ABSTRACT
BACKGROUND: 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. 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 a ML model that predicted blood transfusion in injured adult patients were included. Data extraction and risk of bias assessment was performed using validated frameworks. Data was synthesised narratively due to 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 (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. DISCUSSION: 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. LEVEL OF EVIDENCE: Systematic Review Without Meta-Analysis, Level IV.
ABSTRACT
INTRODUCTION: Medical students across the United Kingdom (UK) report poor satisfaction with surgical teaching. The Surgical Skills Day (SSD) begins to address this by exposing medical students to surgery through an easily accessible one-day practical workshop. This study shows how the SSD encourages undergraduate engagement in surgery. METHOD: Feedback forms were emailed to attendees of the SSD and their anonymised responses were used to evaluate the SSD. RESULTS: A total of 144 students attended the SSD across three years and the feedback response rate was 74% (n = 107). Key findings were that 100% of respondents (n = 107) would like the SSD to be an annual event, 79% (n = 83) were more inclined to pursue a surgical career following the event, and 97% (n = 103) would like to see practical surgical skills incorporated into the curriculum. The SSD was able to engage undergraduates with surgery through mentorship, practical skills, specialty exposure, and teaching of the General Medical Council (GMC) mandated skills. CONCLUSIONS: Undergraduate surgical teaching in the UK is insufficient. The student-led annual SSD showed improved engagement in practical surgical skills and increased enthusiasm for a surgical career. In light of this, the authors feel the SSD or similar event should be integrated into the UK medical school curriculum.
