BIOMARKER-AUGMENTED SEPSIS RECOGNITION FROM SCENE TO ED: A SYSTEMATIC REVIEW OF PARAMEDIC, NURSING, AND LABORATORY INTEGRATION
Abstract Background: Early sepsis recognition in the prehospital/ED pathway is challenging. Beyond vital-sign–based scores, several host-response biomarkers (lactate, procalcitonin, presepsin, suPAR) improve triage and risk stratification. Objective: To synthesize evidence on biomarker-augmented sepsis recognition and risk stratification across the scene-to-ED continuum. Methods: Following PRISMA principles, we analyzed nine included original studies (prehospital/ED) and used 10 additional papers for discussion. Data items were population, setting, index biomarker/tool, comparators, and outcomes (diagnostic/prognostic accuracy, workflow effects, and patient outcomes). Risk of bias and applicability were narratively assessed. Results: Prehospital lactate consistently associated with short-term mortality and improved identification of higher-risk patients, particularly when ≥3 mmol/L, but effects on hard outcomes or bundle adherence were inconsistent. In ED cohorts, presepsin showed strong diagnostic/prognostic discrimination and suPAR improved risk stratification; however, large pragmatic trials found no mortality benefit from routine suPAR use alone. Combining biomarkers with parsimonious clinical scores (NEWS2/qSOFA) generally enhanced discrimination but heterogeneity limits firm recommendations. Conclusions: Biomarkers can sharpen early sepsis risk recognition, with the most reproducible prehospital signal for lactate. Yet, translation to improved patient-level outcomes remains uncertain. Future research should prioritize targeted biomarker-plus-score pathways, workflow integration (pre-alert/antibiotic readiness), and randomized evaluations of patient-centered outcomes.
Institutions
- King Fahd Armed Forces Hospital (SA)
- National Guard Military Academy of Ukraine (UA)
- National Guard Health Affairs (SA)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-09-23
- DOI
- https://doi.org/10.5281/zenodo.17276510
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00