AI Model May Help Identify Trauma Patients Requiring Prehospital Transfusion

An international team of investigators has developed a machine learning model that demonstrated high predictive accuracy in identifying trauma patients who may require prehospital blood transfusion using only readily available prehospital data. The team published findings from the retrospective study in the January issue of The Lancet Digital Health.

The investigators trained the model using trauma registry data from more than 360,000 patients in the United States and externally validated it using data from more than 54,000 patients in Austria, Canada, Germany, Ireland and Switzerland. The models incorporated prehospital variables routinely available to emergency medical services, including vital signs, injury patterns, trauma mechanism and preinjury antithrombotic medication use.

In external validation cohorts, the machine learning model showed strong performance in predicting transfusion need, with an area under the receiver operating characteristic curve of about 0.87, outperforming commonly used predictors such as shock index, revised trauma score and initial hemoglobin levels measured after emergency department arrival. Patients classified by the model as having a high probability of transfusion were more likely to require early operative bleeding control, receive transfusions soon after admission or die from hemorrhage than patients classified as low probability.

According to the investigators, machine learning–based decision support tools could eventually help trauma teams identify patients at high risk for hemorrhagic shock earlier, supporting advance preparation and more timely availability of blood products. However, the authors emphasized that the work represents a development and validation phase rather than a deployable clinical tool, noting that transfusion decisions recorded in trauma registries reflect clinical judgment and institutional practice. Further prospective studies are needed to assess real-time use, integration into emergency workflows and effects on patient outcomes.