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Can Artificial Intelligence Improve the Appropriate Use and Decrease the Misuse of REBOA?

Year: 2025

Type: article

Abstract: ABSTRACT BACKGROUND The use of resuscitative endovascular balloon occlusion of the aorta (REBOA) for control of noncompressible torso hemorrhage remains controversial. We aimed to utilize a novel and transparent/interpretable artificial intelligence (AI) method called Optimal Policy Trees (OPT), to improve the appropriate use and decrease the misuse of REBOA in hemodynamically unstable blunt trauma patients. METHODS We trained then validated OPTs that “prescribe” REBOA in a 50:50 split on all hemorrhagic shock blunt trauma patients in the 2010-2019 ACS-TQIP database based on rates of survival. Hemorrhagic shock was defined as a systolic blood pressure ≤ 90 on arrival or transfusion requirement of ≥ 4 units of blood in the first 4 hours of presentation. The expected 24-hour mortality rate following OPT prescription was compared to the observed 24-hour mortality rate in patients who were or were not treated with REBOA. RESULTS Out of 4.5 million patients, 100,615 were included and 803 underwent REBOA. REBOA patients had a higher rate of pelvic fracture, femur fracture, hemothorax, pneumothorax, and thoracic aorta injury (p<0.001). The 24-hour mortality rate for the REBOA vs. non-REBOA group was 47% vs. 21%, respectively (p<0.001). OPTs resulted in an 18% reduction in 24-hour mortality for REBOA and 0.8% reduction in non-REBOA patients. CONCLUSION Interpretable AI models can improve mortality in unstable blunt trauma patients by optimizing the use and decreasing the misuse of REBOA. These models to date have been used to predict outcomes, but their groundbreaking use will be prescribing interventions and changing outcomes. LEVEL OF EVIDENCE Level IV, Prognostic

Source: Bioengineering

DOI: 10.3390/bioengineering12101025