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Component-resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy
MR. Datema, R. van Ree, R. Asero, L. Barreales, S. Belohlavkova, F. de Blay, M. Clausen, R. Dubakiene, C. Fernández-Perez, P. Fritsche, D. Gislason, K. Hoffmann-Sommergruber, M. Jedrzejczak-Czechowicz, L. Jongejan, AC. Knulst, M. Kowalski, TZ....
Language English Country Denmark
Document type Journal Article, Randomized Controlled Trial, Research Support, Non-U.S. Gov't
Grant support
BBS/E/F/00041800
Biotechnology and Biological Sciences Research Council - United Kingdom
PubMed
28986984
DOI
10.1111/all.13328
Knihovny.cz E-resources
- MeSH
- Allergens immunology MeSH
- Nut Hypersensitivity diagnosis immunology MeSH
- Antigens, Plant immunology MeSH
- Double-Blind Method MeSH
- Immunoglobulin E blood MeSH
- Humans MeSH
- Corylus immunology MeSH
- Multivariate Analysis MeSH
- Area Under Curve MeSH
- ROC Curve MeSH
- Sensitivity and Specificity MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Randomized Controlled Trial MeSH
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 (ORs) and areas under receiver-operating characteristic (ROC) curves (AUCs) 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 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. 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.
Allergy Department 2nd Pediatric Clinic University of Athens Athens Greece
Allergy Department Hospital Clinico San Carlos IdISSC Madrid Spain
Allergy Division Chest Disease Department Strasbourg University Hospital Strasbourg France
Allergy Unit Department of Dermatology University Hospital Zürich Zürich Switzerland
Ambulatorio di Allergologia Clinica San Carlo Paderno Dugnano Italy
Clinic of Allergy and Asthma Medical University of Sofia Sofia Bulgaria
Department of Dermatology and Allergology University Medical Center Utrecht Utrecht The Netherlands
Department of Experimental Immunology Academic Medical Center Amsterdam The Netherlands
Department of Pathophysiology and Allergy Research Medical University of Vienna Vienna Austria
Faculty of Medicine University of Iceland Landspitali University Hospital Reykjavik Iceland
Institute of Immunity and Transplantation University College London London UK
Medical Faculty Pilsen Charles University Prague
References provided by Crossref.org
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