Change in Atrial Fibrillation Burden over Time in Patients with Nonpermanent Atrial Fibrillation
Bibliographic record
Abstract
Introduction. The natural course of atrial fibrillation (AF) is not well defined. We aimed to investigate the change in AF burden over time and its associated risk factors among AF patients. Methods. Fifty-four participants with recently documented paroxysmal or persistent AF were enrolled. Main exclusion criteria were permanent AF or previous catheter ablation for AF. AF burden was calculated as time in AF divided by total recording time using yearly continuous 7-day Holter-ECG recordings. A relative change ≥10% or an absolute change >0.5% in AF burden between two yearly Holter-ECG recordings was considered significant. Results. Mean age was 67 years, 72% were men. The proportion of patients with no recorded AF increased from 53.7% at baseline to 78.6% ( p=0.1 ) after 4 years of follow-up. In 7-day Holter-ECG recordings performed after baseline, 23.7% of participants had a decrease and 23.7% an increase in AF burden. In separate mixed effect models, AF burden over time was associated with prior stroke (β 42.59, 95% CI (23.40; 61.77); p<0.0001 ), BNP (β 0.05, CI (0.02; 0.09); p=0.005 ) end-diastolic (β 0.49, CI (0.23; 0.74); p=0.0003 ) as well as end-systolic (β 0.25, CI (0.05; 0.46); p=0.02 ) left atrial volume, left atrial ejection fraction (β −0.43, CI (−0.76;−0.10); p=0.01 ), E-wave (β 36.67, CI (12.96; 60.38); p=0.003 ), and deceleration time (β −0.1, CI (−0.16; −0.05); p=0.002 ). In a multivariable model, a history of prior stroke (β 29.87, CI (2.61; 57.13); p=0.03 ) and BNP levels (β 0.05, CI (0.01; 0.08); p=0.007 ) remained significantly associated with AF burden. Conclusions. Few patients with paroxysmal or persistent AF have AF episodes on yearly 7-day Holter-ECG recordings, and AF progression is rare. AF burden was independently associated with a history of prior stroke and BNP levels.
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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 | 0.002 |
| 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 | 0.000 |
| 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".