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首页> 外文期刊>Allergy >Component‐resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy
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Component‐resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy

机译：组件已解决的诊断及超越：多变量回归模型，以预测榛子过敏的严重程度

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摘要

Abstract Background 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. Aim To analyze associations between severity and sensitization patterns, patient characteristics and clinical history, and to develop models to improve predictive accuracy. Methods 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 ( OR s) and areas under receiver‐operating characteristic ( ROC ) curves ( AUC s) were used to evaluate their predictive value. Results 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 AUC s 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. Conclusion 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.
机译：摘要背景组分分辨诊断（CRD）揭示了IgE对单个过敏原和榛子过敏的严重程度之间的重要关联。已经注意到它们与预测严重程度的临床因素相结合。旨在分析严重程度与致敏模式，患者特征和临床史之间的关联，以及开发模型以提高预测准确性。方法对12个欧洲城市报告榛子过敏（n = 423）的患者对Ige对单独的榛子过敏原进行了测试。症状（报告和在双盲安慰剂对照食物挑战[dbpcfc]）中分类为轻度，中度和严重。从临床因素和敏化模式（CRD - 和基于提取物）产生多次回归模型以预测严重程度。使用赔率比（或S）和接收器操作特征（ROC）曲线（AUC S）的区域来评估它们的预测值。结果COR 9和14分别是阳性（或10.5和10.1），以及在DBPCFC期间与严重症状相关的COR A负（或0.14），AUC S为0.70-073。结合COR 1和9改善此提高至0.76。一种模型，使用特应性皮炎（风险），花粉过敏（保护），IgE反对COR 14（风险）和核桃（风险）增加了AUC至0.91。敏感度为92％，特异性为76.3％，分别为阳性和阴性预测值62.2％和95.7％。对于报告的症状，协会和生成的模型被证明几乎相同但较弱。结论组合CRD与临床背景和基于提取物的血清学的模型优于CRD，仅在评估对榛子的严重反应的风险方面，特别是在排除严重反应中。

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  • 来源
    《Allergy》 |2018年第3期|共11页
  • 作者

    Datema M. R.; Ree R.; Asero R.; Barreales L.; Belohlavkova S.; Blay F.; Clausen M.; Dubakiene R.; Fernández‐Perez C.; Fritsche P.; Gislason D.; Hoffmann‐Sommergruber K.; Jedrzejczak‐Czechowicz M.; Jongejan L.; Knulst A. C.; Kowalski M.; Kralimarkova T. Z.; Le T.‐M.; Lidholm J.; Papadopoulos N. G.; Popov T. A.; Prado N.; Purohit A.; Reig I.; Seneviratne S. L.; Sinaniotis A.; Versteeg S. A.; Vieths S.; Zwinderman A. H.; Mills E. N. C.; Fernández‐Rivas M.; Ballmer‐Weber B.;

  • 作者单位

    Department of Experimental ImmunologyAcademic Medical CenterAmsterdam The Netherlands;

    Department of Experimental ImmunologyAcademic Medical CenterAmsterdam The Netherlands;

    Ambulatorio di AllergologiaClinica San CarloPaderno Dugnano Italy;

    Allergy DepartmentHospital Clinico San Carlos IdISSCMadrid Spain;

    Medical Faculty PilsenCharles University Prague;

    Allergy DivisionStrasbourg University HospitalStrasbourg France;

    Faculty of MedicineLandspitali University HospitalReykjavik Iceland;

    Medical FacultyVilnius UniversityVilnius Lithuania;

    Allergy DepartmentHospital Clinico San Carlos IdISSCMadrid Spain;

    Allergy UnitUniversity Hospital ZürichZürich Switzerland;

    Faculty of MedicineLandspitali University HospitalReykjavik Iceland;

    Department of Pathophysiology and Allergy ResearchMedical University of ViennaVienna Austria;

    Department of Immunology Rheumatology and AllergyMedical University of LodzLodz Poland;

    Department of Experimental ImmunologyAcademic Medical CenterAmsterdam The Netherlands;

    Department of Dermatology and AllergologyUniversity Medical Center UtrechtUtrecht The Netherlands;

    Department of Immunology Rheumatology and AllergyMedical University of LodzLodz Poland;

    Clinic of Allergy and AsthmaMedical University of SofiaSofia Bulgaria;

    Department of Dermatology and AllergologyUniversity Medical Center UtrechtUtrecht The Netherlands;

    Thermo Fisher ScientificUppsala Sweden;

    Allergy DepartmentUniversity of AthensAthens Greece;

    Clinic of Allergy and AsthmaMedical University of SofiaSofia Bulgaria;

    Clinical epidemiology UnitHospital Clinico San Carlos IdISSCMadrid Spain;

    Allergy DivisionStrasbourg University HospitalStrasbourg France;

    Allergy DepartmentHospital Clinico San Carlos IdISSCMadrid Spain;

    Institute of Immunity and TransplantationUniversity College LondonLondon UK;

    Allergy DepartmentUniversity of AthensAthens Greece;

    Department of Experimental ImmunologyAcademic Medical CenterAmsterdam The Netherlands;

    Division of AllergologyFederal Institute for Vaccines and BiomedicinesLangen Germany;

    Department of Clinical Epidemiology Biostatistics and BioinformaticsAcademic Medical;

    Institute of Inflammation and RepairThe University of ManchesterManchester UK;

    Allergy DepartmentHospital Clinico San Carlos IdISSCMadrid Spain;

    Allergy UnitUniversity Hospital ZürichZürich Switzerland;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 医学免疫学;
  • 关键词

    allergen components; hazelnut allergy; iFAAM; prediction; severity;

    机译：过敏原组分;榛子过敏;IFAAM;预测;严重程度;

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