Clinical predictive model to estimate probability of remission in patients with lupus nephritis

Background: Lupus nephritis (LN) is a major organ complication and cause of morbidity and mortality in patients with systemic lupus erythematosus. This study aims to provide the clinician with a quantitative tool for the prediction of the individual remission probability of LN and obtain new insights for improved clinical management in LN treatment.

Methods: A total of 301 patients with renal biopsy-proven LN were recruited and randomly divided into model construction and validation group. The least absolute shrinkage and selection operator regression analysis was conducted to select significant variables, and a multivariate Cox regression predictive model was established. The performance of the model was verified and tested with 1000-bootstrap validation in the validation group. Finally, the nomogram was constructed, and the performance was evaluated. The predictive accuracy and efficiency were verified through receiver operation characteristic and calibration curves.

Results: A total of 210 and 91 patients who all received renal biopsy were included in the training and validation group, respectively. A final prognostic model was established, which included the course of LN, gender, 24h-proteinuria, creatinine, triglycerides, FIB, Complement C3, anti-dsDNA antibody, tubular atrophy and classification of kidney biopsy. Moreover, an easy-to-use nomogram was built based on the predictive model. The areas under the curve (AUC) of the 1, 2, 5-year prediction were 77.12, 77.98 and 87.01 in the training group, respectively. In the validation group, the AUC of the 1, 2, 5-year prediction were 81.42, 87.20 and 92.81 respectively, which indicated good performance in predicting the remission probability of LN.

Conclusion: This novel model was constructed to predict the remission probability of patients with LN for the first time. This model displayed good predictive performance and was easy to use for clinical practice.