Abstract 2446: Determination of Key Sites of CFAE-targeted AF Ablation and Correlation with Cardiac Ganglionic Plexi
Bibliographic record
Abstract
Substrate-targeted AF ablation relies on accurate locating of sites with Complex Fractionated Atrial Electrograms (CFAE). Our objectives were to determine if key sites of CFAE are predictably located and to determine the overlap of these sites with the locations of ganglionic plexi (GP). Utilizing a cardiac CT via image integration, we mapped all sites of CFAE on 54 pts (age 66 ±10 yrs; 78% male; 26% paroxysmal (pAF), 74% persistent or permanent AF (ppAF)) undergoing AF ablation. All patients were in SR at the end of the procedure. Ibutilide was used in 21% pAF vs. 85% ppAF (p<0.001) & DDCV was used in none of the pAF vs. 20% ppAF (p=ns). Mean number of lesions & mean RF duration were 46 ± 27 & 29.5 ±12 min for pAF; 62 ± 23 & 30 ±12 min for ppAF (p=0.04 & p=ns, respectively). A detailed grid of 44 regions in the LA, RA, & CS was created and applied to each CARTOMERGE (mean alignment 2.39 ± 0.43 mm). The prox CS & septal wall anterior to the RSPV were most common sites of CFAE regardless of AF type. The RSPV, LSPV, & anterior wall medial to the LAA were with next most common in pAF and the anterior wall medial to the LAA, the ridge between the LAA & LSPV, and the septal wall anterior to the RIPV were the next in ppAF. A CTI line was done in 21% pAF and 8% ppAF (p=ns) and targeted ablation within any PV antrum in 86% pAF and 83% ppAF (p=ns). Ablation near a GP occurred in >98% of pts (Figure shows pts with ablation on each GP as %pAF / %ppAF). Conclusion: the distribution of substrate that perpetuates AF (as indicated by CFAE) is not homogeneous, and predominant sites are the CS and areas near the PV’s and LAA. A PVI technique utilizing a wide antrum lesion set would affect similar areas in patients with pAF, but not necessarily ppAF.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".