Carbamazepine, phenytoin, and oral anticoagulants: Drug‐drug interaction and clinical events in a retrospective cohort
Bibliographic record
Abstract
BACKGROUND: Carbamazepine and phenytoin are potent inducers of enzymes that metabolize oral anticoagulants. OBJECTIVES: To determine the clinical impact of drug-drug interactions between these anticonvulsants and oral anticoagulants, and whether they affect the treatment with direct oral anticoagulants (DOACs) or vitamin K antagonists (VKAs). MATERIAL AND METHODS: Data on patients cotreated with carbamazepine or phenytoin and an oral anticoagulant were retrospectively retrieved from medical records from 2011 to 2020. Outcomes were time in therapeutic range (TTR), DOAC levels, thromboembolic events, major bleeding, and all-cause mortality. RESULTS: = 42 [49%]), 53 (62%) were initially treated with VKAs and 32 (38%) with DOACs. TTR in VKA patients was 63%, which improved in year 2. Four of seven trough and five of 12 peak DOAC plasma levels were lower than expected. The incidence rate (95% confidence interval) per 100 person-years for thromboembolism was 3.6 (3.1-4.2) for VKA patients and 4.4 (3.5-5.6) for DOAC patients; for major bleeding 1.8 (1.5-2.1) and 1.5 (1.2-1.9), and for all-cause mortality 3.6 (3.1-4.2) and 1.5 (1.2-1.9), respectively. Incidence rates between VKAs and DOACs and between carbamazepine and phenytoin were similar. CONCLUSION: There was a high incidence of thromboembolism in patients cotreated with anticoagulants and carbamazepine or phenytoin. The incidence rates of thrombotic and bleeding events were similar between VKA and DOAC patients. DOAC levels were lower than expected in 47% of cases tested, without correlation with clinical outcomes.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".