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To develop a claims-based algorithm identifying systemic lupus erythematosus (SLE) flares using a linked claims-electronic medical record (EMR) dataset. This study was a retrospective analysis of linked administrative claims and EMR data spanning 1 January 2003 to 31 March 2019. Included were adult SLE patients with at least 12 months of continuous enrollment in claims data, 12 months of clinical activity in EMR, and an absence of malignancies excluding basal and squamous cell carcinoma. Patient follow-up was divided into 30-day windows, and a proxy SLEDAI-2K score based on the EMR data was calculated for each 30-day period. A flare was defined as an increase of at least 4 from the baseline score. A series of potential flare predictor variables identified in claims were based on a combination of established variables from a previous algorithm, with the addition of other SLE-related indicators based on clinical input. Logistic regression models were built to predict monthly SLE flares. Inclusion criteria identified 2427 patients. Results from a logistic model with forward selection capping the number of variables at 10 performed well with a  c -statistic of 0.76 and a Brier score of 0.07. The top five predictors were any inpatient admission (OR = 4.76), outpatient office visit (OR = 3.04), MRI (OR = 2.26), ER visit (OR = 2.25), and number of rheumatology visits (OR = 1.75);  p  &lt; .01 for all. The final algorithm shows promise in providing an alternative and more streamlined way for identifying likely flares in administrative claims data that will advance the study of SLE within the context of flares.

Development of a claims-based flare algorithm for systemic lupus erythematosus