Trends in the prescription of novel oral anticoagulants in UK primary care
Bibliographic record
Abstract
AIMS: Novel oral anticoagulants (NOACs) are alternatives to vitamin-K antagonists (VKAs) for the prevention of thromboembolism. It is unclear how NOACs have been adopted in the UK since first introduced in 2008. The present study was conducted to describe the trends in the prescription of NOACs in the UK, including dabigatran, rivaroxaban and apixaban. METHODS: Using the UK's Clinical Practice Research Datalink, the rates of new use of NOACs and VKAs from 2009 to 2015 were calculated using Poisson regression. Patient characteristics associated with NOAC initiation were identified using multivariate logistic regression. RESULTS: The overall rate of oral anticoagulant initiation increased by 58% over the study period [rate ratio (RR) 1.58; 95% confidence interval (CI) 1.23, 2.03], even as the rate of new VKA use decreased by 31% (RR 0.69; 95% CI 0.52, 0.93). By contrast, the rate of initiation of NOAC increased, particularly from 2012 onwards, with a 17-fold increase from 2012 to 2015 (RR 17.68; 95% CI 12.16, 25.71). In 2015, NOACs accounted for 56.5% of oral anticoagulant prescriptions, with rivaroxaban prescribed most frequently, followed by apixaban and then dabigatran. Compared to VKAs, new NOAC users were less likely to have congestive heart failure, coronary artery disease and peripheral vascular disease, and more likely to have a history of ischaemic stroke. CONCLUSIONS: In the UK, the rate of initiation of NOACs has increased substantially since 2009, and these agents have now surpassed VKAs as the anticoagulant of choice. Moreover, the characteristics of patients initiated on NOACs have changed over time, and this should be accounted for in future studies comparing NOACs and VKAs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".