Abstract
Milk amyloid A (MAA) has been suggested a promising new biomarker in the diagnosis of mastitis. In absence of a gold standard, we estimated the diagnostic test accuracy of a commercial MAA-ELISA, somatic cell count (SCC), and bacteriological culture (BC) using Bayesian latent class modeling. Intramammary infections are divided in 2 classes: those caused by major pathogen bacteria and those caused by all bacterial species. Of 433 composite milk samples included in this study, 275 (63.5%) contained at least 1 colony of any bacterial species; of those, 56 contained major pathogens and 219 contained minor pathogens. The remaining 158 samples (36.5%) were sterile. We determined 2 different thresholds for the MAA-ELISA using Bayesian latent class modeling: 3.9 μg/mL to detect mastitis caused by major pathogens and 1.6 μg/mL to detect mastitis caused by all pathogens. The optimal SCC threshold for identification of subclinical mastitis was 150,000 cells/mL. Test accuracy for major-pathogen intramammary infections was as follows: SCC, sensitivity (Se) 92.6% and Sp 72.9%; MMA-ELISA, Se 81.4% and Sp 93.4%; BC, Se 23.8% and Sp 95.2%. Test accuracy for all-pathogen intramammary infections was as follows: SCC, sensitivity 90.3% and Sp 71.8%; MAAELISA, Se 88.0% and Sp 65.2%; BC, Se 83.8% and Sp 54.8%. The MAA-ELISA is a valuable addition to existing tools for the diagnosis of subclinical mastitis.
Jaeger, Seraina