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Prediction of serum IgG concentration by indirect techniques with adjustment for age and clinical and laboratory covariates in critically ill newborn calves

机译:通过间接技术预测年龄以及危重新生儿犊牛的临床和实验室协变量通过间接技术预测血清IgG浓度

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摘要

The objective of this study was to develop prediction models for the serum IgG concentration in critically ill calves based on indirect assays and to assess if the predictive ability of the models could be improved by inclusion of age, clinical covariates, and/or laboratory covariates. Seventy-eight critically ill calves between 1 and 13 days old were selected from 1 farm. Statistical models to predict IgG concentration from the results of the radial immunodiffusion test, the gold standard, were built as a function of indirect assays of serum and plasma protein concentrations, zinc sulfate (ZnSO4) turbidity and transmittance, and serum γ-glutamyl transferase (GGT) activity. For each assay 4 models were built: without covariates, with age, with age and clinical covariates (infection and dehydration status), and with age and laboratory covariates (fibrinogen concentration and packed cell volume). For the protein models, dehydration status (clinical model) and fibrinogen concentration (laboratory model) were selected for inclusion owing to their statistical significance. These variables increased the coefficient of determination (R2) of the models by ≥ 7% but did not significantly improve the sensitivity or specificity of the models to predict passive transfer with a cutoff IgG concentration of 1000 mg/dL. For the GGT assay, including age as a covariate increased the R2 of the model by 3%. For the ZnSO4 turbidity test, none of the covariates were statistically significant. Overall, the R2 of the models ranged from 34% to 62%. This study has provided insight into the importance of adjusting for covariates when using indirect assays to predict IgG concentration in critically ill calves. Results also indicate that ZnSO4 transmittance and turbidity assays could be used advantageously in a field setting.
机译:这项研究的目的是建立基于间接测定的重症犊牛血清IgG浓度的预测模型,并评估是否可以通过纳入年龄,临床协变量和/或实验室协变量来提高模型的预测能力。从1个农场中选出1-13天大的78个重病犊牛。建立了基于放射免疫扩散试验(金标准)的结果预测IgG浓度的统计模型,该模型是对血清和血浆蛋白浓度,硫酸锌(ZnSO4)浊度和透射率以及血清γ-谷氨酰转移酶( GGT)活动。对于每种测定,建立了4个模型:无协变量,有年龄,有年龄和临床协变量(感染和脱水状态)以及有年龄和实验室协变量(纤维蛋白原浓度和细胞充盈量)。对于蛋白质模型,由于其统计学意义,因此选择了脱水状态(临床模型)和纤维蛋白原浓度(实验室模型)作为纳入对象。这些变量使模型的确定系数(R 2 )增加了≥7%,但并没有显着提高模型的敏感性或特异性,以预测IgG截止浓度为1000 mg / s时的被动转移dL。对于GGT分析,包括年龄作为协变量,可使模型的R 2 增加3%。对于ZnSO4浊度测试,协变量均无统计学意义。总体而言,模型的R 2 范围从34%到62%。这项研究为使用间接测定法预测重症犊牛的IgG浓度时调整协变量的重要性提供了见识。结果还表明,ZnSO4透射率和浊度测定法可有利地用于野外环境。

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