Simultaneous use of serum IgG and IgM for risk scoring of suspected early Lyme borreliosis: graphical and bivariate analyses
The laboratory diagnosis of early disseminated Lyme borreliosis (LB) rests on IgM and IgG antibodies in serum. The purpose of this study was to refine the statistical interpretation of IgM and IgG by combining the diagnostic evidence provided by the two immunoglobulins and exploiting the whole range of the quantitative variation in test values. ELISA assays based on purified flagella antigen were performed on sera from 815 healthy Danish blood donors as negative controls and 117 consecutive patients with confirmed neuroborreliosis (NB). A logistic regression model combining the standardized units of the IgM and IgG ELISA assays was constructed and the resulting disease risks graphically evaluated by receiver operating characteristic and ‘predictiveness’ curves. The combined model improves the discrimination between NB patients and blood donors. Hence, it is possible to report a predicted risk of disease graded for each individual patient, as is theoretically preferable. The predictiveness curve, when adapted to the local pretest probability of LB, allows high-risk and low-risk thresholds to be defined instead of cut-offs based on the laboratory characteristics only, and it allows the extent of under- and over-treatment to be assessed. It is shown that an example patient with low ELISA results in IgM and IgG, considered negative by the conventional cut-off, has a relatively high risk of belonging to the truly diseased population and a low risk of being false positive. Using a 20% high-risk threshold for advising the clinician to consider treatment, the sensitivity of the assay is increased from 76% to 85%, while the specificity is maintained at around 95%.
Document Type: Research Article
Affiliations: 1: Department of Clinical Microbiology, Næstved Hospital, Region Zealand, Næstved 2: Department of Clinical Microbiology, Aalborg Hospital, Aarhus University Hospital, Aalborg 3: Department of Biostatistics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
Publication date: 2010-04-01