Development and validation of a prognostic model incorporating the blood urea nitrogen-to-albumin ratio for predicting mortality in methicillin-resistant Staphylococcus aureus bloodstream infection

Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is associated with an exceedingly high mortality rate and limited therapeutic options. Early risk stratification is essential to optimize clinical management. This study aimed to evaluate the prognostic value of the blood urea nitrogen-to-albumin ratio (BAR) and to develop a clinically practical model to predict mortality in MRSA bloodstream infections. A retrospective cohort study that included 410 adult patients with microbiologically confirmed MRSA bloodstream infections hospitalized at a university medical center between January 1, 2020, and December 31, 2024. The primary outcome was 90-day in-hospital mortality. Patients were randomly assigned to a training cohort (n = 308) and a validation cohort (n = 102) for model development and internal validation. Multivariable logistic regression was used to identify independent predictors of mortality, and model performance was assessed in terms of discrimination, calibration, and overall predictive accuracy. A total of 91 patients (22.2%) experienced 90-day in-hospital all-cause mortality. The mortality group demonstrated significantly higher BAR values [24.74 (14.29–36.00) vs. 5.37 (3.75–12.86); p < 0.001] compared with survivors. Multivariable analysis identified age [adjusted odds ratio (aOR): 1.037, 95% confidence interval (CI): 1.010–1.065; p = 0.007], pulmonary infection (aOR: 2.021, 95% CI: 1.035–3.946; p = 0.039), and BAR (aOR: 1.070, 95% CI: 1.045–1.096; p < 0.001) as independent predictors of mortality. The final BAR-based model demonstrated good discriminative performance, with an AUC of 0.838 in the training cohort and 0.831 in the validation cohort. The BAR was identified as an independent predictor of mortality in MRSA bacteremia. A practical prediction model with internal validation was developed to support early risk stratification in this high-risk population.