Establishment of a diagnostic model for glomerular micro thrombosis in patients with lupus nephritis based on machine learning
10.3760/cma.j.cn141217-20210906-00359
- VernacularTitle:基于机器学习的狼疮肾炎患者肾小球微血栓形成诊断模型的建立
- Author:
Haofei HU
1
;
Ricong XU
;
Yang LIU
;
Jianyu CHEN
;
Zheyi CHANG
;
Qijun WAN
Author Information
1. 广东省深圳市第二人民医院 深圳大学第一附属医院肾内科，深圳 518035
- Keywords:
Lupus nephritis;
Kidney glomerulus;
Thrombosis;
Machine learning;
Logistic step regression
- From:
Chinese Journal of Rheumatology
2022;26(11):721-729,C11-1
- CountryChina
- Language:Chinese
-
Abstract:
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.
