Abstract 14870: Progression of Paroxysmal to Persistent Af in Patients Awaiting AF Ablation
Bibliographic record
Abstract
Objective: Atrial fibrillation (AF) is a progressive disease for which ablation has become an important treatment option. Success rates have been shown to be significantly higher while AF has not yet progressed to persistent. In a general AF-population the HATCH-score has been proposed to predict the risk of progression to persistent AF. However, little is known about predictors of progression in patients awaiting AF ablation. Methods/Results: We performed a retrospective, single centre investigation of patients with paroxysmal AF at the time they were placed onto our AF ablation waiting list and evaluated possible risk factors for the progression of AF until the time of the actual ablation. Of 564 patients (median age 60.4 (±15.1) years, 194 (34.4%) female) 60 (11%) progressed from paroxysmal to persistent AF during a median waiting time of 291 (±244.3) days. In patients that progressed to persistent AF, ablation took significantly longer (180±99 min vs. 157±85min; p 0.009), had a tendency to require longer RF-energy delivery (68.9±40 min vs. 61.8±44 min; p 0.052) and was associated with a higher rate of recurrence (53.3% vs. 39.1%; p<0.001). Patients that did progress to persistent AF had tied significantly more antiarrhythmic drugs (1 (±2) vs. 1(±1); p 0,048) and more frequently had a history of amiodaron treatment (21.7% vs. 11.9%; p 0.03). The previously proposed HATCH-score was only a poor predictor of AF progression (AUC 0.54). Furthermore, none of the individual HATCH-score parameters was a significantly predicted the progression of AF in our population. However, a left atrial (LA) diameter of more than 45mm (OR 3.46, p< 0.001) and heart failure (OR 3.11, p 0.036) were strong and independent predictors of AF progression in multivariable analysis. Conclusion: Patients with an increased LA-diameter or heart failure have a significantly increased risk to progress to persistent AF. If ablation is considered in such a patient it should be conducted as soon as possible to prevent progression to 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".