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Could machine learning algorithms help us predict massive bleeding at prehospital level?

Marcos Valiente Fernández1, Carlos García Fuentes1, Francisco de Paula Delgado Moya1

  • 1Hospital Universitario 12 de Octubre, UCI de Trauma y Emergencias, Madrid. Spain.

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|July 28, 2023
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Summary

Machine learning algorithms (MLAs) significantly outperform traditional prediction scales (TPS) in predicting massive hemorrhage (MH) in severe traumatic injury (STI) patients. MLAs achieved high predictive accuracy, offering a valuable tool for out-of-hospital emergency care.

Keywords:
Clinical scoresHemorragia masivaMachine learningMassive hemorrhageOut-of-hospitalPrehospitalariaScores clínicosTrauma

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Area of Science:

  • Emergency Medicine
  • Data Science
  • Trauma Surgery

Background:

  • Massive hemorrhage (MH) is a critical concern in severe traumatic injury (STI).
  • Accurate prediction of MH is vital for timely and effective interventions.
  • Traditional prediction scales (TPS) have limitations in predicting MH in STI.

Purpose of the Study:

  • To compare the predictive performance of machine learning algorithms (MLAs) against TPS for MH in STI patients.
  • To evaluate the utility of MLAs in out-of-hospital settings for trauma care.

Main Methods:

  • Retrospective analysis of 473 STI patients with prehospital data.
  • Development and validation of four MLAs (Random Forest, SVM, GBM, NN) using 80% training and 20% validation data.
  • Evaluation of predictive power using Receiver Operating Characteristic (ROC) curves and variable importance via Shapley values.

Main Results:

  • MLAs achieved high predictive accuracy with ROC values exceeding 0.85, with medians near 0.98.
  • No significant differences were found between the performance of the tested MLAs.
  • Key predictive variables identified by MLAs included hemodynamic status, resuscitation factors, and neurological impairment.

Conclusions:

  • Machine learning algorithms demonstrate superior predictive ability for massive hemorrhage compared to traditional scales in severe traumatic injury.
  • MLAs offer a promising advancement for improving prehospital care and patient outcomes in trauma.