Association of Cardiac CT‐Derived Epicardial Adipose Tissue With Atrial Fibrillation in Patients Without Left Atrial Fibrosis as Defined by Endocardial Voltage Mapping
Bibliographic record
Abstract
INTRODUCTION: Epicardial adipose tissue (EAT) is often associated with atrial fibrosis, and both can provide the substrate for atrial fibrillation (AF). However, most AF patients have no evidence of left atrial (LA) fibrosis based on bipolar voltage mapping. We determined whether EAT differs in AF patients without LA fibrosis compared to matched controls without AF. METHODS: Patients undergoing cardiac CT before first-time AF catheter ablation were prospectively enrolled. LA bipolar voltage mapping was performed, and patients were divided into -LVZ (LA low voltage zones < 5% of LA surface area; no fibrosis) and +LVZ (LA low voltage zones ≥ 5%; fibrosis). A control group without AF was matched to -LVZ patients. EAT was quantified on CT using standard signal thresholding to quantify total and regional volumes. AF patients were followed for 1-year postablation to assess atrial arrhythmia (AA) recurrence. RESULTS: -LVZ (n = 50) had higher total EAT volumes than matched controls (n = 48) (79 [58-109] vs. 51 [37-73] cm³, p < 0.001), higher LA EAT (9 [6.3-12] vs. 4.2 [2.9-5.8] cm³, p < 0.001), higher posterior LA EAT (9.7 [6.4-12] vs. 5.9 [2.8-7.2] cm³, p < 0.001) and higher right atrial EAT (7.3 [5.1-9.9] vs. 4.8 [3.2-6.5] cm³, p < 0.001). These differences remained even after correcting EAT for BMI and LA volumes. There were no significant differences in EAT volumes between -LVZ and +LVZ (n = 25). There was no significant association between EAT and AF recurrence postablation. CONCLUSION: EAT volume is greater in AF patients without evidence of LA fibrosis compared to matched controls without AF. These findings support an association of EAT with AF pathogenesis even in the absence of LA fibrosis.
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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) | 0.001 | 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".