Prognostic value of suPAR in sepsis: a potential tool to support patient management in the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: The soluble urokinase plasminogen activator receptor (suPAR) is a well-established biomarker of immune activation, reflecting the severity of systemic inflammation. Recent evidence showed increasing interest in suPAR as a prognostic marker, with elevated levels consistently associated with greater disease severity and mortality, in different clinical settings. METHODS: In this retrospective study, suPAR levels were assayed at Emergency Department admission in patients who received a diagnosis of sepsis or systemic infection during their initial clinical workup, using an automated turbidimetric assay (suPARnostic ViroGates kit on Atellica CH analyzer Siemens). The primary endpoint of this study was to evaluate the association between baseline suPAR levels and mortality, while the secondary endpoint aimed to explore their potential as indicators of clinical severity and predictors of patient outcomes. RESULTS: suPAR levels were elevated in all patients (median 6.99 μg/L) consistent with the severity of their clinical condition. A threshold of 10.2 μg/L was strongly associated with mortality, while a cut-off of 5.96 μg/L identified patients with severe disease and prolonged hospital stays. CONCLUSIONS: suPAR seems to be a reliable, rapid, and clinically useful prognostic biomarker in the Emergency Department in patients with sepsis or systemic infections. Its early measurement by turbidimetric immunoassay in automation can support risk stratification, improve triage decisions, and enhance the management of these patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".