Prediction of Flares in Systemic Lupus Erythematosus During Post-Remission Follow-up

Yu Bai,1 Jiuliang Zhao,1 Qian Wang,1 Dong Xu,1 Xiaofeng Zeng,1 Xinping Tian,1 He-Jun Li,2 Mengtao Li1 1Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, People’s Republic of China; 2Department of Rheumatology, Fujian Medical University Union Hospital, Fuzhou, People’s Republic of ChinaCorrespondence: Mengtao Li, Email [email protected] He-Jun Li, Email [email protected]: Patients at high risk of SLE flares benefit from being identified before flares; this can be done by predictors of flares. This study aimed to explore the predictive factors and model of SLE flares after remission, providing basis for clinical decision-making.Patients and Methods: SLE patients recruited at the Peking Union Medical College Hospital (PUMCH), were all registered in the Chinese SLE treatment and research (CSTAR) registry cohort and had experienced at least one remission before December 31, 2020. Demographic, clinical, and laboratory parameters were collected through CSTAR online registry. The predictive effects of variables were analyzed using a Cox proportional hazards model. A nomogram was formulated to predict flares.Results: A total of 359 patients were included in the analysis, among which, 108 (30.1%) patients had at least one flare. Multivariate Cox regression model showed that younger age (hazard ratio [HR], 0.97; 95% CI, 0.95– 0.99), positive anti-dsDNA at remission (HR, 1.64; 95% CI, 1.08– 2.51), significantly low levels of C3 and C4 (HR, 2.09; 95% CI, 1.17– 3.73) were independent risk factors associated with flares. A nomogram was established based on the multivariate analysis. The internal bootstrap resampling approach suggested the nomogram has a certain degree of discriminatory power with a C-index of 0.654 (95% CI, 0.601– 0.707). The calibration plots also showed good consistency between the prediction and the observation.Conclusion: This study highlights that SLE patients with significantly low levels of C3 and C4, younger age, and elevated anti-dsDNA levels may require closer monitoring and follow-up after remission. Identifying these predictors allows clinicians to better assess the risk of flare and tailor therapeutic strategies accordingly for more effective long-term management.Keywords: SLE, predictors, complements, model