CRD and beyond: multivariable regression models to predict severity of hazelnut allergy

Component-resolved diagnosis (CRD) has revealed significant associations between IgE against individual allergens and severityof hazelnut allergy. Less attention has been given to combining them with clinical factors in predicting severity. To analyze associations between severityand 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 DBPCFC) were categorized in mild, moderate and severe. Multiple regression models to predict severitywere generated from clinical factors and sensitization patterns (CRD- and extract-based). Odds ratios (OR) and areas under receiver operating characteristic (ROC) curves (AUC) 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 severesymptoms 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 severereactions to hazelnut, particular in ruling out severereactions. This article is protected by copyright. All rights reserved