Estimating Degree of Mastitis from Time-Series Measurements in Milk: A Test of a Model Based on Lactate Dehydrogenase Measurements

Abstract

The aim of this study was to test a model for mastitis detection using a logic that allows examination of time-related changes and a progressive scale of mastitis state (i.e., not using specificity/sensitivity). The model produces a mastitis risk (MR) for individual cows on a scale from 0 (completely healthy) to 1 (full-blown mastitis). The main model input was lactate dehydrogenase (LDH; mumol/min per L) x milk yield. Test data containing 253 mastitis cases were used. Proportional samples were collected from each cow at each milking and analyzed for LDH and somatic cell count (SCC). The basis for the health definitions was veterinary treatment records. A refinement of the basic health definitions was made using systematic positive deviations in log(SCC) to indicate untreated infections. Two subsets of cows were identified: mastitic cows and cows completely free of mastitis (healthy controls). The time-profiles of these 2 groups in a 60-d window relative to day of veterinary treatment were examined. Model reliability throughout all stages of lactation and degrees of infection was examined using SCC as a continuous measure of degree of mastitis. The time-profile for the health controls was flat throughout the 60-d window with a median MR of 0.02. In contrast, the profile of the mastitic cows increased above the control cows' baseline from about -6 d, rising to a MR value of 0.20 at d 0, and declining to the control level after treatment. There were significant differences between mastitic and healthy cows from -4 to +2 d relative to veterinary treatment. When cases were time-aligned to peak of infection, rather than veterinary treatment, there was a much sharper peak to the time-profile of mastitic cows. The median MR at peak was 0.62 and the mean was 0.80. Using these data, the MR value of 0.62 had a <1% likelihood of actually coming from a healthy control. Testing against SCC, on the whole data set, showed that only 2.1% of all MR values had an error >0.7. These estimates of model reliability are comparable with the greatest values reported in the literature and, additionally, the model was able to detect significant differences between mastitic and healthy cows 4 d before treatment. It was also found that specificity/sensitivity calculations are inappropriate for evaluating time-related changes and a progressive scale of predicted mastitis state.

Evaluation of quality changes in udder quarter milk from cows with low-to-moderate somatic cell counts

“…LDH has recently been incorporated as a mastitis indicator in Herd Navigator TM , a new management system that performs automatic in-line milk analyses to monitor metabolic status, udder health and reproduction. Friggens et al (2007) find LDH to be an accurate mastitis indicator as it reveals significant differences between healthy and mastitic cows at an early stage. In a study in which quarter foremilk from subclinical cows was collected and compared to foremilk from healthy udder quarters, LDH activity is found to be elevated in those quarters with subclinical mastitis (Batavani et al, 2007).…”

“…Lactoferrin concentration is increased during subclinical mastitis and is significantly correlated with SCC (Hagiwara et al, 2003;Lindmark-Må nsson et al, 2006). Lactate dehydrogenase (LDH) is an enzyme in milk that appears in earlier studies to be a promising marker for mastitis (Friggens et al, 2007). This parameter has also recently been used in-line as an indicator of mastitis in the management programme, Herd Navigator TM (Herd Navigator, 2009).…”

Invited review: Sensors to support health management on dairy farms

“…The relation between the chosen indicator and a reference test has been studied for mastitis (Chagunda et al, 2006a). The models calculate a risk of disease and when a cow should be tested by the sensor system Chagunda et al, 2006b;Friggens et al, 2007). The model for estrus detection has been tested with real cow data (Friggens et al, 2008), and the economic effect of such a sensor system on the farm has been studied (Østergaard et al, 2005).…”

New phenotypes for new breeding goals in dairy cattle

“…Typical phenotypes produced were relative to milk yield and composition, mastitis, milking speed, as well as indicators of their evolution over successive days. Some devices such as Herd Navigator take samples and include an on-farm analyzer for a more accurate diagnosis with biological markers such as lactate dehydrogenase for mastitis (Friggens et al, 2007;Hojsgaard and Friggens, 2010), beta-hydroxyburate for ketosis and progesterone for reproduction.…”