Construction of Predictive Model of Nomogram in Patients with Idiopathic Membranous Nephropathy
Objective To establish a predictive model for predicting the prognosis of patients with idiopathic membranous nephropathy(IMN)by Nomogram diagram.Methods The newly diagnosed patients with IMN diagnosed by renal biopsy in The Affiliated Hospital of Chengde Medical University from January 2018 to December 2020 were selected and followed up for 24 months.The patients were divided into two groups according to whether the patients had renal endpoint events at the end of follow-up.Patients who did not have terminal events at the end of follow-up were enrolled in the group that did not reach the renal endpoint.The influencing factors with P<0.2 in univariate Logistic analysis were analyzed by multivariate Logistic regression analysis,and the optimal Logistic regression model was selected according to Akaike information criterion(AIC)to construct the predictive model of poor prognosis of IMN patients.The discrimination ability of the model was verified and evaluated by the area under the receiver operating characteristic(AUROC),and the calibration curve and decision curve analysis(DCA)were drawn to evaluate the calibration,clinical net income and practicability of the model.Results The predictive factors used to predict the poor prognosis of IMN include age,mean arterial pressure(MAP),duration of disease before renal puncture,albumin and serum creatinine.The AUROC of patients with IMN was 0.729.The statistical value of the Hosmer-Lemeshow test of the calibration curve is 1.44(P=0.49).DCA curve shows that this model is clinically applicable when the predictive probability of IMN is between 0.17 and 0.44.Conclusion In this study,a predictive model for predicting the poor prognosis of IMN patients was constructed.The predictive model has good predictive ability,calibration ability,and clinical net benefit,it can be applied to predict the prognosis of IMN patients.
idiopathic membranous nephropathyrenal end pointprognosisnomograms
万方数据
维普
