessels in defibrillation, although hypothesized to be important, remains to be elucidated. We investigated how vessel-induced virtual electrode polarizations assist reentry termination. METHODS AND RESULTS: A highly anatomically detailed rabbit ventricular slice bidomain computer model was constructed from 25-μm magnetic resonance data, faithfully representing both structural and electric properties of blood vessels. For comparison, an equivalent simplified model with intramural cavities filled in was also built. Following fibrillation induction, 6 initial states were selected, and biphasic shocks (5-70 V) were applied using a realistic implanted cardioverter-defibrillator electrode configuration. A fundamental mechanism of biphasic defibrillation was uncovered in both models, involving successive break excitations (after each shock phase) emanating from opposing myocardial surfaces (in septum and left ventricle), which rapidly closed down excitable gaps. The presence of vessels accelerated this process, achieving more-rapid and successful defibrillation. Defibrillation failed in 5 cases (all because of initiation of new activity) compared with 8 with the simplified model (5/8 failures because of surviving activity). At stronger shocks, virtual electrodes formed around vessels, rapidly activating intramural tissue because of break excitations, assisting the main defibrillation mechanism, and eliminating all activity <15 ms of shock end in 60% of successful shocks (36% in simplified model). Subsequent analysis identified that only vessels >200 μm in diameter participated through this mechanism. Consequently, wavefronts could survive intramurally in the simplified model, leading to reentry and shock failure. CONCLUSIONS: We provide new insight into defibrillation mechanisms by showing how intramural blood vessels facilitate more-effective elimination of existing wavefronts, rapid closing down of excitable gaps, and successful defibrillation and give guidance toward the required resolution of cardiac imaging and model generation endeavors for mechanistic defibrillation analysis.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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 teacher head, 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

Citations26
Published2011
Admission routes1
Has abstractyes

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