Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum β-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83.4% in multiparous cows. These results suggest that predicting serum BHB from continuous test-day milk and performance variables could serve as a valuable diagnostic tool for monitoring HYK in Holstein and Jersey herds.

尽管有用于诊断高血钾症（HYK）的奶牛测试策略，但许多策略劳动强度大且成本高，并且有些缺乏足够的准确性。在常规的牛奶采样过程中通过傅里叶变换红外光谱法预测奶酮体可能会提供更实用的监控策略。这项研究的目的是（1）使用所有可用的试验日牛奶和性能变量开发线性和逻辑回归模型以预测HYK，以及（2）比较预测方法（傅里叶变换红外乳酮体，线性回归模型和logistic回归模型）来确定哪个最能预测HYK。给定可用数据，第二个目的是评估荷斯坦奶牛和泽西岛之间以及种内有或没有HYK的母牛之间的试验日牛奶和性能变量（连续测量）的差异。在5到20天的牛奶（DIM）中，从658头荷斯坦奶牛和468头泽西奶牛的牛奶采样当天采集血样。HYK的诊断是血清β-羟基丁酸酯（BHB）浓度≥1.2mmol / L。牛奶中BHB和丙酮的浓度通过傅立叶变换红外光谱法（Foss Analytical，Hillerød，丹麦）进行了预测。测试了牛奶中BHB和丙酮的阈值的诊断准确性，并从连续变量构建了逻辑模型，以预测该品种初生和多生母牛的HYK。由连续变量为品种内初生和多生母牛的线性变量构建，线性模型为5到11 DIM或12到20 DIM。乳酮体阈值在Holsteins中诊断HYK的准确度为64.0％至92.9％，在Jerseys中诊断为59.1％至86.6％的准确性。逻辑模型预测HYK的准确度为82.6至97.3％。内部交叉验证的多元线性回归模型诊断的荷斯坦奶牛HYK的初产准确性为​​97.8％，多产母牛的准确性为83.3％。初乳的泽西模型准确性为81.3％，多头母牛的准确性为83.4％。这些结果表明，从连续测试日的牛奶和生产性能变量预测血清BHB可以作为监测荷斯坦和泽西牛群HYK的有价值的诊断工具。荷斯坦犬的准确度为0至92.9％，泽西岛的准确度为59.1至86.6％。逻辑模型预测HYK的准确度为82.6至97.3％。内部交叉验证的多元线性回归模型诊断的荷斯坦奶牛HYK的初产准确性为​​97.8％，多产母牛的准确性为83.3％。初乳的泽西模型准确性为81.3％，多头母牛的准确性为83.4％。这些结果表明，从连续测试日的牛奶和生产性能变量预测血清BHB可以作为监测荷斯坦和泽西牛群HYK的有价值的诊断工具。荷斯坦犬的准确度为0至92.9％，泽西岛的准确度为59.1至86.6％。逻辑模型预测HYK的准确度为82.6至97.3％。内部交叉验证的多元线性回归模型诊断的荷斯坦奶牛HYK的初产准确性为​​97.8％，多产母牛的准确性为83.3％。初乳的泽西模型准确性为81.3％，多头母牛的准确性为83.4％。这些结果表明，从连续测试日的牛奶和生产性能变量预测血清BHB可以作为监测荷斯坦和泽西牛群HYK的有价值的诊断工具。多头母牛的准确度为3％。初乳的泽西模型准确性为81.3％，多头母牛的准确性为83.4％。这些结果表明，从连续测试日的牛奶和生产性能变量预测血清BHB可以作为监测荷斯坦和泽西牛群HYK的有价值的诊断工具。多头母牛的准确度为3％。初乳的泽西模型准确性为81.3％，多头母牛的准确性为83.4％。这些结果表明，从连续测试日的牛奶和生产性能变量预测血清BHB可以作为监测荷斯坦和泽西牛群HYK的有价值的诊断工具。