Cohort-level disease prediction using aggregate biomarker data measured at dry-off in transition dairy cattle: a proof-of-concept study

Publication date: Available online 24 May 2019Source: Preventive Veterinary MedicineAuthor(s): L. Wisnieski, B. Norby, S.J. Pierce, T. Becker, J.C. Gandy, L.M. SordilloAbstractDuring the transition from late gestation to early lactation, dairy cattle are at increased risk for disease. Herd-level monitoring for disease risk involves evaluating multiple factors, including food intake, cow density, and biomarkers of nutrient metabolism. Biomarkers that are measured include non-esterified fatty acids (NEFA) and beta-hydroxybutyrate (BHB), which are usually measured in a subset of the herd (i.e. cohort). If a certain proportion of cows in the cohort are above a specific threshold for a biomarker, the cohort is considered at high risk of disease. Few previous studies have investigated other methods to aggregate individual cow-level data to the cohort level. We designed a proof-of-concept study to determine if biomarker aggregation methods may be useful to predict cohort incidence of adverse health events including 1) clinical diseases: mastitis, metritis, retained placenta, ketosis, lameness, pneumonia, milk fever, displaced abomasum, 2) and abortion or death of the calf or the cow. The study design was a prospective cohort study that used cows (N = 277) from five Michigan commercial dairy herds. Multiple cohorts of cows (two to four cohorts per farm, 18 total) were enrolled that shared the same dry-off date. We tested three different methods (central, dispersion, and count) to...
Source: Preventive Veterinary Medicine - Category: Veterinary Research Source Type: research