Change in Trajectories of Adherence to Lipid‐Lowering Drugs Following Non‐Fatal Acute Coronary Syndrome or Stroke
Bibliographic record
Abstract
Background Poor adherence to cardioprotective drugs remains a concern among patients for secondary prevention. A better understanding of adherence fluctuations before and after critical health events may inform approaches for addressing or preventing poor adherence. Therefore, we assessed trajectories of adherence to lipid-lowering drugs before and after acute coronary syndrome (ACS) or stroke and identified post-ACS/stroke trajectories' predictors. Methods and Results We conducted a cohort study of patients hospitalized for ACS or stroke in Alberta, Canada, using administrative health data between 2009 and 2015. Patients using lipid-lowering drugs in the 2 years pre-hospitalization and had post-discharge follow-up ≥365 days were included. We used group-based trajectory modeling to assess adherence trajectories and multinomial logistic regression to assess trajectories' predictors. In total, 10 623 patients were included. The average age was 69 years, and 65% were men. Five trajectories were identified in both periods: nearly perfect, gradual increase, gradual decline, rapid decline, and poor adherence throughout. Of patients who were poor adherers, rapidly or gradually declining pre-hospitalization, 2395/3588 (66.8%) switched to gradual increase or perfect adherence post discharge. Conversely, of patients gradually increasing or nearly perfect before, only 4822/7035 (68.5%) were nearly perfect adherers after. Main predictors of poor post-ACS/stroke trajectories included older age, female sex, lack of immediate post discharge follow-up, and prior trajectories. Conclusions This study suggests that adherence post-ACS/stroke is highly variable and emphasizes the importance for clinicians to recognize that post-discharge adherence will likely change negatively for prior good adherers. Adherence-enhancing interventions should occur both early and late following discharge.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".