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

Abstract

Background: Based 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.

Methods: A 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.

Results: We 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.

Conclusion: In 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.

Keywords: Cysc; discriminant model; malignancy; malignancy-associated membranous nephropathy; membranous nephropathy.

Figure 1

Flowchart of enrollment and exclusion in training and test group. IMN, idiopathic membranous nephropathy; MN, membranous nephropathy; MMN, malignancy-associated membranous nephropathy.

Figure 2

AUC of the MMN risk nomogram model. (A) ROC curve based on potential risk factors showing discrimination rate for MMN and IMN in training group. (B) ROC curve based on potential risk factors showing discrimination rate for MMN and IMN in test group.

Figure 3

Calibration curve of discrimination nomogram in (A) training group or (B) test group. The x-axis represents the predicted probability of MMN. The y-axis represents the actual diagnosed MMN. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction.

Figure 4

Nomogram predicting MMN. Cysc, Cystatin C; CD4, CD4+ T cells count; ALB, albumin; FT3, free triiodothyronine.

Figure 5

Coefficient of determination of the nomogram model. Cysc contributed the most in the model, followed by age, CD4+ T cell counts, FT3 and ALB.

Figure 6

Decision curve analysis for MMN risk nomogram in (A) training group and (B) test group. The y-axis tested the net benefit. The thin gray line meant the assumption that all patients had MMN, while the thick red line represented the assumption that all patients had IMN. The dotted line represented the risk nomogram. In training group, the decision curve showed that if the threshold probability of a patient is between 0.01 and 0.93, using the nomogram in the present study to predict MMN adds more benefit.