Quantification of the effect of clopidogrel on enzymatic infarct size related to a percutaneous coronary intervention in patients with acute coronary syndromes
Bibliographic record
Abstract
OBJECTIVES: Using data from the CHAMPION percutaneous coronary intervention (PCI), we determined the relationship between clopidogrel started at least 5 days before PCI (maintenance of clopidogrel) and PCI-related enzymatic infarct size. BACKGROUND: Clopidogrel is recommended in patients with acute coronary syndrome (ACS) managed with PCI, but its effect on PCI-related myonecrosis in contemporary patients has not been quantified. PATIENTS AND METHODS: Patients with ACS (with or without ST-segment elevation) who underwent PCI and had at least three creatine kinase-MB (CK-MB) samples after PCI were included. Enzymatic infarct size was defined as the peak CK-MB concentration indexed by its upper limit of normal. Associations between maintenance clopidogrel and enzymatic infarct size were explored using multivariable linear regression (with and without missing data imputation) and propensity score analysis using inverse probability weighting. RESULTS: Of 8877 patients randomized, 6327 (71.3%) were included (median age 61 years, 73% male, 13% ACS with ST-segment elevation). Of these 6327 patients, 2015 (31.8%) were on maintenance clopidogrel. After multivariable adjustment, maintenance clopidogrel was associated with a reduction in enzymatic infarct size {β=-0.63; 47% decrease in peak CK-MB [95% confidence interval (CI) 35, 56%]}. Multivariable linear regression with multiple imputations and inverse probability weighting propensity score analysis yielded similar results, with maintenance clopidogrel associated with 44% (95% CI 33, 53%) and 29% (95% CI 24, 33%) infarct size reductions. CONCLUSION: In this subgroup analysis of modern ACS patients, clopidogrel maintenance was independently associated with smaller enzymatic infarct size after PCI. These results are consistent with previous observations suggesting a benefit of clopidogrel on the procedural outcome and quantify this benefit.
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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 | 0.000 | 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".