Application of pre-partum feeding and social behaviour in predicting risk of developing metritis in crossbred cows

Patbandha, T.K.; Mohanty, T.K.; Layek, S. S.; Kumaresan, A.; Behera, Kumaresh

doi:10.1016/j.applanim.2012.03.014

Cited by 20 publications

(15 citation statements)

References 31 publications

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“…Previous work has also shown that cows that engaged in fewer agonistic interactions during the prepartum period were more likely to develop metritis [22] and ketosis [23] during the postpartum period. Moreover, cows that developed metritis postpartum were displaced from the feed bunk more often and displaced other cows less frequently during the prepartum period [27]. Cows later diagnosed with mastitis also engaged in fewer replacements from the feed bunk during the five days before diagnosis [24].…”

Section: Discussionmentioning

confidence: 99%

Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving

Sahar

Beaver

Keyserlingk

et al. 2020

Animals

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Dairy cattle are particularly susceptible to metritis, hyperketonemia (HYK), and mastitis in the weeks after calving. These high-prevalence transition diseases adversely affect animal welfare, milk production, and profitability. Our aim was to use prepartum behavior to predict which cows have an increased risk of developing these conditions after calving. The behavior of 213 multiparous and 105 primiparous Holsteins was recorded for approximately three weeks before calving by an electronic feeding system. Cows were also monitored for signs of metritis, HYK, and mastitis in the weeks after calving. The data were split using a stratified random method: we used 70% of our data (hereafter referred to as the “training” dataset) to develop the model and the remaining 30% of data (i.e., the “test” dataset) to assess the model’s predictive ability. Separate models were developed for primiparous and multiparous animals. The area under the receiver operating characteristic (ROC) curve using the test dataset for multiparous cows was 0.83, sensitivity and specificity were 73% and 80%, positive predictive value (PPV) was 73%, and negative predictive value (NPV) was 80%. The area under the ROC curve using the test dataset for primiparous cows was 0.86, sensitivity and specificity were 71% and 84%, PPV was 77%, and NPV was 80%. We conclude that prepartum behavior can be used to predict cows at risk of metritis, HYK, and mastitis after calving.

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Section: Discussionmentioning

confidence: 99%

Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving

Sahar

Beaver

Keyserlingk

et al. 2020

Animals

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“…As milk lactose from inflamed and non-inflamed quarters showed particular trend, as well as significant difference (p<0.05), hence further analyzed by receiver operating characteristic (ROC) analysis using Sigmaplot 11 software package (Systat Software, Inc., California, USA) to see the accuracy and degree of association with IMI. The ROC analysis produces an area under the curve (AUC), threshold value and their corresponding sensitivity (Se), specificity (Sp) and positive likelihood ratio (LR+) value [ 12 , 13 ]. The AUC is a two-dimensional graph (Se plotted in Y-axis and 1-Sp plotted in X-axis) which interprets the accuracy of the diagnostic test and used to distinguish the inflamed and non-inflamed udder quarters.…”

Section: Methodsmentioning

confidence: 99%

Association of milk components with intra-mammary inflammation in Jaffrabadi buffaloes

et al. 2015

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Aim:To study the alteration of major milk components such as milk fat, protein, lactose, solid not fat (SNF) and total solids (TS) and their association with different degree of intra-mammary inflammation (IMI) in Jaffrabadi buffaloes.Materials and Methods:Milk samples (n=1516) were collected from Jaffrabadi buffaloes separately from each quarter. Milk samples were analyzed for milk fat, protein, lactose, SNF and TS percent on the same day using milk analyzer “LACTOSCAN.” Milk samples were checked for IMI by California mastitis test (CMT), and the results were expressed as negative (0), +, ++, and +++ CMT score. The traits of milk components which showed significant difference (p<0.05) between samples from inflamed and non-inflamed quarters were analyzed by receiver operating characteristic (ROC) analysis to see the accuracy and degree of association with IMI.Results:Among several milk components, milk protein and lactose percent showed a significant difference (p<0.05) between milk samples from normal and inflamed quarters. Though, during the early stage of mammary gland inflammation milk protein percent remained significantly high (p<0.05), later with an increase in the degree of severity of inflammation it did not show any difference. Milk samples from normal udder quarters had significantly higher lactose percent than inflamed quarters (p<0.05). Milk lactose percent decreased gradually with an increase in the degree of severity of inflammation. ROC analysis revealed that milk samples having lactose content below the threshold values had significantly higher chances to come from inflamed udder quarters (p<0.05). Though, the value of the area under curve (AUC) indicated that milk lactose was significantly associated with IMI (p<0.05), the accuracy was moderate (AUC=0.71-0.75).Conclusions:The results of the present study indicated that milk lactose percent gradually and significantly reduced during IMI and can be used as a marker for identification of IMI in buffaloes. However, ROC analysis further confirmed that using milk lactose IMI can be identified with moderate accuracy.

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“…Further, milk lactose was analyzed by ROC analysis using Sigmaplot 11 software package (Systat software, Inc, California, USA) to see the accuracy of the test as indicated by AUC and for the development of optimum threshold value along with their corresponding sensitivity (Se), specificity (Sp) and positive likelihood ratio (LR+) value. Though, ROC analysis produces range of potential threshold values of a diagnostic indicator, the value having highest combines Se and Sp is called as optimum threshold value of that indicator (Patbandha et al, 2012(Patbandha et al, , 2013. The AUC of ROC analysis produces a 2-dimensional graph where Se and 1-Sp are plotted in Y-axis and X-axis, respectively; which measures the accuracy of diagnostic indicators.…”

Section: Discussionmentioning

confidence: 99%

“…Since, then it has been used for the evaluation of accuracy of clinical laboratory diagnostic tests as well as diagnostic indicators or markers for classification of diseased and non diseased cases (Hughes and Bhattacharya, 2013). Additionally, it is being used for development of optimum threshold value of diagnostic indicators in medical and veterinary sciences (Fan et al, 2006;Hughes and Bhattacharya, 2013;Patbandha et al, 2012Patbandha et al, , 2013. The ROC analysis is considered as a simple statistical tool used to characterize a diagnostic variable in terms of area under a ROC curve (AUC) and further provides range of potential threshold values along with their corresponding Sensitivity (Se), Specificity (Sp) and positive likelihood ratio (LR+) values (Patbandha et al, 2012(Patbandha et al, , 2013.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, it is being used for development of optimum threshold value of diagnostic indicators in medical and veterinary sciences (Fan et al, 2006;Hughes and Bhattacharya, 2013;Patbandha et al, 2012Patbandha et al, , 2013. The ROC analysis is considered as a simple statistical tool used to characterize a diagnostic variable in terms of area under a ROC curve (AUC) and further provides range of potential threshold values along with their corresponding Sensitivity (Se), Specificity (Sp) and positive likelihood ratio (LR+) values (Patbandha et al, 2012(Patbandha et al, , 2013. Milk constitutes major components such as fat, protein and lactose which are most important to the dairy farmers, producers and consumers (Reis et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Receiver operating characteristic analysis of milk lactosefor identification of mastitis in buffaloes

Patbandha

Ravikala

Maharana

et al. 2016

IJAR

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Receiver operating characteristic (ROC) analysis is a simple statistical tool used to classify a diagnostic indicator in terms of area under a ROC curve (AUC) and to develop potential threshold values of a diagnostic indicator. Milk lactose was analyzed by ROC analysis to see its accuracy to discriminate infected and healthy udder quarters, and to develope an optimum threshold value along with corresponding sensitivity (Se), specificity (Sp) and positive likelihood ratio (LR+) value. Data for the present study comprised of 1516 milk samples collected from Jaffrabadi buffaloes. Milk lactose was estimated by milk analyzer 'LACTOSCAN' and further samples were checked for sub-clinical mastitis by California mastitis test (CMT). The threshold values of milk lactose for identification of moderate and severe infection were found to be 5.31g% (Se, 58.82%; Sp, 58.28%) and 5.23g% (Se, 70.97%; Sp, 64.41%), respectively by ROC analysis. Milk samples with lactose content below 5.31g% were 1.41 times more likely come from moderately infected quarters (LR+ = 1.41); whereas, below 5.23g% were 1.99 times more likely come from severely infected quarters (LR+ = 1.99). The overall accuracy of milk lactose for discrimination of normal quarters from moderately infected quarters was 64% (AUC=0.64) and from severely infected quarters was 72% (AUC=0.72) (P<0.001). Thus, the present study indicated that milk lactose classified mastitic and healthy udder quarters in Jaffrabadi buffaloes with moderate accuracy.

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Application of pre-partum feeding and social behaviour in predicting risk of developing metritis in crossbred cows

Cited by 20 publications

References 31 publications

Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving

Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving

Association of milk components with intra-mammary inflammation in Jaffrabadi buffaloes

Receiver operating characteristic analysis of milk lactosefor identification of mastitis in buffaloes

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