A Deep Learning-based Model to Predict Limb Amputation in Peripheral Vascular Trauma

Abstract

The objective of this study is to develop an accurate neural network (NN)-based approach to predict limb amputation in patients who sustain blunt traumatic peripheral vascular injury. The 2019 National Trauma Data Bank (NTDB) was queried for International Classification of Disease, Tenth Edition (ICD-10) codes corresponding to blunt peripheral vascular trauma. A total of 15,438 patients were used to train a NN, of which 335 received a traumatic amputation. The neural network was trained 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, heart rate, and presenting systolic blood pressure (SBP). The primary outcome of our study was the area under the receiver-operator characteristics curve (AUC) of our NN with secondary outcomes including the normalized importance ratio of variables used to train the NN. The NN accurately predicted amputation in patients who sustained blunt peripheral vascular injury with an AUC of 0.78. There were no significant differences in age between the amputation and limb salvage cohorts. There was a higher prevalence of males in the amputation cohort in addition to a higher ISS, lower GCS, increased amount of packed RBCs in the first 4 hours of presentation, and a lower presenting SBP. Our NN found ISS followed by blood transfusion requirements to be most predictive of traumatic amputation. A NN-based approach can accurately predict. Additionally, presenting ISS and transfusion requirements within the first 4 hours were found to have the highest predictive value. Furthermore, NN can be utilized as an innovative predictive adjunct in the management of critical trauma.