Abstract 17252: Rates and Predictors of Co-Prescribing Common Interacting Cardiovascular Medications in Atrial Fibrillation Patients on Direct Oral Anticoagulants
Bibliographic record
Abstract
Introduction: Amiodarone and diltiazem are commonly prescribed cardiovascular medications in atrial fibrillation (AF) patients who take direct oral anticoagulants (DOACs). They are known to have drug-drug interactions (DDIs) with DOACs, increasing serum levels of DOACs by 40-60%, and potentially increasing risk of bleeding. Objective: To evaluate frequency of use of amiodarone or diltiazem among continuous users of DOACs in AF patients and assess factors associated with their 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. We used linked databases housed at ICES, Ontario. DOAC fill dates and days supplied per prescription were used to determine treatment durations. A maximum gap of 30 days between prescriptions was allowed. Multivariable logistic regression models were used to identify predictors of prescribing amiodarone or diltiazem among AF patients on DOACs. Results: In total, 5390 AF patients, ≥66 years, with continuous DOAC use were identified. Amiodarone was co-prescribed in 343 (6.4%) patients and diltiazem was co-prescribed in 604 (11.2%) patients. The presence of percutaneous coronary intervention (PCI) or coronary artery bypass surgery (CABG) significantly increased the odds of co-prescribing amiodarone among AF DOAC patients (OR 2.52 [95% CI 1.55, 4.10], p=0.0002 and OR 5.19 [95% CI 3.46, 7.80], p= <0.0001, respectively). The presence of chronic obstructive pulmonary disease was associated with significantly increased diltiazem co-prescription among AF DOAC patients (OR 1.55 [95% CI 1.28, 1.87], p=<0.0001) when adjusted for important patient-level factors (Tables 1&2). Conclusions: Among AF patients with continuous DOAC use, the presence of PCI or CABG was associated with increased amiodarone co-prescription. Future efforts should focus on examining the risk of bleeding in these vulnerable populations exposed to major DOAC 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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".