Objective To explore the risk factors of complicating urogenic sepsis after percutaneous nephrolithotripsy(PCNL)and construct a nomogram prediction model.Methods The data of 291 patients with stage 1 PCNL in 940 Hospital of Joint Logistics Support Force from October 2016 to October 2021 were retrospectively analyzed.The patients were divided into the sepsis group and non-sepsis group according to whether complicating urogenic sepsis after operation.The general data,stone-related data,operation-related data and laboratory detection related data were included.The independent risk factors were screened by the univariate and multivariate logistic regression analysis,and the nomogram prediction model was constructed.Results The results of univariate and multivariate logistic regression analysis showed that age≥60 years old(OR=6.438,95％CI:1.548-26.769),urinary leukocyte 3+(OR=5.651,95％CI:1.614-31.766),urinary nitrite positive(OR=7.117,95％CI:1.190-42.561),operation time≥90 min(OR=4.626,95％CI:1.137-18.817)and perfusion volume 30 L(OR=3.312,95％CI:1.090-10.061)were the independent risk factors of postoperative complicating urogenic sepsis.C-index of the constructed nomogram prediction model in the modeling samples was 0.937,the calibrated C-index was 0.914,and the model predictive efficien-cy was good.Conclusion Age ≥60 years old,urinary leukocyte 3+,urinary nitrite positive,operation time 90 min and perfusion volume ≥30 L are the independent risk factors for complicating urogenic sepsis after PCNL;the constructed nomogram prediction model has a good predictive efficiency for the occurrence of post-operative urogenic sepsis.

Objective:To explore the changes and clinical significance of inflammatory indices of urogenic sepsis with different severity.Methods:A retrospective case-control study was used to analyze the clinical data of 71 patients with urogenic sepsis admitted to 940th Hospital of PLA Joint Logistics Support Force from January 2010 to April 2018, including 34 males and 37 females, aged 39-96 years [(63.1±18.3)years]. The patients were divided into three groups according to the clinical diagnostic criteria for septic shock and sepsis according to the 2014 edition of the Chinese Urology Surgical Guidelines for Diagnosis and Treatment: 21 cases in sepsis group [sequential organ failure assessment (SOFA) score of 3.0 (2.0, 3.0)points], 21 cases in severe sepsis group [SOFA score of 9.0 (6.0, 11.0)points], and 29 cases in septic shock group [SOFA score of 15.0 (14.0, 16.0)points]. Spearman correlation analysis was used to analyze the correlation of inflammatory indicators with SOFA, including white blood cell count, percentage of neutrophils, C-reactive protein, interleukin-6, procalcitonin, fibrinogen, D-dimer, and platelet. Multiple linear regression analysis and stepwise regression weighted analysis were performed to analyze the relation between inflammatory indicators and sepsis severity. Levels of each inflammatory indicator was detected and compared among the groups.Results:① Spearman correlation analysis: percentage of neutrophils, D-dimer, interleukin-6, procalcitonin and SOFA scores were significantly positively correlated, with the r s value of 0.738, 0.712, 0.31, 0.795, respectively ( P<0.01); platelet and SOFA scores were significantly negatively correlated, with the r s value of -0.661 ( P<0.01). ② Multiple linear regression analysis: percentage of neutrophils, platelet, D-dimer, procalcitonin and SOFA score were significantly correlated ( P<0.01); Stepwise regression weighted analysis suggested that the model linear relationship and fit was good. ③ Inflammatory index comparison: percentage of neutrophils in sepsis group, severe sepsis group and septic shock group was 82.30 (76.25, 88.45), 90.50 (86.55, 93.85), 95.10 (92.05, 97.95), respectively; level of platelet was 183.01 (144.50, 246.50)×10 9/L, 149.11 (81.04, 207.00)×10 9/L, 81.26 (50.01, 93.50)×10 9/L, respectively; level of D-dimer was 0.98 (0.71, 1.74)mg/L, 3.45 (1.79, 5.56)mg/L, 7.19 (4.26, 11.63)mg/L, respectively; level of procalcitonin was 0.55 (0.21, 1.09)ng/ml, 5.45 (3.74, 11.80)ng/ml, 17.68 (13.97, 26.75)ng/ml, respectively. There were significant differences in above indicators among the groups ( P<0.05). Conclusions:The serum levels of procalcitonin, percentage of neutrophils, D-dimer and platelet are positively correlated with the severity of urogenic sepsis. While combined detection of those indicators can better predict the severity of the sepsis.

OBJECTIVE: To analyze the association of the clinical inflammatory indices with the severity of urinary sepsis. METHODS: We reviewed the clinical data of 70 patients with urinary sepsis treated in our hospital between January, 2013 and April, 2018. All the patients were diagnosed in line with the Guidelines for Diagnosis and Treatment of Urological Diseases in China (2014 edition), including 22 patients with sepsis, 12 with hypotension and severe sepsis, 17 with septic shock, and 19 with critical septic shock. White blood cell count (WBC), neutrophil percentage (N%), platelets (PLT), fibrinogen (FIB), Ddimer, interleukin-6 (IL-6), procalcitonin (PCT) and C-reactive protein (CRP) were examined in all the cases and compared among the 4 groups. The correlations of these inflammatory markers with the severity of sepsis were analyzed using logistic regression analysis. RESULTS: The 4 groups of patients showed significant differences in N%, PLT, D-dimer, and PCT ( < 0.05) but not in CRP (>0.05). Kruskal-Wallis Pairwise comparisons showed that the N% and PCT in patients with sepsis differed significantly from those in the other 3 groups; platelets in patients with sepsis differed significantly from those in patients with septic shock and critical septic shock; D-dimer differed significantly between patients with sepsis and those with septic shock. Among the 4 groups, the median levels of PLT decreased and PCT and N% increased with the worsening of sepsis. Logistic regression analysis indicated that PCT (=0.186, =0.000), N% (=0.047, =0.035) and PLT (=-0.012, =0.003) were significantly correlated with the severity of sepsis in these patients. CONCLUSIONS PCT, PLT and N% are all significantly correlated with the severity of sepsis, and their combined detection can be informative for assessing the severity of sepsis to facilitate clinical decisions on treatment.
Biomarkers ; blood ; C-Reactive Protein ; analysis ; China ; Fibrin Fibrinogen Degradation Products ; analysis ; Fibrinogen ; analysis ; Humans ; Interleukin-6 ; blood ; Leukocyte Count ; Platelet Count ; Procalcitonin ; blood ; Sepsis ; blood ; diagnosis ; Severity of Illness Index ; Shock, Septic ; blood ; diagnosis ; Statistics, Nonparametric ; Urinary Tract Infections ; diagnosis

Objective    To analyze the risk factors for esophagogastric anastomotic leakage (EGAL) after esophageal cancer surgery, and to establish a risk prediction model for early prevention and treatment. Methods    Clinical data of patients undergoing esophagectomy in our hospital from January 2013 to October 2020 were retrospectively analyzed. The independent risk factors for postoperative EGAL were analyzed by univariate and multivariate logistic regression analyses, and a clinical nomogram prediction model was established. According to whether EGAL occurred after operation, the patients were divided into an anastomotic fistula group and a non-anastomotic fistula group. Results    A total of 303 patiens were enrolled, including 267 males and 36 females with a mean age of 62.30±7.36 years. The incidence rate of postoperative EGAL was 15.2% (46/303). The multivariate logistic regression analysis showed that high blood pressure, chronic bronchitis, peptic ulcer, operation way, the number of lymph node dissected, anastomotic way, the number of intraoperative chest drainage tube, tumor location, no-supplementing albumin in the first three days after operation, postoperative pulmonary infection, postoperative use of bronchoscope were the independent risk factors for EGAL after esophageal cancer surgery (P<0.05). A prognostic nomogram model was established based on these factors with the area under the receiver operating characteristic curve of 0.954 (95%CI 0.924-0.975), indicating a high predictive  value. Conclusion    The clinical prediction model based on 11 perioperative risk factors in the study has a good evaluation efficacy and can promote the early detection, diagnosis and treatment of EGAL.