OPTIMIZATION OF THE ALLERGENS CLASSIFICATION TO THE INTERNATIONAL CLASSIFICATION OF DISEASES (ICD)-11.
Accurate diagnosis of triggers or causative allergens is essential for appropriate risk assessment, providing correct advice to allergic patients and caregivers and personalized treatment. However, allergens have never been represented in the World Health Organization International Classification of Diseases (ICD). In this manuscript, we present the process of selection of allergens to better fit the ICD-11 structure and the outcomes of this process. The Logical Observation Identifiers Names and Codes (LOINC) database, containing 1 444 allergens was used as the basis for the selection process. Two independent experts were responsible for the first selection of the allergens according to specific technical criteria. Second step of the selection process was based on real-life relevance of allergens according to the frequency of requests of each allergen. We selected 1 109 allergens (76.8%) from all 1 444 present in the LOINC database, with a considerable agreement between experts (Cohen kappa: 8.6). After assessing real-life data, 297 more relevant allergens worldwide were selected. Grouped as plants (36.4%), drugs (32.6%), animal proteins (21%), mold and other microorganisms (1.5%), occupational allergens (0.4%) and miscellaneous (0.5%). The stepwise approach allowed us to select the most relevant allergens in practice which is the first step to build a classification of allergens to the WHO ICD-11. Aligned with the achievement in the construction of the pioneer section addressed to the allergic and hypersensitivity conditions in the ICD-11, the introduction of a classification for allergens can be considered timely and much needed in clinical practice.
The Journal of allergy and clinical immunology. 2023 Apr;():.
ISSN 1097-6825
Authors: Luciana Kase Tanno, Yann Briand, Mélissa Mary, David A Khan, James L Sublett, Mark L Corbett, Ruby Pawankar, Stefano Del Giacco, Maria Jose Torres, Ignacio J Ansontegui, Motohiro Ebisawa, Bryan Martin, Pascal Demoly
Copyright © 2023. Published by Elsevier Inc.
PMID 37019392