Neutrophil Percentage-to-Albumin Ratio as a Predictor of Urinary Tract Infection in Patients with Urinary Stone Disease: Development a Novel User-Friendly Tool

Background: Urinary stone disease (USD) is a prevalent condition, and associated urinary tract infections (UTIs) present significant risks, often leading to severe complications. Current diagnostic approaches for UTIs, such as urine culture, are time-consuming, while existing predictive models often lack dynamic biomarkers or are overly complex. The neutrophil-to-albumin ratio (NPAR), a readily available inflammatory marker, merits investigation as a predictor of UTIs in this patient population. Objective: This study aimed to examine the relationship between NPAR and UTIs in patients with USD and to develop a novel, user-friendly predictive tool for assessing UTIs risk. Methods: A retrospective cohort study was conducted at a single center, including 7000 participants with USD (January 2015 to January 2025). The cohort was randomly split into training and validation sets (7:3). The association between NPAR and UTIs was explored using restricted cubic splines (RCS) with three knots. Both traditional logistic regression and LASSO (Least Absolute Shrinkage and Selection Operator) regression were employed, and model performance was assessed via the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). Results: for interaction < 0.001), with a stronger association in males (OR = 2.79, 95% CI: 2.20-3.52). The LASSO regression model demonstrated good discrimination, with AUC of 0.8016 in the training set and 0.8013 in the validation set, comparable to those of the logistic regression model (0.8015 and 0.8008). Additionally, the LASSO model showed better calibration and greater parsimony. A user-friendly, web-based tool was successfully developed. (https://utipredictor.streamlit.app). Conclusion: NPAR is an independent, easily accessible predictor of UTIs in patients with USD. The developed web-based tool may enable rapid UTI risk stratification, with the potential to support timely intervention and personalized treatment. External validation is needed to confirm its generalizability.