Explainable machine learning with routine biomarkers identifies culture-defined bacteremic urosepsis

Urosepsis is a severe complication of urinary tract infection (UTI) and may lead to organ dysfunction and death. Early identification remains challenging at initial presentation, highlighting the need for improved risk stratification using routinely available data. This single-center retrospective study analyzed clinical data from 182 hospitalized patients with culture-confirmed UTI, including 89 with culture-defined bacteremic urosepsis (concurrent positive blood and urine cultures) and 93 with non-bacteremic UTI. Random Forest (RF), Extreme Gradient Boosting (XGBoost), and multivariable logistic regression (LR) models were developed using routine biomarkers obtained within 0-24 h of the index time; outcomes were assigned using culture results within 48-72 h to minimize information leakage. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) with bootstrap 95% confidence interval (CI) on a held-out test set. D-dimer was consistently ranked among the top predictors. Compared with non-bacteremic UTI, bacteremic urosepsis showed higher procalcitonin (PCT), C-reactive protein (CRP), and white blood cell count (WBC) and lower albumin (all p < 0.05). On the held-out test set (n = 37; positives = 18), XGBoost achieved an AUC of 0.886 (95% CI 0.763-0.971), compared with 0.822 (95% CI 0.665-0.938) for RF and 0.822 (95% CI 0.663-0.935) for LR; the AUC difference between XGBoost and RF was not statistically significant (DeLong p = 0.072). Using routine biomarkers available within 24 h, RF and XGBoost demonstrated good discrimination for culture-defined bacteremic urosepsis among inpatients with culture-confirmed UTI. XGBoost yielded a numerically higher AUC than RF, but the difference was not statistically significant in this modest test set. D-dimer, procalcitonin, and albumin emerged as key predictors, supporting the potential utility of routine laboratory indicators for early risk stratification pending external validation.