Construction of a Nomogram Discriminating Malignancy-Associated Membranous Nephropathy From Idiopathic Membranous Nephropathy: A Retrospective Study

BackgroundBased on the etiology, membranous nephropathy (MN) can be categorized into idiopathic membranous nephropathy (IMN) and secondary membranous nephropathy. Malignancy-associated membranous nephropathy (MMN) is a common type of secondary MN. Its incidence is only second to that of lupus nephritis. As the treatment and prognosis of MMN differ significantly from those of other MNs, the identification of MMN is crucial for clinical practice. The purpose of this study was to develop a model that could efficiently discriminate MMN, to guide more precise selection of therapeutic strategies. MethodsA total of 385 with IMN and 62 patients with MMN, who were hospitalized at the First Affiliated Hospital of Zhengzhou University between January 2017 and December 2020 were included in this study. We constructed a discriminant model based on demographic information and laboratory parameters for distinguishing MMN and IMN. To avoid an increased false positivity rate resulting from the large difference in sample numbers between the two groups, we matched MMN and IMN in a 1:3 ratio according to gender. Regression analysis was subsequently performed and a discriminant model was constructed. The calibration ability and clinical utility of the model were assessed via calibration curve and decision curve analysis. ResultsWe constructed a discriminant model based on age, CD4(+) T cell counts, levels of cystatin C, albumin, free triiodothyronine and body mass index, with a diagnostic power of 0.860 and 0.870 in the training and test groups, respectively. The model was validated to demonstrate good calibration capability and clinical utility. ConclusionIn clinical practice, patients demonstrating higher scores after screening with this model should be carefully monitored for the presence of tumors in order to improve their outcome.

Automated summary

A discriminant model based on demographic information and laboratory parameters was developed to efficiently distinguish MMN and IMN, with a diagnostic power of 0.860 and 0.870 in the training and test groups, respectively. The model showed good calibration capability and clinical utility, highlighting the importance of carefully monitoring patients with higher scores for the presence of tumors in clinical practice.