Sepsis is a leading cause of death, contributing to 1 in every 2 or 3 hospital deaths. Early identification and treatment has a dramatic impact on morbidity and mortality. In a cover article published in Science Translational Medicine, Dr. Saria and colleagues showed that routinely available vital signs and lab results could be used to predict which patients would experience septic shock. TREWScore (Targeted Real-Time Early Warning Score) was more accurate than a routine screening protocol and another score used clinically for predicting septic shock (MEWS). TREWScore identified patients 28.2 hours (median) before onset with ⅔ of cases identified before any sepsis-related organ dysfunction.
This was the first study to show that applying machine learning techniques to clinical data could be used to proactively identify patients at risk of sepsis. Since then, over 200 related papers have been published.
Read the full research paper here.