Clinically Interpretable Machine Learning for Glycemic Risk Screening in Trauma Patients

Abstract

Undiagnosed diabetes and prediabetes are common among trauma patients and increase perioperative complication rates, yet routine glycemic screening at hospital admission is rarely performed. We compared 10 supervised machine learning (ML) algorithms for early detection of impaired glucose metabolism in 1789 trauma surgery patients (1095 normal; 694 prediabetes/diabetes) using questionnaire data and preoperative laboratory values within a nested cross-validation framework with hyperparameter optimization. On the combined feature set, XGBoost achieved the highest area under the receiver operating characteristic curve (ROC-AUC; 0.815), followed by multilayer perceptron (0.814) and Gradient Boosting (0.813); adding blood parameters significantly improved performance for nine of ten algorithms (DeLong’s test, p < 0.05). SHapley Additive exPlanations (SHAP) and XGBoost feature gains identified six overlapping predictors, yielding a simplified model with comparable mean discrimination. Inter-model differences were small, and a fully interpretable logistic regression model remained within one percentage point of XGBoost. The best-performing model demonstrated strong calibration and a higher net benefit than either universal testing or no testing across the evaluated threshold probabilities. In routine trauma practice, such a model could identify patients who would benefit from confirmatory glycated hemoglobin (HbA1c) testing, enabling earlier detection of dysglycemia, risk-stratified glucose management, and timely referral for lifestyle intervention in patients with prediabetes.