A prediction model for massive hemorrhage in trauma: a retrospective observational study
Related Experiment Video
Updated: Aug 22, 2025
A New Hybrid Quantitative Evaluation Model for Axillary Junctional Hemorrhage in Swine
Published on: December 6, 2024
Chengyu Guo1,2, Minghui Gong3, Lei Ji4
1Present Address: School of Medicine, Nankai University, Tianjin, 300071, China.
Background:
Massive hemorrhage is the main cause of preventable death after trauma. This study aimed to establish prediction models for early diagnosis of massive hemorrhage in trauma.
Methods:
Using the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were fit to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done using the least absolute shrinkage and selection operator (LASSO) method. The first model was constructed based on LASSO feature selection results. The second model was constructed based on the first vital sign recordings of trauma patients after admission. Finally, a web calculator was developed for clinical use.
Results:
A total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01-1.02; P<0.001), pulse pressure (OR: 0.99; 95% CI: 0.98-0.99; P = 0.004), base excess (OR: 0.90; 95% CI: 0.87-0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI: 0.95-0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48-3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21-2.18; P = 0.001). Model 1, which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894; 95% CI: 0.875-0.912), good calibration (P = 0.405), and clinical utility. In addition, the predictive power of model 1 was better than that of model 2 (AUC: 0.718; 95% CI: 0.679-0.757). Model 1 was deployed as a public web tool ( http://82.156.217.249:8080/ ).
Conclusions:
Our study developed and validated prediction models to assist medical staff in the early diagnosis of massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the research results.
