Prehospital triage of trauma patients: predicting major surgery using artificial intelligence as decision support

BACKGROUND: Matching the necessary resources and facilities to attend to the needs of trauma patients is traditionally performed by clinicians using criteria-directed triage protocols. In the present study, it was hypothesized that an artificial intelligence (AI) model should be able to predict the need for major surgery based on data available at the scene.

METHODS: Prehospital and in-hospital electronic health record data were available for 4578 patients in the Danish Prehospital Trauma Data set. Data included demographics (age and sex), clinical scores (airway, breathing, circulation, disability (ABCD) and Glasgow Coma Scale scores), and sequential vital signs (heart rate, blood pressure, and oxygen saturation). The data from the first 5, 10, and 20 min of prehospital contact were used for predicting the need for surgery up to 12 h after hospital arrival. Surgeries were stratified into all major surgical procedures and specialty-specific procedures (neurosurgery, abdominal surgery, and vascular surgery). The data set was split into training (70%), validation (20%) and holdout test (10%) data sets. Three hybrid neural networks were trained and performance was evaluated on the holdout test data set using the area under the receiver operating characteristic curve (ROC-AUC).

RESULTS: Overall, the model achieved an ROC-AUC of 0.80-0.86 for predicting the need for major surgery. For predicting the need for major neurosurgery the ROC-AUC was 0.90-0.95, for predicting the need for major vascular surgery the ROC-AUC was 0.69-0.88, and for predicting the need for major abdominal surgery the ROC-AUC was 0.77-0.84.

CONCLUSION: Utilizing AI early in the prehospital phase of a trauma patient's trajectory can predict specialized surgical needs. This approach has the potential to aid the early triage of trauma patients.