Development and validation of a simple lupus severity index using ACR criteria for classification of SLE. Issue 1 (10th March 2016)

• Record Type: Journal Article
• Authors: Bello, Ghalib A; Brown, Michael A; Kelly, Jennifer A; Thanou, Aikaterini; James, Judith A; Montgomery, Courtney G
• Is Part Of: Lupus science & medicine. Volume 3:Issue 1(2016)
• Publication Date: 2016-03-10
• Subjects: Lupus Severity -- Severity Index -- ACR criteria; Systemic lupus erythematosus -- Periodicals; 616.772005
• DOI: 10.1136/lupus-2015-000136
• Languages: English
• ISSNs: 2398-8851

Abstract

Abstract : 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.