Simultaneous Use of Amiodarone Influences Warfarin Maintenance Dose but Is Not Associated with Adverse Events
Bibliographic record
Abstract
BACKGROUND: Drug interaction studies selecting patients in a real-life setting are scarce, and most studies to date are characterized by a small sample size. OBJECTIVE: To evaluate the effect of amiodarone on warfarin maintenance dose and adverse events in an anticoagulation cohort from a tertiary cardiovascular service. METHODS: This study recruited 866 patients, and oral anticoagulant therapy was monitored by the prothrombin time expressed as the international normalized ratio (INR). Genotyping of CYP2C9*2, CYP2C9*3, and VKORC1 3673 polymorphisms was performed. RESULTS: Of the 866 patients, 111 (12.8%) were taking amiodarone and warfarin simultaneously, and 514 (59.4%) reached the therapeutic target dose. The warfarin maintenance dose was significantly lower in patients simultaneously using amiodarone (23.8 ± 11.3 mg/wk) compared with other patients (29.5 ± 14.3 mg/wk; P < 0.001). Patients taking amiodarone had higher INR/current dose ratios (0.83 ± 0.04 per mg) compared with patients not using amiodarone (0.71 ± 0.02 per mg, P = 0.001). Adverse event frequency was not different between the groups (P = 0.40). No genotype effect was noted on the odds of bleeding associated with amiodarone use. CONCLUSIONS: Simultaneous use of amiodarone influences warfarin maintenance dose, but is not associated with adverse events.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".