Early non-persistence with dabigatran and rivaroxaban in patients with atrial fibrillation
Bibliographic record
Abstract
OBJECTIVE: Dabigatran and rivaroxaban are novel oral anticoagulants (NOACs) approved for stroke prevention in atrial fibrillation (AF). Although NOACs are more convenient than warfarin, their lack of monitoring may predispose patients to non-persistence. Limited information is available on NOAC non-persistence rates and related clinical outcomes in clinical practice. METHODS: We conducted a retrospective cohort study using administrative data from Ontario, Canada, from January 1998 to March 2014 of patients with AF who were dispensed dabigatran or rivaroxaban. Non-persistence was defined as a gap in dabigatran or rivaroxaban prescriptions ≥14 days. A multivariable Cox proportional hazards model was used to estimate the primary composite outcome of stroke, transient ischaemic attack (TIA) and mortality associated with non-persistence. RESULTS: The cohort consisted of 15 857 dabigatran (age 80.7±6.7 year) and 10 119 rivaroxaban users (age 77.0±7.1 year) with women comprising 52% of each medication group. At 6 months, 36.4% of patients were non-persistent to dabigatran, while 31.9% of patients were non-persistent to rivaroxaban. Stroke/TIA/death was significantly higher for those non-persistent to dabigatran (HR 1.76 (95% CI 1.60 to 1.94); p<0.0001) or rivaroxaban (HR 1.89 (95% CI 1.64 to 2.19); p<0.0001) compared with those who were persistent. Risk of stroke/TIA was markedly higher in non-persistent patients to dabigatran (HR 3.75 (95% CI 2.59 to 5.43); p<0.0001) and rivaroxaban (HR 6.25 (95% CI 3.37 to 11.58); p<0.0001) than those persistent. CONCLUSIONS: NOAC non-persistence rates are high in clinical practice, with approximately one in three patients becoming non-persistent to dabigatran or rivaroxaban within 6 months after drug initiation. Non-persistence with either dabigatran or rivaroxaban is significantly associated with worse clinical outcomes of stroke/TIA/death.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".