Identifying trigger cues for hospital blood transfusions based on ensemble of machine learning methods.

ABSTRACT:

Background

Traumatic shock is the leading cause of preventable death with most patients dying within the first six hours from arriving to the hospital. This underscores the importance of prehospital interventions, and growing evidence suggests prehospital transfusion improves survival. Optimizing transfusion triggers in the prehospital setting is key to improving outcomes for patients in hemorrhagic shock. Our objective was to identify factors associated with early in-hospital transfusion requirements available to prehospital clinicians in the field to develop a simple algorithm for prehospital transfusion, particularly for patients with occult shock.

Methods

We included trauma patients transported by a single critical care transport service to a level I trauma center betwee

SUBMITTER: Zadorozny EV 

PROVIDER: S-EPMC11186116 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature