Predictive Model Integrating Imaging and Inflammatory Biomarkers for Preoperative Urinary Tract Infection in Patients with Ureteral Calculi and Metabolic Syndrome

Abstract

Objective: Urinary tract infection (UTI) frequently occurs in patients with ureteral calculi and metabolic syndrome (MetS). Timely recognition of patients at elevated risk remains a clinical challenge. This study aimed to construct and internally validate a nomogram for assessing the risk of UTI in this population.

Materials and methods: We retrospectively reviewed the data of 254 patients diagnosed with ureteral calculi and MetS who were hospitalised between January 2022 and March 2025. Baseline patient characteristics, clinical parameters, laboratory test results and imaging findings were systematically collected. Factors showing significant differences (p < 0.05) between patients with and without UTI were examined for multicollinearity and then entered into a multivariable regression framework to determine independent predictors. A risk-prediction nomogram based on these key determinants was subsequently constructed. The model's performance was assessed through the area under the receiver operating characteristic curve (AUC), calibration plot, Hosmer-Lemeshow goodness-of-fit test, Brier score and decision curve analysis (DCA). Internal validation was conducted using bootstrap resampling.

Results: Multivariate analysis identified periureteral fat stranding, tissue rim sign, neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio and positive urine nitrite as independent predictors of UTI. The constructed nomogram exhibited robust discriminative ability (AUC = 0.783; 95% confidence interval (CI): 0.702-0.863) and satisfactory calibration (χ²= 6.867, p= 0.551; Brier score = 0.118). Bootstrap validation confirmed model stability (AUC = 0.783). DCA indicated that the nomogram yielded a superior net clinical benefit compared with strategies of treating all or none with a probability threshold of 0.1-0.85.

Conclusions: The proposed nomogram accurately predicts the risk of UTI in patients with ureteral calculi and MetS. The integration of imaging features, haematologic inflammatory markers and urinalysis results enables individualised risk assessment,facilitating the early detection of high-risk patients and informing timely preventive and therapeutic interventions.

Keywords: metabolic syndrome; nomogram; risk factor; ureteral calculi; urinary tract infection.