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

Comparison of the predictive ability of various machine learning algorithms (MLA) versus traditional prediction scales (TPS) for massive hemorrhage (MH) in patients with severe traumatic injury (STI). On a database of a retrospective cohort with prehospital clinical variables and MH outcome, a treatment of the database was performed to be able to apply the different AML, obtaining a total set of 473 patients (80% training, 20% validation). For modeling, proportional imputation and cross validation were performed. The predictive power was evaluated with the ROC metric and the importance of the variables using the Shapley values. Out-of-hospital care of patients with STI. Patients with STI treated out-of-hospital by a out-of-hospital medical service from January 2010 to December 2015 and transferred to a trauma center in Madrid. None. Obtaining and comparing the “Receiver Operating Characteristic curve” (ROC curve) metric of four MLAs: "random forest" (RF), "vector support machine" (SVM), "gradient boosting machine" (GBM) and "neural network" (NN) with the results obtained with TPS. The different AML reached ROC values higher than 0.85, having medians close to 0.98. We found no significant differences between AMLs. Each AML offers a different set of more important variables with a predominance of hemodynamic, resuscitation variables and neurological impairment. MLA may be helpful in patients with HM by outperforming TPS. Comparación de la capacidad predictiva de diferentes algoritmos de machine learning (AML) respecto a escalas tradicionales de predicción (ETP) de hemorragia masiva (HM) en pacientes con enfermedad traumática grave (ETG). Sobre una base de datos de una cohorte retrospectiva con variables clínicas out-of-hospitalarias y de resultado de HM se realizó un tratamiento de la base de datos para poder aplicar los AML, obteniéndose un conjunto total de 473 pacientes (80% entrenamiento, 20% validación). Para la modelización se realizó imputación proporcional y cross validation. El poder predictivo se evaluó con la métrica ROC y la importancia de las variables mediante los valores Shapley. Atención extrahospitalaria del paciente con ETG. Pacientes con ETG atendidos en el medio extrahospitalario por un servicio médico extrahospitalario desde enero de 2010 hasta diciembre de 2015 y trasladados a un centro de trauma en Madrid. Ninguna. obtención y comparación de la métrica “Receiver Operating Characteristic curve” (curva ROC) de cuatro AML: “random forest”