Related Experiment Video
Updated: Jun 4, 2026
Integrated Compensatory Responses in a Human Model of Hemorrhage
Published on: November 20, 2016
Predicting Massive Transfusion Needs in Trauma Using Machine Learning: Systematic Review and Meta-Analysis
Chunyan Han1, Guixia Zhang, Hui Gao
1Author Affiliations: Emergency Critical Care Unit, Qingdao Municipal Hospital, Qingdao, China (Ms Han); First People's Hospital of Jinan, Jinan, China (Ms Zhang); Institute of Marine Science and Technology, Shandong University, Qingdao, China (Mr Gao); and The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (Ms Zhang).
Background:
Uncontrolled bleeding after trauma is the main preventable cause of death and often requires rapid fluid resuscitation and massive blood transfusion. Accurately predicting the need for massive transfusion is critical to improving outcomes in patients with trauma.
Objective:
To systematically assess and quantify the effectiveness of machine learning (ML) models in predicting massive transfusion needs in patients with trauma.
Methods:
A comprehensive search of 7 databases was conducted from inception to March 2024. Studies were screened based on prespecified inclusion criteria. Data were extracted using a standardized checklist and evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Pooled performance metrics were calculated using a random-effects model, including area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and diagnostic odds ratio (DOR). Heterogeneity was assessed using Cochran Q and I2 statistics, and potential sources of variation were identified through meta-regression.
Results:
After screening, 12 studies were included in the final analysis. The pooled AUC for ML models predicting transfusion needs was 0.89, with sensitivity and specificity values of 0.83[95% CI: 0.84-0.83] and 0.84(95% CI: 0.83-0.85), respectively. Despite overall strong predictive performance, the included models showed considerable methodological heterogeneity, particularly in feature selection, handling of missing data, and model validation.
Conclusions And Implications:
Although the ML models assessed across the included studies demonstrated robust performance in predicting massive transfusion needs in patients with trauma, the studies showed methodological inconsistencies. To ensure reliable clinical implementation, future research should focus on developing standardized protocols for model development and validation.
Registration:
PROSPERO #CRD42024565253.