Abstract 15603: Discovery of Plasminogen Activator Inhibitor-1 Platelet-derived Extracellular Vesicles to Predict Major Adverse Cardiac Events
Bibliographic record
Abstract
Objective: To evaluate the utility of plasminogen activator inhibitor-1 positive platelet-derived extracellular vesicles (PAI-1 + PEV) as a biomarker for major adverse cardiac events (MACE) following angiography. Background: The stented coronary artery is at high-risk for complications, predominantly in the form of stent thrombosis and in-stent restenosis. Clinical risk scores have been attempted but no current models nor biomarkers yet accurately identifies the high-risk cohort following revascularization. Methods: PAI-1 + PEV was measured by flow cytometry in 172 patients undergoing coronary angiography. Biological characteristics and utility of PAI-1 + PEV as a biomarker were evaluated. The primary outcome was the incidence of MACE (composite of death, myocardial infarction, cerebrovascular accident, and unplanned revascularization) at 12 months. Results: During a median follow-up period of 377 days (IQR, 269.5 to 442.5 days), 38 patients (20.9%) experienced MACE. In this study, the existence of PAI-1 + PEV complex was validated by flow cytometry (Figure 1A). Furthermore, low log-transformed PAI-1 + PEV levels were associated with MACE (4.17 0.40 vs. 4.33 0.59 logPAI-1 + PEV, p=0.02). After adjustment for known clinical risk factors, low PAI-1 + PEV levels were independently associated with MACE with a hazard ratio of 7.79 (95% CI, 1.87 to 32.4, p=0.005). Finally, low plasma PAI-1 + PEV levels was predictive of MACE in both the discovery and validation cohort (Figure 1B-C). Conclusion: Our results demonstrate the existence of a PAI-1 + PEV and its potential utility as a biomarker to predict MACE. Low plasma PAI-1 + PEV levels was predictive of MACE in both the discovery and validation cohort.
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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 | 0.002 |
| 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) | 0.002 | 0.001 |
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".