TREWS: Statistical Machine Learning Driven Approaches for Timely Recognition and Management of Acute Deterioration Due to Complications like Sepsis and Infection
Sepsis is a major cause of death, which remains difficult to treat despite modern antibiotics. Early aggressive treatment of this disease improves patient mortality, but the tools currently available in the clinic do not predict who will develop sepsis and its late manifestation, septic shock, until the patients are already in advanced stages of the disease. The projects draws on electronic health records of intensive care unit patients to develop an algorithm that combines 27 factors into a Targeted Real-time Early Warning Score –TREWScore. TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality.