title: Development of a multiplex assay for the detection of antibodies to Borrelia burgdorferi in horses and its validation using Bayesian and conventional statistical methods.
abstract: Lyme disease is a zoonotic, vector-borne disease and occurs in mammals including horses. The disease is induced by infection with spirochetes of the Borrelia burgdorferi sensu lato group. Infection of mammalian hosts requires transmission of spirochetes by infected ticks during tick bites. Lyme disease diagnosis is based on clinical signs, possible exposure to infected ticks, and antibody testing which is traditionally performed by ELISA and Western blotting (WB). This report describes the development and validation of a new fluorescent bead-based multiplex assay for the detection of antibodies to B. burgdorferi outer surface protein A (OspA), OspC and OspF antigens in horse serum. Testing of 562 equine sera was performed blindly and in parallel by using WB and the new multiplex assay. Because a true gold standard is missing for Lyme antibody testing, we performed and compared different statistical approaches to validate the new Lyme multiplex assay. One approach was to use WB results as a 'relative gold standard' in ROC-curve and likelihood-ratio analyses of the new test. Cut-off values and interpretation ranges of the multiplex assay were established by the analysis. The second statistical approach used a Bayesian model for the calculation of diagnostic sensitivities and specificities of the multiplex assay. The Bayesian analysis takes into consideration that no true gold standard exists for detecting antibodies to B. burgdorferi and estimated sensitivities and specificities of both tests that were compared. Therefore, the Bayesian analysis also resulted in an evaluation of diagnostic sensitivity and specificity of WB. Overall, the new assay was characterized by low background values and a wide dynamic quantification range for the detection of antibodies to OspA, OspC and OspF antigens of B. burgdorferi. The diagnostic sensitivity and specificity for the OspA bead-based assay were calculated as 49% and 85%, respectively, and by a standard ROC curve analysis only because the Bayesian model could not be run on this parameter. The Bayesian-derived diagnostic sensitivities of the OspC and OspF assays were 80% and 86%, respectively. For comparison, the Bayesian-derived estimates for WB resulted in sensitivities of 72% for OspC and 80% for OspF. The Bayesian diagnostic specificities of the multiplex assay were 79% and 69% for OspC and OspF, respectively. WB analysis had specificities of 92% for OspC and 77% for OspF. Although the analysis of a new assay in the absence of a true gold standard remains challenging, the approach used here can help to address this problem when new technologies and traditionally used test standards differ significantly in their analytical sensitivities, which consequently causes problems in the calculation of diagnostic sensitivity and sensitivity values for the new assay. In summary, the new multiplex assay for the detection of antibodies to B. burgdorferi OspA, OspC and OspF antigens in horse serum has improved analytical and diagnostic sensitivities compared to WB analysis. Multiplex analysis is a valuable quantitative tool that simultaneously detects antibodies indicative for natural infection with and/or vaccination against the Lyme pathogen.
authors: Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY, USA. bw73@cornell.edu
authors: Bettina
authors: B
authors: Wagner
authors: Heather
authors: H
authors: Freer
authors: Alicia
authors: A
authors: Rollins
authors: Hollis N
authors: HN
authors: Erb
authors: Zhao
authors: Z
authors: Lu
authors: Yrjo
authors: Y
authors: Gröhn
mesh_terms: D000818:Animals; D000907:Antibodies, Bacterial / Q000276:immunology*; D000942:Antigens, Bacterial / Q000276:immunology; D000954:Antigens, Surface / Q000276:immunology; D001425:Bacterial Outer Membrane Proteins / Q000276:immunology; D001428:Bacterial Vaccines / Q000276:immunology; D001499:Bayes Theorem; D025065:Borrelia burgdorferi / Q000276:immunology*; D003627:Data Interpretation, Statistical; D005453:Fluorescence; D006734:Horse Diseases / Q000175:diagnosis / Q000276:immunology*; D006736:Horses / Q000276:immunology; D007118:Immunoassay / Q000379:methods / Q000662:veterinary*; D016013:Likelihood Functions; D008074:Lipoproteins / Q000276:immunology; D008193:Lyme Disease / Q000175:diagnosis / Q000276:immunology / Q000662:veterinary*; D008863:Microspheres; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D000907:Antibodies, Bacterial; D000942:Antigens, Bacterial; D000954:Antigens, Surface; D001425:Bacterial Outer Membrane Proteins; D001428:Bacterial Vaccines; D008074:Lipoproteins; C072045:OspA protein; C079616:OspC protein; C084999:OspF protein, Borrelia burgdorferi