Left atrial deceleration outperforms regional conduction velocity in predicting arrhythmia recurrence following atrial fibrillation ablation
Bibliographic record
Abstract
Background The slowest regional conduction velocity (CV min ) is associated with atrial arrhythmia (AA) recurrence following atrial fibrillation (AF) ablation; however, the role of conduction deceleration has not been investigated. Objective The study sought to assess whether true deceleration (TD) is a better marker than CV min in identifying abnormal left atrial (LA) substrate and AA recurrence in patients undergoing de novo pulmonary vein isolation (PVI). Methods Eighty AF patients and 6 control subjects underwent LA electroanatomic mapping during atrial pacing. The LA was divided into 6 anatomical regions and the regional low-voltage area (LVA), CV min , and maximum true deceleration (TD max ) were quantified. TD was calculated as the largest continuous decline in CV along the propagating wavefront divided by the change in activation time. AF patients underwent PVI and AA recurrence was assessed during 12-month follow-up. Results A median of 1 to 2 TDs were found in each LA region of AF patients, and the TD max only weakly correlated with the regional CV min . AF patients with AA recurrence had a significantly larger LVA, lower CV min , and greater TD max on the anterior wall. Multivariate modeling demonstrated that the TD max (when >110 m/s 2 ) and not the CV min (when <0.2 m/s) predicted AA recurrence (C-statistic = 0.74). Clinical TD sites (defined as TD max >110 m/s 2 ) only colocalized with LVA sites in a minority of LA regions (range 13%–44%) and were absent from control subjects. Conclusion TD is a novel metric for quantifying LA remodeling and predicting AA recurrence post-PVI that outperforms CV min . This may guide future trials focusing on improving success from substrate-based AF ablation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".