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Predicting prehospital blood need after MVC trauma

Predicting Prehospital Blood Transfusion After Motor Vehicle Trauma
Prehospital Emergency Care · Added 29 August 2026

Machine learning models using NEMSIS data achieved strong discrimination (AUC 0.906) for predicting prehospital blood transfusion after motor vehicle trauma, identifying physiologic measures and shock designation as key predictors.

Study type

Registry

Design

Retrospective registry cohort with machine learning (NEMSIS)

Participants

2,078,452 EMS activations for MVC; derivation cohort 303,383

Main finding

Ridge-selected model AUC 0.942 (sensitivity 63.9%, specificity 97.7%); random forest-selected model AUC 0.901; physiologic composites and shock designation were key predictors; ~3.4–3.6% of national cohort predicted high probability of prehospital blood transfusion

Caveat

US registry data; models predict transfusion given rather than true need, and derivation from agencies already running blood programs may bias predictors

Bottom line

Hypothesis-generating

Learning goal

Recognise early physiologic indicators of shock in motor vehicle trauma that should prompt consideration of prehospital blood.

More

• Topic: Trauma
• Daily scan: 29 August 2026

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