Characterization of long-term oral anticoagulant adherence trajectories among patients with atrial fibrillation: a retrospective observational study
Bibliographic record
Abstract
Abstract Background Medication taking is a dynamic behaviour that changes over time. Conventional adherence summary measures (e.g. proportion days covered) used in the OAC adherence studies conducted so far, however, are insensitive to the fluid nature of adherence. For example, identical PDC values can be calculated for patients with initial good adherence followed by poor adherence, and for those with periodic non-adherence throughout the course of therapy. Purpose The objective of this study was to characterize atrial fibrillation (AF) patients' long-term unique oral anticoagulant (OAC) adherence trajectories. Methods Using linked, population-based administrative data containing physician billings, hospitalization and prescription records of 4.8 million British Columbians (1996–2019), incident adult cases of AF were identified. Only patients who had prescription refill data available for five years were included in the analysis. The primary measure of OAC adherence was the proportion of days covered (PDC) over consecutive 90-day rolling windows. We modelled continuous 90-day PDC values over time. The time variable was number of years since OAC initiation. Group-Based Trajectory Modelling (GBTM) was used to identify patients' unique longitudinal adherence trajectories. To determine the best model, a relative comparison was done between models using Bayesian information criteria (BIC), and the Akaike information criterion (AIC). Results The study cohort was 19,749 AF patients [mean age 70.6y (SD 10.64), 56% male, mean CHA2DS2-VASc score 2.77 (SD 1.39]. The model that best fit our data identified four distinct OAC adherence trajectories (Figure). These were “consistent good adherence” (n=14,631 patients, 74.1% of the cohort), “rapid decline and discontinuation” (n=2327, 11.8%), “rapid decline with recovery” (n=1973, 9.99%), and “slow decline and discontinuation” (n=819, 4.2%). Our results show that there is heterogeneity among non-adherers. PDC dropped significantly in the first year after therapy initiation for those with “rapid decline and discontinuation” trajectory. Patients exhibiting “rapid decline with recovery” also displayed a rapid decline in adherence in the first year but showed improvements around the third year. Those in the “slow decline and discontinuation” trajectory displayed slow decline in adherence over first three years which eventually led to permanent discontinuation of therapy. Conclusion In this retrospective study we distinguished between the different kinds of non-adherence in terms of timing and rate. While a majority of our cohort adhered to their medications, we identified three unique trajectories displaying declining adherence over time at varying rates. Our results emphasize the importance of early intervention and have direct implications for improving the design of adherence interventions. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Canadian Institutes of Health Research grant
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".