Reproductive tract disease in Irish grazing dairy cows: Retrospective observational study examining its association with reproductive performance and accuracy of 2 diagnostic tests

The detection of reproductive tract disease (RTD) 3 wk postpartum is important because of its effect on subsequent reproductive outcomes. Numerous methods for the diagnosis of RTD are described, some of which are more practical and instantaneous in terms of diagnosis. Two of these methods involve identification of purulent vaginal discharge (PVD) and evidence of ultrasonographic uterine changes indicative of endometritis (UE). The objectives of our retrospective observational study were (1) to assess the association of PVD or UE score at the prebreeding examination (PBE) with the hazard of pregnancy within the subsequent breeding season; (2) to determine the test sensitivity (Se) and specificity (Sp) at the point of sampling of both tests using a Bayesian latent class model; and (3) to determine the effect of varying positivity thresholds on test accuracy. To achieve these objectives, we analyzed an initial data set of 5,049 PBE from 2,460 spring-calved cows in 8 herds between 2014 and 2018. Each PBE was conducted once between 25 and 86 d in milk. At each PBE, vaginal discharge was obtained with a Metricheck device (Simcro) whereas uterine contents were assessed using transrectal ultrasonography. Purulent vaginal discharge was scored on a scale of 0 to 3 depending on discharge character, and UE was scored on a scale of 0 to 4 depending on the presence and consistency of intraluminal fluid. Cows with scores of ≥2 in either test had received treatment. Fertility data were available from 4,756 PBE after data exclusion. The association between PVD or UE score at the PBE and subsequent hazard of pregnancy was analyzed using a Cox proportional hazards model. Cows with a PVD score of 2 or 3 were less likely to conceive than cows with a PVD score 0 [score 2 hazard ratio (HR) = 0.74; 95% confidence interval (CI): 0.59-0.94; score 3 HR = 0.65; 95% CI: 0.51-0.84]. Cows with a UE score of 1, 2, 3, or 4 were less likely to conceive than cows with a UE score of 0 (score 1 HR = 0.82; 95% CI: 0.73-0.93; score 2 HR = 0.79; 95% CI: 0.62-1.00; score 3 HR = 0.43; 95% CI: 0.43-0.90; score 4 HR = 0.39; 95% CI: 0.26-0.58). To determine the Se and Sp of PVD or UE score for diagnosis of RTD at the time of PBE, a Bayesian latent class model was fitted on 2,460 individual cow PBE. Flat priors were used for the Se and Sp of UE, whereas informative priors were used for PVD Se (mode = 65%, 5th percentile = 45%) and Sp (mode = 90%, 5th percentile = 80%) and RTD prevalence (mode = 20%, 5th percentile = 10%). Posterior estimates (median and 95% Bayesian probability intervals; BPI) were obtained using 'rjags' (R Studio). The optimal test thresholds (PVD and UE score ≥1) were selected by assessing the effect of different thresholds on test estimates and using a misclassification cost analysis. Based on these, median (95% BPI) Se for PVD and UE score ≥1 were 44% (29-60%) and 67% (33-100%), respectively. Median Sp for PVD and UE score ≥1 were 90% (86-93%) and 91% (86-93%), respectively. Higher scores in both tests were associated with impaired fertility, and UE scoring with a threshold of ≥1 had the highest test Se and Sp estimates although test Se was conditional on days in milk when the PBE occurred.

Prediction of lameness using automatically recorded activity, behavior and production data in post-parturient Irish dairy cows

BACKGROUND

Although visual locomotion scoring is inexpensive and simplistic, it is also time consuming and subjective. Automated lameness detection methods have been developed to replace the visual locomotion scoring and aid in early and accurate detection. Several types of sensors are measuring traits such as activity, lying behavior or temperature. Previous studies on automatic lameness detection have been unable to achieve high accuracy in combination with practical implementation in a on farm commercial setting. The objective of our research was to develop a prediction model for lameness in dairy cattle using a combination of remote sensor technology and other animal records that will translate sensor data into easy to interpret classified locomotion information for the farmer. During an 11-month period, data from 164 Holstein-Friesian dairy cows were gathered, housed at an Irish research farm. A neck-mounted accelerometer was used to gather behavioral metrics, additional automatically recorded data consisted of milk production and live weight. Locomotion scoring data were manually recorded, using a one-to-five scale (1 = non-lame, 5 = severely lame). Locomotion scores where then used to label the cows as sound (locomotion score 1) or unsound (locomotion score ≥ 2). Four supervised classification models, using a gradient boosted decision tree machine learning algorithm, were constructed to investigate whether cows could be classified as sound or unsound. Data available for model building included behavioral metrics, milk production and animal characteristics.

RESULTS

The resulting models were constructed using various combinations of the data sources. The accuracy of the models was then compared using confusion matrices, receiver-operator characteristic curves and calibration plots. The model which achieved the highest performance according to the accuracy measures, was the model combining all the available data, resulting in an area under the curve of 85% and a sensitivity and specificity of 78%.

CONCLUSION

These results show that 85% of this model's predictions were correct in identifying cows as sound or unsound, showing that the use of a neck-mounted accelerometer, in combination with production and other animal data, has potential to replace visual locomotion scoring as lameness detection method in dairy cows.

Cow-level risk factors for reproductive tract disease diagnosed by 2 methods in pasture-grazed dairy cattle in Ireland

The cow-side diagnosis of reproductive tract disease (RTD) involves identifying the presence of purulent vaginal discharge (PVD) and ultrasonographic endometritis (UE). The objectives of our study were to obtain prevalence estimates for RTD diagnosed by 2 methods (PVD and UE scoring) and to investigate the risk factors for increased probability of RTD if these methods are used in isolation or in combination. Our retrospective observational cohort study tested the hypothesis that RTD assessed by 2 methods would have similar risk factors, and that those would be mainly cow- and calving-related factors. We analyzed data from 5,049 pre-breeding examinations (PBE) from 2,460 spring-calved cows on 8 farms between 2014 and 2018. Cow-related details assessed were days in milk at PBE, breed, lactation number, dry period length, body condition score at calving and PBE, 305-d milk yield, predicted transmitting ability for production and fertility, the presence of a corpus luteum at PBE, and positive diagnosis the previous year. Calving details assessed were type of sire, calf sex, twinning, stillbirth, calving difficulty score, and retained fetal membranes. We conducted statistical analyses using 4 multivariable logistic regression models to identify the risk of RTD diagnosed by (1) PVD in isolation, (2) UE in isolation, (3) the presence of either PVD or UE; and (4) the presence of both PVD and UE. We accounted for herd, cow, and year as random effects in all 4 models. The overall prevalence of RTD in models 1, 2, 3, and 4 were 7.5, 6.7, 11.6, and 2.6%, respectively. Days in milk at PBE, the interaction between days in milk and retained fetal membranes, twinning, and the predicted transmitting ability for calving interval were consistently significant risk factors for positive scores in all 4 models. Considerable calving difficulty [adjusted odds ratio (AOR) = 13.64], Holstein Friesian dam breed (AOR = 2.58), first lactation (AOR = 2.39), and body condition score at PBE (AOR = 1.64) were risk factors for a positive PVD score but not for a positive UE score. Fifth lactation (AOR = 1.69), a beef-sired calf (AOR = 1.46), and the absence of a corpus luteum at PBE (AOR = 1.57) were risk factors for a positive UE score but not for a positive PVD score. These results support the hypothesis that most of the risk factors for PVD and UE are the same but some are distinctly different, implying that in some instances the 2 methods diagnose separate components of the RTD complex.