Optimal adherence thresholds for oral anticoagulants in patients with atrial fibrillation using machine learning and population administrative data
Bibliographic record
Abstract
BACKGROUND: Adherence to oral anticoagulants (OACs) for atrial fibrillation (AF) stroke prevention is traditionally defined as taking 80% of doses as prescribed, but this threshold has not been clinically validated. OBJECTIVES: We identified OAC adherence thresholds maximally associated with risk of clinical events in patients with AF and compared them with the conventional 80% threshold. METHODS: This was a cohort study using retrospective data of patients newly diagnosed with AF who were new OAC users from 1996 to 2019, based on population-based administrative data from British Columbia, Canada. We used Cox proportional hazards models with OAC proportion of days covered (PDC) as the main exposure captured during 90 days before outcome events or at the end of follow-up, then applied least absolute shrinkage and selection operator-estimated coefficient paths to identify candidate thresholds, and identified which were optimal in terms of outcomes. RESULTS: A total of 44 172 patients were included. For all outcomes, vitamin K antagonist (VKA) optimal thresholds were between PDC 0.85 and 0.95, and the direct OAC (DOAC) optimal threshold was PDC 0.9. Above vs below threshold outcome hazard reduction was greater for DOACs than for VKAs (DOACs: 19%-52% stroke risk reduction; VKAs: not significant for most outcomes). The PDC 0.8 threshold had inferior model fit and outcome effect compared with the optimal thresholds identified. CONCLUSION: Optimal adherence thresholds are higher than conventionally assumed and, especially for DOACs, are strongly associated with clinical outcomes. These results provide a basis for considering updating the definition of OAC nonadherence to PDC <0.9 for DOACs and to PDC <0.9 or <0.95 for VKAs, depending on the outcome.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".