Abstract 11269: Comparing Measures of Adherence and Persistence to Ticagrelor Therapy in Patients With Acute Coronary Syndromes
Bibliographic record
Abstract
Introduction: There have been efforts to accurately measure adherence to ticagrelor to identify suboptimal medication therapy in the first year post-ACS as nonadherence during this crucial period is a major obstacle to optimizing clinical outcomes. Our study aims to examine ticagrelor adherence and persistence using different methods to better understand adherence patterns. Methods: We conducted a retrospective cohort study of patients aged ≥65 years who had filled a ticagrelor prescription within 7 days post-ACS discharge in Ontario, Canada between 4/2014-3/2018. We estimated mean proportion of days covered [PDC], the proportion of patients with “good” adherence of PDC≥80%, both at 1 year and the proportion of patients who were persistently taking ticagrelor at 1-year, using permissible gaps between prescriptions of 3, 7, 14 and 30 days. Results: There were 9,763 ticagrelor users (mean age 73.6; 65.4% men). The mean 1-year PDC (±SD) was 80.8±29.2, while only 73.0% of the cohort showed good adherence (PDC≥80%). Using a permissible gap definition of 14 days, only 55.7% of patients were persistent with ticagrelor in the year post-ACS. The 1-year persistence rates were as high as 62.6% with an allowable gap of 30 days and as low as 49.7% for a 7-day gap and 39.3% for a 3-day gap. Conclusions: Adherence and persistence estimates varied widely based on the definition used. While the PDC estimates implied reasonable 1-year ticagrelor adherence, PDC methods overestimated continuous use of ticagrelor, yet persistence methods with small gaps were likely too stringent. Readers of adherence and persistence studies should pay close attention to the methods and definitions used.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".