Distinct Longitudinal Trajectories of SLEDAI-2K Scores Predict Prognosis in Systemic Lupus Erythematosus Based on Group-Based Trajectory Modeling.

Abstract

Objective: To apply group-based trajectory modeling (GBTM) to longitudinal real world data from patients with systemic lupus erythematosus (SLE) to identify distinct disease activity trajectories and factors associated with 6 month clinical status.

Methods: A total of 91 patients with SLE treated between 2017 and 2024 were included. SLE Disease Activity Index 2000 (SLEDAI-2K) scores were assessed at baseline, 3 months, and 6 months. GBTM was used to characterize longitudinal disease activity patterns. Heatmap clustering analysis was performed to compare clinical laboratory variables and SLEDAI-2K scores across trajectory groups and to further explore their association with 6 month clinical status.

Results: Two distinct disease activity trajectories were identified. Class 1, comprising patients with higher initial SLEDAI-2K scores, was predominantly composed of nonremission cases (90.91%). Class 2, characterized by lower initial SLEDAI-2K scores, showed a more balanced distribution of nonremission (46.81%) and remission (53.19%) patients. At 6 months, the remission subgroup had significantly lower SLEDAI-2K scores and lower urinary biomarker levels, including 24 h urinary microalbumin (24 h-UMA), 24 h urinary micrototal protein (24 h-UMTP), UMA, and UMTP, than the nonremission subgroup.

Conclusions: Longitudinal monitoring of SLEDAI-2K trajectories may help identify patients at risk for persistent high disease activity or nonremission at 6 months and those who may benefit from closer monitoring and treatment adjustment. These findings support a stratified management approach in SLE based on trajectory profiles.