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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".