Use of inline measures of l-lactate dehydrogenase for classification of posttreatment mammary Staphylococcus aureus infection status in dairy cows.

Abstract

An automated method for determining whether dairy cows with subclinical mammary infections recover after antibiotic treatment would be a useful tool in dairy production. For that purpose, inline l-lactate dehydrogenase (LDH) measurements was modeled using a dynamic linear model; the variance parameters were estimated using the expectation-maximization algorithm. The method used to classify cows as infected or uninfected was based on a multiprocess Kalman filter. Two learning data sets were created: infected and uninfected. The infected data set consisted of records from 48 cows with subclinical Staphylococcus aureus infection from 4 herds collected in 2010. The uninfected data set came from 35 uninfected cows collected during 2013 from 2 herds. Bacteriological culturing was used as gold standard. To test the model, we collected data from the 48 infected cows 50 d after antibiotic treatment. As a result of the treatment, this test data set consisted of 25 cows that still had a subclinical infection and 23 cows that were recovered. Model sensitivity was 36.0% and specificity was 82.6%. To a large extent, l-lactate dehydrogenase reflected the cow's immune response to the presence of pathogens in the udder. However, cows that were classified correctly before treatment had a better chance of correct classification after treatment. This indicated a variation between cows in immune response to subclinical mammary infection that may complicate the detection of subclinically infected cows and determination of recovery.