Prevalence, Predictors, and Outcomes of Primary Nonadherence After Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Secondary prevention after acute myocardial infarction (AMI) is achieved primarily through medications. However, patients must take their medications to benefit. Medication adherence research has focused primarily on continuation of medications rather than not filling the first prescription written (primary nonadherence). Our objectives were to characterize, to determine factors of, and to measure outcomes associated with primary nonadherence after AMI. METHODS AND RESULTS: We conducted a population-based cohort study using an AMI registry linked with administrative data in Ontario, Canada. The primary outcome was 1-year mortality. There were 4591 post-AMI patients >65 years of age included with 12 832 prescriptions written, of which 73% and 79% were filled within 7 and 120 days, respectively. By 120 days after discharge, more cardiac than noncardiac prescriptions were filled (82% versus 35%, respectively; P<0.0001). Only 74% of patients filled all their discharge prescriptions by 120 days after discharge after the exclusion of acetylsalicylic acid, which is also available over the counter in Ontario. Factors associated with filling all compared with filling no discharge prescriptions included younger age, low income, discharge medication counseling, in-hospital attending cardiologist, and fewer medications before AMI. The adjusted 1-year mortality rate was higher in patients who filled some versus all (odds ratio, 1.44; 95% confidence interval, 1.15 to 1.79; P=0.001) and none versus all (odds ratio, 1.80; 95% confidence interval, 1.35 to 2.42; P<0.0001) of their discharge medications. CONCLUSIONS: Patients fill most of their discharge prescriptions within 1 week after AMI. The 1-year mortality rate was higher for those patients who did not fill all of their discharge medications after AMI. Factors such as discharge medication counseling and postdischarge follow-up may help to increase the filling rate of medications after AMI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".