Early Prediction of Anesthetic and Hemorrhage Control Requirements in Severely Injured Trauma Patients: A Systematic Review
Keywords:
Wounds and Injuries, Hemorrhage, Blood Transfusion, Artificial IntelligenceAbstract
Background: Severe trauma requires minutes-level decisions on airway control, hemodynamic support, transfusion, and definitive hemorrhage control. Artificial intelligence (AI) models are increasingly used in trauma prediction, but their role in anticipating anesthetic and hemorrhage-control requirements remains unclear.
Methods: PubMed was searched for human studies evaluating AI or machine-learning models for early prediction of transfusion, hemorrhage-control resuscitation, urgent hemorrhage intervention, traumatic coagulopathy, hemorrhagic shock, or related operative needs in severe trauma. Eligible studies were synthesized narratively without meta-analysis.
Results: Eleven cohort studies met the inclusion criteria; 10 were retrospective and 1 was prospective multicenter, and no randomized clinical trials were identified. Study size ranged from 1,292 to 326,758 patients. The most mature evidence concerned blood product prediction, with model performance ranging from area under the receiver-operating characteristic curve (AUC) 0.81 to 0.95; a prehospital ensemble model achieved AUC 0.837 in both internal and external validation, and one emergency-department study reported mean AUC 0.83. For hemorrhage-control outcomes, one model achieved external-validation AUC 0.875, while combined clinician-plus-machine prediction reached sensitivity 83% (95% confidence interval [CI] 77-88%) and specificity 73% (95% CI 70-75%). Coagulopathy and hemorrhagic-shock models also performed strongly, with external AUC 0.91 and AUC 0.935-0.968, respectively.
Conclusions: AI showed meaningful promise for early prediction of transfusion need, hemorrhage-control escalation, traumatic coagulopathy, and hemorrhagic shock in severely injured trauma patients. However, the evidence remained heterogeneous and mostly retrospective, and direct prediction of anesthetic support requirements was sparse, supporting the need for prospective multicenter validation and explicit anesthesia-centered outcomes.
