用农场收集的数据,代谢和炎症生物标志物以及在诊断时测量的血液图变量来预测甲状腺炎治愈的预测模型
P R Menta1, J Prim2, E de Oliveira3
1Department of Veterinary Sciences, Davis College of Agricultural Sciences and Natural Resources, Texas Tech University, Lubbock, TX 79409.
Journal of dairy science
|March 1, 2024
概括
通过使用农场数据和生物标志物,可以预测牛甲状炎的治愈. 添加生物标志物提高了自发治愈 (SC) 模型的准确性,而ceftiofur治疗模型显示了更高的准确性,生物标志物提供了边际收益.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 动物繁殖 动物繁殖
- 牛的健康状况
背景情况:
- 甲状炎是乳牛中常见的产后子宫疾病.
- 准确预测甲状炎治愈对于有效的治疗策略和群体管理至关重要.
- 当前的预测模型往往缺乏全面的数据集成.
研究的目的:
- 评估预测模型对自发性甲状炎治愈 (SC) 和治疗后治愈的准确性.
- 评估农场收集的数据,血液图变量和生物标志物 (BM) 对模型准确性的影响.
- 为了比较使用不同数据组合的模型的预测能力.
主要方法:
- 一个随机的临床试验,涉及4个群体中的422头公牛.
- 数据收集包括平价,分娩问题,身体状况得分,直肠温度和牛奶中的几天.
- 血液样本被分析为完整血清 (CBC),矿物质和炎症生物标志物 (BM).
- 多变量逻辑回归模型被用来预测甲状腺炎.
主要成果:
- 仅使用农场数据预测自发治愈 (SC) 的模型的AUC为0.70,使用生物标志物 (BM) 改善为0.76.
- 完全血清 (CBC) 变量没有改善SC模型.
- 预测Ceftiofur治疗奶牛治愈的模型显示AUC从0.75 (仅农场数据) 到0.80 (农场+BM或农场+CBC+BM) 之间.
- 生物标志物稍微提高了模型准确性,而CBC数据没有.
结论:
- 牛甲状炎治愈的预测模型显示出相当的准确性.
- 预测受治疗的奶牛治愈的模型比预测自发治愈的模型更准确.
- 整合生物标志物在预测甲状腺炎治愈的准确性方面提供了微小的改善.
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