Objective:To investigate the correlation between serum C3 and glomerular microthrombosis in patients with lupus nephritis (LN).Methods:Patients who were diagnosed as LN by renal biopsy hospitalized in Department of Nephrology, the First Affiliated Hospital of Shenzhen University from January 2010 to February 2019 were retrospectively analyzed and they were divided into glomerular microthrombosis group (GMT group) and non-glomerular microthrombosis group (non-GMT group). The demographic data, clinical characteristics, pathology and prognosis of the two groups were compared. Logistic regression and smooth curve fitting of generalized additive mixed model analysis were used to explore the correlation between serum C3 and glomerular microthrombosis. Renal prognosis of the two groups were compared by the Kaplan-Meier survival curve.Results:A total of 116 patients were enrolled, aged (32.79±11.43) years old, in which 108 cases (93.10%) were female. Thirty-seven patients (31.90%) were confirmed to be combined with GMT (GMT group) and 79 cases were not (non-GMT group). Compared with the non-GMT group, patients in the GMT group were relatively older ( t=-2.876, P=0.002), with higher proportion of hypertension ( χ2=7.492, P=0.006), higher urine protein quantitation ( Z=-2.115, P=0.003), lower levels of eGFR and serum complement C3 ( Z=3.469, P<0.001; t=1.744, P<0.001), higher systemic lupus erythematosus disease activity index ( t=-2.758, P=0.007). As to the pathological characteristics, type IV LN patients were the majority (72.97%). Proportion of crescents and pathological activity indicators of the GMT group were higher ( Z=-1.866, P=0.002; t=-5.005, P<0.001), nuclear fragmentation, endothelial hyperplasia and renal tubular atrophy were more serious ( χ2=14.987, P<0.001; χ2=15.695, P<0.001; χ2=4.130, P=0.042). Multivariate logistic regression analysis indicated that serum complement C3 was a relational factor of the formation of GMT in LN patients ( OR=0.966, 95% CI 0.938-0.995, P=0.023). Smooth curve fitting of generalized additive mixed model analysis indicated that level of complement C3 had a linear relationship with the changing trend of GMT. The Kaplan-Meier curve showed that there were statistical differences between the two groups in terms of complete remission of urine protein (Log-rank χ2=5.858, P=0.016) and doubled serum creatinine/end-stage renal disease (Log-rank χ2=3.945, P=0.047). Conclusions:Serum C3 is closely related to the formation of GMT in LN patients, and statistical differences were demonstrated in the renal prognosis of GMT group and non-GMT group.

Objective:To establish a diagnostic model for glomerular micro thrombosis (GMT) in lupus nephritis through clinical indicators.Methods:A continuous collection of patients diagnosed with lupus nephritis (LN) by renal biopsy in the Department of Nephrology, Shenzhen Second People's Hospital, from January 2010 to March 2021. All patients were admitted and discharged through the inclusion and exclusion criteria. Demographic data, clinical characteristics, biochemical indicators, and immune indicators were collected. A GMT diagnosis model was established from the most important variables among the abovementioned variables through machine learning and Logistic stepwise regression analysis. The model was presented through a nomogram. The receiver operating characteristic curve (ROC), the clinical decision curve and the calibration curve were used to evaluate the model discrimination, clinical use and accuracy, respectively. The internal verification of the model was carried out by repeated sampling 500 times by the Bootstrap method.Results:There were a total of 129 patients with lupus nephritis including the study, including 117 females (90.7%); the average age was (34±11) years. There were 39 patients with GMT (30.2%). Using machine learning to screen out the top 10 important variables from 47 candidate variables, then through logistic stepwise regression analysis, five variables were further screened to establish the diagnostic model of GMT, namely hemoglobin [ OR(95% CI)=0.966(0.943, 0.990), P=0.005], serum C3 [ OR(95% CI)=0.133(0.022, 0.819), P=0.030], systolic blood pressure [ OR(95% CI)=1.027(1.005, 1.049), P=0.017], lymphocyte count [ OR(95% CI)=0.462(0.213, 0.999), P=0.049], and TT [ OR(95% CI)=1.260(0.993, 1.597), P=0.057]. Draw up the equation of the GMT diagnosis model of lupus nephritis and establish a nomogram to present the model. The area under curve (AUC) of the diagnostic model was 0.823, 95% CI(0.753, 0.893), indicating that the model had a reasonable degree of discrimin-ation. The Hosmer-Lemeshow test showed a perfect fit between the predicted GMT risk and the observed GMT risk ( χ2= 14.62, P=0.067). The clinical decision curve and clinical impact curve reflect that the model had a good clinical application value, especially when the threshold probability is between 0.4 and 0.6, the application value is more significant. In addition, after 500 repeated samplings by the Bootstrap method, the average AUC was 0.825, 95% CI(0.753, 0.893), which is basically the same as the AUC obtained by the original model. Conclusion:We established a diagnostic model of GMT inLN based on clinical indicators through machine learning and logistic stepwise regression analysis. It is used for early diagnosis of the risk of GMT before the renal biopsy.