Observational studyBMC emergency medicine2022
A prediction model for massive hemorrhage in trauma: a retrospective observational study.
Observational study in BMC emergency medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Graph Neural Networks for Realistic Bleeding Prediction in Surgical Simulators.Journal of imaging informatics in medicine · 2026Article
- Real-time, artificial intelligence-guided intraoperative resuscitation and fluid management in trauma anesthesia.Current opinion in anaesthesiology · 2026Review
- Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging.BMC medical informatics and decision making · 2026Article
- Independent risk of death from hemorrhage and traumatic brain injury in polytrauma: findings from a prospective, multi-center study in the Western Cape of South Africa.World journal of emergency surgery : WJES · 2026Observational
- Narrowed pulse pressure as a predictor of active hemorrhage in hemodynamically stable blunt trauma: Insights from an Asian cohort.Pakistan journal of medical sciences · 2025Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
- Recent trends in perioperative blood transfusion during elective kidney transplantation.Korean journal of transplantation · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMassive 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.
methodsUsing 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.
resultsA 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/ ).
conclusionsOur 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.
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