Abstract: Objective To analyze the risk factors of systemic inflammatory response syndrome (SIRS) in patients with upper urinary calculi (UUC) after lithotripsy (PCNL), to build a predictive model, and to verify the predictive value of the model. 
Methods A total of 78 UUC patients were selected as the research subjects, all of whom received PCNL treatment. They were divided into a SIRS group (n=21) and a non-SIRS group (n=57) based on occurrence of SIRS after surgery. Detailed records and comparisons of general information between two groups of patients were conducted. Multivariate Logistic regression analysis was used to analyze the relevant risk factors for SIRS, and a predictive model was constructed. The Bootstrap internal validation method was used to perform consistency and discrimination tests on the predictive model. The receiver operating characteristic (ROC) curve was used to determine the diagnostic cutoff point and evaluate the predictive value of the model. 
Results The preoperative renal malformation, postoperative body temperature ≥ 38 ℃, postoperative heart rate, diabetes, recurrent urinary tract infection, white blood cell (WBC) count, neutrophil count, monocyte count, C-reactive protein (CRP) and procalcitonin (PCT) in SIRS group were higher than those in non-SIRS group. The preoperative uric acid value and high-density lipoprotein cholesterol (HDL-C) were lower than those in non-SIRS group, and the difference was statistically significant (P＜0.05). Multivariate logistic regression analysis showed that diabetes, recurrent urinary tract infection, HDL-C, CRP and PCT were the risk factors for SIRS after PCNL in UUC patients (P＜0.05). Based on the above five risk factors, a predictive model for the risk of SIRS after PCNL in UUC patients was established and validated using the Bootstrap internal validation method. It was found that the predicted values were basically consistent with the measured values, indicating good consistency of the predictive model. The calculated C-index was 0.955 (95%CI: 0.918-0.992), which had good discrimination, and the area under the ROC curve of the prediction model was 0.955, indicating that the prediction value of the prediction model was high. After verification, the sensitivity of the prediction model was 87.50%, the specificity was 91.11%, and the accuracy was 89.85%. 
Conclusion Diabetes, recurrent urinary tract infection, low HDL-C level and high CRP and PCT levels are the risk factors for SIRS after PCNL in UUC patients. Building a nomogram prediction model based on influencing factors can help predict the risk of SIRS in UUC patients after PCNL, and this model has a high value. 

Key words: urinary calculi, lithotripsy, systemic inflammatory response syndrome