Patterns of long‐term use of non‐vitamin K antagonist oral anticoagulants for non‐valvular atrial fibrillation: Quebec observational study
Bibliographic record
Abstract
Abstract Purpose Studies on long‐term utilization of non‐vitamin K antagonist oral anticoagulants (NOACs) in non‐valvular atrial fibrillation (NVAF) are scarce. We evaluated predictors of use and long‐term persistence of NOACs in a real‐world setting. Methods This population‐based cohort study used the computerized databases of the Canadian Province of Quebec's health insurance. Patients with a first NVAF diagnosis from 2011 until 2014 were included. A logistic regression model yielded adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for predictors of treatment initiation with NOACs versus VKAs. Cox proportional hazards models yielded adjusted hazard ratios (HRs) and 95% CIs for predictors of switching from VKAs to NOACs versus remaining on VKAs, and for predictors of discontinuation of anticoagulation treatment. Results Of the 62 867 newly diagnosed NVAF patients, 14 646 initiated NOACs and 17 685 VKAs. Initiation with NOACs was less likely for patients ≥ 80 years old (OR 0.55, 95% CI 0.41–0.73) or with CHA 2 DS 2 ‐VASc ≥ 2 (OR 0.49, 95% CI 0.42–0.57). Switching from VKAs to NOACs was less likely for patients with chronic kidney disease (HR 0.53, 95% CI 0.48–0.59). After 3 years, persistence was 54% with NOACs and 25% with VKAs. Discontinuation of anticoagulation treatment was less likely for patients ≥ 80 years old (HR 0.47, 95% CI 0.40–0.55) or with CHA 2 DS 2 ‐VASc ≥ 2 (HR 0.64, 95% CI 0.57–0.70). Conclusions Older, high‐risk patients are less likely to initiate NOACs than VKAs. NOAC users show a higher long‐term persistence than VKA users, and older, high‐risk patients are less likely to discontinue anticoagulation treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".