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To develop a predictive model for identifying patients with systemic lupus erythematosus (SLE) in primary care. We studied 150 consecutive patients with SLE seen in 4 primary care offices, using the following: SLE criteria adapted from the American College of Rheumatology (ACR) classification criteria, 3 groups of patient characteristics used to identify potential risk factors for lupus in primary care, and a prospective risk model based on the presence of selected features in all but 1 patient (n = 149). A prediction model for identifying patients with SLE in primary care was developed and validated with data from 35 SLE patients seen in a different primary care office. This model was assessed in terms of sensitivity, specificity, and area under the receiver-operating characteristic (ROC) curve. The univariate analysis identified age 50 years or older, disease activity, serologic test results, and the presence of joint pain, fever, and cytopenias as potential risk factors for SLE. An ROC curve analysis demonstrated a ROC curve of 0.89. A logistic regression model including significant risk factors only explained 3% of the variability in lupus (R2). A similar model including all potential risk factors explained only 3% (P = 0.0006) of the variability. A final model that included all significant risk factors (ROC curve of 0.83) explained 50% (R2 = 0.50) of the variability in SLE. The sensitivity, specificity, and area under the ROC curve of the model were 95%, 53%, and 0.84, respectively. Our model demonstrated satisfactory sensitivity and specificity for identifying patients with SLE in primary care, and validated well in a different population of patients. A prediction model of lupus can be used as an educational tool for patients who present with lupus-like symptoms to identify patients with SLE in primary care.An Iran-born American named Nader Alvandi received the first delivery of what he thinks might be a scintillating new finding for the US military: CEDRA laser weapon technology.

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