Effectiveness of Preprocedural Statin Therapy on Clinical Outcomes for Patients With Stable Coronary Artery Disease After Percutaneous Coronary Interventions
Bibliographic record
Abstract
BACKGROUND: Data have shown that preprocedural statin therapy reduces periprocedural myocardial infarction after percutaneous coronary intervention (PCI). However, whether preprocedural statins improve clinical outcomes in patients with stable coronary artery disease (CAD) has not been established. We aimed to evaluate the clinical effectiveness of preprocedural statin therapy in patients with stable CAD undergoing PCI. METHODS AND RESULTS: We conducted an observational study of 12 980 patients, age >65 years with stable CAD, who underwent PCI from December 1, 2003, to March 31, 2008. Using propensity score-matching analysis, 3098 unique matched pairs (6196 patients) who had similar likelihood of receiving preprocedural statins were identified. Additional analyses adjusting for postprocedural statins as a time-varying variable were performed. The main outcome measure was a composite of death or recurrent acute coronary syndrome. In the propensity-matched cohort, at 90 days, the primary outcome of death and acute coronary syndrome occurred in 5.6% in the preprocedural statin group as compared with 7.4% in the no-pretreatment group (P=0.005). Improved clinical outcomes associated with preprocedural statins were still observed at 2 years (16.7% versus 19.3%, P=0.007). The effectiveness of preprocedural statins was most pronounced at 90 days after PCI (adjusted hazard ratio, 0.80; 95% confidence interval, 0.65 to 0.98) but was no longer significant at 1 year (adjusted hazard ratio, 0.92; 95% confidence interval, 0.79 to 1.07) after accounting for postprocedural statin therapy. CONCLUSIONS: Preprocedural statin therapy was associated with significant reduction in the risk of death or recurrent acute coronary syndrome in stable CAD patients after PCI. These findings support the routine use of preprocedural statins for suitable candidates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".