COMPARISON OF CONTINUOUS VITAL SIGNS DATA ANALYSIS VERSUSVENOUS LACTATE FOR THE PREDICTION OF LIFESAVING INTERVENTIONS IN PATIENTS WITH TRAUMATIC SHOCK

Introduction:The prehospital environment is fraught with operational constraints, making it difficult to assess the need for resources such as lifesaving interventions (LSI) for adults with traumatic injuries. While invasive methods such as lactate have been found to be highly predictive for estimating injury severity and resource requirements, noninvasive methods, to include continuous vital signs (VS), have the potential to provide prognostic information that can be quickly acquired, interpreted, and incorporated into decision making. In this work, we hypothesized that an analysis of continuous VSwould have predictive capacity comparable to lactate and other laboratory tests for the prediction of injury severity, need for LSIs and intensive care unit admission. Methods:In this preplanned secondary analysis of 300 prospectively enrolled patients, venous blood samples were collected in the prehospital environment aboard a helicopter and analyzed with a portable lab device. Patients were transported to the primary adult resource center for trauma in the state of Maryland. Continuous VSwere simultaneously collected. Descriptive statistics were used to describe the cohort and predictive models were constructed using a regularized gradient boosting framework with 10-fold cross-validation with additional analyses using Shapley additive explanations (SHAP). Results:Complete VSand laboratory data from 166 patients were available for analysis. The continuous VSmodels had better performance for prediction of receiving LSIs and intensive care unit length of stay compared to single (initial) VSmeasurements. The continuous VSmodels had comparable performance to models using only laboratory tests in predicting discharge within 24 h (continuous VSmodel: area under the receiver operating curve [AUROC] 0.71; 95% CI, 0.68–0.75 vs.lactate model: AUROC, 0.65; 95% CI, 0.68; 95% CI, 0.66–0.71). The model using all laboratory data yielded the highest sensitivity and sensitivity (AUROC, 0.77; 95% CI, 0.74–0.81). Discussion:The results from this study suggest that continuous VSobtained from autonomous monitors in an aeromedical environment may be helpful for predicting LSIs and the critical care requirements for traumatically injured adults. The collection and use of noninvasively obtained physiological data during the early stages of prehospital care may be useful for in developing user-friendly early warning systems for identifying potentially unstable trauma patients.