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Outcomes from cardiopulmonary resuscitation (CPR) remain distressingly poor. Overuse of CPR is attributable to unrealistic expectations, unintended consequences of existing policies andfailure to honour patient refusal of CPR. We analyzed the CPR outcomes literature using the bioethical principles of beneficence, non-maleficence, autonomy andjustice and developed a proposalfor selective use of CPR.Beneficence supports use of CPR when most effective.

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). Participants were asked to push a computer key when they believed the patient was becoming unstable and needed medical intervention. RESULTS The average time of 11.0 minutes (95% confidence interval, 8.7–13.3 minutes) required by the hemorrhage + CRM group to identify an unstable patient (i.e., stop the video sequence) was less by more than 40% (p < 0.01) compared with 18.9 minutes (95% confidence interval, 17.2–20.5 minutes) in the hemorrhage group. CONCLUSION The use of a machine-learning monitoring technology designed to measure the capacity to compensate for central blood volume loss resulted in reduced time required by combat medics to identify impending hemodynamic instability. LEVEL OF EVIDENCE Diagnostic, level IV.

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