Amiodarone vs Dronedarone for Atrial Fibrillation: A Retrospective Cohort Study
Bibliographic record
Abstract
BackgroundAtrial fibrillation is one of the most common arrhythmias, but the optimal drug choice for a rhythm-control strategy remains uncertain.MethodsThis article reports on a retrospective cohort claims database study conducted using the Truven Health Market Scan Commercial Claims and Encounters and Medicare Supplemental databases. Patients with a new diagnosis of atrial fibrillation, and a discharge date between 2011 and 2015, were included. The exposure variables of interest were a discharge prescription for amiodarone or dronedarone. The average treatment effect for the composite of total mortality or a repeat cardiovascular (CV)-related hospitalization was the primary outcome. Sensitivity analyses with other treatment effect metrics were performed. Baseline covariate imbalances between the groups were adjusted using propensity-score methods with inverse probability weighting.ResultsA total of 1735 patients were discharged on amiodarone, and 338 were discharged on dronedarone, with a median follow-up time of 357 days. A total of 43 (12.7%) CV-related hospitalizations occurred in the dronedarone group, and 146 (8.4%) occurred in the amiodarone group (risk difference 4.3%, 95% confidence interval [CI] 0.4%-8.3%, P = 0.02). A total of 4 (1.2%) deaths occurred in the dronedarone group, and 31 (1.8%) deaths occurred with amiodarone (risk difference -0.6%, 95% CI -2.1%-0.9%, P = 0.6). After adjusting for baseline covariates, the dronedarone hazard ratio for the composite endpoint was 1.47 (95% CI 1.01-2.12). This result was generally robust to sensitivity analyses.ConclusionIn this incident cohort of patients hospitalized for atrial fibrillation, compared to those discharged on amiodarone, patients who received a dronedarone discharge prescription had an increase in the composite endpoint of recurrent CV-related hospitalization and death, over a median 1-year follow-up period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".