Uptake of non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation – a prospective cohort study
Bibliographic record
Abstract
AIMS: We aimed to assess the uptake of non-vitamin K antagonist oral anticoagulants (NOACs) among patients with atrial fibrillation between 2010 and 2015 in Switzerland. METHODS: We performed a prospective observational cohort study. At the baseline examination and during yearly follow-ups, we used questionnaires to obtain information about clinical characteristics and antithrombotic treatment. Stroke risk was assessed using the CHA2DS2-VASc score. RESULTS: 1545 patients were enrolled across seven centres in Switzerland. Mean age was 68 ± 12 years and 29.5% were female. The percentage of anticoagulated patients with an indication for oral anticoagulation (CHA2DS2-VASc score ≥2 in women and ≥1 in men) was 75% in 2010 and 80% in 2015 (p = 0.2). There was a gradual increase in the use of NOACs from 0% in 2010 to 29.8% in 2015 (p <0.0001). Out of 888 patients, who initially received a vitamin K antagonist (VKA), 86 (9.7%) were switched to an NOAC during follow-up. Use of aspirin as a monotherapy decreased from 23% in 2010 to 11% in 2015 (p <0.0001). CONCLUSION: After regulatory approval, the use of NOACs in Switzerland steadily increased to about 30% in 2015, whereas switches from VKAs to NOACs were infrequent. In parallel, the prescription of aspirin as monotherapy was more than halved, suggesting significant guideline-concordant improvements in oral anticoagulation use among patients with atrial fibrillation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".