Component‐resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy
Year: 2017
Type: article
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 (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.
Source: Allergy
DOI: 10.1111/all.13328
Authors: M. R. 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, A. C. Knulst, M. Kowalski, T. Z. Kralimarkova, T.‐M. Le, J. Lidholm, N. G. Papadopoulos, T. A. Popov, N. del Prado, A. Purohit, I. Reig, S. L. Seneviratne, A. Sinaniotis, S. A. Versteeg, S. Vieths, A. H. Zwinderman, E. N. C. Mills, M. Fernández‐Rivas, B. Ballmer‐Weber
Institutions: Amsterdam UMC Location University of Amsterdam, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Charles University, Hôpitaux Universitaires de Strasbourg, University of Iceland, National University Hospital of Iceland, Vilnius University, University Hospital Zurich, Medical University of Vienna, Medical University of Lodz, University Medical Center Utrecht, Medical University of Sofia, Thermo Fisher Scientific (Sweden), National and Kapodistrian University of Athens, University of Manchester, University College London, Paul Ehrlich Institut, Manchester Academic Health Science Centre
Language: English
Subfield: Immunology and Allergy
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2763065026