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

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

Prehospital transfusion, Early hospital transfusion, Hemorrhagic shock, Prehospital lactate concentration,
Fast frugal trees, Bayesian analysis, Decision support models

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 set‐
ting 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
between 2012 and 2019. We used logistic regression, Fast and Frugal Trees (FFTs), and Bayesian analysis to identify fac‐
tors associated with early in-hospital blood transfusion as a potential trigger for prehospital transfusion.
Results We included 2,157 patients transported from the scene or emergency department (ED) of whom 207
(9.60%) required blood transfusion within four hours of admission. The mean age was 47 (IQR=28 – 62) and 1,480
(68.6%) patients were male. From 13 clinically relevant factors for early hospital transfusions, four were incorporated
into the FFT in following order: 1) SBP, 2) prehospital lactate concentration, 3) Shock Index, 4) AIS of chest (sensitiv‐
ity=0.81, specificity=0.71). The chosen thresholds were similar to conventional ones. Using conventional thresholds
resulted in lower model sensitivity. Consistently, prehospital lactate was among most decisive factors of hospital
transfusions identified by Bayesian analysis (OR=2.31; 95% CI 1.55 – 3.37).
Conclusions Using an ensemble of frequentist statistics, Bayesian analysis and machine learning, we developed
a simple, clinically relevant prehospital algorithm to help identify patients requiring transfusion within 4 h of hospital
arrival.
Keywords Prehospital transfusion, Early hospital transfusion, Hemorrhagic shock, Prehospital lactate concentration,
Fast frugal trees, Bayesian analysis, Decision support models

Eva V. Zadorozny1* , Tyler Weigel2

, Samuel M. Galvagno Jr.3

, Christian Martin‐Gill4

, Joshua B. Brown5 and

Francis X. Guyette4

https://doi.org/10.1186/s12245-024-00650-0

2024

Peri Irawan

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