Low adherence to statin treatment during the 1st year after an acute myocardial infarction is associated with increased 2nd-year mortality risk—an inverse probability of treatment weighted study on 54 872 patients
Bibliographic record
Abstract
AIMS: Experiencing an acute myocardial infarction (AMI) is a life-threatening event and use of statins can reduce the probability of recurrence and improve long-term survival. However, the effectiveness of statins in the real-world setting may be lower than the reported efficacy in randomized clinical trials. Therefore, we aimed to investigate whether low statin treatment adherence during the year following an AMI episode is associated with increased 2nd-year mortality. METHODS AND RESULTS: We analysed all 54 872 AMI patients aged ≥45 years, admitted to Swedish hospitals between 2010 and 2012, and who survive at least 1 year after the AMI episode. We defined low adherence as a medication possession ratio <50% or non-use of statins. Applying inverse probability of treatment weighting (IPTW), we investigated the association between low adherence and all-cause, cardiovascular disease (CVD), and non-CVD mortality during the 2nd year. Overall, 20% of the patients had low adherence during the 1st year and 8% died during the 2nd year. In the IPTW analysis, low adherence was associated with an increased risk of all-cause [absolute risk difference (ARD) = 0.048, number needed to harm (NNH) = 21, relative risk (RR) = 1.71], CVD (ARD = 0.035, NNH = 29, RR = 1.62), and non-CVD mortality (ARD = 0.013, NNH = 77, RR = 2.17). CONCLUSION: In the real-world setting, low statin adherence during the 1st year after an AMI episode is associated with increased mortality during the 2nd year. Our results reaffirm the importance of achieving a high adherence to statin treatment after suffering from an AMI.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".