Abstract

Background: It is of prime importance to manage trauma patients in the early hours and use easy trauma severity scoring systems to make decisions and evaluate patient prognosis. Objectives: The present study aimed to design a predictive model of the mortality of multi-trauma patients due to traffic accidents. Methods: This cross-sectional analytical study was performed on 600 patients who suffered from multi-trauma caused by traffic accidents from December 2019 to September 2021. Collected data included age, sex, vital signs, trauma mechanism, involved vehicle in the accident, accident location, and hospital outcome. Results: In this study, 600 multi-trauma cases caused by traffic accidents were evaluated. Among the significant variables included in the regression model, age, Mean Arterial Pressure (MAP), Glasgow Coma Scale (GCS), AVPU (Alert, Verbal response, Pain response, Unresponsive), and vehicle versus fixed objects (in Vehicle 2) in the presence of other variables in the model, significantly predicted patient outcomes. Therefore, with the other variables being constant, one unit increase in the age variable increases the probability of death by 1.04 times, one unit increase in the score of the two variables of MAP and GCS, and also the transfer of trauma mechanism from the fixed object to the vehicle reduces death by 0.92, 0.62, and 0.10 times, respectively. In the AVPU variable, the transition from Alert to Verbal, the transition from Verbal to Pain, and the transition from Pain to Unresponsive increases the probability of death by 32, 104, and 567, respectively. Conclusion: In this study, AVPU, age, MAP, primary GCS, and trauma mechanism due to hitting a vehicle with a fixed object had significantly the highest predictive power of hospital mortality in patients with multiple trauma due to traffic accidents, respectively. It is suggested that further studies be performed to replace the AVPU variable with GCS in the newly designed formulas for calculating the severity of trauma to simplify these scores.

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