AI model predicts blood transfusion needs in trauma before hospital

The researchers developed and validated machine-learning models designed to predict whether trauma patients will require blood transfusions before reaching hospital. The models rely exclusively on data typically available to emergency medical services (EMS) teams, including:

• vital signs
• injury patterns
• medication history

Co-author Prof Patricia Maguire (University College Dublin) said: “These findings show that AI-driven decision support could enable earlier and more precise identification of patients at highest risk of haemorrhagic shock, using data already available to emergency services. This has clear potential to support more timely transfusion decisions, although prospective evaluation will be needed before clinical implementation.”

The study analyzed trauma registry data from 364,350 patients in the United States. The models were then tested in 54,210 additional patients from Germany, Austria, Switzerland, Ireland and Canada. In these evaluations, the AI system achieved high predictive accuracy for identifying both:

• any transfusion need
• need for packed red blood cells

Importantly, the models were evaluated against traditional risk classification approaches applied after arrival at an emergency department. The AI approach more accurately identified patients who later required transfusion, emergency surgery for bleeding control, or who died from haemorrhage, based only on pre-hospital information.

Prof Maguire also noted: “This work shows how AI can use prehospital data to anticipate transfusion needs before arrival, enabling trauma teams to prepare earlier and respond faster when minutes matter most.”

The authors stress that the work represents a development and validation phase rather than a deployable clinical system. Further studies are needed to understand performance in real-time decision-making, how clinicians interact with AI decision support, and whether its use improves outcomes in prospective trials. For emergency physicians and surgeons, as well as paramedics and nurses, the central question will be how reliably such tools can support triage, pre-alerting and blood product logistics under real-world conditions.

MEDICA-tradefair.com; Source: UCD Research and Innovation