Modelling autoimmune rheumatic disease: a likelihood rationale

Immunoglobulins (Igs) and autoantibodies are commonly tested in sera from patients with suspected rheumatic disease. To evaluate the clinical utility of the tests in combination, we investigated sera from 351 patients with autoimmune rheumatic disease (ARD) rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and Sjögren's syndrome (SS) and 96 patients with nonautoimmune rheumatic disease (NAD) (fibromyalgia, osteoarthritis, etc.). Antinuclear antibodies (ANA), rheumatoid factor (RF), antibodies against DNA and extractable nuclear antigens (anti-ENA), IgG, IgA and IgM were measured for all patients. Logistic regression analysis of test results was used to calculate each patient's probability for belonging to the ARD or NAD group as well as likelihood ratios for disease. Test accuracy was investigated using receiver-operating characteristic (ROC) plots and nonparametric ROC analysis. Neither concentrations of IgG, IgA, IgM, anti-DNA nor anti-ENA gave a significant effect on diagnostic outcome. Probabilities for disease and likelihood ratios calculated by combining RF and ANA performed significantly better at predicting ARD than utilization of the diagnostic tests in isolation (P < 0.001). At a cut-off level of P = 0.73 and likelihood ratio = 1, the logistic model gave a specificity of 93% and a sensitivity of 75% for the differentiation between ARD and NAD. When compared at the same level of specificity, ANA gave a sensitivity of 37% and RF gave a sensitivity of 56.6%. Dichotomizing ANA and RF as positive or negative did not reduce the performance characteristics of the model. Combining results obtained from serological analysis of ANA and RF according to this model will increase the diagnostic utility of the tests in rheumatological practice.