Abstract 12122: Persistence With Antiarrhythmic Agents in Atrial Fibrillation: Insights From the Veterans Health Administration
Bibliographic record
Abstract
Background: There is limited information on patterns of persistence with antiarrhythmic drugs (AADs) in atrial fibrillation (AF). Nonpersistence may represent failure of AADs or patient/physician choice, therefore, we characterized AAD persistence patterns following discharge in patients admitted to the hospital with AF. Methods: This retrospective cohort study of patients with AF, diagnosed on index AF admission in the Veterans Health Administration from 2001-2010 used national inpatient, outpatient, pharmacy claims, and vital status data to identify patients and characterize persistence. We identified prescriptions for all initial AAs dispensed after AF diagnosis. Patients were considered nonpersistent if they had >30-day gap in filling a repeat AAD prescription. Results: Of 206,331 patients with AF, 43,262 (21%) received an AAD, age was 68.1±10.4 years, 98% were male, and CHA2DS2-VASc score was 2 (IQR: 1-3). Patients were followed for 2 years after their index AAD prescription. Among AAD-treated patients, most received amiodarone (68%). In the 2 years after their first AAD was dispensed, 43% discontinued their AAD. The lowest rates of nonpersistence were with dofetilide (29%) and sotalol (33%). Median time to nonpersistence was 143 days (IQR 90, 289). Of patients who were nonpersistent (>30-day gap), 46% restarted the same AAD, 7% switched to a different AAD, 33% switched to a rate control strategy, and 14% stopped the AAD strategy completely. Conclusion: Among patients with AF, 4 in 10 patients are nonpersistent with their initial AAD. Nearly 50% of patients restart their same AAD after nonpersistence, suggesting intermittent medication use likely due to patient nonadherence. One-third of patients who had initiated a rhythm control strategy with an AAD switched to a rate control strategy, indicating possible AAD failure. Given the magnitude of nonpersistence, future studies on predictors and outcomes of nonpersistence with AAD in AF are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".