TiME OUT: Time-specific Machine-learning Evaluation to Optimize Ultra-massive Transfusion

Abstract

Abstract 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. Methods A retrospective review was conducted at a Level I trauma (2018-2021) and included trauma patients meeting criteria for UMT, defined as >20 red blood cell products within 24 h of admission. Cross-sectional data was obtained from the blood bank and trauma registries and time-specific data was obtained from the electronic medical record. Time-specific decision-tree models (TS-DTM) predicating mortality were generated and evaluated using AUC. Results In the 180 patients included, mortality rate was 40.5% at 48-hours and 52.2% overall. The deceased received significantly more blood products with a median of 71.5 total units compared to 55.5 in the survivors (p < 0.001) and significantly greater rates of pRBC and FFP at each time interval. TS-DTM predicted mortality with an accuracy as high as 81%. In the early time intervals, hemodynamic stability, undergoing an emergency department thoracotomy and injury severity were most predictive of survival while in the later intervals, markers of adequate resuscitation such as arterial pH and lactate level became more prominent. Conclusions This study supports that the decision of “when to stop” in UMT resuscitation is not based exclusively on the number of units transfused, but rather the complex integration of patient and time-specific data. ML is an effective tool to investigate this concept and further research is needed to refine and validate these TS-DTM. Level of Evidence IV, Retrospective cohort review