OBJECTIVE: To develop a simple systemic lupus erythematosus (SLE) severity index 
that requires knowledge of only American College of Rheumatology (ACR) criteria 
and subcriteria.
METHODS: This study used demographic, mortality and medical records data of 1915 
patients with lupus from the Lupus Family Registry and Repository. The data were 
randomly split (2:1 ratio) into independent training and validation sets. A 
logistic regression with ridge penalty was used to model the probability of 
being prescribed major immunosuppressive drugs-a surrogate indicator of lupus 
severity. ACR criteria and subcriteria were used as predictor variables in this 
model, and the resulting regression coefficient estimates obtained from the 
training data were used as item weightings to construct the severity index.
RESULTS: The resulting index was tested on the independent validation dataset 
and was found to have high predictive accuracy for immunosuppressive use and 
early mortality. The index was also found to be strongly correlated with a 
previously existing severity score for lupus. In addition, demographic factors 
known to influence lupus severity (eg, age of onset, gender and ethnicity) all 
showed robust associations with our severity index that were consistent with 
observed clinical trends.
CONCLUSIONS: This new index can be easily computed using ACR criteria, which may 
be among the most readily available data elements from patient medical records. 
This tool may be useful in lupus research, especially large dataset analyses to 
stratify patients by disease severity, an important prognostic indicator in SLE.