Predicting Mortality in Blunt Abdominal Aortic Trauma- a Novel Approach Utilizing Neural Networks

Abstract

The aim of this study was to develop an accurate neural network (NN)-based approach to predict in-hospital mortality in patients who sustain blunt traumatic aortic injury (BTAI). Secondary objectives include identifying factors that may play a contributory role in mortality. The 2019 National Trauma Data Bank (NTDB) was queried for International Classification of Diseases, Tenth Edition (ICD-10) codes corresponding to blunt abdominal aortic trauma. Patients with no signs of life upon presentation were excluded from this study. A total of 113 patients were used to train a NN using the following input variables: age, sex, Injury Severity Scale (ISS), presenting Glasgow Coma Scale (GCS), packed red blood cells (RBCs) used in the first 4 hours of presentation, and presenting systolic blood pressure (SBP). The primary outcome of our study was the area under the receiver-operator characteristics curve (AUC), sensitivity, and specificity of our NN with secondary outcomes including the normalized importance ratio of variables used to train the NN. The NN accurately predicted mortality in patients who sustained BTAI with an AUC of 0.93 (Fig), sensitivity of 72%, and specificity of 89%. There were no significant differences in age between the mortality and survival cohorts. There was a higher prevalence of males in the mortality group in addition to a higher ISS, lower GCS, increased number of packed RBCs in the first 4 hours of presentation and a lower presenting SBP. Our NN found transfusion requirements to be the most predictive of mortality followed by ISS. A NN-based approach can accurately predict mortality in BTAI. Additionally, transfusions requirements within the first 4 hours and presenting ISS were found to have the highest predictive value. Furthermore, NN can be utilized as an innovative predictive adjunct in the management of critical trauma.