Modelling long-term outcomes for patients with systemic lupus erythematosus

Area of Science:

• Rheumatology
• Immunology
• Medical Statistics

Background:

• Systemic lupus erythematosus (SLE) standard of care (SoC) involves glucocorticoids (GCs), associated with long-term organ damage.
• Newer SLE treatments aim for better tolerability and disease control.
• Predicting long-term outcomes with current SoC is essential for guiding future therapeutic strategies.

Purpose of the Study:

• To develop natural history models for SLE.
• To identify predictors of long-term outcomes, including disease activity, flare rate, GC use, organ damage, and mortality.
• To analyze data from the Toronto Lupus Cohort (TLC) spanning 1997-2020.

Main Methods:

• Utilized generalized linear and parametric accelerated failure time (AFT) survival models.
• Performed longitudinal retrospective analysis of prospectively collected TLC data.
• Employed bivariate and multivariate regression to select significant covariates for best-fit models.

Main Results:

• Included 1255 subjects (89% female, 65% Caucasian) with a mean follow-up of 10.5 years.
• Found that SLE disease activity (SLEDAI-2K) and average GC dose predict changes in SLEDAI-2K, GC use, and flare rates.
• Demonstrated that SLEDAI-2K and GC dose predict mortality and organ damage across multiple organ systems.

Conclusions:

• Longitudinal predictive models confirm disease activity and GC use as key predictors of organ damage and mortality in moderate-to-severe SLE.
• Findings deepen the understanding of SLE natural history.
• Highlights the need for novel treatments that reduce disease activity and GC reliance to improve long-term SLE prognosis.