Computational assessment of arrhythmia potential in the heterogeneously perfused ventricle
Bibliographic record
Abstract
Abstract Background The heterogeneity in the human left ventricle is augmented by heterogeneous perfusion defects in dialysis patients. We hypothesized that ischemic zones generated by heterogeneous perfusion are a cause of clinically observed post-dialysis arrhythmia. This preliminary study assessed the arrhythmia potential in a heterogeneously perfused 2D human ventricle computational model. Aim Our aim was to ascertain a relationship between the number of ischemia zones and incidence of multiple re-entrant waves in a 2D model of the human ventricle. Methods A human ventricle action potential model was modified to include the adenosine triphosphate (ATP) sensitive potassium current. Within ischemic zones, cell electrophysiological alterations due to ischemia were implemented as increased extracellular potassium, reduced intracellular ATP concentrations, as well as reduced upstroke current conductances. The cell model was incorporated into a spatial 2D model. The inter-cellular gap junction coupling was adjusted to simulate slow conduction in dialysis patient hearts. CT imaging data of the heart obtained during dialysis was analysed to estimate the approximate spatial size of ischemic zones. An ischemic border zone between the normal and central ischemic zones was implemented which had smoothly varying electrophysiological parameters. Arrhythmic potential was assessed using the paths of the centres of the re-entrant waves, called tip trajectories, and dominant frequency maps. Results Extracellular potassium elevated the resting potential and I KATP reduced the action potential’s duration. In the absence of ischemic zones, the propensity of the model to induce multiple re-entrant waves was low. The inclusion of ischemic zones provided the substrate for initiation of re-entrant wave fibrillation. The dominant frequency which measured the highest rate of pacing in the tissue increased drastically with the inclusion of ischemic zones, going from 3 Hz in the pre-dialysis state to over 6 Hz in the post-dialysis state. Re-entrant wave tip numbers increased from 1 tip in the pre-dialysis case to 34 in the post-dialysis case, a 34 fold increase. The increase of tip number was found to be strongly correlated to tissue heterogeneity in terms of ischemic zone numbers. Computational factors limiting a more extensive simulation of cause-effect combinations were identified. Conclusions A dialysis session restores systemic homeostasis, but promotes deleterious arrhythmias. Structure-function mechanistic modelling will permit patient-specific assessment of health status. Such an effort is expected to lead to application of wider physical sciences methods in the improvement of the lives of critically ill patients. High performance computing is a crucial requirement for such mechanistic assessment of health status.
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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 | 0.000 |
| 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".