Background  Traumatic shock (TS) is an important cause of death in trauma patients. It is of great significance of focusing on the prognosis of TS patients.

  Objective  To construct machine learning models based on early indicators including vital signs and arterial blood gas analysis on admission to predict clinical outcome in TS patients for medical decision-making.

  Methods  Clinical data about TS patients admitted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from Jun. 2014 to Dec. 2021 were retrospectively collected. In-hospital mortality prediction models were constructed based on three classification algorithms: decision tree (DT), logistic regression (LR), and random forest (RF). The prediction performance of different models was compared and the best model was selected for external validation.

  Results  In the modeling process, 281 TS patients who met the inclusion and exclusion criteria were screened, and divided into survival group (n=218) and death group (n=63) according to whether the patients were alive at discharge. In the internal validation set, the RF model obtained the highest area under receiver operator characteristic curve (AUC), which was 0.856 (95% CI: 0.847-0.865). The AUC of DT model was 0.756 (95% CI: 0.740-0.772). The AUC of LR model was 0.801 (95% CI: 0.780-0.822). The mean values of classifier performance metrics of the RF model including accuracy (0.807), precision (0.886), recall (0.866), F-value (0.876), and AUC (0.856) were all greater than 0.8. And the RF model had similar performance in the external validation set, suggesting that the RF model had a strong ability to identify the risk of death in TS patients. The calibration and clinical usefulness of the three models were evaluated by calibration curves and decision curves, suggesting that the RF model also outperformed the LR and DT models.

  Conclusion  Prediction models based on machine learning have a promising application in predicting in-hospital mortality of TS patients.