Abstract 281: Adherence and Persistence for Antiarrhythmic and Rate Control Agents in Newly Diagnosed Atrial Fibrillation: Findings from the TREAT-AF Study
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is an emerging epidemic, often treated with medications for rate or rhythm control. Adherence to these medications is important since patients may not receive optimal benefit if not taken as intended. Our study assessed patient adherence to AF medications in the Veterans Administration (VA), the largest US integrated healthcare system. Methods: The Retrospective Evaluation and Assessment of Therapies in AF (TREAT-AF) study is a retrospective cohort study of patients with newly diagnosed AF treated in the VA. National inpatient, outpatient, pharmacy claims and lab data were used to identify patients between 10/1/04 - 9/30/08 and who survived at least one year following diagnosis. We identified drug prescriptions for all antiarrhythmic (AA) and rate control drugs (RC) dispensed within 90 days after first AF diagnosis (index date). For each drug, we estimated adherence by calculating the 1-year medication possession ratio (MPR) and persistence by calculating continuous use within 1 year. Results: In 115,081 patients with newly diagnosed AF, the most common initially prescribed AAs were amiodarone, sotalol, and propafenone, and RCs were metoprolol, digoxin, and diltiazem. Among AAs, adherence was highest with sotalol (MPR 0.82+/-0.31) (Table) . Among RCs, adherence was highest with metoprolol (MPR 0.85+/-0.32) (Table). Good adherence (>80% MPR) was variable for AAs (27%-68%) and for RCs (44%-66%). At 1-year, persistence to AAs and RCs was low in general, with persistence ≤70% for each of the top three medications in each category. Conclusion: Among patients with newly diagnosed AF, adherence and persistence rates with AAs and RCs are variable and low in general. Continuous persistence is lower for both AAs and RCs compared with mean adherence using the MPR measure. Effectiveness of AF therapies may be compromised by poor medication adherence and persistence to these commonly prescribed medications. Future studies on predictors and outcomes of poor adherence in AF are needed.
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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) | 0.001 | 0.001 |
| 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".