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Prev Vet Med. 2017 May 1;140:122-133. doi: 10.1016/j.prevetmed.2017.03.008. Epub 2017 Mar 28.

Bayesian estimation of sensitivity and specificity of a milk pregnancy-associated glycoprotein-based ELISA and of transrectal ultrasonographic exam for diagnosis of pregnancy at 28-45 days following breeding in dairy cows.

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Département de pathologie et microbiologie, Faculté de médecine vétérinaire, Université de Montréal, C. P. 5000, Saint-Hyacinthe, QC, J2S 7C6, Canada. Electronic address:
Valacta, 555 boul. des Anciens-Combattants, Sainte-Anne-de Bellevue, QC, H9X 3R4, Canada.
Département de sciences cliniques, Faculté de médecine vétérinaire, Université de Montréal, C.P. 5000, Saint-Hyacinthe, QC, J2S 7C6, Canada.
Technology Assessment Unit, Royal Victoria Hospital, 687 Pine Avenue W, QC, H3A 1A1, Canada.


Using a milk sample for pregnancy diagnosis in dairy cattle is extremely convenient due to the low technical inputs required for collection of biological materials. Determining accuracy of a novel pregnancy diagnostic test that relies on a milk sample is, however, difficult since no gold standard test is available for comparison. The objective of the current study was to estimate diagnostic accuracy of the milk PAG-based ELISA and of transrectal ultrasonographic (TUS) exam for determining pregnancy status of individual dairy cows using a methodology suited for test validation in the absence of gold standard. Secondary objectives were to evaluate whether test accuracy varies with cow's characteristics and to identify the optimal ELISA optical density threshold for PAG test interpretation. Cows (n=519) from 18 commercial dairies tested with both TUS and PAG between 28 and 45days following breeding were included in the study. Other covariates (number of days since breeding, parity, and daily milk production) hypothesized to affect TUS or PAG test accuracy were measured. A Bayesian hierarchical latent class model (LCM) methodology assuming conditional independence between tests was used to obtain estimates of tests' sensitivities (Se) and specificities (Sp), to evaluate impact of covariates on these, and to compute misclassification costs across a range of ELISA thresholds. Very little disagreement was observed between tests with only 23 cows yielding discordant results. Using the LCM model with non-informative priors for tests accuracy parameters, median (95% credibility intervals [CI]) TUS Se and Sp estimates of 0.96 (0.91, 1.00) and 0.99 (0.97, 1.0) were obtained. For the PAG test, median (95% CI) Se of 0.99 (0.98, 1.00) and Sp of 0.95 (0.89, 1.0) were observed. The impact of adjusting for conditional dependence between tests was negligible. Test accuracy of the PAG test varied slightly by parity number. When assuming false negative to false positive costs ratio≥3:1, the optimal ELISA optical density threshold allowing minimization of misclassification costs was 0.25. In conclusion, both TUS and PAG showed excellent accuracy for pregnancy diagnosis in dairy cows. When using the PAG test, a threshold of 0.25 could be used for test interpretation.


Bayesian estimation; Dairy cattle; Diagnostic accuracy; Latent class models; Pregnancy-associated glycoprotein; Sensitivity and specificity

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