Performance of the StaphGold ELISA test in determining subclinical Staphylococcus aureus infections in dairy cows using a Gaussian mixture model
Loading...
Date
2022-07-20
DOI
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
John Wiley and Sons Ltd
Rights
(c) The author/s
CC BY-NC-ND
CC BY-NC-ND
Abstract
BACKGROUND: A novel ELISA test has been developed to detect antigen-specific IgG in early and late lactation cows in New Zealand.
OBJECTIVES: This study was to evaluate the discriminatory ability of the ELISA based on the detection of S. aureus-specific IgG as a screening test.
METHODS: The ELISA was used for the composite milk samples taken during routine herd testing in 2018-2019 milking season in New Zealand. In the absence of a gold standard test, the diagnostic specificity and sensitivity was estimated using a Gaussian mixture model.
RESULTS: The ELISA test had a high accuracy (AUC = 0.98) to detect antigen-specific IgG in early and late lactation cows with high somatic cell count due to either subsequent to or contemporaneous with the S. aureus invasion. Using an S/P ratio = 0.3 as the cut-off value, the ELISA test has sensitivity of 0.9 and specificity of 0.95, while the sensitivity increased to 0.94 at a cost of a decreased specificity of 0.9 at a lower cut-off value 0.26.
CONCLUSIONS: The integration of the ELISA test as a screening tool into specific control programs may be useful to reduce the spread of S. aureus infections, to aid with treatment decisions, and to establish a correct milking order.
Description
Keywords
Staphylococcus aureus, dairy cattle, latent class analysis, receiver operating characteristic curve, serology, subclinical mastitis, unsupervised learning, Animals, Cattle, Cattle Diseases, Enzyme-Linked Immunosorbent Assay, Female, Immunoglobulin G, Mastitis, Bovine, Sensitivity and Specificity, Staphylococcal Infections, Staphylococcus aureus
Citation
Yang DA, Laven RA. (2022). Performance of the StaphGold ELISA test in determining subclinical Staphylococcus aureus infections in dairy cows using a Gaussian mixture model.. Vet Med Sci. 8. 4. (pp. 1632-1639).