Optimal adherence thresholds for oral anticoagulants in patients with atrial fibrillation using machine learning and population administrative data
Bibliographic record
Abstract
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 validated in terms of clinical outcomes. Objectives We sought to determine the OAC adherence thresholds maximally associated with risk of clinical events in patients with AF. We also sought to compare these thresholds to the conventional 80% threshold. Methods This was a cohort study using retrospective data of patients newly diagnosed with AF who were new users of OACs from 1996 to 2019 from population-based administrative data from British Columbia, Canada. We built Cox proportional hazard models with OAC adherence, measured as proportion of days covered (PDC), as the main exposure captured during 90 days before outcome events or end of follow-up and applied LASSO estimated coefficient paths to identify candidate thresholds, followed by steps to identify which were truly optimal in terms of outcomes. Results A total of 44,172 patients were included. For all outcomes, VKA optimal PDC thresholds were between 0.85 and 0.95 and the DOAC optimal threshold was 0.9. Outcome hazard reduction for adherence above vs. below the optimal threshold was greater for DOAC than VKA. For DOAC, PDC ≥0.9 yielded risk reductions of 19 to 52% compared to being below it for stroke outcomes, while for these were not significant for VKA for most outcomes. The conventional PDC 0.8 threshold had inferior model fit and clinical relevance compared to the optimal thresholds identified. Conclusions Optimal adherence thresholds are higher than conventionally assumed and, especially with DOACs, are strongly associated with clinical outcomes. Our results highlight the need to consider updating the definition of nonadherence to PDC <0.9 for DOACs, and to PDC<0.9 or <0.95 for VKA, 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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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".