Different Patterns of Statin Use in Patients with Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Statins have well-established cardiovascular benefits, and recent evidence suggests that discontinuing statin therapy after acute myocardial infarction (AMI) is harmful. Our objective was to assess the association between statin discontinuation post-AMI and 1-year all-cause mortality in a real world setting. METHODS: Data on survivors of AMI between 2000 and 2007 were extracted from the hospital discharge summary database of Quebec and the provincial physician and drug claims database. Statin prescription filling was used to establish cohort groups. Previous statin use was defined as having filled a statin prescription in the 90 days pre-AMI, while post-AMI statin use was filling a prescription between discharge from hospital post-AMI and 90 days post-discharge. AMI patients who survived 90 days (n=48,229) were divided into 4 groups: i) non-users (n=11,657), did not receive statins pre- or post- AMI (reference group), ii) starters (n=22,452), received statins only post-AMI, iii) stoppers (n=488), received statins pre- but not post-AMI, and, iv) users (n=13,632), received statins pre- and post-AMI. Cox proportional hazards models were used to calculate hazard ratios (HR). RESULTS: Compared with non-users, stoppers had increased 1-year all-cause mortality (adjusted HR 1.36; 95% CI 1.08- 1.70, P=0.008). Starters (HR 0.65; 95% CI 0.59-0.71, P<0.0001) and users (HR 0.81; 95% CI 0.74-0.88, P<0.0001) had lower mortality than non-users. CONCLUSION: Discontinuation of statins in survivors of AMI was associated with an increase in 1-year all-cause mortality. Physicians should use caution when discontinuing statins post-AMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".