Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, PO Box 80151, 3508 TD Utrecht, the Netherlands.
Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, PO Box 80151, 3508 TD Utrecht, the Netherlands.
J Dairy Sci. 2012 Sep;95(9):4886-4898. doi: 10.3168/jds.2011-4417.
The objective of this study was to assess the quality of a diagnostic model for the detection of hyperketonemia in early lactation dairy cows at test days. This diagnostic model comprised acetone and β-hydroxybutyrate (BHBA) concentrations in milk, as determined by Fourier transform infrared (FTIR) spectroscopy, in addition to other available test-day information. Plasma BHBA concentration was determined at a regular test day in 1,678 cows between 5 and 60 d in milk, originating from 118 randomly selected farms in the Netherlands. The observed prevalence of hyperketonemia (defined as plasma BHBA ≥1,200 µmol/L) was 11.2%. The value of FTIR predictions of milk acetone and milk BHBA concentrations as single tests for hyperketonemia were found limited, given the relatively large number of false positive test-day results. Therefore, a multivariate logistic regression model with a random herd effect was constructed, using parity, season, milk fat-to-protein ratio, and FTIR predictions of milk acetone and milk BHBA as predictive variables. This diagnostic model had 82.4% sensitivity and 83.8% specificity at the optimal cutoff value (defined as maximum sum of sensitivity and specificity) for the detection of hyperketonemia at test days. Increasing the cutoff value of the model to obtain a specificity of 95% increased the predicted value of a positive test result to 56.5%. Confirmation of test-positive samples with wet chemistry analysis of milk acetone or milk BHBA concentrations (serial testing) improved the diagnostic performance of the test procedure. The presented model was considered not suitable for individual detection of cows with ketosis due to the length of the test-day interval and the low positive predictive values of the investigated test procedures. The diagnostic model is, in our opinion, valuable for herd-level monitoring of hyperketonemia, especially when the model is combined with wet chemistry analysis of milk acetone or milk BHBA concentrations. By using the diagnostic model in combination with wet chemistry milk BHBA analysis, 84% of herds were correctly classified at a 10% alarm-level prevalence. As misclassification of herds may particularly occur when only a limited number of fresh cows are sampled, we suggest using prevalence estimates over several consecutive test days to evaluate feeding and management practices in smaller dairy farms.
本研究旨在评估一种用于检测泌乳早期奶牛酮血症的诊断模型在检测日的检测质量。该诊断模型包括通过傅里叶变换红外（FTIR）光谱法测定的牛奶中的丙酮和β-羟丁酸（BHBA）浓度，以及其他可用的检测日信息。在荷兰 118 个随机选择的农场中，对 1678 头处于泌乳 5-60d 的奶牛，在常规检测日测定血浆 BHBA 浓度。观察到的酮血症（定义为血浆 BHBA≥1200μmol/L）的患病率为 11.2%。由于假阳性检测日结果数量较多，FTIR 预测牛奶丙酮和牛奶 BHBA 浓度作为酮血症单一检测方法的价值有限。因此，构建了一个具有随机畜群效应的多元逻辑回归模型，使用胎次、季节、乳脂-蛋白比以及 FTIR 预测的牛奶丙酮和牛奶 BHBA 作为预测变量。该诊断模型在最佳截断值（定义为检测日酮血症检测的灵敏度和特异性之和最大）时，对检测日酮血症的检测具有 82.4%的灵敏度和 83.8%的特异性。将模型的截断值增加到 95%特异性，可将阳性检测结果的预测值提高到 56.5%。通过对牛奶丙酮或牛奶 BHBA 浓度进行湿化学分析（连续检测）对阳性检测样本进行确认，提高了检测程序的诊断性能。由于检测日间隔较长且所研究检测程序的阳性预测值较低，因此该模型不适合用于个体检测奶牛酮病。我们认为，该诊断模型对于酮血症的群体监测是有价值的，特别是当模型与牛奶丙酮或牛奶 BHBA 浓度的湿化学分析相结合使用时。通过使用诊断模型结合湿化学牛奶 BHBA 分析，在 10%的报警水平患病率下，84%的畜群得到了正确分类。由于仅对有限数量的新产奶牛进行采样时可能会发生畜群的错误分类，因此我们建议在几个连续的检测日使用患病率估计值来评估小型奶牛场的饲养和管理实践。
