A Bayesian latent class model to estimate the accuracy of pregnancy diagnosis by transrectal ultrasonography and laboratory detection of pregnancy-associated glycoproteins in dairy cows

Abstract

Accurate diagnosis of pregnancy is an essential component of an effective reproductive management plan for dairy cattle. Indirect methods of pregnancy detection can be performed soon after breeding and offer an advantage over traditional direct methods in not requiring an experienced veterinarian and having potential for automation. The objective of this study was to estimate the sensitivity and specificity of pregnancy-associated glycoprotein (PAG) detection ELISA and transrectal ultrasound (TRUS) in dairy cows of South Africa using a Bayesian latent class approach. Commercial dairy cattle from the five important dairy regions in South Africa were enrolled in a short-term prospective cohort study. Cattle were examined at 28–35days after artificial insemination (AI) and then followed up 14days later. At both sampling times, TRUS was performed to detect pregnancy and commercially available PAG detection ELISAs were performed on collected serum and milk. A total of 1236 cows were sampled and 1006 had complete test information for use in the Bayesian latent class model. The estimated sensitivity (95% probability interval) and specificity for PAG detection serum ELISA were 99.4% (98.5, 99.9) and 97.4% (94.7, 99.2), respectively. The estimated sensitivity and specificity for PAG detection milk ELISA were 99.2% (98.2, 99.8) and 93.4% (89.7, 96.1), respectively. Sensitivity of veterinarian performed TRUS at 28–35days post-AI varied between 77.8% and 90.5% and specificity varied between 94.7% and 99.8%. In summary, indirect detection of pregnancy using PAG ELISA is an accurate method for use in dairy cattle. The method is descriptively more sensitive than veterinarian-performed TRUS and therefore could be an economically viable addition to a reproductive management plan.