Early Risk Stratification of Severe Trauma in the Emergency Department: Integrating Clinical Scoring Systems, Dynamic...

Severe trauma remains a major global cause of death and disability, particularly among individuals younger than 45 years. Emergency departments are the main gateway to trauma care, where rapid risk stratification guides resuscitation, surgical decision-making, intensive care admission, and resource allocation. Conventional tools, including the Revised Trauma Score, Glasgow Coma Scale, Injury Severity Score, and Trauma and Injury Severity Score, standardize early assessment but rely largely on static variables and may not capture dynamic physiological changes during resuscitation. Dynamic biomarkers offer additional biological information. Glial fibrillary acidic protein, S100B, neuron-specific enolase, serum lactate, and lactate clearance may reflect neuronal injury, tissue hypoperfusion, metabolic stress, and how effectively resuscitation is restoring perfusion. Artificial intelligence and machine-learning models can integrate vital signs, laboratory findings, imaging data, biomarkers, and demographic variables to generate individualized predictions of mortality, clinical deterioration, intensive care requirements, and complications. Recent studies suggest that some machine-learning models may outperform conventional scoring systems; however, limitations involving data heterogeneity, algorithmic bias, interpretability, external validation, privacy, and clinical workflow integration remain. This narrative review synthesizes current evidence on conventional trauma scoring systems, dynamic biomarkers, and artificial intelligence-based prediction models for early risk stratification of severe trauma in emergency departments. It also examines the potential of multimodal frameworks that combine physiological, anatomical, molecular, and computational information. Although integrated prediction systems may improve accuracy and clinical decision-making, prospective multicenter validation and evidence of real-world clinical benefit are required before routine implementation.