Utilization and long‐term persistence of direct oral anticoagulants among patients with nonvalvular atrial fibrillation and liver disease
Bibliographic record
Abstract
AIMS: We characterized the utilization and long-term treatment persistence of direct oral anticoagulants (DOACs) in patients with nonvalvular atrial fibrillation (NVAF) and liver disease. METHOD: Using the UK Clinical Practice Research Datalink, we assembled a population-based cohort of NVAF patients with liver disease initiating oral anticoagulants between 2011 and 2020. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs) of the association between patient characteristics and initiation of DOACs vs vitamin K antagonists (VKAs). Cox proportional hazards models estimated hazard ratios (HRs) and 95% CIs of the association between patient characteristics and the switch from VKAs to DOACs vs remaining on VKAs. We also assessed the 5-year treatment persistence with DOACs vs VKAs, and whether ischemic stroke or bleeding preceded treatment discontinuation. RESULTS: Our cohort included 3167 NVAF patients with liver disease initiating DOACs (n = 2247, 71%) or VKAs (n = 920, 29%). Initiators of DOACs were more likely to have prior ischemic stroke (OR 1.44, 95% CI 1.12-1.85) than VKA initiators but less likely to have used antiplatelet agents (OR 0.66, 95% CI 0.53-0.82). Patients switching to DOACs were more likely to have used selective serotonin reuptake inhibitors (HR 1.64, 95% CI 1.13-2.37) than those remaining on VKAs. At 5 years, 31% of DOAC initiators and 9% of VKA initiators remained persistent. Only few patients were diagnosed with ischemic stroke or bleeding prior to treatment discontinuation. CONCLUSION: Most NVAF patients with liver disease initiated treatment with DOACs. Long-term persistence with DOACs was higher than with VKAs but remained relatively low.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".