The clinical value of the SII for predicting the development of urosepsis after percutaneous nephrolithotripsy

The clinical value of the SII for predicting the development of urosepsis after percutaneous nephrolithotripsy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The clinical value of the SII for predicting the development of urosepsis after percutaneous nephrolithotripsy Huang Wu, Fuyan Lian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4868534/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Based on accumulating evidence, biomarkers related to the inflammatory response are closely associated with tumors. However, there are fewer studies related to urosepsis. The aim of this research was to investigate the importance of the SII as a predictor of the development of urosepsis after percutaneous nephrolithotripsy, utilizing a retrospective research design. Materials and Methods This study encompassed a cohort of 639 individuals diagnosed with kidney stones between January 2019 and August 2022. The patients were categorized into a modeling group consisting of 439 individuals and a validation group comprising 200 individuals, following a ratio of 7:3. R software was used to perform multivariate logistic regression analysis after screening with LASSO regression. The risk line graph model, ROC curve, calibration curve, and decision curve of the modeling group were drawn and visualized using R statistical software. These findings were also drawn and verified in the validation cohort. Results In a cohort of 439 patients, the prevalence of urosepsis was found to be 9.11% (40/439). Subsequently, a multivariate logistic regression analysis was conducted following a screening process utilizing LASSO regression. Our results suggested four risk factors for PCNL-US, namely, positive urinary nitrite (OR = 3.176, 95%CI: 1.390–7.097, P < 0.001), preoperative fever (OR = 2.762, 95%CI: 1.021–7.104, P = 0.039), positive urine culture (OR = 2.447, 95%CI: 1.077–5.476, P = 0.030), and high preoperative SII (OR = 4.943, 95%CI: 2.323–10.776, P < 0.001). According to above four factors, we constructed a column-line graph prediction model of risk factors for PCNL-US. The area under the ROC curve (AUC) of the modeling group was 0.818 (95% CI: 0.739–0.898). The area under the ROC curve (AUC) of the validation group was 0.794 (95% CI: 0.679–0.909). The Hosmer-Lemeshow test was greater than 0.05 in both groups, indicating a good calibration curve and good clinical decision-making performance. Conclusions This study suggested that positive urinary nitrite levels, preoperative fever, and positive urine culture are risk factors for PCNL-US. Additionally, a high preoperative SII level is recognized as a separate risk factor for the occurrence of urosepsis. The clinical prediction model constructed based on these four risk factors may serve as a reference for preventing the occurrence of PCNL-US. Percutaneous nephrolithotripsy Risk factors SII Urosepsis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Urolithiasis is the most common disease in urology, and occurs in approximately 11% of men and 7% of women. 1 Epidemiological data from Europe and the United States indicate that the prevalence of urolithiasis ranges from 1 to 20%. 2 Furthermore, the recurrence rate of stones is remarkably high, with 50% occurring within 5–10 years and 75% occurring within 20 years, significantly impacting the overall physical and mental well-being of the population. 3 Percutaneous nephrolithotomy (PCNL) is considered the gold standard surgical procedure for treating kidney stones, especially those larger than 2 cm or staghorn stones. It has a clearance rate of up to 95%. 4 However, none of these methods can completely prevent perioperative complications such as hemorrhage, organ and peripheral tissue injury, infection, and sepsis. The early detection of urosepsis after percutaneous nephrolithotomy (PCNL-US) is often challenging due to the lack of effective diagnostic markers and the insidious onset of the disease. It is often difficult to detect urinary sepsis early. Treatment can be life-threatening if left untreated or improperly treated. Several studies have indicated that urosepsis is the leading cause of perioperative death after PCNL. Urosepsis is the most frequent cause of perioperative mortality after PCNL. 5 Urosepsis refers to sepsis caused by urinary tract infections. Urinary tract obstruction is a common cause of urosepsis, and accounts approximately 78% of cases. The remaining 22% of cases are attributed to urodynamic abnormalities resulting from urinary tract pathology. Common causes of obstruction include urolithiasis (43%), urologic tumors (18%), benign prostatic hyperplasia (25%), and abscess infection (6%). 6 Urosepsis also occurs after genitourinary tract interventions such as ureteroscopy, percutaneous nephrolithotripsy, or transrectal prostate biopsy. It has been reported that it occurs in approximately 17% of patients after urologic intervention. 7 The incidence of undergoing PCNL-US ranged from 0.3–4.7%. Approximately 40% of urosepsis cases progress to uremic shock, while 20–40% of patients with uremic shock may die. 8 Several blood markers (calcitoninogen levels, the calcitoninogen/albumin ratio, C-reactive protein levels, the NLR, the PLR, the LMR, and IL-6) have been shown to provide clinicians with useful information. 9 However, the sensitivity and specificity of these tests are limited. It is still not possible to confirm the diagnosis solely based on changes in the indexes. Clinical diagnosis relies more on the physician's experience. Numerous studies have demonstrated that the systemic immune-inflammation index (SII) is closely related to tumor development. 10 , 11 Fewer studies have demonstrated that the SII has a greater predictive value for the development of SIRS in PCNL patients than other traditional inflammatory indicators. 12 However, the relationship between the SII and urosepsis is unclear. To the best of our understanding, this study represents the inaugural examination of the diagnostic utility of the SII in forecasting PCNL-US outcomes. 2. Materials and Methods 2.1. Study Cohort We retrospectively selected 639 patients diagnosed with kidney stones who underwent PCNL only at the Urology Department of Xiaogan Central Hospital from January 2019 to August 2022. The participants were divided into two groups according to the definition of urosepsis: the urosepsis group and the non-urosepsis group (Fig. 1 ). The sepsis-3 diagnostic criteria were applied to diagnose ursepsis, which included the following: 1. Urinary tract infection accompanied by clinical symptoms, and 2. A Sequential Organ Failure Assessment (SOFA) score of ≥ 2 points was given. The specific scores are shown in Table 1 . Table 1 The Sequential Organ Failure Assessment (SOFA) score Organ Failure Variant 0 1 2 3 4 Respiratory system PaO 2 /FiO 2 , mmHg ≥ 400 < 400 < 300 < 200, with respiratory support < 100, with respiratory support Nervous system Glasgow coma scale 15 13–14 10–12 6–10 70 5 >15 Dobutamine, µg/kg/min any dose Epinephrine µg/kg/min . ≤ 0.1 >0.1 Norepinephrine, µg/kg/min ≤ 0.1 >0.1 Liver Bilirubin, (mg/dL) 12.0 Kidneys Creatinine, (mg/dL) 5 Urine volume, (ml/d) ≥ 500 < 500 < 200 Coagulation Platelets, 10 9 /L ≥ 150 < 150 < 100 < 50 < 20 The criteria for inclusion were established as follows: (1) patients diagnosed with upper urinary tract stones and clinical symptoms by computed tomography (CT), plain film, ultrasound, magnetic resonance imaging (MRI), etc. ; and (2) patients with stones who underwent percutaneous nephrolithotomy in our hospital were admitted according to the 2019 edition《the Chinese Guidelines for Diagnosis and Treatment of Urology and Andrology Diseases》. The exclusion criteria were as follows:(1) It is coupled with additional surgical techniques like ureteroscopic lithotripsy;(2) Patients with tumors, hematologic and immune system disorders;(3) Congenital malformations such as polycystic kidneys and horseshoe kidneys;(4) Preoperative urologic CT scan images could not be obtained;(5) Age < 18 years; 2.2. Data collection We included the following data, including age, sex, BMI (body mass index), comorbidities (hypertension, diabetes mellitus), stone-related characteristics (CT value, single or multiple, surface area, staghorn stones), history of surgery, urinary nitrites, urine culture, preoperative fever, serum creatinine, urea nitrogen, hemoglobin,blood leucocytes, AGR (albumin-to-globulin ratio), NLR (neutrophil-to-lymphocyte ratio), LMR (lymphocyte-to-lymphocyte ratio),PLR (Platelet-to-Lymphocyte Ratio), and SII (SII = platelet× neutrophil/ lymphocyte), bleeding, operative time, and residual stone,serum albumin. 2.3. Statistical Analysis The baseline data were statistically analyzed using SPSS 23.0 software. The Kolmogorov‒Smirnov test was utilized to evaluate the normality of continuous variables. Variables that followed a normal distribution are presented as the mean ± standard deviation, and independent sample t tests were used for comparisons between groups. Variables that were not normally distributed are presented as medians (P25, P75), and between-group comparisons were analyzed using the Mann‒Whitney U test. Unordered categorical variables are expressed as percentages, and comparisons were analyzed using the chi-square test or Fisher's exact test. Risk factor analyses were performed using multivariate logistic regression analysis in the R language (R 3.6.2, Institute of Statistics and Mathematics, Vienna, Austria). P values less than 0.05 were considered to indicate statistical significance. Based on the results of the logistic regression model and the combination of clinically significant variables, a column-line graph prediction model was constructed and validated using the RMS package of R software. The receiver operating characteristic (ROC) curve was calculated to determine the model's discriminatory ability. Calibration curves were plotted, and the Hosmer-Lemeshow test was performed to evaluate the model's goodness of fit. Additionally, clinical decision curve testing was used to evaluate the accuracy of the model. 3. Results 3.1 Patient Characteristics The modeling group of this study summarized the clinical data of 449 patients who underwent percutaneous nephrolithotomy, with 399 patients having non-urosepsis and 40 patients having urosepsis. The baseline data of the two groups were analyzed: female (P < 0.001); diabetes mellitus (P = 0.010); positive urine culture (P < 0.001); positive urinary nitrite (P < 0.001); preoperative fever (P < 0.001); AGR (P = 0.006), PLR (P = 0.006); LMR (P < 0.001); blood urea nitrogen (P = 0.046); hemoglobin (P = 0.001); serum albumin (P = 0.042); blood leukocytes (P = 0.030); NLR (P < 0.001); residual stones (P = 0.002); multiple stones (P = 0.006); staghorn stones (P = 0.002); age (P = 0.879); BMI (P = 0.838); history of previous ipsilateral surgery (P = 0.245); hypertension (P = 0.520); blood creatinine (P = 0.417); CT value (P = 0.274); surface area (P = 0.436); operative time (P = 0.858); and bleeding (P = 0.110).Table 2 displays the specific outcomes. No notable statistical variance was found in the indices observed among the groups (P > 0.05), and Table 3 displays the specific outcomes. Table 2 Baseline demographics and clinical characteristics of the patients Parameter Non-urosepsis n = 399 (%) Urosepsis n = 40 (%) P-value Sex < 0.001 Male 290 (72.68) 16 (40.00) Female 109 (27.32) 24 (60.00) History of surgery 0.245 No 362 (90.73) 34 (85.00) Yes 37 (9.27) 6 (15.00) Hypertension 0.520 No 344 (86.22) 33 (82.50) Yes 55 (13.78) 7 (17.50) Diabetes mellitus 0.010 No 368 (92.23) 32 (80.00) Yes 31 (7.77) 8 (20.00) Urine culture < 0.001 Negative 330 (82.71) 20 (50.00) Positive 69 (17.29) 20 (50.00) Urinary nitrite < 0.001 Negative 334 (83.71) 21 (52.50) Positive 65 (16.29) 19 (47.50) Preoperative fever < 0.001 No 368 (92.23) 28 (70.00) Yes 31 (7.77) 12 (30.00) Residual calculus 0.002 No 331 (82.96) 25 (62.50) Yes 68 (17.04) 15 (37.50) Multiple stones 0.006 No 211 (52.88) 12 (30.00) Yes 188 (47.12) 28 (70.00) Staghorn calculus 0.002 No 367 (92.21) 31 (77.50) Yes 31 (7.79) 9 (22.50) Age (y) 49.00 (38.00, 55.00) 48.00(37.30, 56.00) 0.879 BMI (kg/m 2 ) 24.09 (21.70, 26.20) 23.97(21.10, 26.40) 0.838 PLR 108.90(84.90, 135.10) 132.23 (102.90, 193.30) 0.006 LMR 4.57 (3.60, 5.60) 3.645 (2.10, 4.80) < 0.001 AGR 1.73 (1.50, 2.00) 1.550 (1.40, 1.80) 0.006 Blood urea nitrogen (µmol/L) 5.60 (4.70, 6.70) 4.975 (4.30, 6.00) 0.046 Serum creatinine (µmol/L) 70.00 (61.00, 81.00) 73.00(54.40, 90.00) 0.417 Serum albumin (g/L) 42.30(39.40, 45.30) 39.70 (37.90, 44.30) 0.042 Blood leukocytes (10 9 /L) 6.07 (5.20, 7.30) 6.54 (5.40, 9.10) 0.030 NLR 1.71(1.30, 2.30) 2.56(1.80, 5.90) < 0.001 SII 420.90 (388.47, 453.33) 908.56 (511.52, 1305.61) < 0.001 Hemoglobin (g/L) 149.00 (138.00, 160.00) 133.50 (122.00, 149.80) < 0.001 CT value (HU) 1100.00 (882.00, 1266.00) 1174.00 (860.50, 1396.30) 0.274 Surface area (cm ) 2 2.13 (1.50, 3.10) 2.69 (1.40, 3.50) 0.436 Surgical time (min) 113.00 (94.00, 140.00) 120.00(96.00, 138.80) 0.858 Bleeding (ml) 10.00 (5.00, 20.00) 20.00 (5.00, 68.80) 0.110 Table 3 Comparison of basic characteristics between the validation group and the training group Parameter Modeling group n = 439 (%) Validation group n = 200 (%) P-value Sex 0.350 Male 306 (69.70) 132 (66.00) Female 133 (30.30) 68 (34.00) History of surgery 0.751 No 396 (90.21) 182 (91.00) Yes 43 (9.79) 18 (9.00) Hypertension 0.270 No 377 (85.88) 165 (82.50) Yes 62 (14.12) 35 (17.50) Diabetes mellitus 0.051 No 400 (91.12) 191 (95.50) Yes 39 (8.88) 9 (4.50) Urine culture 0.521 Negative 350 (79.73) 155 (77.50) Positive 89 (20.27) 45 (22.50) Urinary nitrite Negative 355 (80.87) 163 (81.50) 0.849 Positive 84 (19.13) 37 (18.50) Preoperative fever 0.113 No 396 (90.21) 188 (94.00) Yes 43 (9.79) 12 (6.00) Residual calculus 0.745 No 356 (81.09) 160 (80.00) Yes 83 (18.91) 40 (20.00) Multiple stones 0.222 No 223 (50.80) 112 (56.00) Yes 216 (49.20) 88 (44.00) Staghorn calculus No 398 (90.87) 187 (93.50) 0.264 Yes 40 (9.13) 13 (6.50) Age (y) 49 (38, 55) 49 (37, 56) 0.689 BMI (kg/m 2 ) 24.03 (21.70, 26.20) 23.69(21.50, 25.90) 0.254 PLR 111.20(85.50, 143.30) 108.31 (85.30, 142.00) 0.814 LMR 4.48 (3.50, 5.50) 4.70 (3.50, 5.90) 0.092 AGR 1.72 (1.50, 2.00) 1.69(1.50,2.00) 0.750 Blood urea nitrogen (µmol/L) 5.50(4.60, 6.70) 5.50 (4.80, 6.50) 0.923 Serum creatinine (µmol/L) 70.20 (60.80, 81.60) 72.00 (61.30, 83.80) 0.445 Serum albumin (g/L) 42.10 (39.30, 45.30) 42.60(39.80, 45.60) 0.416 Blood leukocytes (10 9 /L) 6.12 (5.20, 7.40) 6.38 (5.10, 7.60) 0.524 NLR 1.77 (1.30, 2.40) 1.82 (1.40, 2.30) 0.560 SII 468.16 (424.10, 512.20) 464.05 (418.71, 509.41) 0.077 Hemoglobin (g/L) 148.00 (136.00, 159.00) 148.00 (132.00, 162.00) 0.895 CT value (HU) 1100.00 (880.00, 1274.00) 1034.50 (856.00, 1247.80) 0.262 Surface area (cm) 2 2.16 (1.50, 3.10) 2.13 (1.60, 2.70) 0.291 Surgical time (min) 133.00 (96.00, 160.00) 125.00 (90.00, 155.00) 0.567 Bleeding (ml) 40 .00(20.00, 60.00) 38.00(18.00, 54.00) 0.727 3.2 Independent prognostic factors associated with urosepsis We used the least absolute shrinkage and selection operator (LASSO) to determine the optimal penalty coefficient (0.04317417) for the outcome variable "urosepsis" after conducting 72 internal cross-validations. We identified 6 non-zero coefficient variables from a total of 26 variables, which included sex, positive urine culture, positive urine nitrite, preoperative fever, preoperative hemoglobin, and SII (Fig. 2 ). The above six screened factors were analyzed by multivariate logistic regression analysis, and the final results showed that positive urinary nitrite (OR = 3.176, 95% CI: 1.390–7.097, P < 0.001), preoperative fever (OR = 2.762, 95% CI: 1.021–7.104, P = 0.039), positive urine culture (OR = 2.447, 95% CI: 1.077–5.476, P = 0.030), and the SII (OR = 4.943, 95% CI = 2.323–10.776, P < 0.001) were significantly different (Table 4 ). In addition, we constructed a forest plot of the results (Fig. 3 ). Table 4 Multivariate Logistic Regression Analysis for PCNL-US in Training Cohort Parameter estimated value standard error Z-value P OR lower limit limit Sex 0.787 0.443 1.774 0.076 2.196 0.922 5.297 Positive urine culture 0.895 0.413 2.169 0.030 2.447 1.077 5.476 Positive urinary nitrite 1.156 0.412 2.808 < 0.001 3.176 1.39 7.097 Preoperative fever 1.016 0.4912 2.067 0.039 2.762 1.021 7.104 SII 1.597 0.389 4.110 < 0.001 4.943 2.323 10.776 Preoperative hemoglobin -0.013 0.009 -1.296 0.195 0.987 0.969 1.007 3.3 Construction and Assessment of the Nomogram Model Based on the logistic regression results, four risk factors, positive urinary nitrite, preoperative fever, positive urine culture, and SII, were ultimately used in the production of the column chart. R language is used as a visualization tool for risk factor graphs, and the total score is calculated based on whether the patient has the aforementioned risk factors. Then, the predicted value corresponding to the total score was subsequently used to determine the probability of PCNL-US (Fig. 4 ). For example, If a patient's urine nitrite is positive, positive urine culture, and a SII > 492.85, and their total score is 240 points, the probability of postoperative occurrence of urosepsis in that patient is approximately 60%. The area under the ROC curve for our modeling group was 0.818 (95% CI: 0.739–0.898) (Fig. 5 A). The area under the ROC curve for the validation group was 0.794 (95% CI: 0.679–0.909) (Fig. 5 B). We also performed ROC curves for four independent risk factors, as shown in Fig. 5 C. The area under the AUC curve for positive urinary nitrites was 0.656 (95% CI: 0.558–0.754), for preoperative fever was 0.611 (95% CI: 0.509–0.713), and for positive urinary cultures was 0.664 (95% CI). The AUC curve area for SII was 0.727 (95% CI: 0.638–0.817). It can be seen that the area under the curve for SII is larger than that of the remaining three factors. See Table 5 for details. Table 5 ROC area for different risk factors Area Under the Curve Test Result Variable(s) Area Std. Error a Asymptotic Sig. b Asymptotic 95% Confidence Interval Lower Bound Upper Bound Positive urine culture .664 .050 .001 .566 .761 Positive urine nitrites .656 .050 .001 .558 .754 Preoperative fever .611 .052 .020 .509 .713 SII .727 .046 .000 .638 .817 We used the bootstrap method to sample 1000 times in order to validate the column line plots of our data. The results of the Hosmer-Lemeshow goodness-of-fit test (modeling group: P = 0.4934 > 0.05, validation group: P = 0.0741 > 0.05) indicated a better fit. The calibration curves of the line plots for both groups closely aligned with the actual calibration curve and are generally consistent. This further indicates that the model has objective accuracy (Fig. 6 ). Whether in the modeling group or the validation group, the decision curve is higher than the two extreme values, indicating a greater net return and a wider high-risk threshold. Therefore, the clinical prediction model constructed in this study has certain clinical application value and reference significance (Fig. 7 ). 4. Discussion Since the introduction of PCNL in 1976, the safety and efficacy of this minimally invasive technique have significantly improved. As a result, PCNL has become the preferred treatment for complex and large stones. However, One of the most serious consequences is still post-PCNL infection. 13 Research indicates that the occurrence of infectious complications following PCNL surgery varies between 2.8% and 32.1%.Urosepsis is a potentially serious complication, with the pathogenesis of which ranges from infection to systemic inflammatory response syndrome, followed by progression to sepsis. However, due to the unique pathogenesis of urosepsis, it is often difficult to detect this disease early in the clinic. Delays in the diagnosis and treatment of sepsis increase mortality, prolong hospitalization, and increase costs. 14 Nonetheless, the negative effects can be lessened through prompt identification and management of sepsis. 15 As a result, it is critical to identify risk factors for urosepsis early in order to avoid serious postoperative complications. The occurrence of PCNL-US has been found to be associated with a variety of factors, including positive urine cultures, positive urinary nitrites, urinary leukocytes, blood leukocytes, staghorn stones, stone loading, duration of surgery, bleeding, diabetes mellitus, and sex. Regarding the risk factors mentioned above, there is no consensus on the concept of a positive urine culture. Preoperative urine cultures are routinely performed prior to PCNL to assess the risk of infection and sepsis. Teh, K. Y 16 demonstrated that Patients exhibiting positive preoperative urine cultures demonstrated an almost fourfold increased likelihood of developing post-percutaneous nephrolithotomy (PCNL) sepsis compared to those with negative cultures, with incidence rates of 8.41% and 2.2%, respectively. However, several studies have found that a negative bladder urine culture does not necessarily indicate that there are no bacteria present in the stone or renal pelvis. This may be due to underlying urinary stone obstruction. 17 Several studies have shown pelvic urinary infections in up to one-third of patients with negative bladder urine cultures, half of whom had positive stone cultures. 18 Eswara. et al. 19 reported that urosepsis occurred in 3% (11/328) of patients, 8 of whom had a positive stone culture (SC). However, none of the patients had a positive preoperative mid-stream urine culture. The authors suggested that preoperative midstream urine culture results did not directly correlate with pyelocentesis urine results or stone culture results. The reason for this discrepancy was that the uropathogens detected in mid-stream urine were not consistent with those found in the renal pelvis or in stones. Specifically, Staphylococcus aureus was predominant in stone cultures, while Escherichia coli was predominant in urine cultures.A total of 14 studies involving 3,540 patients were analyzed via meta-analysis. The findings of these studies concluded that renal pelvic urine cultures or stone cultures were more reliable in predicting postoperative systemic inflammatory response syndrome (SIRS) and urosepsis. Additionally, these cultures were found to be effective in identifying causative microorganisms and guiding antibiotic therapy in patients with PCNL, as compared to preoperative mid-stream urine cultures 20 .The guidelines from the American Urological Association (AUA) and the European Association of Urology (EAU) for patients with positive urine cultures do not provide recommendations for the duration of perioperative antibiotics. However, they strongly recommend collecting stone samples after lithotripsy to guide the selection of postoperative antibiotics. 8 , 21 Recent studies have shown that patients with Positive urine cultures should be managed with suitable antibiotics for a minimum duration of seven days prior to surgical intervention. 22 , 23 Similarly, the Chinese Urological Association (CUA) guidelines recommend a 1–2 week course of antibiotics for patients with positive urine cultures. A cross-sectional survey conducted by Zhang et al. examined the use of antibiotics for PCNL in China. The study found that urologists in China commonly used cephalosporins as the primary antibiotic prior to PCNL, followed by quinolones. 24 Many studies from various regions of China, however, have shown that the majority of uropathogens isolated from urine or stones of patients with urinary tract stones exhibit high resistance to cephalosporins (such as cefuroxime and ceftriaxone) and quinolones (such as ciprofloxacin and levofloxacin). Therefore, antibiotics should be selected rationally based on the local bacterial spectrum and drug sensitivity. Therefore, stone culture and renal pelvic urine culture should be routinely tested as much as possible in actual clinical practice. This will further enhance the ability to predict and prevent the occurrence of urosepsis. Urinary nitrites are formed through bacterial reduction reactions of nitrates in the urine. Because urinary tract infections caused by gram-negative bacteria can be diagnosed quickly but indirectly by testing for urinary nitrites, a positive test for urinary nitrites indicates the presence of gram-negative bacteria in the urethra, especially Escherichia coli. 25 The more virulent the Gram-negative bacteria, the more likely they are to be present. The more virulent Gram-negative bacteria are more active at higher urate concentrations, which often indicates a more severe infection in the patient. 26 Gu et al. showed that patients with preoperative positive urinary nitrite had up to 3.33 times higher risk of urosepsis compared to patients with negative urinary nitrite. 27 Our study also suggests a higher risk of positive urinary nitrite levels. Urinary nitrite has a high specificity (98%) but low sensitivity (23%-43%) due to factors such as urination within 4 hours, low dietary nitrate intake, and urine dilution, which may result in false negatives. 28 For these reasons, several studies have combined levels with leukocyte and leukocyte esterase levels to increase the diagnostic precision of urinary tract infections. 29 The findings of these studies are summarized in the table below. Other researchers have found that the combination of urinary leukocytes and urinary nitrites has a sensitivity of 92% and a specificity of 98% when compared to the combination of urine and stone cultures. This combination is superior for early prediction of PCNL-US. The reason for this is that urine bacterial cultures are time-consuming and can also be affected by sample contamination. Because normal urine tests are less expensive and time-consuming, clinical urologists prefer to use them as a diagnostic tool in their daily practice. 30 In a recent study, a urine nitrite-based model showed a greater net clinical benefit than a urine culture-based model for post-PCNL infections. 31 This is similar to the findings of our study. Most studies have focused on the factors associated with postoperative fever after PCNL, and there are fewer reports on whether preoperative fever affects the occurrence of PCNL-US. We defined preoperative fever as a body temperature above 38°C. Preoperative fever also implies the presence of underlying urinary tract infections (UTI), most of which are caused by Escherichia coli.Bacterial adherence within the urinary tract plays a significant role in both colonization and invasion, as well as in the formation of biofilms and the damage to host cells.Biofilms can contribute to the persistence of urothelial and biomaterial surfaces by protecting bacteria from hydrodynamic scavenging, as well as host defense mechanisms and the killing activity of antibiotics. The persistence of biofilms leads to persistent UTIs and, consequently, stone formation. 32 Some studies have shown that preoperative UTI can lead to a decline in renal function one month after percutaneous nephrolithotomy in patients with single-kidney staghorn stones. 33 Additionally, a history of recurrent UTI is an independent risk factor for postoperative SIRS after PCNL. 34 Yang et al discovered a strong association between preoperative stone fever and unfavorable postoperative outcomes in patients with complex upper urinary tract kidney stones, emphasizing the importance of adequate preoperative treatment. 35 In addition, an analysis of a predictive model for infectious stones showed that preoperative fever was a significant predictor of infectious stone formation. Patients with preoperative fever were 2.37 times more likely than patients without fever to have infectious stones. Therefore, patients with preoperative fever should be identified and treated before surgery. The SII is a multi-marker index that objectively reflects the balance between inflammation and immunity in patients with malignant tumors. It can be used as a prognostic indicator in cancer research. 36 Studies have reported that elevated SII levels are associated with a poorer prognosis and higher mortality in patients with cardiovascular disease. 37 It has also been documented that SII is a marker for chronic obstructive pulmonary disease. 38 Although LMR, NLR, and PLR were not strongly correlated with urosepsis in this study, SII was strongly correlated. The exact mechanism needs to be further explored, but this is first to study the relationship between SII and urosepsis. This study also has some limitations. First, single-center studies can bias the results and their value. Therefore, further validation is needed to determine whether systemic inflammatory markers have clinical value, particularly through multi-center studies with large sample sizes. Conclusion Our study suggested that positive urinary nitrite, preoperative fever, and positive urine culture are risk factors for PCNL-US. Additionally, a high preoperative SII level serves as an independent risk factor for the development of urosepsis.A clinical prediction model constructed based on these four risk factors may serve as a reference for preventing the occurrence of PCNL-US. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Xiaogan Central Hospital Affiliated to Wuhan University of Science and Technology. Informed written consent was obtained from all individual participants included in the study. Details that disclose the identity of the subjects under study were omitted. Consent for publication Not applicable. Competing interests No competing financial interests exist. Funding This study received no funds from any sources. Author Contribution WH had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: LFY. Acquisition of data:WH . Analysis and interpretation of data: WH. Drafting of the manuscript: WH. Critical revision of the manuscript for important intellectual content:LFY. Supervision: LFY. All authors read and approved the final manuscript. Acknowledgements Not applicable for that section. Data Availability The data and materials can be obtained by contacting the corresponding author. References R.C. WangManaging Urolithiasis. Ann Emerg Med, 2016. 67(4): p. 449–54. Pathan SA, Mitra B, Straney LD, et al. Delivering safe and effective analgesia for management of renal colic in the emergency department: a double-blind, multigroup, randomised controlled trial. Lancet. 2016;387(10032):1999–2007. Lopez Martinez JM, Sierra Del Rio A. Luque GalvezMedical treatment of renal stones. Arch Esp Urol. 2021;74(1):63–70. Preminger GM, Assimos DG, Lingeman JE, et al. Chapter 1: AUA guideline on management of staghorn calculi: diagnosis and treatment recommendations. J Urol. 2005;173(6):1991–2000. Armitage JN, Irving SO, Burgess NA, et al. Percutaneous nephrolithotomy in the United kingdom: results of a prospective data registry. Eur Urol. 2012;61(6):1188–93. Dreger NM, Degener S, Ahmad-Nejad P et al. Urosepsis–Etiology, Diagnosis, and Treatment. Dtsch Arztebl Int, 2015. 112(49): p. 837 – 47; quiz 848. Wagenlehner FM, Pilatz A. W. WeidnerUrosepsis–from the view of the urologist. Int J Antimicrob Agents, 2011. 38 Suppl: pp. 51 – 7. Turk C, Petrik A, Sarica K, et al. EAU Guidelines on Interventional Treatment for Urolithiasis. Eur Urol. 2016;69(3):475–82. Guliciuc M, Maier AC, Maier IM et al. Urosepsis-A Literature Rev Med (Kaunas), 2021. 57(9). Nost TH, Alcala K, Urbarova I, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol. 2021;36(8):841–8. Yang M, Lin SQ, Liu XY, et al. Association between C-reactive protein-albumin-lymphocyte (CALLY) index and overall survival in patients with colorectal cancer: From the investigation on nutrition status and clinical outcome of common cancers study. Front Immunol. 2023;14:1131496. Peng C, Li J, Xu G, et al. Significance of preoperative systemic immune-inflammation (SII) in predicting postoperative systemic inflammatory response syndrome after percutaneous nephrolithotomy. Urolithiasis. 2021;49(6):513–9. Kreydin EI. EisnerRisk factors for sepsis after percutaneous renal stone surgery. Nat Rev Urol. 2013;10(10):598–605. Garnacho-Montero J, Ortiz-Leyba C, Herrera-Melero I, et al. Mortality and morbidity attributable to inadequate empirical antimicrobial therapy in patients admitted to the ICU with sepsis: a matched cohort study. J Antimicrob Chemother. 2008;61(2):436–41. M.W. MoyerNew biomarkers sought for improving sepsis management and care. Nat Med. 2012;18(7):999. Teh KY. ThamPredictors of post-percutaneous nephrolithotomy sepsis: The Northern Malaysian experience. Urol Ann. 2021;13(2):156–62. Mariappan P, Smith G, Bariol SV, et al. Stone and pelvic urine culture and sensitivity are better than bladder urine as predictors of urosepsis following percutaneous nephrolithotomy: a prospective clinical study. J Urol. 2005;173(5):1610–4. Korets R, Graversen JA, Kates M, et al. Post-percutaneous nephrolithotomy systemic inflammatory response: a prospective analysis of preoperative urine, renal pelvic urine and stone cultures. J Urol. 2011;186(5):1899–903. Eswara JR, Shariftabrizi A. D. SaccoPositive stone culture is associated with a higher rate of sepsis after endourological procedures. Urolithiasis. 2013;41(5):411–4. Liu M, Chen J, Gao M, et al. Preoperative Midstream Urine Cultures vs Renal Pelvic Urine Culture or Stone Culture in Predicting Systemic Inflammatory Response Syndrome and Urosepsis After Percutaneous Nephrolithotomy: A Systematic Review and Meta-Analysis. J Endourol. 2021;35(10):1467–78. Wolf JS Jr., Bennett CJ, Dmochowski RR, et al. Best practice policy statement on urologic surgery antimicrobial prophylaxis. J Urol. 2008;179(4):1379–90. Xu P, Zhang S, Zhang Y, et al. Preoperative antibiotic therapy exceeding 7 days can minimize infectious complications after percutaneous nephrolithotomy in patients with positive urine culture. World J Urol. 2022;40(1):193–9. Zeng T, Chen D, Wu W, et al. Optimal perioperative antibiotic strategy for kidney stone patients treated with percutaneous nephrolithotomy. Int J Infect Dis. 2020;97:162–6. Zhang S, Li G, Qiao L, et al. The antibiotic strategies during percutaneous nephrolithotomy in China revealed the gap between the reality and the urological guidelines. BMC Urol. 2022;22(1):136. Jiang E, Guo H, Yang B, et al. Predicting and comparing postoperative infections in different stratification following PCNL based on nomograms. Sci Rep. 2020;10(1):11337. Amier Y, Zhang Y, Zhang J, et al. Analysis of Preoperative Risk Factors for Postoperative Urosepsis After Mini-Percutaneous Nephrolithotomy in Patients with Large Kidney Stones. J Endourol. 2022;36(3):292–7. Gu J, Liu J, Hong Y, et al. Nomogram for predicting risk factor of urosepsis in patients with diabetes after percutaneous nephrolithotomy. BMC Anesthesiol. 2022;22(1):87. Pallin DJ, Ronan C, Montazeri K, et al. Urinalysis in acute care of adults: pitfalls in testing and interpreting results. Open Forum Infect Dis. 2014;1(1):ofu019. Zhu Z, Cui Y, Zeng H, et al. The evaluation of early predictive factors for urosepsis in patients with negative preoperative urine culture following mini-percutaneous nephrolithotomy. World J Urol. 2020;38(10):2629–36. Chen D, Jiang C, Liang X, et al. Early and rapid prediction of postoperative infections following percutaneous nephrolithotomy in patients with complex kidney stones. BJU Int. 2019;123(6):1041–7. Ruan S, Chen Z, Zhu Z, et al. Value of preoperative urine white blood cell and nitrite in predicting postoperative infection following percutaneous nephrolithotomy: a meta-analysis. Transl Androl Urol. 2021;10(1):195–203. Maheswari UB, Palvai S, Anuradha PR, et al. Hemagglutination and biofilm formation as virulence markers of uropathogenic Escherichia coli in acute urinary tract infections and urolithiasis. Indian J Urol. 2013;29(4):277–81. Wang J, Bai Y, Yin S, et al. Risk factors for deterioration of renal function after percutaneous nephrolithotomy in solitary kidney patients with staghorn calculi. Transl Androl Urol. 2020;9(5):2022–30. Akkas F, Karadag S. A. HaciislamogluDoes the duration between urine culture and percutaneous nephrolithotomy affect the rate of systemic inflammatory response syndrome postoperatively? Urolithiasis. 2021;49(5):451–6. Yang J, Huang Y, Li Y et al. Efficacy of Flexible Ureteroscopic Lithotripsy and Percutaneous Nephrolithotomy in the Treatment of Complex Upper Urinary Tract Nephrolithiasis. Comput Math Methods Med, 2022. 2022: p. 2378113. Chen JH, Zhai ET, Yuan YJ, et al. Systemic immune-inflammation index for predicting prognosis of colorectal cancer. World J Gastroenterol. 2017;23(34):6261–72. Fest J, Ruiter R, Mulder M, et al. The systemic immune-inflammation index is associated with an increased risk of incident cancer-A population-based cohort study. Int J Cancer. 2020;146(3):692–8. Ye C, Yuan L, Wu K, et al. Association between systemic immune-inflammation index and chronic obstructive pulmonary disease: a population-based study. BMC Pulm Med. 2023;23(1):295. Additional Declarations No competing interests reported. Supplementary Files Validationgroupdata.xlsx Modelinggroupdata.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4868534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351000507,"identity":"6b3f5be9-0de6-4bec-84ab-24e6e6a2c1c1","order_by":0,"name":"Huang Wu","email":"","orcid":"","institution":"The Central Hospital of Xiao gan","correspondingAuthor":false,"prefix":"","firstName":"Huang","middleName":"","lastName":"Wu","suffix":""},{"id":351000510,"identity":"16a64fd9-a090-408c-900a-1ddcfb1c33cd","order_by":1,"name":"Fuyan Lian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACPmYgUQFisTc2PvhAjBY2kJYzIBbP4WbDGURpYYBpkUhvk+YgSgs787MHByoOy5tLPmyQZmCwk9NtIOgwNnODA2cOG+6cndhgXMCQbGx2gKAWBjPpj22HGTfcTmxInsFwIHEbYS3s3yQOth2233DzYMNhHuK08JiBtCRuuMHY2EysljKJA2fSkzecSWxmnGFAhF/4+Y9vkzhQYW274fjx5z8+VNjJEdQCBc1Q2oA45SBQR7zSUTAKRsEoGHkAADOEQ62XtrLTAAAAAElFTkSuQmCC","orcid":"","institution":"Intensive Care Medicine Department，Gansu Provincial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fuyan","middleName":"","lastName":"Lian","suffix":""}],"badges":[],"createdAt":"2024-08-06 12:38:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4868534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4868534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66571264,"identity":"6cad7a73-7c63-4721-bdf4-8a51ddba732d","added_by":"auto","created_at":"2024-10-14 11:51:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96669,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of this study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/6b838240c36194d62e0c77c0.png"},{"id":66571267,"identity":"64733e86-04f0-46fe-b31a-490a08a74ad1","added_by":"auto","created_at":"2024-10-14 11:51:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95188,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Plot of each clinical characteristic coefficient against log(λ) obtained by adjusting the parameter λ. (B) Cross-validation plot of the LASSO regression model. A vertical line is drawn at the optimum with the minimum criterion and 1 SE of the minimum criterion. When λ =0.04317417, we obtain 6 variables for further analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/4ddc4655c7489e41b7975bb1.png"},{"id":66571272,"identity":"0e3e5e4f-6c73-4cbd-be30-bbd8359b2eec","added_by":"auto","created_at":"2024-10-14 11:51:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66403,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of results of multivariate logistic regression analysis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/ff52c8b79a1320a81f86fd18.png"},{"id":66571270,"identity":"12da6643-f7e6-433a-bbd6-ea377a8c0ccc","added_by":"auto","created_at":"2024-10-14 11:51:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49358,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram for predicting the occurrence of urosepsis after PCNL.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/c4970dbf2216a0f228b34f8c.png"},{"id":66572967,"identity":"4737eff8-c35b-4a3d-b974-d466f05df3b0","added_by":"auto","created_at":"2024-10-14 12:07:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60855,"visible":true,"origin":"","legend":"\u003cp\u003e(A) ROC curve of the training set. (B) ROC curve of the validation set. (C) ROC curves for each risk factor in the training set.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/604610a0cc0bd44e162df152.png"},{"id":66572470,"identity":"0661ff3b-563b-41fa-9a57-a8bc4cd7f8e2","added_by":"auto","created_at":"2024-10-14 11:59:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63007,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram for the training cohort (A) and the validation cohort (B).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/20c22618d3f4eaf1c2c210a2.png"},{"id":66571269,"identity":"f6ffbfa2-08d8-4500-b7cd-a25081f6bbb0","added_by":"auto","created_at":"2024-10-14 11:51:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":75282,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the training cohort (A) and the validation cohort (B).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/b73b3c13ce2fec511cbc5e7e.png"},{"id":94650084,"identity":"31e0d86b-a043-4a07-9193-69448bd1be0b","added_by":"auto","created_at":"2025-10-29 09:25:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1528872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/c0ad688d-1809-4be0-b98d-a401b1e3b353.pdf"},{"id":66571265,"identity":"2e1de961-c82a-440b-9420-385acf0617c9","added_by":"auto","created_at":"2024-10-14 11:51:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44139,"visible":true,"origin":"","legend":"","description":"","filename":"Validationgroupdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/a427c813b6a10bc94f705390.xlsx"},{"id":66572471,"identity":"7268a79d-1159-4c59-8c7d-25542a020758","added_by":"auto","created_at":"2024-10-14 11:59:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":87677,"visible":true,"origin":"","legend":"","description":"","filename":"Modelinggroupdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4868534/v1/72293744f211778b5d843d39.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The clinical value of the SII for predicting the development of urosepsis after percutaneous nephrolithotripsy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrolithiasis is the most common disease in urology, and occurs in approximately 11% of men and 7% of women.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eEpidemiological data from Europe and the United States indicate that the prevalence of urolithiasis ranges from 1 to 20%.\u003csup\u003e2\u003c/sup\u003eFurthermore, the recurrence rate of stones is remarkably high, with 50% occurring within 5\u0026ndash;10 years and 75% occurring within 20 years, significantly impacting the overall physical and mental well-being of the population.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003ePercutaneous nephrolithotomy (PCNL) is considered the gold standard surgical procedure for treating kidney stones, especially those larger than 2 cm or staghorn stones. It has a clearance rate of up to 95%.\u003csup\u003e4\u003c/sup\u003eHowever, none of these methods can completely prevent perioperative complications such as hemorrhage, organ and peripheral tissue injury, infection, and sepsis. The early detection of urosepsis after percutaneous nephrolithotomy (PCNL-US) is often challenging due to the lack of effective diagnostic markers and the insidious onset of the disease. It is often difficult to detect urinary sepsis early. Treatment can be life-threatening if left untreated or improperly treated. Several studies have indicated that urosepsis is the leading cause of perioperative death after PCNL. Urosepsis is the most frequent cause of perioperative mortality after PCNL. \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUrosepsis refers to sepsis caused by urinary tract infections. Urinary tract obstruction is a common cause of urosepsis, and accounts approximately 78% of cases. The remaining 22% of cases are attributed to urodynamic abnormalities resulting from urinary tract pathology. Common causes of obstruction include urolithiasis (43%), urologic tumors (18%), benign prostatic hyperplasia (25%), and abscess infection (6%).\u003csup\u003e6\u003c/sup\u003eUrosepsis also occurs after genitourinary tract interventions such as ureteroscopy, percutaneous nephrolithotripsy, or transrectal prostate biopsy. It has been reported that it occurs in approximately 17% of patients after urologic intervention.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003eThe incidence of undergoing PCNL-US ranged from 0.3\u0026ndash;4.7%. Approximately 40% of urosepsis cases progress to uremic shock, while 20\u0026ndash;40% of patients with uremic shock may die. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSeveral blood markers (calcitoninogen levels, the calcitoninogen/albumin ratio, C-reactive protein levels, the NLR, the PLR, the LMR, and IL-6) have been shown to provide clinicians with useful information.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003eHowever, the sensitivity and specificity of these tests are limited. It is still not possible to confirm the diagnosis solely based on changes in the indexes. Clinical diagnosis relies more on the physician's experience. Numerous studies have demonstrated that the systemic immune-inflammation index (SII) is closely related to tumor development.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eFewer studies have demonstrated that the SII has a greater predictive value for the development of SIRS in PCNL patients than other traditional inflammatory indicators.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eHowever, the relationship between the SII and urosepsis is unclear. To the best of our understanding, this study represents the inaugural examination of the diagnostic utility of the SII in forecasting PCNL-US outcomes.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Cohort\u003c/h2\u003e \u003cp\u003eWe retrospectively selected 639 patients diagnosed with kidney stones who underwent PCNL only at the Urology Department of Xiaogan Central Hospital from January 2019 to August 2022. The participants were divided into two groups according to the definition of urosepsis: the urosepsis group and the non-urosepsis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sepsis-3 diagnostic criteria were applied to diagnose ursepsis, which included the following: 1. Urinary tract infection accompanied by clinical symptoms, and 2. A Sequential Organ Failure Assessment (SOFA) score of \u0026ge;\u0026thinsp;2 points was given. The specific scores are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Sequential Organ Failure Assessment (SOFA) score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrgan Failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e /FiO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200, with respiratory support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100, with respiratory support\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlasgow coma scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCardiovascular system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean arterial pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDopamine, \u0026micro;g/kg/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDobutamine, \u0026micro;g/kg/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eany dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpinephrine \u0026micro;g/kg/min .\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorepinephrine, \u0026micro;g/kg/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilirubin, (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u0026ndash;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0-5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0-11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKidneys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine, (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u0026ndash;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0-3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5\u0026ndash;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrine volume, (ml/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatelets, 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe criteria for inclusion were established as follows: (1) patients diagnosed with upper urinary tract stones and clinical symptoms by computed tomography (CT), plain film, ultrasound, magnetic resonance imaging (MRI), etc. ; and (2) patients with stones who underwent percutaneous nephrolithotomy in our hospital were admitted according to the 2019 edition《the Chinese Guidelines for Diagnosis and Treatment of Urology and Andrology Diseases》.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows:(1) It is coupled with additional surgical techniques like ureteroscopic lithotripsy;(2) Patients with tumors, hematologic and immune system disorders;(3) Congenital malformations such as polycystic kidneys and horseshoe kidneys;(4) Preoperative urologic CT scan images could not be obtained;(5) Age\u0026thinsp;\u0026lt;\u0026thinsp;18 years;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data collection\u003c/h2\u003e \u003cp\u003eWe included the following data, including age, sex, BMI (body mass index), comorbidities (hypertension, diabetes mellitus), stone-related characteristics (CT value, single or multiple, surface area, staghorn stones), history of surgery, urinary nitrites, urine culture, preoperative fever, serum creatinine, urea nitrogen, hemoglobin,blood leucocytes, AGR (albumin-to-globulin ratio), NLR (neutrophil-to-lymphocyte ratio), LMR (lymphocyte-to-lymphocyte ratio),PLR (Platelet-to-Lymphocyte Ratio), and SII (SII\u0026thinsp;=\u0026thinsp;platelet\u0026times; neutrophil/ lymphocyte), bleeding, operative time, and residual stone,serum albumin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe baseline data were statistically analyzed using SPSS 23.0 software. The Kolmogorov‒Smirnov test was utilized to evaluate the normality of continuous variables. Variables that followed a normal distribution are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and independent sample t tests were used for comparisons between groups. Variables that were not normally distributed are presented as medians (P25, P75), and between-group comparisons were analyzed using the Mann‒Whitney U test. Unordered categorical variables are expressed as percentages, and comparisons were analyzed using the chi-square test or Fisher's exact test. Risk factor analyses were performed using multivariate logistic regression analysis in the R language (R 3.6.2, Institute of Statistics and Mathematics, Vienna, Austria). P values less than 0.05 were considered to indicate statistical significance. Based on the results of the logistic regression model and the combination of clinically significant variables, a column-line graph prediction model was constructed and validated using the RMS package of R software. The receiver operating characteristic (ROC) curve was calculated to determine the model's discriminatory ability. Calibration curves were plotted, and the Hosmer-Lemeshow test was performed to evaluate the model's goodness of fit. Additionally, clinical decision curve testing was used to evaluate the accuracy of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Characteristics\u003c/h2\u003e \u003cp\u003eThe modeling group of this study summarized the clinical data of 449 patients who underwent percutaneous nephrolithotomy, with 399 patients having non-urosepsis and 40 patients having urosepsis. The baseline data of the two groups were analyzed: female (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); diabetes mellitus (P\u0026thinsp;=\u0026thinsp;0.010); positive urine culture (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); positive urinary nitrite (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); preoperative fever (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); AGR (P\u0026thinsp;=\u0026thinsp;0.006), PLR (P\u0026thinsp;=\u0026thinsp;0.006); LMR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); blood urea nitrogen (P\u0026thinsp;=\u0026thinsp;0.046); hemoglobin (P\u0026thinsp;=\u0026thinsp;0.001); serum albumin (P\u0026thinsp;=\u0026thinsp;0.042); blood leukocytes (P\u0026thinsp;=\u0026thinsp;0.030); NLR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); residual stones (P\u0026thinsp;=\u0026thinsp;0.002); multiple stones (P\u0026thinsp;=\u0026thinsp;0.006); staghorn stones (P\u0026thinsp;=\u0026thinsp;0.002); age (P\u0026thinsp;=\u0026thinsp;0.879); BMI (P\u0026thinsp;=\u0026thinsp;0.838); history of previous ipsilateral surgery (P\u0026thinsp;=\u0026thinsp;0.245); hypertension (P\u0026thinsp;=\u0026thinsp;0.520); blood creatinine (P\u0026thinsp;=\u0026thinsp;0.417); CT value (P\u0026thinsp;=\u0026thinsp;0.274); surface area (P\u0026thinsp;=\u0026thinsp;0.436); operative time (P\u0026thinsp;=\u0026thinsp;0.858); and bleeding (P\u0026thinsp;=\u0026thinsp;0.110).Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the specific outcomes. No notable statistical variance was found in the indices observed among the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the specific outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographics and clinical characteristics of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-urosepsis n\u0026thinsp;=\u0026thinsp;399 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrosepsis n\u0026thinsp;=\u0026thinsp;40 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e290 (72.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 (27.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e362 (90.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (9.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344 (86.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (82.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55 (13.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (17.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e368 (92.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (7.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330 (82.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (17.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary nitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e334 (83.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (52.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (16.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (47.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e368 (92.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (7.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual calculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e331 (82.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (62.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (17.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (37.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple stones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e211 (52.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188 (47.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaghorn calculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367 (92.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (77.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (7.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.00 (38.00, 55.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.00(37.30, 56.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.09 (21.70, 26.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.97(21.10, 26.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.90(84.90, 135.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132.23 (102.90, 193.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.57 (3.60, 5.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.645 (2.10, 4.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.73 (1.50, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.550 (1.40, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.60 (4.70, 6.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.975 (4.30, 6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.00 (61.00, 81.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.00(54.40, 90.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.30(39.40, 45.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.70 (37.90, 44.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood leukocytes (10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.07 (5.20, 7.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.54 (5.40, 9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.71(1.30, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.56(1.80, 5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e420.90 (388.47, 453.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e908.56 (511.52, 1305.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149.00 (138.00, 160.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133.50 (122.00, 149.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT value (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1100.00 (882.00, 1266.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1174.00 (860.50, 1396.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface area (cm )\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13 (1.50, 3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.69 (1.40, 3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113.00 (94.00, 140.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.00(96.00, 138.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeding (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00 (5.00, 20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.00 (5.00, 68.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of basic characteristics between the validation group and the training group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModeling group n\u0026thinsp;=\u0026thinsp;439 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation group n\u0026thinsp;=\u0026thinsp;200 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306 (69.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (66.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (30.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e396 (90.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (91.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (85.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (82.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (14.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (17.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (91.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (95.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (8.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350 (79.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (77.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (20.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary nitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e355 (80.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (81.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (19.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (18.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e396 (90.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188 (94.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual calculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356 (81.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (18.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple stones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (50.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (56.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (49.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaghorn calculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398 (90.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187 (93.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (6.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (38, 55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (37, 56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.03 (21.70, 26.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.69(21.50, 25.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.20(85.50, 143.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.31 (85.30, 142.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.48 (3.50, 5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.70 (3.50, 5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72 (1.50, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69(1.50,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.50(4.60, 6.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.50 (4.80, 6.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.20 (60.80, 81.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.00 (61.30, 83.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.10 (39.30, 45.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.60(39.80, 45.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood leukocytes (10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.12 (5.20, 7.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.38 (5.10, 7.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77 (1.30, 2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82 (1.40, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e468.16 (424.10, 512.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e464.05 (418.71, 509.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148.00 (136.00, 159.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.00 (132.00, 162.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT value (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1100.00 (880.00, 1274.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1034.50 (856.00, 1247.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface area (cm)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16 (1.50, 3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13 (1.60, 2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.00 (96.00, 160.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.00 (90.00, 155.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeding (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 .00(20.00, 60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.00(18.00, 54.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Independent prognostic factors associated with urosepsis\u003c/h2\u003e \u003cp\u003eWe used the least absolute shrinkage and selection operator (LASSO) to determine the optimal penalty coefficient (0.04317417) for the outcome variable \"urosepsis\" after conducting 72 internal cross-validations. We identified 6 non-zero coefficient variables from a total of 26 variables, which included sex, positive urine culture, positive urine nitrite, preoperative fever, preoperative hemoglobin, and SII (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe above six screened factors were analyzed by multivariate logistic regression analysis, and the final results showed that positive urinary nitrite (OR\u0026thinsp;=\u0026thinsp;3.176, 95% CI: 1.390\u0026ndash;7.097, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), preoperative fever (OR\u0026thinsp;=\u0026thinsp;2.762, 95% CI: 1.021\u0026ndash;7.104, P\u0026thinsp;=\u0026thinsp;0.039), positive urine culture (OR\u0026thinsp;=\u0026thinsp;2.447, 95% CI: 1.077\u0026ndash;5.476, P\u0026thinsp;=\u0026thinsp;0.030), and the SII (OR\u0026thinsp;=\u0026thinsp;4.943, 95% CI\u0026thinsp;=\u0026thinsp;2.323\u0026ndash;10.776, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly different (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In addition, we constructed a forest plot of the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Logistic Regression Analysis for PCNL-US in Training Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eestimated value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003estandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003elower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003elimit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive urine culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive urinary nitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative hemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction and Assessment of the Nomogram Model\u003c/h2\u003e \u003cp\u003eBased on the logistic regression results, four risk factors, positive urinary nitrite, preoperative fever, positive urine culture, and SII, were ultimately used in the production of the column chart. R language is used as a visualization tool for risk factor graphs, and the total score is calculated based on whether the patient has the aforementioned risk factors. Then, the predicted value corresponding to the total score was subsequently used to determine the probability of PCNL-US (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For example, If a patient's urine nitrite is positive, positive urine culture, and a SII\u0026thinsp;\u0026gt;\u0026thinsp;492.85, and their total score is 240 points, the probability of postoperative occurrence of urosepsis in that patient is approximately 60%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe area under the ROC curve for our modeling group was 0.818 (95% CI: 0.739\u0026ndash;0.898) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The area under the ROC curve for the validation group was 0.794 (95% CI: 0.679\u0026ndash;0.909) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). We also performed ROC curves for four independent risk factors, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. The area under the AUC curve for positive urinary nitrites was 0.656 (95% CI: 0.558\u0026ndash;0.754), for preoperative fever was 0.611 (95% CI: 0.509\u0026ndash;0.713), and for positive urinary cultures was 0.664 (95% CI). The AUC curve area for SII was 0.727 (95% CI: 0.638\u0026ndash;0.817). It can be seen that the area under the curve for SII is larger than that of the remaining three factors. See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC area for different risk factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTest Result Variable(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAsymptotic Sig.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAsymptotic 95% Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive urine culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive urine nitrites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe used the bootstrap method to sample 1000 times in order to validate the column line plots of our data. The results of the Hosmer-Lemeshow goodness-of-fit test (modeling group: P\u0026thinsp;=\u0026thinsp;0.4934\u0026thinsp;\u0026gt;\u0026thinsp;0.05, validation group: P\u0026thinsp;=\u0026thinsp;0.0741\u0026thinsp;\u0026gt;\u0026thinsp;0.05) indicated a better fit. The calibration curves of the line plots for both groups closely aligned with the actual calibration curve and are generally consistent. This further indicates that the model has objective accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhether in the modeling group or the validation group, the decision curve is higher than the two extreme values, indicating a greater net return and a wider high-risk threshold. Therefore, the clinical prediction model constructed in this study has certain clinical application value and reference significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSince the introduction of PCNL in 1976, the safety and efficacy of this minimally invasive technique have significantly improved. As a result, PCNL has become the preferred treatment for complex and large stones. However, One of the most serious consequences is still post-PCNL infection.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Research indicates that the occurrence of infectious complications following PCNL surgery varies between 2.8% and 32.1%.Urosepsis is a potentially serious complication, with the pathogenesis of which ranges from infection to systemic inflammatory response syndrome, followed by progression to sepsis. However, due to the unique pathogenesis of urosepsis, it is often difficult to detect this disease early in the clinic. Delays in the diagnosis and treatment of sepsis increase mortality, prolong hospitalization, and increase costs.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eNonetheless, the negative effects can be lessened through prompt identification and management of sepsis.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003eAs a result, it is critical to identify risk factors for urosepsis early in order to avoid serious postoperative complications.\u003c/p\u003e \u003cp\u003eThe occurrence of PCNL-US has been found to be associated with a variety of factors, including positive urine cultures, positive urinary nitrites, urinary leukocytes, blood leukocytes, staghorn stones, stone loading, duration of surgery, bleeding, diabetes mellitus, and sex. Regarding the risk factors mentioned above, there is no consensus on the concept of a positive urine culture. Preoperative urine cultures are routinely performed prior to PCNL to assess the risk of infection and sepsis. Teh, K. Y\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e demonstrated that Patients exhibiting positive preoperative urine cultures demonstrated an almost fourfold increased likelihood of developing post-percutaneous nephrolithotomy (PCNL) sepsis compared to those with negative cultures, with incidence rates of 8.41% and 2.2%, respectively. However, several studies have found that a negative bladder urine culture does not necessarily indicate that there are no bacteria present in the stone or renal pelvis. This may be due to underlying urinary stone obstruction. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003eSeveral studies have shown pelvic urinary infections in up to one-third of patients with negative bladder urine cultures, half of whom had positive stone cultures.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eEswara. et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003ereported that urosepsis occurred in 3% (11/328) of patients, 8 of whom had a positive stone culture (SC). However, none of the patients had a positive preoperative mid-stream urine culture. The authors suggested that preoperative midstream urine culture results did not directly correlate with pyelocentesis urine results or stone culture results. The reason for this discrepancy was that the uropathogens detected in mid-stream urine were not consistent with those found in the renal pelvis or in stones. Specifically, Staphylococcus aureus was predominant in stone cultures, while Escherichia coli was predominant in urine cultures.A total of 14 studies involving 3,540 patients were analyzed via meta-analysis. The findings of these studies concluded that renal pelvic urine cultures or stone cultures were more reliable in predicting postoperative systemic inflammatory response syndrome (SIRS) and urosepsis. Additionally, these cultures were found to be effective in identifying causative microorganisms and guiding antibiotic therapy in patients with PCNL, as compared to preoperative mid-stream urine cultures\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.The guidelines from the American Urological Association (AUA) and the European Association of Urology (EAU) for patients with positive urine cultures do not provide recommendations for the duration of perioperative antibiotics. However, they strongly recommend collecting stone samples after lithotripsy to guide the selection of postoperative antibiotics.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003eRecent studies have shown that patients with Positive urine cultures should be managed with suitable antibiotics for a minimum duration of seven days prior to surgical intervention.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003eSimilarly, the Chinese Urological Association (CUA) guidelines recommend a 1–2 week course of antibiotics for patients with positive urine cultures. A cross-sectional survey conducted by Zhang et al. examined the use of antibiotics for PCNL in China. The study found that urologists in China commonly used cephalosporins as the primary antibiotic prior to PCNL, followed by quinolones.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003eMany studies from various regions of China, however, have shown that the majority of uropathogens isolated from urine or stones of patients with urinary tract stones exhibit high resistance to cephalosporins (such as cefuroxime and ceftriaxone) and quinolones (such as ciprofloxacin and levofloxacin). Therefore, antibiotics should be selected rationally based on the local bacterial spectrum and drug sensitivity. Therefore, stone culture and renal pelvic urine culture should be routinely tested as much as possible in actual clinical practice. This will further enhance the ability to predict and prevent the occurrence of urosepsis.\u003c/p\u003e \u003cp\u003eUrinary nitrites are formed through bacterial reduction reactions of nitrates in the urine. Because urinary tract infections caused by gram-negative bacteria can be diagnosed quickly but indirectly by testing for urinary nitrites, a positive test for urinary nitrites indicates the presence of gram-negative bacteria in the urethra, especially Escherichia coli.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003eThe more virulent the Gram-negative bacteria, the more likely they are to be present. The more virulent Gram-negative bacteria are more active at higher urate concentrations, which often indicates a more severe infection in the patient.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003eGu et al. showed that patients with preoperative positive urinary nitrite had up to 3.33 times higher risk of urosepsis compared to patients with negative urinary nitrite.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003eOur study also suggests a higher risk of positive urinary nitrite levels. Urinary nitrite has a high specificity (98%) but low sensitivity (23%-43%) due to factors such as urination within 4 hours, low dietary nitrate intake, and urine dilution, which may result in false negatives.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003eFor these reasons, several studies have combined levels with leukocyte and leukocyte esterase levels to increase the diagnostic precision of urinary tract infections.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003eThe findings of these studies are summarized in the table below. Other researchers have found that the combination of urinary leukocytes and urinary nitrites has a sensitivity of 92% and a specificity of 98% when compared to the combination of urine and stone cultures. This combination is superior for early prediction of PCNL-US. The reason for this is that urine bacterial cultures are time-consuming and can also be affected by sample contamination. Because normal urine tests are less expensive and time-consuming, clinical urologists prefer to use them as a diagnostic tool in their daily practice.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e In a recent study, a urine nitrite-based model showed a greater net clinical benefit than a urine culture-based model for post-PCNL infections.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eThis is similar to the findings of our study.\u003c/p\u003e \u003cp\u003eMost studies have focused on the factors associated with postoperative fever after PCNL, and there are fewer reports on whether preoperative fever affects the occurrence of PCNL-US. We defined preoperative fever as a body temperature above 38°C. Preoperative fever also implies the presence of underlying urinary tract infections (UTI), most of which are caused by Escherichia coli.Bacterial adherence within the urinary tract plays a significant role in both colonization and invasion, as well as in the formation of biofilms and the damage to host cells.Biofilms can contribute to the persistence of urothelial and biomaterial surfaces by protecting bacteria from hydrodynamic scavenging, as well as host defense mechanisms and the killing activity of antibiotics. The persistence of biofilms leads to persistent UTIs and, consequently, stone formation.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003eSome studies have shown that preoperative UTI can lead to a decline in renal function one month after percutaneous nephrolithotomy in patients with single-kidney staghorn stones.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003eAdditionally, a history of recurrent UTI is an independent risk factor for postoperative SIRS after PCNL.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003eYang et al discovered a strong association between preoperative stone fever and unfavorable postoperative outcomes in patients with complex upper urinary tract kidney stones, emphasizing the importance of adequate preoperative treatment.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003eIn addition, an analysis of a predictive model for infectious stones showed that preoperative fever was a significant predictor of infectious stone formation. Patients with preoperative fever were 2.37 times more likely than patients without fever to have infectious stones. Therefore, patients with preoperative fever should be identified and treated before surgery.\u003c/p\u003e \u003cp\u003eThe SII is a multi-marker index that objectively reflects the balance between inflammation and immunity in patients with malignant tumors. It can be used as a prognostic indicator in cancer research.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003eStudies have reported that elevated SII levels are associated with a poorer prognosis and higher mortality in patients with cardiovascular disease.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e It has also been documented that SII is a marker for chronic obstructive pulmonary disease.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003eAlthough LMR, NLR, and PLR were not strongly correlated with urosepsis in this study, SII was strongly correlated. The exact mechanism needs to be further explored, but this is first to study the relationship between SII and urosepsis.\u003c/p\u003e \u003cp\u003eThis study also has some limitations. First, single-center studies can bias the results and their value. Therefore, further validation is needed to determine whether systemic inflammatory markers have clinical value, particularly through multi-center studies with large sample sizes.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eOur study suggested that positive urinary nitrite, preoperative fever, and positive urine culture are risk factors for PCNL-US. Additionally, a high preoperative SII level serves as an independent risk factor for the development of urosepsis.A clinical prediction model constructed based on these four risk factors may serve as a reference for preventing the occurrence of PCNL-US.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Ethics Committee of Xiaogan Central Hospital Affiliated to Wuhan University of Science and Technology. Informed written consent was obtained from all individual participants included in the study. Details that disclose the identity of the subjects under study were omitted.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eNo competing financial interests exist.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study received no funds from any sources.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWH had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: LFY. Acquisition of data:WH . Analysis and interpretation of data: WH. Drafting of the manuscript: WH. Critical revision of the manuscript for important intellectual content:LFY. Supervision: LFY. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable for that section.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e The data and materials can be obtained by contacting the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eR.C. WangManaging Urolithiasis. Ann Emerg Med, 2016. 67(4): p. 449\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathan SA, Mitra B, Straney LD, et al. Delivering safe and effective analgesia for management of renal colic in the emergency department: a double-blind, multigroup, randomised controlled trial. Lancet. 2016;387(10032):1999\u0026ndash;2007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez Martinez JM, Sierra Del Rio A. Luque GalvezMedical treatment of renal stones. Arch Esp Urol. 2021;74(1):63\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreminger GM, Assimos DG, Lingeman JE, et al. Chapter 1: AUA guideline on management of staghorn calculi: diagnosis and treatment recommendations. J Urol. 2005;173(6):1991\u0026ndash;2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmitage JN, Irving SO, Burgess NA, et al. Percutaneous nephrolithotomy in the United kingdom: results of a prospective data registry. Eur Urol. 2012;61(6):1188\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDreger NM, Degener S, Ahmad-Nejad P et al. Urosepsis\u0026ndash;Etiology, Diagnosis, and Treatment. Dtsch Arztebl Int, 2015. 112(49): p. 837\u0026thinsp;\u0026ndash;\u0026thinsp;47; quiz 848.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagenlehner FM, Pilatz A. W. WeidnerUrosepsis\u0026ndash;from the view of the urologist. Int J Antimicrob Agents, 2011. 38 Suppl: pp. 51\u0026thinsp;\u0026ndash;\u0026thinsp;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurk C, Petrik A, Sarica K, et al. EAU Guidelines on Interventional Treatment for Urolithiasis. Eur Urol. 2016;69(3):475\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuliciuc M, Maier AC, Maier IM et al. Urosepsis-A Literature Rev Med (Kaunas), 2021. 57(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNost TH, Alcala K, Urbarova I, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol. 2021;36(8):841\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Lin SQ, Liu XY, et al. Association between C-reactive protein-albumin-lymphocyte (CALLY) index and overall survival in patients with colorectal cancer: From the investigation on nutrition status and clinical outcome of common cancers study. Front Immunol. 2023;14:1131496.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng C, Li J, Xu G, et al. Significance of preoperative systemic immune-inflammation (SII) in predicting postoperative systemic inflammatory response syndrome after percutaneous nephrolithotomy. Urolithiasis. 2021;49(6):513\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKreydin EI. EisnerRisk factors for sepsis after percutaneous renal stone surgery. Nat Rev Urol. 2013;10(10):598\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarnacho-Montero J, Ortiz-Leyba C, Herrera-Melero I, et al. Mortality and morbidity attributable to inadequate empirical antimicrobial therapy in patients admitted to the ICU with sepsis: a matched cohort study. J Antimicrob Chemother. 2008;61(2):436\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM.W. MoyerNew biomarkers sought for improving sepsis management and care. Nat Med. 2012;18(7):999.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeh KY. ThamPredictors of post-percutaneous nephrolithotomy sepsis: The Northern Malaysian experience. Urol Ann. 2021;13(2):156\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMariappan P, Smith G, Bariol SV, et al. Stone and pelvic urine culture and sensitivity are better than bladder urine as predictors of urosepsis following percutaneous nephrolithotomy: a prospective clinical study. J Urol. 2005;173(5):1610\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorets R, Graversen JA, Kates M, et al. Post-percutaneous nephrolithotomy systemic inflammatory response: a prospective analysis of preoperative urine, renal pelvic urine and stone cultures. J Urol. 2011;186(5):1899\u0026ndash;903.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEswara JR, Shariftabrizi A. D. SaccoPositive stone culture is associated with a higher rate of sepsis after endourological procedures. Urolithiasis. 2013;41(5):411\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu M, Chen J, Gao M, et al. Preoperative Midstream Urine Cultures vs Renal Pelvic Urine Culture or Stone Culture in Predicting Systemic Inflammatory Response Syndrome and Urosepsis After Percutaneous Nephrolithotomy: A Systematic Review and Meta-Analysis. J Endourol. 2021;35(10):1467\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf JS Jr., Bennett CJ, Dmochowski RR, et al. Best practice policy statement on urologic surgery antimicrobial prophylaxis. J Urol. 2008;179(4):1379\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu P, Zhang S, Zhang Y, et al. Preoperative antibiotic therapy exceeding 7 days can minimize infectious complications after percutaneous nephrolithotomy in patients with positive urine culture. World J Urol. 2022;40(1):193\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng T, Chen D, Wu W, et al. Optimal perioperative antibiotic strategy for kidney stone patients treated with percutaneous nephrolithotomy. Int J Infect Dis. 2020;97:162\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang S, Li G, Qiao L, et al. The antibiotic strategies during percutaneous nephrolithotomy in China revealed the gap between the reality and the urological guidelines. BMC Urol. 2022;22(1):136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang E, Guo H, Yang B, et al. Predicting and comparing postoperative infections in different stratification following PCNL based on nomograms. Sci Rep. 2020;10(1):11337.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmier Y, Zhang Y, Zhang J, et al. Analysis of Preoperative Risk Factors for Postoperative Urosepsis After Mini-Percutaneous Nephrolithotomy in Patients with Large Kidney Stones. J Endourol. 2022;36(3):292\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu J, Liu J, Hong Y, et al. Nomogram for predicting risk factor of urosepsis in patients with diabetes after percutaneous nephrolithotomy. BMC Anesthesiol. 2022;22(1):87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePallin DJ, Ronan C, Montazeri K, et al. Urinalysis in acute care of adults: pitfalls in testing and interpreting results. Open Forum Infect Dis. 2014;1(1):ofu019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Cui Y, Zeng H, et al. The evaluation of early predictive factors for urosepsis in patients with negative preoperative urine culture following mini-percutaneous nephrolithotomy. World J Urol. 2020;38(10):2629\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen D, Jiang C, Liang X, et al. Early and rapid prediction of postoperative infections following percutaneous nephrolithotomy in patients with complex kidney stones. BJU Int. 2019;123(6):1041\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan S, Chen Z, Zhu Z, et al. Value of preoperative urine white blood cell and nitrite in predicting postoperative infection following percutaneous nephrolithotomy: a meta-analysis. Transl Androl Urol. 2021;10(1):195\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaheswari UB, Palvai S, Anuradha PR, et al. Hemagglutination and biofilm formation as virulence markers of uropathogenic Escherichia coli in acute urinary tract infections and urolithiasis. Indian J Urol. 2013;29(4):277\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Bai Y, Yin S, et al. Risk factors for deterioration of renal function after percutaneous nephrolithotomy in solitary kidney patients with staghorn calculi. Transl Androl Urol. 2020;9(5):2022\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkkas F, Karadag S. A. HaciislamogluDoes the duration between urine culture and percutaneous nephrolithotomy affect the rate of systemic inflammatory response syndrome postoperatively? Urolithiasis. 2021;49(5):451\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Huang Y, Li Y et al. Efficacy of Flexible Ureteroscopic Lithotripsy and Percutaneous Nephrolithotomy in the Treatment of Complex Upper Urinary Tract Nephrolithiasis. Comput Math Methods Med, 2022. 2022: p. 2378113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen JH, Zhai ET, Yuan YJ, et al. Systemic immune-inflammation index for predicting prognosis of colorectal cancer. World J Gastroenterol. 2017;23(34):6261\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFest J, Ruiter R, Mulder M, et al. The systemic immune-inflammation index is associated with an increased risk of incident cancer-A population-based cohort study. Int J Cancer. 2020;146(3):692\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe C, Yuan L, Wu K, et al. Association between systemic immune-inflammation index and chronic obstructive pulmonary disease: a population-based study. BMC Pulm Med. 2023;23(1):295.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Percutaneous nephrolithotripsy, Risk factors, SII, Urosepsis","lastPublishedDoi":"10.21203/rs.3.rs-4868534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4868534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eBased on accumulating evidence, biomarkers related to the inflammatory response are closely associated with tumors. However, there are fewer studies related to urosepsis. The aim of this research was to investigate the importance of the SII as a predictor of the development of urosepsis after percutaneous nephrolithotripsy, utilizing a retrospective research design.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThis study encompassed a cohort of 639 individuals diagnosed with kidney stones between January 2019 and August 2022. The patients were categorized into a modeling group consisting of 439 individuals and a validation group comprising 200 individuals, following a ratio of 7:3. R software was used to perform multivariate logistic regression analysis after screening with LASSO regression. The risk line graph model, ROC curve, calibration curve, and decision curve of the modeling group were drawn and visualized using R statistical software. These findings were also drawn and verified in the validation cohort.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn a cohort of 439 patients, the prevalence of urosepsis was found to be 9.11% (40/439). Subsequently, a multivariate logistic regression analysis was conducted following a screening process utilizing LASSO regression. Our results suggested four risk factors for PCNL-US, namely, positive urinary nitrite (OR\u0026thinsp;=\u0026thinsp;3.176, 95%CI: 1.390\u0026ndash;7.097, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), preoperative fever (OR\u0026thinsp;=\u0026thinsp;2.762, 95%CI: 1.021\u0026ndash;7.104, P\u0026thinsp;=\u0026thinsp;0.039), positive urine culture (OR\u0026thinsp;=\u0026thinsp;2.447, 95%CI: 1.077\u0026ndash;5.476, P\u0026thinsp;=\u0026thinsp;0.030), and high preoperative SII (OR\u0026thinsp;=\u0026thinsp;4.943, 95%CI: 2.323\u0026ndash;10.776, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). According to above four factors, we constructed a column-line graph prediction model of risk factors for PCNL-US. The area under the ROC curve (AUC) of the modeling group was 0.818 (95% CI: 0.739\u0026ndash;0.898). The area under the ROC curve (AUC) of the validation group was 0.794 (95% CI: 0.679\u0026ndash;0.909). The Hosmer-Lemeshow test was greater than 0.05 in both groups, indicating a good calibration curve and good clinical decision-making performance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study suggested that positive urinary nitrite levels, preoperative fever, and positive urine culture are risk factors for PCNL-US. Additionally, a high preoperative SII level is recognized as a separate risk factor for the occurrence of urosepsis. The clinical prediction model constructed based on these four risk factors may serve as a reference for preventing the occurrence of PCNL-US.\u003c/p\u003e","manuscriptTitle":"The clinical value of the SII for predicting the development of urosepsis after percutaneous nephrolithotripsy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 11:51:09","doi":"10.21203/rs.3.rs-4868534/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe568b7c-ed33-49f8-baa4-e4ddeae21f93","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T09:24:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 11:51:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4868534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4868534","identity":"rs-4868534","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}