Oral anticoagulant switching in patients with atrial fibrillation: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Oral anticoagulants (OACs) prevent stroke in patients with atrial fibrillation (AF). Several factors may cause OAC switching. OBJECTIVES: To examine the phenomenon of OAC switching in patients with AF, including all available evidence; frequency and patterns of switch, clinical outcomes, adherence, patient-reported outcomes, reasons for switch, factors associated with switch and evidence gaps. DESIGN: Scoping review. DATA SOURCES: MEDLINE, Embase and Web of Science, up to January 2022. RESULTS: Of the 116 included studies, 2/3 examined vitamin K antagonist (VKA) to direct-acting OAC (DOAC) switching. Overall, OAC switching was common and the definition of an OAC switch varied across. Switching from VKA to dabigatran was the most prevalent switch type, but VKA to apixaban has increased in recent years. Patients on DOAC switched more to warfarin than to other DOACs. OAC doses involved in the switches were hardly reported and patients were often censored after the first switch. Switching back to a previously taken OAC (frequently warfarin) occurred in 5%-21% of switchers.The risk of ischaemic stroke and gastrointestinal bleeding in VKA to DOAC switchers compared with non-switchers was conflicting, while there was no difference in the risk of other types of bleeding. The risk of ischaemic stroke in switchers from DOAC versus non-switchers was conflicting. Studies evaluating adherence found no significant changes in adherence after switching from VKA to DOAC, however, an increase in satisfaction with therapy were reported. Reasons for OAC switch, and factors associated with OAC switch were mostly risk factors for stroke and bleeding. Clinical outcomes, adherence and patient-reported outcomes were sparse for switches from DOACs. CONCLUSIONS: OAC switching is common in patients with AF and patients often switch back to an OAC they have previously been on. There are aspects of OAC switching that have received little study, especially in switches from DOACs.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".