Performance of plasminogen activator inhibitor-1 as a biomarker in patients undergoing coronary angiography: Analytical and biological considerations
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Target lesion failure continues to limit the efficacy of percutaneous coronary intervention despite advancements in stent design and medical therapy. Identification of biomarkers to risk stratify patients after percutaneous coronary intervention has the potential to focus therapies on cohorts with increased benefits. Plasminogen activator inhibitor-1 has been identified as a candidate biomarker. Herein, we evaluate biological variables which impact plasminogen activator inhibitor-1 levels and analytical characteristics which impact its utility as a biomarker in humans. METHODS: Plasma plasminogen activator inhibitor-1 was measured in 689 patients undergoing coronary angiography. Plasminogen activator inhibitor-1 levels were measured. Clinical and procedural characteristics were collected in a prospective registry. RESULTS: = 118.6%) variations with reference change value of 122.3% were calculated. Plasminogen activator inhibitor-1 levels were elevated in patients with cardiovascular risk factors, including type 2 diabetes, pre-diabetes, smokers, obesity, hypertension, and daytime variation in procedure and blood draw. CONCLUSION: Variation in plasma plasminogen activator inhibitor-1 levels is influenced by multiple biological and procedural characteristics. The performance of plasma plasminogen activator inhibitor-1 is consistent with biomarkers in clinical use (N-terminal pro-B-type natriuretic peptide and C-reactive protein) and its applicability is promising.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".