Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission

Background: The effective management of trauma patients necessitates efficient triaging, timely activation of Massive Blood Transfusion Protocols (MTP), and accurate prediction of in-hospital outcomes. Machine learning (ML) algorithms have emerged as up-and-coming tools in the domains of optimizing triage decisions, improving intervention strategies, and predicting clinical outcomes, consistently outperforming traditional methodologies. This study aimed to develop, assess, and compare several ML models for the triaging processes, activation of MTP, and mortality prediction. Methods: In a 10-year retrospective study, the predictive capabilities of seven ML models for trauma patients were systematically assessed using on-admission patients' hemodynamic data. All patient's data were randomly divided into training (80 %) and test (20 %) sets. Employing Python for data preprocessing, feature scaling, and model development, we evaluated K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machines (SVM) with RBF kernels, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). We employed various imputation techniques and addressed data imbalance through down-sampling, up-sampling, and synthetic minority for the over-sampling technique (SMOTE). Hyperparameter tuning, coupled with 5-fold cross-validation, was performed. The evaluation included essential metrics like sensitivity, specificity, F1 score, accuracy, Area Under the Receiver Operating Curve (AUC ROC), and Area Under the Precision recall Curve (AUC PR), ensuring robust predictive capability. Result: This study included 17,390 adult trauma patients; of them, 19.5 % (3385) were triaged at a critical level, 3.8 % (664) required MTP, and 7.7 % (1335) died in the hospital. The model's performance improved using imputation and balancing techniques. The overall models demonstrated notable performance metrics for predicting triage, MTP activation, and mortality with F1 scores of 0.75, 0.42, and 0.79, sensitivities of 0.73, 0.82, and 0.9, and AUC ROC values of 0.89, 0.95 and 0.99 respectively. Conclusion: Machine learning, especially RF models, effectively predicted trauma triage, MTP activation, and mortality. Featured critical hemodynamic variables include shock indices, systolic blood pressure, and mean arterial pressure. Therefore, models can do better than individual parameters for the early management and disposition of patients in the ED. Future research should focus on creating sensitive and interpretable models to enhance trauma care. © 2024 Elsevier Ltd