Abstract 11913: Early Nonadherence With Dabigatran and Rivaroxaban in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background: Dabigatran and rivaroxaban are novel oral anticoagulants (NOACs) approved recently for stroke prevention in atrial fibrillation (AF). Although NOACs are more convenient than warfarin, their lack of monitoring, and for dabigatran, dosing frequency may predispose patients to nonadherence. Limited information is available on the adherence rates and related clinical outcomes for dabigatran and rivaroxaban in clinical practice. Methods: We conducted a population-based cohort study using administrative data of patients aged 65 years and over with AF, linking hospital discharge abstract and prescription claims databases in Ontario, Canada from April 2012 to March 2014. Nonadherence was measured as proportion of patients discontinuing dabigatran or rivaroxaban, defined as a gap in dabigatran or rivaroxaban prescription for ≥14 days within the first 6 months of therapy, and time to discontinuation. A multivariate Cox proportional hazards model was used to examine the association between drug discontinuation at any time, and the composite outcome of hospitalization for stroke, or death. Results: The cohort consisted of 15,857 dabigatran users and 10,119 rivaroxaban users, with women comprising 52% of each medication group. Mean age was 80.7±6.7 years for dabigatran, and 77.0±7.1 years for rivaroxaban patients. At 6-months, 36.5% of patients discontinued dabigatran (110mg: 37.4%; 150mg: 34.1%), while 32.1% of patients discontinued rivaroxaban. Median time to discontinuation was 240 days (IQR: 78-523) for dabigatran and 140 days (IQR: 52-283) for rivaroxaban. Risk of the composite outcome (stroke or death) was significantly higher for those who discontinued dabigatran [HR 1.78 (95% CI 1.62-1.95);p<0.0001] or discontinued rivaroxaban [HR 3.07 (95% CI 2.54-3.72);p<0.0001] compared with those who did not discontinue the medication. Conclusions: Within 6 months of initiation, discontinuation rates are high in clinical practice, with 1 in 4 patients discontinuing dabigatran, and 1 in 3 patients discontinuing rivaroxaban. There is an association between nonadherence with either dabigatran or rivaroxaban and significantly worse clinical outcomes following medication discontinuation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".