Investigating the Role of the Coronary Vasculature in the Mechanisms of Defibrillation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".