Development and validation of a diagnostic nomogram model for predicting monoclonal gammopathy of renal significance

Abstract

AbstractIn patients with kidney disease, the presence of monoclonal gammopathy necessitates the exploration of potential causal relationships. Therefore, in this study, we aimed to address this concern by developing a nomogram model for the early diagnosis of monoclonal gammopathy of renal significance (MGRS). Univariate and multivariate logistic regression analyses were employed to identify risk factors for MGRS. Verification and evaluation of the nomogram model's differentiation, calibration, and clinical value were conducted using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. The study encompassed 347 patients who underwent kidney biopsy, among whom 116 patients (33.4%) were diagnosed with MGRS and 231 (66.6%) with monoclonal gammopathy of undetermined significance. Monoclonal Ig-related amyloidosis (n = 86) and membranous nephropathy (n = 86) was the most common renal pathological type in each group. Notably, older age, abnormal serum-free light chain ratio, and the absence of microscopic hematuria were identified as independent prognostic factors for MGRS. The areas under the ROC curves for the training and verification sets were 0.848 and 0.880, respectively. In conclusion, the nomogram model demonstrated high accuracy and clinical applicability for diagnosing MGRS, potentially serving as a valuable tool for noninvasive early MGRS diagnosis.