Prehospital National Early Warning Score as a predictor of massive transfusion in adult trauma patients.

BACKGROUND: Previous studies have shown that an elevated prehospital National Early Warning Score (preNEWS) is associated with increased levels of adverse outcomes in patients with trauma. However, whether preNEWS is a predictor of massive transfusion (MT) in patients with trauma is currently unknown. This study investigated the accuracy of preNEWS in predicting MT and hospital mortality among trauma patients. METHODS: We analyzed adult trauma patients who were treated and transported by emergency medical services (EMS) between January 2018 and December 2019. The main exposure was the preNEWS calculated for the scene. The primary outcome was the predictive ability for MT, and the secondary outcome was 24 h mortality. We compared the prognostic performance of preNEWS with the shock index, modified shock index, and reverse shock index, and reverse shock index multiplied by Glasgow Coma Scale in the prehospital setting. RESULTS: In total, 41,852 patients were included, and 1456 (3.5%) received MT. preNEWS showed the highest area under the receiver operating characteristic (AUROC) curve for predicting MT (0.8504; 95% confidence interval [CI], 0.840-0.860) and 24 h mortality (AUROC 0.873; 95% CI, 0.863-0.883). The sensitivity of preNEWS for MT was 0.755, and the specificity of preNEWS for MT was 0.793. All indicies had a high negative predictive value and low positive predictive value. CONCLUSION: preNEWS is a useful, rapid predictor for MT and 24 h mortality. Calculation of preNEWS would be helpful for making the decision at the scene such as transfer straightforward to trauma center and advanced treatment.

Time-saving effects using helicopter transportation: comparison to a ground transportation time predicted using a social navigation software.

ABSTRACT: Previous comparison studies regarding 2 types of transportation, helicopter (HEMS) versus ground emergency medical services (GEMS), have shown underlying heterogeneity as these options have completely different routes and consequent times with reference to one patient. To compare the 2 types of transportation on a case-by-case basis, we analyzed the retrospectively reviewed HEMS and predicted GEMS data using an open-source navigation software.Patients transferred by military HEMS from 2016 to 2019 were retrospectively enrolled. The HEMS records on the time of notification, injury point and destination address, and time required were reviewed. The GEMS data on distance and the predicted time required were acquired using open-source social navigation systems. Comparison analyses between the two types of transportation were conducted. Furthermore, linear logistic regression analyses were performed on the distance and time of the two options.A total of 183 patients were enrolled. There was no statistical difference (P = .3021) in the distance between the 2 types of transportation, and the HEMS time was significantly shorter than that of GEMS (61.31 vs 116.92 minutes, P < .001). The simple linear curves for HEMS and GEMS were separately secured, and two graphs presented the statistical significance (P) as well as reasonable goodness-of-fit (R2). In general, the HEMS graph demonstrates a more gradual slope and narrow distribution compared to that of GEMS.Ideally, HEMS is identified as a better transportation modality because it has a shorter transportation time (56 minutes saved) and a low possibility of potential time delays (larger R2). With a strict patient selection, HEMS can rescue injured or emergent patients who are "out of the golden hour."