The latest medical research on Trauma

Haemorrhage 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 modelling. This systematic review aimed to identify and evaluate all ML models that predict blood transfusion in trauma.

Systematic Review Without Meta-Analysis, Level IV.

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 (AUROC >0.9) and nine models achieved good discrimination (AUROC >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 due to retrospective datasets, small sample size and lack of external validation.

This review identified twenty-five 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 individualised blood transfusion prediction but further research is critically required to narrow the gap between ML model development and clinical application.

Little is known about how major trauma survivors access health services in the years following their injury. Our study sought to characterize patterns of health services use in trauma survivors following discharge from a provincial trauma centre and to identify sociodemographic factors associated with service utilization.

Retrospective cohort study, Level IV.

The study cohort consisted of a total of 273,406 individuals: 55,060 trauma survivors and 218,346 controls. Trauma survivors were predominately males (71%) with a median age of 46 years (IQR: 26-65 years). Health service use in trauma survivors peaked within a year of hospital discharge but remained increased throughout the follow up period. Trauma survivorship was associated with a 56% increase in overall health services use (Adjusted Rate Ratio 1.56, 95% CI: 1.55-1.57), including an 88% increase in hospital admissions (Adjusted Rate Ratio 1.88, 95% CI: 1.85-1.92). Male sex and rural residence were associated with a reduced overall use of health services but greater use of ED services.

Major trauma survivors have long-term health services needs that persist for years after discharge from the trauma centre. Future research should focus on the understanding why trauma survivors have prolonged health services requirements and ensure care needs are aligned with service delivery.