Researchers from Lund University have identified molecular signatures associated with sepsis that could improve diagnosis and prognosis. This research presented at ECCMID 2024 in Barcelona aims to target specific therapies at patients who would benefit most. Sepsis is a life-threatening condition triggered by infection, with no singular diagnostic test available. The study analyzed plasma samples from patients with suspected sepsis, using mass spectrometry to identify protein patterns predictive of outcomes. A machine-learning model was developed to predict patients at high risk of septic shock with accuracy. The findings suggest the potential for a fast test to provide accurate sepsis diagnosis and predict poorer outcomes.
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Scientists have a new tool in the race to improve the diagnosis and prognosis of sepsis
