Explainable Prediction Model of the Need for Emergency Hemostasis Using Field Information During Physician-Staffed Helicopter Emergency Medical Service Interventions: A Single-Center, Retrospective, Observational Pilot Study

Abstract

ObjectiveEarly recognition of hemostasis is important to prevent trauma-related deaths. We conducted a pilot study of a predictive model of hemostatic need using factors that can be collected during helicopter emergency medical service (HEMS) interventions until transport hospital selection using cases from our institution. MethodsThis single-center, retrospective, observational pilot study included 251 trauma patients aged ≥ 18 years treated with HEMS between April 2017 and March 2022, in Nara Medical University. Cardiac arrest and pre-HEMS treatment patients were excluded. Emergency hemostatic surgery prediction models were constructed using the light gradient boosting machine cross-validation method using objective data that could be collected before hospital determination. The accuracy of this model was compared with that of the ground emergency medical service–based model, and factors influencing outcome were visualized using Shapley additive explanations. ResultsThe predictive accuracy of the model with HEMS intervention factors was an area under the receiver operating characteristic curve of 0.80, superior to the 0.73 accuracy area under the receiver operating characteristic curve for ground emergency medical services constructed with contact information. Clinically important factors, such as shock index, blood pressure changes, and ultrasound findings, had a significant impact on outcomes, with nonmonotonic effects observed across factors. ConclusionThis pilot study suggests that predictive models of emergency hemostasis can be built using limited prehospital information. To validate this model, a larger, multicenter study is recommended.