Predictive models to identify Holstein cows at risk of metritis and clinical cure and reproductive/productive failure following antimicrobial treatment.

Precision dairy farming, specifically the design of management strategies according to the animal's needs, may soon become the norm since automated technologies that generate large amounts of data for each individual are becoming more affordable. Our objectives were to determine whether the use of behavioral changes could improve the accuracy of prediction of the risk of metritis and the risk of clinical cure of cows diagnosed with metritis. Addition of behavioral data to the algorithms to predict the outcomes of interest increased their accuracy by 7 to 32%. The incidence of metritis in postpartum dairy cows ranges from 20 to 40%. Unfortunately, approximately 30% of cows treated with antimicrobials following the diagnosis of metritis fail to cure and have impaired reproductive performance. Automated behavior monitoring devices have become more affordable and accessible. In the current study, we investigated whether behavioral changes recorded by automated devices improve models for the prediction, within 42 h of calving, of metritis and acute metritis. Furthermore, we determined whether behavioral changes aid on the prediction, 24 h before the diagnosis of metritis, of cure in response to antimicrobial treatments and the reproductive (failure to become pregnant)/productive (bottom quartile of milk yield) success within 200 d in milk (DIM). At enrollment, Holstein cows (n = 555) from two farms were fitted with an automated device (HR-LDn tag, SCR Engineers Ltd., Netanya, Israel) 21 d before the expected calving date. Cows were examined for metritis (fetid, watery, red/brown uterine discharge) and were randomly assigned to receive ampicillin trihydrate or ceftiofur crystalline free acid treatments. Contemporary cows with no clinical diseases (NoCD = 362) were paired with cows with metritis. Cure from metritis was defined as the absence of fetid, watery, pink/brown uterine discharge and rectal temperature < 39.5 °C, 11 d after diagnosis. In addition, cows in the lowest quartile of milk production, within lactation and farm, and that were not pregnant by 200 DIM were classified as failure. We built models containing: routinely-available data [lactation number (1, 2, ≥3), calf sex, still birth, twining, dystocia, vaginal laceration score, days on the close-up diets], body condition score (BCS) and BCS change from enrollment to calving (ΔBCS), behavior (feeding, rumination, idle, and active time), and their interactions. The area under the curve (AUC) of the models containing routinely-available data, ΔBCS, and behavior data at 2 DIM to predict metritis [AUC = 0.82, 95% confidence interval (CI) = 0.78, 0.85] and acute metritis (AUC = 0.87, 95% CI = 0.83, 0.89) were (P < 0.01) excellent; whereas the models predicting cure (AUC = 0.92, 95% CI = 0.85, 0.95) and failure (AUC = 0.90, 95% CI = 0.84, 0.94) were outstanding. Behavioral changes peripartum contribute for the identification of cows at risk for metritis, allowing the development of preventive strategies. In addition, predicting whether cows will respond to antimicrobial treatment and succeed during lactation may allow for earlier decision-making regarding treatment and culling.

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