Abstract 11306: Trajectories of Ticagrelor Adherence in Patients With Acute Coronary Syndromes
Bibliographic record
Abstract
Introduction: Adherence is often measured using proportion of days covered (PDC), where the single average PDC that is estimated conceals the heterogeneity of the adherence patterns, each of which may require unique solutions. Novel group-based trajectory methods allow one to distinguish subgroups of adherence patterns. We examined the magnitude of variation in PDC estimates in these subgroups. Methods: We conducted a retrospective cohort study of patients aged ≥65 years who had filled ticagrelor within 7 days post-ACS discharge in Ontario, Canada between 4/2014-3/2018. Longitudinal patterns of adherence were measured using group-based trajectory models over 1 year and compared with traditional adherence metrics using PDC for the entire cohort and for each trajectory group. Results: We identified 9,764 ticagrelor users (mean age 73.6; 65.4% men). Three distinct trajectory patterns of ticagrelor adherence were identified: a consistently adherent, a gradually nonadherent, and a rapidly nonadherent group, comprising 67.8%, 17.1% and 15.1% of the cohort, respectively. The 1-year mean PDC (±SD) was 80.8±29.2 for the whole ticagrelor post-ACS cohort. After differentiating by adherence trajectory group, mean PDC (±SD) was 97.4±4.5 % in the consistently adherent, while it was 69.1±15.6% and 20.2±11.6 over 1 year in the gradually and rapidly non-adherent trajectory groups, respectively. Conclusions: The overall ticagrelor adherence of ~80% 1-year post-ACS instills a false sense of comfort. The 3 distinct trajectory groups revealed divergent patterns not reflected by this overall mean estimate. The two-thirds of patients who were consistently adherent maintained nearly perfect ticagrelor adherence, while the rapidly non-adherent group only took ticagrelor on average for 2.5 months of the year. The trajectory method approach allows us to distinguish adherence subgroups, and better identify patients at risk of nonadherence who need targeted interventions.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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".