Department of Experimental Immunology, Academic Medical Center, Amsterdam, The Netherlands.
Department of Otorhinolaryngology, Academic Medical Center, Amsterdam, The Netherlands.
Allergy. 2018 Mar;73(3):549-559. doi: 10.1111/all.13328. Epub 2017 Nov 24.
Component-resolved diagnosis (CRD) has revealed significant associations between IgE against individual allergens and severity of hazelnut allergy. Less attention has been given to combining them with clinical factors in predicting severity.
To analyze associations between severity and sensitization patterns, patient characteristics and clinical history, and to develop models to improve predictive accuracy.
Patients reporting hazelnut allergy (n = 423) from 12 European cities were tested for IgE against individual hazelnut allergens. Symptoms (reported and during Double-blind placebo-controlled food challenge [DBPCFC]) were categorized in mild, moderate, and severe. Multiple regression models to predict severity were generated from clinical factors and sensitization patterns (CRD- and extract-based). Odds ratios (ORs) and areas under receiver-operating characteristic (ROC) curves (AUCs) were used to evaluate their predictive value.
Cor a 9 and 14 were positively (OR 10.5 and 10.1, respectively), and Cor a 1 negatively (OR 0.14) associated with severe symptoms during DBPCFC, with AUCs of 0.70-073. Combining Cor a 1 and 9 improved this to 0.76. A model using a combination of atopic dermatitis (risk), pollen allergy (protection), IgE against Cor a 14 (risk) and walnut (risk) increased the AUC to 0.91. At 92% sensitivity, the specificity was 76.3%, and the positive and negative predictive values 62.2% and 95.7%, respectively. For reported symptoms, associations and generated models proved to be almost identical but weaker.
A model combining CRD with clinical background and extract-based serology is superior to CRD alone in assessing the risk of severe reactions to hazelnut, particular in ruling out severe reactions.
成分分辨诊断（CRD）已经揭示了针对单一过敏原的 IgE 与榛子过敏严重程度之间的显著关联。然而，在预测严重程度方面，将其与临床因素结合起来的研究相对较少。
分析严重程度与致敏模式、患者特征和临床病史之间的关联，并建立模型以提高预测准确性。
来自 12 个欧洲城市的报告榛子过敏的患者（n=423）接受了针对榛子中单个过敏原的 IgE 检测。根据症状（报告的和在双盲安慰剂对照食物挑战[DBPCFC]期间）将其分为轻度、中度和重度。从临床因素和致敏模式（基于 CRD 和提取物）生成预测严重程度的多元回归模型。使用比值比（OR）和接收者操作特征（ROC）曲线下面积（AUC）评估其预测价值。
Cor a 9 和 14 与 DBPCFC 期间的严重症状呈正相关（OR 分别为 10.5 和 10.1），而 Cor a 1 与严重症状呈负相关（OR 为 0.14），AUC 为 0.70-0.73。将 Cor a 1 和 9 结合起来可以将 AUC 提高到 0.76。使用特应性皮炎（风险）、花粉过敏（保护）、Cor a 14 的 IgE（风险）和核桃（风险）组合的模型将 AUC 提高到 0.91。在 92%的敏感性下，特异性为 76.3%，阳性预测值为 62.2%，阴性预测值为 95.7%。对于报告的症状，关联和生成的模型几乎相同，但强度较弱。
与 CRD 相比，将 CRD 与临床背景和基于提取物的血清学相结合的模型在评估榛子严重反应的风险方面更具优势，特别是在排除严重反应方面。
