Clinical Predictors of Arrhythmia Recurrences Following Pulmonary Vein Antrum Isolation for Atrial Fibrillation: Predicting Arrhythmia Recurrence Post-PVAI
Bibliographic record
Abstract
INTRODUCTION: Pulmonary vein antrum isolation (PVAI) is an accepted treatment for atrial fibrillation (AF) refractory to medical therapy. The purpose of this study was to identify the patient, procedural, and follow-up factors associated with arrhythmia recurrences following PVAI. METHODS AND RESULTS: Clinical data were prospectively collected on all 385 consecutive patients who had 530 PVAI (age 58 ± 11 years, 63% paroxysmal AF-PAF, follow-up 2.8 ± 1.2 years) between February 2004 and March 2009. ECGs were recorded at each follow-up visit with Holter monitoring 1, 3, 6, and 12 months following PVAI and every 6 months thereafter. Recurrences < 3 months post-PVAI were defined as early, 3 months-1 year post-PVAI as late, and > 1 year post-PVAI as very late. Relationship between predictor variables and outcomes was modeled using Cox proportional hazards analysis. Late recurrences occurred in 42% with a lower rate among PAF versus non-PAF patients (39% vs 56%, P = 0.001). Of the 256 patients with ≥ 1-year follow-up, 121 (47%) had no arrhythmia off antiarrhythmic drugs (AADs) 1 year post-PVAI; 36 (30%) of these had a very late recurrence. In multivariate analysis, non-PAF, hypertension, and prior AAD failure predicted recurrence. When entered into the model, early recurrences remained the only predictor of late recurrences. CONCLUSION: Patients with non-PAF, hypertension, and prior failure of multiple AAD were more likely to experience arrhythmia recurrence post-PVAI. Early recurrences were the strongest predictor of late recurrences. Late and very late recurrences following PVAI were common and should be considered when planning long-term AF patient management.
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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 | 0.004 |
| 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 | 0.001 |
| 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".