An interpretable machine learning model for early prediction of ESBL-producing bacteraemia in the emergency...

Empirical antibiotic therapy in the emergency department (ED) is often initiated before susceptibility results are available, risking treatment failure in bloodstream infections caused by extended-spectrum β-lactamase (ESBL)-producing Escherichia coli and Klebsiella spp. We retrospectively analysed 3,138 adult sepsis episodes (training: n = 1,863, ESBL + = 419, ESBL- = 1,444; test: n = 1,275, ESBL + = 373, ESBL- = 902) treated at Inha University Hospital between 2013 and 2022 and trained a machine-learning model to predict ESBL-producing organisms using routinely available clinical and laboratory data. We used stratified 5-fold cross-validation and computed SHapley Additive exPlanations (SHAP) values within folds to avoid information leakage. We performed SHAP-guided stepwise feature elimination based on training performance. The final model, trained on the full training cohort (2013-2019), achieved an AUROC of 0.78 on an independent test set (2020-2022). This interpretable pipeline may support empirical antibiotic selection and antimicrobial stewardship in the ED.