Development and validation of prediction models for prehospital triage of military trauma patients.

Introduction: The introduction of wireless sensors will enable military care providers to continuously and remotely assess/monitor vital signs. Prediction models are needed to use such data adequately and aid military care providers in their on-scene decision-making to optimise prehospital triage and improve patient outcomes.

Methods: A prospective cohort comprising data from eight Emergency Medical Services and seven inclusive trauma regions was used to develop and validate prediction models that could aid military care providers in their prehospital triage decisions. Healthy (American Society of Anesthesiologists physical status classification 1 or 2) admitted adult trauma patients (aged ≥16 and ≤50 years), who suffered from a trauma mechanism that could occur to military personnel and were transported by ambulance from the scene of injury to a hospital, were included. A full model strategy was used, including prehospital predictors that are expected to be automaticly collectible by wireless sensors or to be incorporated in a personalised device that could run the models. Models were developed to predict early critical-resource use (ECRU), severe head injury (Abbreviated Injury Scale (AIS) ≥4), serious thoracic injury (AIS ≥3) and severe internal bleeding (>20% blood loss). Model performance was evaluated in terms of discrimination and calibration.

Results: Prediction models were developed with data from 4625 patients (80.0%) and validated with data from 1157 patients (20.0%). The models had good to excellent discriminative performance for the predicted outcomes in the validation cohort, with an area under the curve of 0.80 (95% CI 0.76 to 0.84) for ECRU, 0.83 (0.76 to 0.91) for severe head injury, 0.75 (0.70 to 0.80) for serious thoracic injury and 0.85 (0.78 to 0.93) for severe internal bleeding. All models showed satisfactory calibration in the validation cohort.

Conclusion: The developed models could reliably predict outcomes in a simulated military trauma population and potentially support prehospital care providers in their triage decisions.