Incidence and Predictors of Atrial Fibrillation Progression
Bibliographic record
Abstract
Background The incidence and predictors of atrial fibrillation (AF) progression are currently not well defined, and clinical AF progression partly overlaps with rhythm control interventions (RCIs). Methods and Results We assessed AF type and intercurrent RCIs during yearly follow-ups in 2869 prospectively followed patients with paroxysmal or persistent AF. Clinical AF progression was defined as progression from paroxysmal to nonparoxysmal or from persistent to permanent AF. An RCI was defined as pulmonary vein isolation, electrical cardioversion, or new treatment with amiodarone. During a median follow-up of 3 years, the incidence of clinical AF progression was 5.2 per 100 patient-years, and 10.9 per 100 patient-years for any RCI. Significant predictors for AF progression were body mass index (hazard ratio [HR], 1.03; 95% CI, 1.01-1.05), heart rate (HR per 5 beats/min increase, 1.05; 95% CI, 1.02-1.08), age (HR per 5-year increase 1.19; 95% CI, 1.13-1.27), systolic blood pressure (HR per 5 mm Hg increase, 1.03; 95% CI, 1.00-1.05), history of hyperthyroidism (HR, 1.71; 95% CI, 1.16-2.52), stroke (HR, 1.50; 95% CI, 1.19-1.88), and heart failure (HR, 1.69; 95% CI, 1.34-2.13). Regular physical activity (HR, 0.80; 95% CI, 0.66-0.98) and previous pulmonary vein isolation (HR, 0.69; 95% CI, 0.53-0.90) showed an inverse association. Significant predictive factors for RCIs were physical activity (HR, 1.42; 95% CI, 1.20-1.68), AF-related symptoms (HR, 1.84; 95% CI, 1.47-2.30), age (HR per 5-year increase, 0.88; 95% CI, 0.85-0.92), and paroxysmal AF (HR, 0.61; 95% CI, 0.51-0.73). Conclusions Cardiovascular risk factors and comorbidities were key predictors of clinical AF progression. A healthy lifestyle may therefore reduce the risk of AF progression.
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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 | 0.000 |
| 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) | 0.000 | 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".