Relationship Between Time-to-First Atrial Tachyarrhythmia Recurrence and Atrial Fibrillation Burden: Implications for Trial Design
Bibliographic record
Abstract
BACKGROUND: Atrial tachyarrhythmia recurrence remains the primary end point of clinical trials evaluating therapeutic pharmacological and nonpharmacological interventions for atrial fibrillation (AF). We sought to examine the relationship between the timing of first atrial tachyarrhythmia recurrence and subsequent AF burden. METHODS: We performed a patient-level analysis of 2 multicenter prospective parallel-group, single-blinded randomized clinical trials that used continuous rhythm monitoring after rhythm intervention. Patients with paroxysmal AF were stratified based on the month where the first recurrence of atrial tachyarrhythmia was observed, after a 2-month blanking period. AF burden was calculated as the time spent in AF at 1 year after first recurrence and over 3 years of follow-up. RESULTS: A total of 51.7% of patients experienced a recurrence of atrial tachyarrhythmia in the first year of follow-up. A first recurrence of atrial tachyarrhythmia occurred in 56.5% of patients within the third month post treatment initiation, with 79.5% of all recurrences detected by month 6 and 90.2% detected by month 9. The median postrecurrence AF burden was significantly greater in those with first recurrence in month 3 (1.04% [interquartile range, 0.23–5.05]) when compared with those patients with first recurrence between months 4 to 12 (0.13% [interquartile range, 0.04–0.63]; P <0.0001 versus month 3) and those with first recurrence after month 12 (0.05% [interquartile range, 0.01–0.20]; P <0.0001 versus month 3). CONCLUSIONS: Atrial tachyarrhythmia recurrence after rhythm control intervention for paroxysmal AF is not uniform, with earlier recurrences being associated with higher AF burden on follow-up. These findings suggest that the timing of arrhythmia recurrence is of critical importance, with later recurrences being of progressively lesser clinical significance.
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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 | 0.407 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".