Background

Peanut allergy (PA) is associated with an economic and psychological burden on patients and families. Its diagnosis includes tests for peanut specific immunoglobulin E (sIgE), the values of which usually are categorized as positive or negative using a predefined cutoff (usually 0.35 kU/L). With the use of Bayes' theorem, this categorization can be replaced with a continuous interpretation of sIgE, which accounts for the prevalence of PA and history of ingestion.

Objective

To evaluate a method for estimating the likelihood ratio (LR) for each value of sIgE by performing a pilot investigation with the results of oral food challenges. The LR could be used to estimate the probability of PA.

Methods

The outcomes of oral food challenges and serum IgE values from 117 children seen in an allergy clinic between January 2017 and November 2019 were obtained. Polynomial regression of the receiver operation characteristics curve was used to determine an LR for each value of sIgE. Linear regression was used to estimate an LR for each value of sIgE.

Results

sIgE ranged from less than 0.1 kU/L to 35 kU/L. Bayes' theorem and a receiver operation characteristics curve were used to estimate LRs for each value of peanut sIgE. The value of IgE associated with an LR of 1 was 0.22 kU/L, which is comparable to other studies that used a value of 0.35 kU/L to separate positive from negative results.

Conclusion

When combined with estimates of pretest probability, this method should permit the development of computerized decision-making algorithms to estimate the probability that a patient has PA.

中文翻译：

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使用连续似然比诊断花生过敏。

背景

花生过敏（PA）与患者和家庭的经济和心理负担有关。它的诊断包括对花生特异性免疫球蛋白E（sIgE）的测试，通常使用预定义的临界值（通常为0.35 kU / L）将其值分为阳性或阴性。通过使用贝叶斯定理，可以用对sIgE的连续解释来代替此分类，这可以解释PA的流行和摄入史。

目的

通过对口腔食物挑战的结果进行试点研究，评估评估sIgE每个值的似然比（LR）的方法。LR可以用来估计PA的可能性。

方法

从2017年1月至2019年11月在过敏诊所中看到的117名儿童获得了口服食物挑战和血清IgE值的结果。接收器工作特性曲线的多项式回归用于确定每个sIgE值的LR。使用线性回归来估计每个sIgE值的LR。

结果

sIgE范围从小于0.1 kU / L到35 kU / L。使用贝叶斯定理和接收器操作特性曲线来估计花生sIgE的每个值的LR。LR为1的IgE值为0.22 kU / L，这与其他研究使用0.35 kU / L来区分阳性结果和阴性结果的研究相当。

结论

当与预测概率的估计值结合使用时，该方法应允许开发计算机化的决策算法来估计患者患有PA的概率。