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Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma.

Matt Strickland^1,2, Anthony Nguyen³, Shinyi Wu³

¹Department of Surgery, University of Southern California, LAC+USC Medical Center (The work was done at LAC+USC Medical Center), Los Angeles, CA, USA.

World Journal of Surgery

|June 30, 2023

Summary

Machine learning models accurately predict massive blood transfusion needs, outperforming existing scores. This technology can improve patient care and resource management in trauma settings.

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Area of Science:

Medical informatics
Trauma surgery
Machine learning applications

Background:

Predicting massive blood transfusion (MBT) needs is crucial for patient outcomes and resource allocation.
Current methods for identifying patients requiring MBT activation may be suboptimal.

Purpose of the Study:

To develop and validate machine learning (ML) models for accurate prediction of MBT necessity.
To compare the performance of ML models against established scoring systems.

Main Methods:

Utilized institutional trauma registry data from June 2015 to August 2019.
Explored various ML algorithms including logistic regression, SVM, random forest, and neural networks.
Assessed model performance using sensitivity, specificity, and area under the curve (AUC), comparing against ABC and RABT scores.

Main Results:

A total of 2438 patients were analyzed, with 4.9% receiving MBT.
Most ML models achieved an AUC > 0.75, with a range of 0.75-0.83.
ML models demonstrated higher sensitivity than ABC and RABT scores while maintaining comparable specificity.

Conclusions:

Machine learning models significantly outperformed existing scores in predicting MBT needs.
Integration of ML models into electronic health records or mobile devices could enhance clinical usability and decision-making.