Courtney H Meyer, Jonathan Nguyen, Andrew ElHabr, Nethra Venkatayogi, Tyler Steed, Judy Gichoya, Jason D Sciarretta, James Sikora, Christopher Dente, John Lyons, Craig M Coopersmith, Crystal Nguyen, Randi N Smith
BACKGROUND: Ultra-massive transfusion (UMT) is a resource-demanding intervention for trauma patients in hemorrhagic shock and associated mortality rates remains high. Current research has been unable to identify a transfusion ceiling, or point where UMT transitions from life-saving to futility. Furthermore, little consideration has been given to how time-specific patient data points impact decisions with ongoing high-volume resuscitation. Therefore, this study sought to utilize time-specific machine learning (ML) modeling to predict mortality and identify parameters associated with survivability in trauma patients undergoing UMT...
November 13, 2023: Journal of Trauma and Acute Care Surgery