Trajectories of <scp>P2Y12</scp> inhibitor adherence in patients with acute coronary syndromes
Bibliographic record
Abstract
PURPOSE: P2Y12 inhibitors (P2Y12i) reduce cardiac events after acute coronary syndromes (ACS). However, suboptimal P2Y12i adherence persists. We aimed to examine P2Y12i non-adherence using group-based trajectory methods and to identify adherence predictors. METHODS: We conducted a population-based, retrospective cohort study using administrative data in Ontario, Canada of patients ≥65 years admitted for ACS between April 2014 and March 2018 with a P2Y12i dispensed within 7 days of discharge. We used group-based trajectory models to characterize longitudinal 1-year adherence patterns. Predictors associated with each adherence trajectory were identified by multinomial logistic regression. RESULTS: We included 11 917 patients using clopidogrel and 9763 using ticagrelor, aged [mean ± SD]: 77.33 ± 8.31/73.59 ± 6.79 years; men: 56.2%/65.4%, respectively. We identified 3 longitudinal adherence trajectories, that differed by agent: 75% of clopidogrel and 68% of ticagrelor patients showed a consistently adherent trajectory, while 13%/17% were gradually, and 12%/15% were rapidly non-adherent, respectively (p < 0.001). Differing baseline characteristics in each cohort were associated with observed adherence trajectories. Concomitant atrial fibrillation and prior bleeding history were associated with non-adherence among clopidogrel users. Among ticagrelor users, women and older persons were more likely to be rapidly non-adherent, adherence declining steeply starting 1 month post-ACS. CONCLUSIONS: We identified distinct adherence trajectories for clopidogrel and ticagrelor post-ACS, with 3 out of 4 clopidogrel patients but only 2 out of 3 ticagrelor patients in the consistently adherent trajectory. Intensive interventions targeted to the period of steep adherence decline post-ACS, particularly for women and older persons initiating ticagrelor, and patients with atrial fibrillation on clopidogrel should be considered and investigated further.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".