Novel Oral Anticoagulant Use Among Patients With Atrial Fibrillation Hospitalized With Ischemic Stroke or Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND: Novel oral anticoagulants (NOACs) have been shown to be at least as good as warfarin for preventing stroke or transient ischemic attack in patients with atrial fibrillation, yet diffusion of these therapies and patterns of use among atrial fibrillation patients with ischemic stroke and transient ischemic attack have not been well characterized. METHODS AND RESULTS: Using data from Get With The Guidelines-Stroke, we identified a cohort of 61 655 atrial fibrillation patients with ischemic stroke or transient ischemic attack hospitalized between October 2010 and September 2012 and discharged on warfarin or NOAC (either dabigatran or rivaroxaban). Multivariable logistic regression was used to identify factors associated with NOAC versus warfarin therapy. In our study population, warfarin was prescribed to 88.9%, dabigatran to 9.6%, and rivaroxaban to 1.5%. NOAC use increased from 0.04% to a 16% to 17% plateau during the study period, although anticoagulation rates among eligible patients did not change appreciably (93.7% versus 94.1% from first quarter 2011 to second quarter 2012), suggesting a trend of switching from warfarin to NOACs rather than increased rates of anticoagulation among eligible patients. Several bleeding risk factors and CHA2DS2-VASc scores were lower among those discharged on NOAC versus warfarin therapy (47.9% versus 40.9% with CHA2DS2-VASc ≤5, P<0.001 for difference in CHA2DS2-VASc). CONCLUSIONS: NOACs have had modest but growing uptake over time among atrial fibrillation patients hospitalized with stroke or transient ischemic attack and are prescribed to patients with lower stroke risk compared with warfarin.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".