Utilizing machine learning to establish a predictive model for transfusion requirements in patients with severe trauma: A comprehensive analysis

Abstract Background Blood transfusion plays a crucial role in the emergency care of trauma patients, significantly impacting their survival rates and prognoses, thereby saving millions of lives annually. Early and rapid recognition of transfusion needs in trauma patients is essential. This study aims to establish a predictive model for emergency transfusions in patients with severe trauma using machine learning (ML). Methods Data were obtained from a comprehensive and anonymized set of medical records. LASSO regression was employed for feature selection. Six ML algorithms were utilized to develop predictive models. The performance of these models was assessed based on their identification accuracy, calibration, and clinical utility. Additionally, the SHapley Additive exPlanations (SHAP) method was applied to visualize model features and predictions on an individual case basis. Results A total of 1716 trauma patients were included in the study and 278 (16.2%) receive blood transfusion after emergency room admission. A model with 11 variables was built, with XGBoost performing best, achieving an area under the curve of 0.884 (95% CI: 0.847–0.921) and brier score of 0.0878 (0.0734–0.1071). Key predictors included, shock index, systolic blood pressure, heart rate, traumatic brain injury, hepatic insufficiency, age, gender, respiratory rate, percutaneous arterial oxygen saturation, pelvic fracture, and femoral fracture. The model also showed robust net benefit across a threshold probability (0.1–0.75). Conclusion We developed a ML model to predict the need of transfusion in trauma patients and conducted a comprehensive assessment of 6 models in terms of discrimination, calibration, and clinical utility. The SHAP method was employed to visually interpret the influence of each variable, thereby enabling clinicians to better understand the underlying mechanisms of ML.