Risk of bleeding with the concurrent use of amiodarone and DOACs: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Amiodarone is frequently prescribed alongside direct oral anticoagulants (DOACs) in atrial fibrillation. There are concerns regarding drug-drug interactions (DDIs) between amiodarone and DOACs. The literature is conflicting on the clinical implications of this DDI, hence we conducted a meta-analysis to compare bleeding risk among patients receiving DOACs, with and without concurrent amiodarone. METHODS AND RESULTS: A systematic search was conducted for studies published between 1 January 2009 and 26 June 2024 in MEDLINE via PubMed, Embase, and CENTRAL. Included studies compared major bleeding in patients on concurrent amiodarone and DOACs to those on DOACs without amiodarone. Event rates were used to calculate odds ratios (ORs), which were pooled with a random-effects model. Nine studies were identified, which included 124 813 patients on amiodarone/DOACs, and 314 074 on DOACs. The average age was 77.2 years in the amiodarone/DOAC group, compared to 74.4 years in the DOAC group (P = 0.21). Among DOAC patients, there was a statistically significant increase in major bleeding with concurrent amiodarone (OR 1.22, 95% confidence interval (CI) 1.03-1.44, P = 0.02, I2 = 88%). Intracranial bleeding rate was numerically higher in the amiodarone/DOAC group (1.0 vs. 0.4%), but the difference did not reach statistical significance (OR 2.20, 95% CI 0.53-9.06, P = 0.27, I2 = 100%). There were no significant differences in gastrointestinal bleeding (OR 1.10, 95% CI 0.98-1.23, P = 0.12, I2 = 62%) and all-cause mortality (OR 1.38, 95% CI 0.70-2.73, P = 0.35, I2 = 99%). CONCLUSION: Concurrent use of amiodarone and DOACs was associated with an increase in major bleeding. This should be considered when co-prescribing these medications.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.048 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".