Point-of-Care Measured Platelet Inhibition Correlates With a Reduced Risk of an Adverse Cardiac Event After Percutaneous Coronary Intervention
Bibliographic record
Abstract
BACKGROUND: The optimal level of platelet inhibition with a glycoprotein (GP) IIb/IIIa antagonist necessary to minimize thrombotic complications in patients undergoing a percutaneous coronary intervention (PCI) is currently unknown. METHODS AND RESULTS: Five hundred patients undergoing a PCI with the planned use of a GP IIb/IIIa inhibitor had platelet inhibition measured at 10 minutes, 1 hour, 8 hours, and 24 hours after the initiation of therapy with the Ultegra Rapid Platelet Function Assay (Accumetrics). Major adverse cardiac events (MACES: composite of death, myocardial infarction, and urgent target vessel revascularization) were prospectively monitored, and the incidence correlated with the measured level of platelet function inhibition at all time points. One quarter of all patients did not achieve >/=95% inhibition 10 minutes after the bolus and experienced a significantly higher incidence of MACEs (14.4% versus 6.4%, P=0.006). Patients whose platelet function was <70% inhibited at 8 hours after the start of therapy had a MACE rate of 25% versus 8.1% for those >/=70% inhibited (P=0.009). By multivariate analysis, platelet function inhibition >/=95% at 10 minutes after the start of therapy was associated with a significant decrease in the incidence of a MACE (odds ratio 0.46, 95% CI 0.22 to 0.96, P=0.04). CONCLUSIONS: Substantial variability in the level of platelet function inhibition is achieved with GP IIb/IIIa antagonist therapy among patients undergoing PCI. The level of platelet function inhibition as measured by a point-of-care assay is an independent predictor for the risk of MACEs after PCI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".