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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., Competing Interests: Declaration of competing interest All the authors have nothing to disclose and no conflict of interest. The Authors did not use AI and AI-assisted technologies in the writing process of this manuscript., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: High-quality cardiopulmonary resuscitation (CPR) can restore spontaneous circulation (ROSC) and neurological function and save lives. We conducted an umbrella review, including previously published systematic reviews (SRs), that compared mechanical and manual CPR; after that, we performed a new SR of the original studies that were not included after the last published SR to provide a panoramic view of the existing evidence on the effectiveness of CPR methods., Methods: PubMed, EMBASE, and Medline were searched, including English in-hospital (IHCA) and out-of-hospital cardiac arrest (OHCA) SRs, and comparing mechanical versus manual CPR. A Measurement Tool to Assess Systematic Reviews (AMSTAR-2) and GRADE were used to assess the quality of included SRs/studies. We included both IHCA and OHCA, which compared mechanical and manual CPR. We analyzed at least one of the outcomes of interest, including ROSC, survival to hospital admission, survival to hospital discharge, 30-day survival, and survival to hospital discharge with good neurological function. Furthermore, subgroup analyses were performed for age, gender, initial rhythm, arrest location, and type of CPR devices., Results: We identified 249 potentially relevant records, of which 238 were excluded. Eleven SRs were analyzed in the Umbrella review (January 2014-March 2022). Furthermore, for a new, additional SR, we identified eight eligible studies (not included in any prior SR) for an in-depth analysis between April 1, 2021, and February 15, 2024. The higher chances of using mechanical CPR for male patients were significantly observed in three studies. Two studies showed that younger patients received more mechanical treatment than older patients. However, studies did not comment on the outcomes based on the patient's gender or age. Most SRs and studies were of low to moderate quality. The pooled findings did not show the superiority of mechanical compared to manual CPR except in a few selected subgroups., Conclusions: Given the significant heterogeneity and methodological limitations of the included studies and SRs, our findings do not provide definitive evidence to support the superiority of mechanical CPR over manual CPR. However, mechanical CPR can serve better where high-quality manual CPR cannot be performed in selected situations., (© 2024. The Author(s).)

Background: Accurate prediction of in-hospital mortality is essential for better management of patients with traumatic brain injury (TBI). Machine learning (ML) algorithms have been shown to be effective in predicting clinical outcomes. This study aimed to identify predictors of in-hospital mortality in TBI patients using ML algorithms., Materials and Method: A retrospective study was performed using data from both the trauma registry and electronic medical records among TBI patients admitted to the Hamad Trauma Center in Qatar between June 2016 and May 2021. Thirteen features were selected for four ML models including a Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XgBoost), to predict the in-hospital mortality., Results: A dataset of 922 patients was analyzed, of which 78% survived and 22% died. The AUC scores for SVM, LR, XgBoost, and RF models were 0.86, 0.84, 0.85, and 0.86, respectively. XgBoost and RF had good AUC scores but exhibited significant differences in log loss between the training and testing sets (% difference in logloss of 79.5 and 41.8, respectively), indicating overfitting compared to the other models. The feature importance trend across all models indicates that aPTT, INR, ISS, prothrombin time, and lactic acid are the most important features in prediction. Magnesium also displayed significant importance in the prediction of mortality among serum electrolytes., Conclusions: SVM was found to be the best-performing ML model in predicting the mortality of TBI patients. It had the highest AUC score and did not show overfitting, making it a more reliable model compared to LR, XgBoost, and RF.