Association between oral anticoagulant adherence and serious clinical outcomes in patients with atrial fibrillation: A long-term retrospective cohort study
Bibliographic record
Abstract
ABSTRACT Background Patients with atrial fibrillation (AF) are frequently nonadherent to oral anticoagulants (OACs) prescribed for stroke and systemic embolism (SSE) prevention. We quantified the relationship between OAC adherence and AF clinical outcomes using methods not previously applied to this problem. Methods Retrospective observational cohort study of incident cases of AF from population-based administrative data over 23 years. The exposure of interest was proportion of days covered (PDC) during 90 days before an event or end of follow-up. Cox proportional hazard models were used to evaluate time to first SSE and the composite of SSE, TIA, or death, and several secondary outcomes. Results 44,172 patients were included with median follow-up of 6.7 years. For DOACs, each 10% decrease in adherence was associated with a 14% increased hazard of SSE and 5% increased hazard of SSE, TIA, or death. For VKA the corresponding increase in SSE hazard was 3%. Receiving DOAC or VKA was associated with primary outcome hazard reduction across most the PDC spectrum. Differences between VKA and DOAC were statistically significant for all efficacy outcomes and at most adherence levels. Conclusions Even small reductions in OAC adherence in patients with AF were associated with significant increases in risk of stroke, with greater magnitudes for DOAC than VKA. DOAC recipients may be more vulnerable than VKA recipients to increased risk of stroke and death even with small reductions in adherence. The worsening efficacy outcomes associated with decreasing adherence occurred without the benefit of major bleeding reduction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".