Outcomes of Direct Oral Anticoagulants in Patients with Atrial Fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common chronic arrhythmia in clinical practice. The incidence of AF in Canada is up to 4.5% per year, with lifetime risk estimated at 25% among those older than 40 years. A core principle of the management of AF is the prevention of AF-related stroke and systemic embolism. It is most commonly achieved through the prescription of oral anticoagulation therapy. For decades, this was achieved through the use of oral vitamin K antagonists (VKA, e.g. warfarin), however the use of direct oral anticoagulants (DOAC) have supplanted VKAs as the preferred oral anticoagulant for most patients with non-valvular AF owing to a favorable risk-benefit profile. Anticoagulant-associated bleeding continues to be one of the most common adverse drug reactions among older patients, accounting for 6.6% of adverse drug reaction-related hospitalizations in those older than 65 years.In the last few years, use of DOAC prescriptions have increased substantially among patients with nonvalvular AF requiring oral anticoagulation therapy. DOACs were assumed to overcome some of the limitations of VKA due to fewer drug interactions. However, recent data have highlighted potential drug-drug interactions (DDIs) among DOAC users leading to an increased risk of bleeding. This is most often related to the absorption, metabolism, and elimination of DOACs that are dependent on the permeability glycoprotein (P-gp) transporter system and cytochrome P450 3A4 enzymes. Amiodarone and diltiazem are commonly recommended cardiovascular medications for use in AF patients; pharmacodynamic and pharmacokinetic data have suggested DDIs between these common cardiovascular medications and DOACs. However, there are limited clinical data to support this. This thesis aimed to address knowledge some gaps in the use of DOACs. The thesis consisted of four projects, conducted using population-based healthcare administrative databases from Ontario, Canada. These databases were linked to aggregate information on clinical characteristics, cardiac tests, and medication use. A wide range of statistical methods was employed, including regression models and propensity score-based methods. Our first project aimed to evaluate the frequency of the use of amiodarone or diltiazem among continuous users of DOACs in AF patients and to determine factors associated with their co-use. We found that amiodarone was co-prescribed in 1 in 16 patients and diltiazem was co-prescribed in 1 in 9 patients. The second and third projects aimed to assess the risk of major bleeding with co-prescription of amiodarone or diltiazem and DOACs among adults with AF. We found that in older patients with AF on a DOAC and concurrent use of amiodarone, there was 53% increased odds of major bleeding. Also, the concurrent use of diltiazem and a DOAC among AF patients was associated with increased odds of major bleeding. Finally, the fourth project compared the risk of major bleeding and thromboembolic events of the most commonly prescribed DOACs, apixaban versus rivaroxaban in older patients with AF. The results showed that among AF patients 66 years or older, treatment with apixaban was associated with reduced risk of the primary composite outcome of major bleeding, thromboembolic events, or MI, primarily driven by lower rates of major bleeding events without significant differences in thromboembolic events compared with rivaroxaban. Collectively, the thesis findings highlight the outcomes of common DDIs among patients with AF. Further, the lower bleeding rates observed with apixaban in our study should be taken into consideration when choosing between apixaban and rivaroxaban for stroke prevention in older AF patients. The thesis results may be used to guide clinical decisions, aiming to reduce cardiovascular events in this large vulnerable patient population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".