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

Ayesha Salgadu; Allison Cheung; Meghan L Schibrowski; Nadeeka K Wawegama; Timothy J Mahony; Mark A Stevenson; Glenn F Browning; Tamsin S Barnes; Simon M Firestone

doi:10.1016/j.prevetmed.2022.105694

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

Prev Vet Med. 2022 Aug:205:105694. doi: 10.1016/j.prevetmed.2022.105694. Epub 2022 Jun 14.

Authors

Ayesha Salgadu¹, Allison Cheung¹, Meghan L Schibrowski², Nadeeka K Wawegama¹, Timothy J Mahony³, Mark A Stevenson¹, Glenn F Browning¹, Tamsin S Barnes², Simon M Firestone⁴

Affiliations

¹ Asia-Pacific Centre for Animal Health, Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, Australia.
² The University of Queensland, Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia and Gatton, Queensland, Australia; The University of Queensland, School of Veterinary Science, Gatton, Queensland, Australia.
³ The University of Queensland, Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia and Gatton, Queensland, Australia.
⁴ Asia-Pacific Centre for Animal Health, Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, Australia. Electronic address: simon.firestone@unimelb.edu.au.

PMID: 35751981
DOI: 10.1016/j.prevetmed.2022.105694

Abstract

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.

Keywords: Cut-off; Diagnostic sensitivity; Diagnostic specificity; MilA ELISA; Mycoplasma bovis; Serum.

MeSH terms

Animals
Antibodies, Bacterial
Australia
Bayes Theorem
Cattle
Cattle Diseases* / diagnosis
Cattle Diseases* / epidemiology
Enzyme-Linked Immunosorbent Assay / veterinary
Latent Class Analysis
Mycoplasma bovis*
Sensitivity and Specificity
Seroepidemiologic Studies

Substances

Antibodies, Bacterial