Use of Direct Oral Anticoagulants in Canadian Primary Care Practice 2010–2015: A Cohort Study From the Canadian Primary Care Sentinel Surveillance Network
Bibliographic record
Abstract
Background As questions have been raised about the appropriateness of direct oral anticoagulant ( DOAC ) dosing among outpatients with atrial fibrillation, we examined this issue in patients being managed by primary care providers. Methods and Results This was a retrospective cohort new‐user study using electronic medical records from 744 Canadian primary care clinicians. Potentially inappropriate DOAC prescribing was defined as prescribing lower or higher doses than those recommended by guidelines for patients with nonvalvular atrial fibrillation. Of the 6658 patients with nonvalvular atrial fibrillation who were prescribed a DOAC (mean age: 74.8; 55% male), 626 (9.4%) had a CHADS 2 score of 0, and 168 (2.5%) had a CHADS ‐ VAS c score of 0. Of the DOAC prescriptions, 527 (7.7%) were deemed potentially inappropriate: 496 (7.2%) were potentially underdosed, and 31 (0.5%) were prescribed a dose that was higher than recommended. Patients were more likely to be prescribed lower‐than‐recommended doses if they were female (adjusted odds ratio [ aOR ]: 1.3 [95% confidence interval ( CI ), 1.0–1.5]), had multiple comorbidities ( aOR: 1.4 [95% CI, 1.1–1.8])—particularly heart failure ( aOR : 1.6 [95% CI, 1.2–2.0]) or dementia ( aOR : 1.4 [95% CI, 1.1–1.8])—or if they were also taking aspirin ( aOR : 1.7 [95% CI, 1.3–2.1]) or nonsteroidal anti‐inflammatory drugs ( aOR : 1.2 [95% CI, 1.02–1.5]). Potentially inappropriate DOAC dosing was more common in rural practices ( aOR : 2.1 [95% CI, 1.7–2.6]) or smaller practices ( aOR : 1.9 [95% CI, 1.6–2.4] for practices smaller than median). Conclusions The vast majority of DOAC prescriptions in our cohort of primary care–managed patients appeared to be for appropriate doses, particularly since prescribing a reduced dose of DOAC may be appropriate in frail patients or those taking other medications that predispose to bleeding.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".