Effectiveness and safety of direct oral anticoagulants among patients with non-valvular atrial fibrillation and liver disease: A multinational cohort study
Bibliographic record
Abstract
BACKGROUND AND AIMS: The effects of direct oral anticoagulants (DOACs) in patients with non-valvular atrial fibrillation (NVAF) and liver disease remain poorly understood. Our multinational cohort study assessed the effectiveness and safety of DOACs in this high-risk population. METHODS: We assembled two population-based cohorts in United Kingdom and in Québec of NVAF patients with liver disease initiating DOACs or vitamin K antagonists (VKAs) between 2011 and 2020. Using an as-treated exposure definition, we compared DOACs to VKAs and apixaban to rivaroxaban. After inverse probability of treatment weighting, Cox proportional hazards models estimated site-specific hazard ratios (HRs) and 95 % confidence intervals (CIs) of ischemic stroke and major bleeding. Site-specific estimates were pooled using random-effects models. Analyses were repeated among NVAF patients with cirrhosis. RESULTS: There were 11,881 NVAF patients with liver disease (2683 with cirrhosis). Among those, 8815 initiated DOACs (4414 apixaban, 2497 rivaroxaban) and 3696 VKAs. The HRs (95 % CIs) for DOACs compared to VKAs were 1.01 (0.76-1.34) for ischemic stroke and 0.87 (0.77-0.99) for major bleeding. Results were consistent among patients with cirrhosis. The HRs (95 % CIs) for apixaban compared to rivaroxaban were 0.85 (0.60-1.20) for ischemic stroke and 0.80 (0.68-0.95) for major bleeding. This decreased bleeding risk was not observed among patients with cirrhosis (HR, 1.01; 95 % CI 0.72-1.43). CONCLUSIONS: Among NVAF patients with liver disease, DOACs were as effective and slightly safer than VKAs, and apixaban was as effective but safer than rivaroxaban. The safety benefit with apixaban was not present among patients with cirrhosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".