Development and Validation of a Nomogram for Predicting Systemic Inflammatory Response Syndrome Following Percutaneous Nephrolithotomy.

To develop and validate a nomogram for predicting the occurrence of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL), aiming to enhance clinical decision-making and treatment planning. Clinical data of 1,047 patients undergoing PCNL at a single-center hospital between 2017 and 2023 were retrospectively analyzed. Independent risk factors influencing SIRS occurrence were identified through multi-variable logistic regression analysis, and a predictive model was constructed. The model's accuracy and reliability were evaluated through internal training and validation set. Multi-variable regression analysis identified six key predictive factors: gender, diabetes, urine culture results, stone surface, staghorn stones, and operative time, leading to the establishment of a nomogram predictive model. Internal validation and validation set data demonstrated the model's high predictive accuracy and reliability, with areas under the receiver operating characteristic curve of 0.718 and 0.723, respectively. A nomogram predictive model for assessing SIRS following PCNL was successfully developed and validated. This model provides clinicians with a valuable tool for personalized treatment planning and implementing preventive measures.