Gene expression signature to predict sepsis mortality

Unmet Need

Sepsis is responsible for half of the in-hospital deaths in the US. It is characterized by organ dysfunction resulting from an uncontrolled response to an infection. The standard treatment for sepsis includes source control, general antibiotics, and supportive care, but attempts to develop a specific treatment for sepsis have been unsuccessful. The failure of sepsis therapies is thought to be due to patient differences and the lack of tools to accurately categorize sepsis at the molecular level. Although clinical severity scores and blood lactate levels are used for risk stratification, they do not adequately quantify the abnormal response displayed by patients. There is a need for a test that allows clinicians to molecularly define the microbial agents and assess the severity of the host response in sepsis.

Technology

Duke inventors have developed a prognostic model to improve the prediction of sepsis mortality and enhance risk stratification for septic patients. This model is intended as a test to identify high-risk patients and ultimately reduce sepsis-related mortality. Specifically, by focusing on gene expression profiles, the researchers were able to identify changes in genes related to neutrophil, hypoxia, and energy expenditure that are correlated with patients who do not survive sepsis. This data provided important insights into the biological response of hosts during sepsis. The researchers validated four different prognostic models in external patient cohorts with either community-acquired sepsis or hospital-acquired infections. This advancement allows clinicians to better predict sepsis prognosis and stratify patient risk based on gene expression profiles at the time of diagnosis.

Other Applications

This technology could also be used molecular for phenotyping in clinical trials, enhancing the selection of candidates for specific treatments.

Advantages

• Enhanced accuracy in sepsis prognosis to optimize patient care.

• Quantitative assessment of host response profiles.

• Improved risk stratification of septic patients for more effective resource allocation and treatment strategies.

• Mortality prediction at the time of prognosis to inform clinical decision-making.