Drug-drug interactions between direct oral anticoagulants and anticonvulsants and clinical outcomes: A systematic review
Bibliographic record
Abstract
Background: Direct oral anticoagulants (DOACs) are widely used in patients with atrial fibrillation and venous thromboembolism. However, DOACs have important potential drug-drug interactions (DDIs) with several classes of drugs. In particular, antiepileptic (AE) drugs may induce cytochrome P450 3A4 or P-glycoprotein. Co-administration of DOACs and AE drugs may result in lower DOAC drug levels and reduced DOAC efficacy. However, the clinical significance of such DDIs is uncertain. Objectives: The aim of this systematic review was to generate an updated review of these DDIs and their clinical relevance, given the rapidly evolving knowledge relating to DOAC and AE DDIs. Methods: We searched the MEDLINE and Embase databases for studies reporting clinical adverse outcomes (thrombotic events, bleeding events, and all-cause mortality) in patients concomitantly taking DOACs and AE drugs. Results: We retrieved 874 studies of which 15 were deemed eligible for this review, including 4 congress abstracts, 3 case reports, 2 letters to the editor, 5 retrospective cohorts, and 1 prospective cohort study. No randomized clinical trials were found. Most of the included studies reported thrombotic events, 3 studies reported major bleeding, and one study reported all-cause mortality associated with DOAC and AE drug administration. Substantial differences in the study designs did not allow for a meta-analysis to be performed. Conclusion: The current literature assessing these adverse clinical outcomes from DOAC and AE drug co-administration is limited. Although the available data point to a possible increased risk of thrombotic events, they are insufficient to draw definitive conclusions. Well-designed clinical studies are of utmost importance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".