Bayesian latent class analysis to estimate the optimal cut-off for the MilA ELISA for the detection of Mycoplasma bovis antibodies in sera, accounting for repeated measures.

The MilA ELISA has been identified as a highly effective diagnostic tool for the detection of Mycoplasma bovis specific antibodies and has been validated for serological use in previous studies. This study aimed to estimate the optimal cut-off and corresponding estimates of diagnostic sensitivity (DSe) and diagnostic specificity (DSp) of the MilA ELISA for testing bovine serum. Serum samples from 298 feedlot cattle from 14 feedlots across four Australian states were tested on entry into the feedlot and approximately 42 days later. The paired serum samples were tested with the MilA ELISA, BIO K302 (Bio-X Diagnostics, Belgium) and BIO K260 (Bio-X Diagnostics, Belgium). A cut-off of 135 AU was estimated to be optimal using Bayesian latent class analysis with three tests in multiple populations, accounting for conditional dependence between tests. At this cut-off, the DSe and DSp of the MilA ELISA were estimated to be 92.1 % (95 % highest probability density [HPD] interval: 87.4, 95.8) and 95.5 % (95 % HPD: 92.4, 97.8), respectively. The DSes of the BIO K260 and BIO K302 ELISAs were estimated to be 60.5 % (95 % HPD: 54.0, 66.9) and 44.6 % (95 % HPD: 38.7, 50.7), respectively. DSps were 95.6 % (95 % HPD: 92.9, 97.7) and 97.8 % (95 % HPD: 95.9, 99.0), respectively. Mycoplasma bovis seroprevalence was remarkably high at follow-up after 42 days on the feedlots. Overall, this study estimated a cut-off, DSe and DSp for the MilA ELISA with less dependence on prior information than previous analyses and demonstrated that the MilA ELISA has higher DSe than the BIO K260 and BIO K302 assays.