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Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma
Gemma Postill1,2,3, Anglin Dent1,2,3,4, Richard Cheng3,5
1Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Scientific Reports
|May 20, 2026
Summary
Machine learning models show promise for predicting massive hemorrhage protocols (MHP) in trauma patients, outperforming traditional methods. However, rigorous validation and transparent reporting are needed for clinical integration.
Area of Science:
- Trauma Care
- Medical Informatics
- Machine Learning Applications
Background:
- Timely activation of massive hemorrhage protocols (MHP) is crucial for trauma patient survival.
- Current tools for predicting MHP have limited accuracy.
- Machine learning (ML) offers potential for improved early recognition of life-threatening hemorrhage.
Purpose of the Study:
- To systematically review and meta-analyze studies on ML models for predicting MHP need in trauma patients.
- To assess the performance and methodological quality of existing ML models.
- To identify gaps and future directions for ML in trauma care.
Main Methods:
- Systematic review and meta-analysis of 21 studies (50 ML models) identified from major databases.
- Performance assessed using pooled AUROC, with random forest, neural networks, and XGBoost showing high pooled AUROC.
- Risk of bias and methodological quality assessed using PROBAST, TRIPOD+AI, and APPRAISE-AI.
Main Results:
- ML models, particularly random forest (0.89), neural networks (0.88), and XGBoost (0.86), demonstrated superior discrimination compared to non-ML methods.
- Most ML models lacked robust subgroup analysis, external validation, and clinical deployment feasibility.
- Median APPRAISE-AI quality score was 51/100, and reporting quality (TRIPOD+AI) was moderate, with high or unclear risk of bias in many studies.
Conclusions:
- While ML models show high predictive performance for MHP need, methodological weaknesses necessitate cautious interpretation of pooled results.
- Further rigorously designed, transparently reported ML studies are essential for trauma care.
- Clinical translation requires collaborative development, robust validation, and a focus on real-world deployment to improve patient outcomes.
