Modeling the influence of the VV delay for CRT on the electrical activation patterns in absence of conduction through the AV node
Bibliographic record
Abstract
From epidemiological studies, it has been shown that 0.2% of men and 0.1% of women suffer from a degree of atrioventricular (AV) block. In recent years, the palliative treatment for third degree AV block has included Cardiac Resynchronization Therapy (CRT). It was found that patients show more clinical improvement in the long term with CRT compared with single chamber devices. Still, an important group of patients does not improve their hemodynamic function as much as could be expected. A better understanding of the basis for optimizing the devices settings (among which the VV delay) will help to increase the number of responders. In this work, a finite element model of the left and right ventricles was generated using an atlas-based approach for their segmentation, which includes fiber orientation. The electrical activity was simulated with the electrophysiological solver CARP, using the Ten Tusscher et al. ionic model for the myocardium, and the DiFrancesco-Noble for Purkinje fibers. The model is representative of a patient without dilated or ischemic cardiomyopathy. The simulation results were analyzed for total activation times and latest activated regions at different VV delays and pre-activations (RV pre-activated, LV pre-activated). To optimize the solution, simulations are compared against the His-Purkinje network activation (normal physiological conduction), and interventricular septum activation (as collision point for the two wave fronts). The results were analyzed using Pearson's coefficient of correlation for point to point comparisons between simulation cases. The results of this study contribute to gain insight on the VV delay and how its adjustment might influence response to CRT and how it can be used to optimize the treatment.
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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 | 0.000 |
| 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 | 0.001 | 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".