Soluble Urokinase Plasminogen Activator Receptor as a Diagnostic and Prognostic Biomarker in Cardiac Disease
Bibliographic record
Abstract
This review summarizes the published literature referring to the use and validity of the biomarker soluble urokinase plasminogen activator receptor (suPAR) when used for the assessment of patients with cardiac diseases. It is measured by enzyme-linked immunosorbent assay (ELISA) in plasma samples. In cardiology a cut-off value range of 3.5 - 4.5 ng/mL has been commonly utilized. Different cut-off values may be applied based on the measuring kit used, the patient population and the clinical setting. A PubMed/Medline search was conducted aiming to identify all publications relevant to the use of suPAR in patients with cardiac diseases. A total of 39 studies were included in this review. suPAR as a marker of inflammation has been used more extensively in recent years, alone or in combination with other biomarkers of inflammation and cardiac pathology in the assessment of patients with acute and chronic cardiac diseases. suPAR is closely related to the pathophysiology of cardiac disease, and a number of publications encourages its use as a valuable biomarker in the assessment of patients presenting to the cardiology service. It may be most valuable in the risk assessment of patients with acute coronary syndromes and congestive heart failure, as suPAR elevation may be an independent predictor of mortality in these conditions. In conclusion, among several biomarkers used for clinical entities with underlying inflammatory pathophysiology including cardiac diseases, suPAR is a novel attractive index for the prognostic risk stratification of cardiac patients. More research is warranted to confirm its diagnostic and prognostic validity, alone or combined with other cardiac and inflammatory biomarkers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".