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Prediction of infectious complications after supine percutaneous nephrolithotomy: development and internal validation
María Elena Martínez-Corral1,2, Rocío Martínez-Corral3,4, Daniel A Pérez-Fentes3,4
1Urology Department, Endourology Unit, University Hospital Complex of Santiago de Compostela, A Coruña, Spain. maria.elena.martinez.corral@sergas.es.
Abstract:
To identify independent predictors of postoperative infectious complications following supine percutaneous nephrolithotomy (PCNL) and to develop and internally validate a predictive nomogram for individualized risk stratification. A prospective cohort study was performed including 906 renal units undergoing supine PCNL at a single tertiary referral center between October 2008 and December 2025. Patients were categorized into two groups according to postoperative infectious outcomes: those without infection (n = 804) and those with infection (n = 102), defined by the occurrence of postoperative fever or urosepsis. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of infectious complications. Based on these predictors, a nomogram was developed, and its performance was assessed in terms of discrimination using the area under the receiver operating characteristic curve (AUC) and calibration using calibration plots. Multivariable logistic regression analysis identified five independent predictors of postoperative infectious complications after PCNL: positive preoperative urine culture (OR 7.07; p < 0.001), multiple surgical steps (OR 1.82; p = 0.049), multiple percutaneous accesses (OR 4.25; p = 0.001), standard-caliber percutaneous tracts (OR 1.80; p = 0.019) and prolonged operative time (OR 1.01; p = 0.032). The resulting nomogram demonstrated good discriminatory performance (AUC = 0.805) and adequate calibration. Postoperative infectious complications after supine PCNL appear to result from the interplay between preoperative urinary infection and intraoperative procedural complexity, with positive urine culture emerging as the strongest predictor. The proposed nomogram enables individualized risk stratification and may support perioperative decision-making aimed at reducing infectious morbidity.
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