Continuously recorded oxygen saturation and heart rate during prehospital transport outperform initial measurement in prediction of mortality after trauma.

INTRODUCTION

Available trauma scoring systems that predict need for higher echelons of care require data not available in the field. We hypothesized that analysis of continuous vital sign data in comparison to trauma registry data predicts mortality early in trauma patient management.

METHODS

A real-time vital signs wave form and data capture system collected trauma patient data during prehospital management from Propaq 206E physiologic monitors. Analysis using statistical and mathematical software calculated receiver operator characteristic curves to evaluate the sensitivity and specificity of continuous vital sign waveforms in predicting mortality. The area under the curve (AUC) was calculated to determine nonsurvival by a particular vital sign (oxygen saturation [SpO2], heart rate, and systolic blood pressure) from these data, compared with a single value in the trauma registry, and to standard trauma scoring systems.

RESULTS

The average transport time from field to hospital for all patients was 25 minutes. Eight of 120 patients (7%) died; 5 of 8 patients (62%) died within the first 24 hours. Receiver operator characteristic analysis of mean SpO2 <90% versus mortality yielded an AUC of 0.76 (p = 0.005) with a sensitivity of 62% and specificity of 86% The initial SpO2 <90% measurement from the trauma registry yielded an AUC of 0.59. Preadmission Glasgow Coma Scale score yielded an AUC of 0.74 (p = 0.009). Injury Severity Score and Trauma-Injury Severity Score produced AUCs of 0.91 and 0.96, respectively. Revised Trauma Score gave an AUC of 0.73, no different from automated predictions of mortality from SpO2.

CONCLUSION

Injury Severity Score and Trauma-Injury Severity Score are predictive of mortality but rely on the inclusion of intra-abdominal and intrathoracic diagnostic data that are not readily available during field assessment. Automated vital signs data collection and analysis from a single noninvasive device with decision support has the potential to alleviate the dual burdens of patient triage and documentation required of the prehospital provider.