Amiodarone, Verapamil, or Diltiazem Use With Direct Oral Anticoagulants and the Risk of Hemorrhage in Older Adults
Bibliographic record
Abstract
Background: Routinely used cardiac medications, based on pharmacokinetics, are hypothesized to increase drug levels of direct oral anticoagulants (DOACs), with the potential to increase the risk of hemorrhage. We set out to compare the risk for hemorrhage following initiation of amiodarone, verapamil, or diltiazem (moderate cytochrome P450 3A4 and/or P-glycoprotein activity) vs metoprolol or amlodipine (weak or no activity), among older adults prescribed DOACs. Methods: = 295,038) who were newly prescribed amiodarone (n = 4872), verapamil (n = 1284), or diltiazem (n = 14,638), compared with metoprolol or amlodipine, from Ontario, Canada (2009-2016). The outcome was hospital admission or emergency room visit with a major hemorrhage (upper or lower gastrointestinal tract, intracranial), examined using weighted models. Results: A total of 1737 hemorrhage events occurred (amiodarone, 80 [1.6%] vs metoprolol 503 [2.3%]; verapamil, 32 [2.5%] vs amlodipine, 406 [1.6%]; diltiazem, 312 [2.1%] vs amlodipine, 404 [1.5%]). The weighted risk of major hemorrhage was not elevated with amiodarone, verapamil, or diltiazem initiation in DOAC users, compared to metoprolol or amlodipine, during the full follow-up period (hazard ratio [HR; 95% confidence interval]: amiodarone HR 0.77 [0.61-0.97]; verapamil HR 1.32 [0.88-1.98]; diltiazem HR 0.99 [0.85-1.15]). This finding was consistent with a broader definition of bleeding, adjusting for kidney function, by DOAC type or dosage. Conclusions: Hemorrhage risk with amiodarone, verapamil, and diltiazem was similar to that with comparators, among DOAC users aged > 66 years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".