Abstract
This study aimed to identify preoperative risk factors for systemic inflammatory response syndrome (SIRS) in patients with metabolic syndrome (MetS) undergoing percutaneous nephrolithotomy (PCNL) and to develop a predictive nomogram for individualized risk stratification. A retrospective analysis was conducted on 245 MetS patients who underwent PCNL between January 2021 and December 2024, among whom 27.8% developed postoperative SIRS. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was initially applied to select candidate predictors, followed by univariate and multivariate logistic regression analyses to identify independent risk factors. A nomogram was subsequently constructed based on the significant variables and evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Multivariate analysis identified five independent predictors of postoperative SIRS: higher standard deviation of stone density, reduced renal parenchymal thickness, increased lateral and posterior perirenal fat thicknesses, and the presence of staghorn calculi (P < 0.05). The nomogram demonstrated good discriminative ability, with an area under the ROC curve (AUC) of 0.888 (95% CI: 0.834-0.942) in the training cohort and 0.882 (95% CI: 0.802-0.962) in the validation cohort. The calibration curve and the Hosmer-Lemeshow test (P = 0.1485) indicated good model calibration and fit. DCA further confirmed the clinical utility of the model. This nomogram offers a reliable preoperative tool for SIRS risk stratification in MetS patients undergoing PCNL, aiding early intervention and personalized perioperative management.