Abstract 10843: P2Y12 Inhibitor Adherence Patterns in Patients with Acute Coronary Syndrome
Bibliographic record
Abstract
Introduction: Dual antiplatelet therapy with P2Y12 inhibitors (P2Y12i) and ASA reduce cardiac events after acute coronary syndrome (ACS). Use has shifted from clopidogrel to ticagrelor, a more potent P2Y12i with higher bleeding risk, dosed twice rather than once daily. As these factors may impact adherence, a better understanding of adherence patterns by agent is needed. Methods: We conducted a population-based cohort study using administrative claims data in Ontario, Canada between 4/2014-3/2018. We identified individuals ≥65 years of age who were admitted for myocardial infarction or unstable angina and had filled a P2Y12i prescription within 7 days of discharge. We excluded patients who died within 1 year after discharge. We calculated “good” adherence [proportion of days covered (PDC) 1 year post-ACS ≥80%, and also used group-based trajectory models to characterize longitudinal patterns of good adherence by time over 1 year. Results: We included 21,680 ACS patients with 45.0% (n=9,763) prescribed ticagrelor and 55.0% (n=11,917) prescribed clopidogrel [mean age±SD: 73.6±6.8/77.3±8.3 years, 65.4%/56.2% male, PCI during index admission 87.4%/48.5%, respectively]. Overall, the good adherence rate was 73.0% for ticagrelor and 78.9% for clopidogrel. We identified 3 adherence patterns in the year post-ACS: 67.8% of ticagrelor and 75.0% of clopidogrel patients were in the adherent group (PDC≥80%), while 17.1 %/13.4% were in the gradually non-adherent, and 15.1%/11.6% were in the rapidly non-adherent groups, respectively. (Figure) Conclusions: Adherence rates and trajectories appeared more favorable for clopidogrel in the first year post-ACS. However, patient characteristics differed between the clopidogrel and ticagrelor populations and these may be associated with the observed adherence patterns. Identification of nonadherence risk factors and barriers specific to each trajectory group is needed to improve adherence and clinical outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".