Specificity of prediction with an autoantibody marker reflects the chance that a person without that marker will remain disease-free. It is calculated by dividing the number of subjects in a cohort without that autoantibody marker who do not go on to develop disease by the total number of subjects who do not develop the disease. Specificity is important if a disease marker is to be used to identify individuals either for counseling or for therapy to prevent the disease from developing. A reciprocal relationship exists between sensitivity and specificity (5). The higher the threshold for autoantibody positivity based on the normal population, the more specifically the autoantibody assay identifies patients with clinical disease, but at the cost of excluding many patients with low autoantibody signals.

If an autoantibody is to be used to predict disease, then ideally every subject with the autoantibody, but without clinical disease, will eventually develop clinical disease. That is, the test should display high disease positive predictive value. The positive predictive value is calculated by dividing the number of autoantibody-positive subjects in the initial sample who go on to develop clinical disease by the overall number of autoantibody-positive subjects. The prognostic significance of any marker varies in populations at differing levels of risk. If the disease risk is high then the predictive power can be high, but when the disease risk is low, as in the general population, then there is a corresponding reduction in predictive power (5). Predictions based on cross-sectional analyses must be verified in prospective studies. Many studies use predictive values based on cross-sectional data of cases with established clinical disease. In general, this approach is invalid and relates to identification of disease cases, not prediction.