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Background: Trauma is a serious medical and economic problem worldwide, and patients with trauma injuries have a poor survival rate following cardiac arrest. This study aimed to create a prediction model specific to prehospital trauma care and to achieve greater accuracy with techniques of machine learning.Methods: This retrospective observational study investigated data of patients who had blunt trauma injuries due to traffic accident and fall trauma from January 1, 2018, to December 31, 2019, using the National Emergency Medical Services Information System, which stores emergency medical service activity records nationwide in the United States. Random forest was used to develop a machine learning model. Results:Per the prediction model, the area under the curve of the predictive model was 0.95 and negative predictive value was 0.99. The feature importance of the predictive model was the highest for the AVPU scale (an acronym from "Alert, Verbal, Pain, Unresponsive"), followed by oxygen saturation (SpO2). Among patients who were progressing to cardiac arrest, the cutoff value was 89% for SpO2 in unalert patients.Conclusions: Patients whose conditions did not progress to cardiac arrest could be identified with high accuracy by machine learning model techniques.

Background: In Japan, increasing the number of ambulance requests, the case with the use of respiratory assistance devices in prehospital care by paramedics is also increasing. When patient experiences respiratory failure, the first responders frequently select a respiratory assist device (RAD) such as Bag Valve Mask (BVM), Jackson Rees (JR), or BVM with Gas Supply Valve Ⓡ (BVM+GSV). This is based on both evaluation and experience as there is no study indicating which RAD is the best choice at the pre-hospital emergency site. This study clarified the precautions when using BVM, JR, and BVM+GSV in pre-hospital emergency medical care with healthy volunteers. Methods: Twenty healthy adults were fitted with a RAD while breathing spontaneously, and changes in vital signs and ETCO2 were observed.Results: The level of ETCO2 became elevated after each RAD was attached. The value was significantly higher in the JR group than in the others.Conclusions: The study showed that even in the presence of spontaneous breathing, ETCO2 increased markedly with the application of respiratory assist devices that are used in pre-hospital care for conditions such as hypoxemia and ventilatory disturbance. The increase in ETCO2 was particularly significant in the