Prescribing of two potentially interacting cardiovascular medications in atrial fibrillation patients on direct oral anticoagulants
Bibliographic record
Abstract
BACKGROUND: Amiodarone and diltiazem are commonly recommended cardiovascular medications for use in atrial fibrillation (AF) patients. They are known to have drug-drug interactions (DDIs) with direct oral anticoagulants (DOACs). We aimed to evaluate frequency of use of amiodarone or diltiazem among continuous users of DOACs in AF patients and to determine factors associated with their co-use. METHODS: The study population included all AF patients with continuous DOAC use in Ontario, Canada, ≥66 years, from April 1, 2017 to March 31, 2018. Concurrent use of amiodarone or diltiazem was determined by identifying the presence of an overlapping prescription. Multivariable logistic regression models were used to identify predictors of amiodarone or diltiazem use. RESULTS: In total, 5,390 AF patients, ≥66 years, with continuous DOAC use were identified. Amiodarone was co-prescribed in 6.4% patients and diltiazem was co-prescribed in 11.2% patients. Prior percutaneous coronary intervention (PCI) and coronary artery bypass surgery (CABG) were associated with significantly increased odds of amiodarone co-use (OR 2.51 [95% CI 1.54, 4.09], p = 0.0002 and OR 5.28 [95% CI 3.52, 7.93], p= <0.001, respectively). Patients with a heart failure (HF) history also had increased co-use of amiodarone (OR 2.05 [95% CI 1.57, 2.67], p < 0.001). The presence of chronic obstructive pulmonary disease (COPD) was associated with significantly increased odds of diltiazem co-use (OR 1.58 [95% CI 1.31, 1.9], p=<0.001). CONCLUSIONS: Among AF patients with continuous DOAC use, amiodarone was co-prescribed in 1 in 16 patients and diltiazem was co-prescribed in 1 in 9 patients. Predictors such as history of HF, PCI, CABG or COPD help identify vulnerable populations at increased risk of DDIs.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".