Analysis of risk factors and development of a nomogram prediction model for lupus nephritis in systemic lupus erythematosus patients

Background: This study aimed to explore risk factors for lupus nephritis (LN) in systemic lupus erythematosus (SLE) patients and establish a Nomogram prediction model based on LASSO-logistic regression.

Methods: The clinical and laboratory data of SLE patients in Meishan People's Hospital from July 2012 to December 2021 were analyzed retrospectively. All SLE patients were divided into two groups with or without LN. Risk factors were screened based on LASSO-logistic regression analysis, and a Nomogram prediction model was established. The receiver operating characteristic curve, calibration curves, and decision curve analysis were adopted to evaluate the performance of the Nomogram model.

Results: A total of 555 SLE patients were enrolled, including 303 SLE patients with LN and 252 SLE patients without LN. LASSO regression and multivariate logistic regression analyses showed that ESR, mucosal ulcer, proteinuria, and hematuria were independent risk factors for LN in SLE patients. The four clinical features were incorporated into the Nomogram prediction model. Results showed that calibration curve was basically close to the diagonal dotted line with slope 1 (ideal prediction case), which proved that the prediction ability of the model was acceptable. In addition, the decision curve analysis showed that the Nomogram prediction model could bring net clinical benefits to patients when the threshold probability was 0.12-0.54.

Conclusion: Four clinical indicators of ESR, mucosal ulcer, proteinuria, and hematuria were independent risk factors for LN in SLE patients. The predictive power of the Nomogram model based on LASSO-logistic regression was acceptable and could be used to guide clinical work.