Estimating the Risk of Severe Peanut Allergy Using Clinical Background and IgE Sensitization Profiles - Datema, Mareen R.; Lyons, Sarah A.; Fernández-Rivas, Montserrat; Ballmer-Weber, Barbara; Knulst, André C.; Asero, Riccardo; Barreales, Laura; Belohlavkova, Simona; de Blay, Frédéric; Clausen, Michael; Dubakiene, Ruta; Fernández-Perez, Cristina; et al. (Frontiers in Allergy, 2021)
Background: It is not well understood why symptom severity varies between patients with peanut allergy (PA). Objective: To gain insight into the clinical profile of subjects with mild-to-moderate and severe PA, and investigate individual and collective predictive accuracy of clinical background and IgE to peanut extract and components for PA severity. Methods: Data on demographics, patient history and sensitization at extract and component level of 393 patients with probable PA (symptoms ≤ 2 hours + IgE sensitization) from 12 EuroPrevall centres were analyzed. Univariable and penalized multivariable regression analyses were used to evaluate risk factors and biomarkers for severity. Results: Female sex, age at onset of PA, symptoms elicited by skin contact with peanut, family atopy, atopic dermatitis, house dust mite and latex allergy were independently associated with severe PA; birch pollen allergy with mild-to-moderate PA. The cross-validated AUC of all clinical background determinants combined (0.74) was significantly larger than the AUC of tests for sensitization to extract (0.63) or peanut components (0.54-0.64). Although larger skin prick test wheal size, and higher IgE to peanut extract, Ara h 1 and Ara h 2/6, were associated with severe PA, and higher IgE to Ara h 8 with mild-to-moderate PA, addition of these measurements of sensitization to the clinical background model did not significantly improve the AUC. Conclusions: Models combining clinical characteristics and IgE sensitization patterns can help establish the risk of severe reactions for peanut allergic patients, but clinical background determinants are most valuable for predicting severity of probable PA in an individual patient.