Intracardiac electrogram analysis may allow for prediction of lesion transmurality after pulsed field ablation of atria in a porcine model
Bibliographic record
Abstract
Background: Pulsed field ablation (PFA) is a novel cardiac ablation modality with an increasing clinical acceptance in treatment of atrial fibrillation due to its clinical efficacy and excellent safety profile. However, intraprocedural guidance for PFA to ensure durable pulmonary vein isolation (PVI) is lacking. Objective: We quantified changes in intracardiac electrograms (iEGMs) following PFA and radiofrequency ablation (RFA) and investigated their applicability for prediction of lesion transmurality. Methods: We induced 38 atrial lesions using PFA or RFA in 5 swine and monitored iEGMs continuously for up to 30 minutes postablation. The most characteristic changes in iEGMs were quantified after the decomposition using discrete wavelet transform, which allowed us to analyze the effects in separate frequency bandwidths. Results: After the ablation, we observed a reduction of bipolar iEGM amplitude (for PFA and RFA) and an increase in unipolar iEGM amplitude (predominantly for PFA). These changes were due to 2 mechanisms with different frequency content. The low-frequency content of unipolar iEGMs (1-16 Hz) further enabled us to discriminate between transmural and nontransmural lesions in the case of PFA. The rate of reduction of initially increased current-of-injury effect reflected in the low-frequency content of unipolar iEGMs within the first few minutes postablation was significantly higher and more pronounced for nontransmural lesions. Conclusion: This study shows that unipolar iEGMs can be used to differentiate between transmural and nontransmural atrial lesions within minutes after PFA in a porcine model, with implications for development of intraprocedural guidance of PFA procedures.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".