Prehospital continuous vital signs predict need for resuscitative endovascular balloon occlusion of the aorta and resuscitative thoracotomy prehospital continuous vital signs predict resuscitative endovascular balloon occlusion of the aorta.

Background: Rapid triage and intervention to control hemorrhage are key to survival following traumatic injury. Patients presenting in hemorrhagic shock may undergo resuscitative thoracotomy (RT) or resuscitative endovascular balloon occlusion of the aorta (REBOA) as adjuncts to rapidly control bleeding. We hypothesized that machine learning along with automated calculation of continuously measured vital signs in the prehospital setting would accurately predict need for REBOA/RT and inform rapid lifesaving decisions.
Methods: Prehospital and admission data from 1,396 patients transported from the scene of injury to a Level I trauma center via helicopter were analyzed. Utilizing machine learning and prehospital autonomous vital signs, a Bleeding Risk Index (BRI) based on features from pulse oximetry and electrocardiography waveforms and blood pressure (BP) trends was calculated. Demographics, Injury Severity Score and BRI were compared using Mann-Whitney-Wilcox test. Area under the receiver operating characteristic curve (AUC) was calculated and AUC of different scores compared using DeLong's method.
Results: Of the 1,396 patients, median age was 45 years and 68% were men. Patients who underwent REBOA/RT were more likely to have a penetrating injury (24% vs. 7%, p < 0.001), higher Injury Severity Score (25 vs. 10, p < 0.001) and higher mortality (44% vs. 7%, p < 0.001). Prehospital they had lower BP (96 [70-130] vs. 134 [117-152], p < 0.001) and higher heart rate (106 [82-118] vs. 90 [76-106], p < 0.001). Bleeding risk index calculated using the entire prehospital period was 10× higher in patients undergoing REBOA/RT (0.5 [0.42-0.63] vs. 0.05 [0.02-0.21], p < 0.001) with an AUC of 0.93 (95% confidence interval [95% CI], 0.90-0.97). This was similarly predictive when calculated from shorter periods of transport: BRI initial 10 minutes prehospital AUC of 0.89 (95% CI, 0.83-0.94) and initial 5 minutes AUC of 0.90 (95% CI, 0.85-0.94).
Conclusion: Automated prehospital calculations based on vital sign features and trends accurately predict the need for the emergent REBOA/RT. This information can provide essential time for team preparedness and guide trauma triage and disaster management.
Level of Evidence: Therapeutic/care management, Level IV.
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