Abstract 18707: The Baseline Image Intensity Ratio, a Surrogate for Pre-existing Left Atrial Fibrosis, is Associated With Recurrence of Atrial Fibrillation Following Pulmonary Vein Isolation
Bibliographic record
Abstract
Introduction: Catheter ablation of AF is beneficial for many patients, but it is associated with a high AF recurrence rate that previously has been correlated with left atrial (LA) fibrosis on late gadolinium enhanced (LGE) MRI. We sought to test the association of LA fibrosis with recurrent post-ablation AF, using the image intensity ratio (IIR), a novel and standardized measure for quantification and inter-patient comparisons of LA fibrosis. Methods: Fifty consecutive AF patients (59.2±9.0 years, 74% men, 44% persistent AF) were enrolled and underwent LGE-MRI prior to initial AF ablation procedure. The local IIR was defined as the LA myocardial signal intensity of each sector in axial plane divided by the mean LA blood pool image intensity. The cohort was divided into four groups using mean IIR thresholds of 0.95. The mean follow-up was 9.0±4.6 months, and recurrences were documented with routine follow-up and symptom-prompted electrocardiography as well as phone calls. Results: Of 50 total patients, 11 (22%) experienced AF recurrence (4/28 paroxysmal AF (14.3%), and 7/ 22 persistent AF (31.8%)). The baseline IIR was higher in paroxysmal AF patients that experienced recurrence (1.0±0.18 vs. 0.81±0.08, p=0.001), but was similar in persistent AF patients with or without recurrence (0.84±0.12 vs. 0.88±0.14, p=NS). The Figure illustrates Kaplan-Meier survival analyses based upon baseline IIR derived patient groups. Conclusions: Baseline left atrial fibrosis is strongly associated with AF recurrence in patients with paroxysmal AF, but is unassociated with outcomes in patients with persistent AF.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".