Patterns and Predictors of Use of Anticoagulants for the Treatment of Venous Thromboembolism Following Approval of Rivaroxaban
Bibliographic record
Abstract
BACKGROUND: Few studies have identified patterns and predictors of use of direct oral anticoagulants for venous thromboembolism (VTE). OBJECTIVE: To describe the use of anticoagulants and assess predictors associated with the prescription of rivaroxaban over vitamin K antagonist (VKA) for the subsequent treatment of VTE. METHODS: This cross-sectional study was built with all consecutive patients newly diagnosed with acute VTE admitted between February 18, 2013, and September 18, 2013, in an academic tertiary care center in Quebec, Canada. Patient characteristics and VTE treatments were described. Univariate analyses and a multiple forward stepwise logistic regression were performed to assess predictors of rivaroxaban use over VKA for the subsequent treatment of VTE. RESULTS: The study included 256 patients, 36.7% with a diagnosis of deep vein thrombosis (DVT) and 63.3% with pulmonary embolism (PE). Mean age was 63.1 years, and 28.1% of patients had cancer-associated VTE. Overall, rivaroxaban was prescribed in 1.6% of patients for the initial treatment and in nearly 20% of patients for the subsequent treatment of VTE. Low-molecular-weight heparin and VKA were mostly prescribed. Independent predictors associated with the prescription of rivaroxaban over VKA were as follows: age < 65 years (OR: 2.86, 95% CI 1.29-6.37), a diagnosis of DVT versus PE (OR 2.54, 95% CI 1.20-5.40), and an emergency department visit rather than a hospitalization (OR 2.24, 95% CI 1.06-4.71). CONCLUSION: Several months following its availability, rivaroxaban was rarely prescribed for acute VTE disease. It also appears to be prescribed in different patient populations than VKA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".