Combat medic testing of a novel monitoring capability for early detection of hemorrhage.

Background: Current out-of-hospital protocols to determine hemorrhagic shock in civilian trauma systems rely on standard vital signs with military guidelines relying on heart rate and strength of the radial pulse on palpation, all of which have proven to provide little forewarning for the need to implement early intervention prior to decompensation. We tested the hypothesis that addition of a real-time decision-assist machine-learning algorithm, the compensatory reserve measurement (CRM), used by combat medics could shorten the time required to identify the need for intervention in an unstable patient during a hemorrhage profile as compared with vital signs alone.

Methods: We randomized combat medics from the Army Medical Department Center and School Health Readiness Center of Excellence into three groups: group 1 viewed a display of no simulated hemorrhage and unchanging vital signs as a control (n = 24), group 2 viewed a display of simulated hemorrhage and changing vital signs alone (hemorrhage; n = 31), and group 3 viewed a display of changing vital signs with the addition of the CRM (hemorrhage + CRM; n = 22).