Clinically Important Drug–Drug Interactions Between Antiarrhythmic Drugs and Anticoagulants
Bibliographic record
Abstract
Until the last decade, vitamin K antagonists (VKAs) were the only agents available for oral anticoagulation. Although effective and accessible, their use was complicated by a narrow therapeutic window, the need for regular monitoring of the international normalized ratio, and an associated susceptibility to interactions with both food and numerous medications. Furthermore, the onset of action was delayed, often requiring bridging with intravenous agents. In more recent years, we have enjoyed the development of nonvitamin-K-dependent, direct oral anticoagulants (DOACs), which either directly inhibit the activity of factor IIa (eg, dabigatran) or factor Xa (eg, rivaroxaban, apixaban, edoxaban). These medications boast a more rapid onset of action, predictable pharmacokinetics, wider therapeutic window, and equal or superior safety profiles. Although these medications appear to have fewer drug-drug interactions than VKAs, their interactions remain of clinical importance, particularly in one of the largest populations requiring anticoagulation: patients with atrial fibrillation. These patients are rarely on single medications, with the majority of them requiring some form of rate or rhythm control due to their arrhythmia. Unfortunately, data on interactions between DOACs and antiarrhythmic medications, despite their common coadministration, remain limited. Here, we summarize the interactions between antiarrhythmics and VKAs and review existing knowledge regarding their interactions with 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".