Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis

BACKGROUND: Hemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential but can be difficult. Machine learning (ML) is a field of artificial intelligence that is emerging within medicine for accurate prediction modeling. This systematic review aimed to identify and evaluate all ML models that predict blood transfusion in trauma. METHODS: This systematic review was registered on the International Prospective register of Systematic Reviews (CRD4202237110). MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were systematically searched. Publications reporting an ML model that predicted blood transfusion in injured adult patients were included. Data extraction and risk of bias assessment were performed using validated frameworks. Data were synthesized narratively because of significant heterogeneity. RESULTS: Twenty-five ML models for blood transfusion prediction in trauma were identified. Models incorporated diverse predictors and varied ML methodologies. Predictive performance was variable, but eight models achieved excellent discrimination (area under the receiver operating characteristic curve, >0.9) and nine models achieved good discrimination (area under the receiver operating characteristic curve, >0.8) in internal validation. Only two models reported measures of calibration. Four models have been externally validated in prospective cohorts: the Bleeding Risk Index, Compensatory Reserve Index, the Marsden model, and the Mina model. All studies were considered at high risk of bias often because of retrospective data sets, small sample size, and lack of external validation. DISCUSSION: This review identified 25 ML models developed to predict blood transfusion requirement after injury. Seventeen ML models demonstrated good to excellent performance in silico, but only four models were externally validated. To date, ML models demonstrate the potential for early and individualized blood transfusion prediction, but further research is critically required to narrow the gap between ML model development and clinical application.