• Ze Long, Shengzhi Tan, Baisheng Sun, Yong Qin, Shengjie Wang, Zhencan Han, Tao Han, Feng Lin, and Mingxing Lei.
• Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, 410028, China.
• Shock. 2024 Dec 3.

AbstractPurpose: This study aims to establish and validate machine learning-based models to predict death in hospital among critical orthopaedic trauma patients with sepsis or respiratory failure.Methods: This study collected 523 patients from the Medical Information Mart for Intensive Care database. All patients were randomly classified into a training cohort and a validation cohort. Six algorithms, including logistic regression (LR), extreme gradient boosting machine (eXGBM), support vector machine (SVM), random forest (RF), neural network (NN), and decision tree (DT), were used to develop and optimize models in the training cohort, and internal validation of these models were conducted in the validation cohort. Based on a comprehensive scoring system, which incorporated ten evaluation metrics, the optimal model was obtained with the highest scores. An artificial intelligence (AI) application was deployed based on the optimal model in the study.Results: The in-hospital mortality was 19.69%. Among all developed models, the eXGBM had the highest area under the curve (AUC) value (0.951, 95%CI: 0.934-0.967), and it also showed the highest accuracy (0.902), precise (0.893), recall (0.915), and F1 score (0.904). Based on the scoring system, the eXGBM had the highest score of 53, followed by the RF model (43) and the NN model (39). The scores for the LR, SVM, and DT were 22, 36, and 17, respectively. The decision curve analysis confirmed that both the eXGBM and RF models provided substantial clinical net benefits. However, the eXGBM model consistently outperformed the RF model across multiple evaluation metrics, establishing itself as the superior option for predictive modeling in this scenario, with the RF model as a strong secondary choice. The SHAP analysis revealed that SAPS II, age, respiratory rate, OASIS, and temperature were the most important five features contributing to the outcome.Conclusions: This study develops an artificial intelligence application to predict in-hospital mortality among critical orthopaedic trauma patients with sepsis or respiratory failure.Copyright © 2024 by the Shock Society.

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