Prevalence of endoepicardial asynchrony and breakthrough patterns in a bilayer computational model of heterogeneous endoepicardial dissociation in the left atrium
Bibliographic record
Abstract
BACKGROUND: Transmural propagation and endoepicardial delays in activation observed in patients with atrial fibrillation are hypothesized to be associated with structural remodeling and endoepicardial dissociation. We aim to explore in a computational model how the distribution of delays and the rate of endo- and epicardial breakthrough activation patterns are affected by fibrosis and heterogeneous layer dissociation. METHODS: A bilayer interconnected cable model of the left atrium was used to simulate a total of 4,800 episodes of atrial fibrillation on 960 different arrhythmogenic substrates with up to 30% epicardium-only diffuse fibrosis. Endoepicardial connections were heterogeneously distributed following random spatial patterns (characteristic length scale from 1.6 to 11.4 mm). Intermediate nodes were introduced in the transmural connections to enable the simulation of weaker coupling. This heterogeneous interlayer dissociation divided the atrial bilayer into connected and disconnected regions (from 27 to 48,000 connected regions). Activation time series were extracted in both layers to compute endoepicardial delays and detect breakthrough patterns. RESULTS: Because of epicardial fibrosis, fibrillatory waves were driven by the endocardium, which generated endoepicardial delays. The delays in the connected regions (up to 10 ms, but generally < 5 ms) were prolonged by higher fibrosis density and weaker coupling. Disconnected regions allowed longer delays (> 15 ms) and promoted the occurrence of breakthroughs. These breakthroughs had short lifespan (< 10-20 ms) and were more prevalent with higher fibrosis density and heterogeneous dissociation (larger disconnected regions). Severe remodeling (< 500 connected regions) was needed to produce clinically reported rates (> 0.1 breakthrough/cycle/cm2). CONCLUSION: Heterogeneous endoepicardial dissociation aggravates activation delays and increases the prevalence of epicardial breakthroughs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".