Non-Medical Switching or Discontinuation Patterns among Patients with Non-Valvular Atrial Fibrillation Treated with Direct Oral Anticoagulants in the United States: A Claims-Based Analysis
Bibliographic record
Abstract
This study assessed direct-acting oral anticoagulant (DOAC) switching/discontinuation patterns in patients with non-valvular atrial fibrillation (NVAF) in 2019, by quarter (Q1–Q4), and associated socioeconomic risk factors. Adults with NVAF initiating stable DOAC treatment (July 2018–December 2018) were selected from Symphony Health Solutions’ Patient Transactional Datasets (April 2017–January 2021). Switching/discontinuation rates were reported in 2019 Q1–Q4, separately. Non-medical switching/discontinuation (NMSD) was defined as the difference between switching/discontinuation rates in Q1 and mean rates across Q2–Q4. The associations of socioeconomic factors with switching/discontinuation were assessed. Of 46,793 patients (78.7% ≥ 65 years; 52.6% male; 7.7% Black), 18.0% switched/discontinued their initial DOAC in Q1 vs. 8.8% on average in Q2–Q4, corresponding to an NMSD of 9.2%. During the quarter following the switch/discontinuation, more patients who switched/discontinued in Q1 remained untreated (Q1: 77.0%; Q2: 74.3%; Q3: 71.2%) and fewer reinitiated initial DOAC (Q1: 17.6%; Q2: 20.8%; Q3: 24.0%). Factors associated with the risk of switching/discontinuation in Q1 were race, age, gender, insurance type, and household income (all p < 0.05). More patients with NVAF switched/discontinued DOACs in Q1 vs. Q2–Q4, and more of them tended to remain untreated relative to those who switched/discontinued later in the year, suggesting a potential long-term impact of NMSD. Findings on factors associated with switching/discontinuation highlight potential socioeconomic discrepancies in treatment continuity.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".