An algorithm to safely manage oral food challenge in an office-based setting for children with multiple food allergies

Abstract

Background: In France, from 30% to 35% of children suffer from multiple food allergies (MFA). The gold standard to diagnosis a food allergy is the oral food challenge (OFC) which is conducted in a hospital setting due to risk of anaphylaxis. The aim of this study was to evaluate an algorithm to predict OFCs at low risk of anaphylaxis that could safely be performed in an office-based setting. Methods: Children with MFA and at least one open OFC reactive or non-reactive to other allergens were included. The algorithm was based on multiple clinical and biological parameters related to food allergens, and designed mainly to predict “low-risk” OFCs i.e., practicable in an office-based setting. The algorithm was secondarily tested in a validation cohort. Results: Ninety-one children (median age 9 years) were included; 94% had at least one allergic comorbidity with an average of three OFCs per child. Of the 261 OFCs analyzed, most (192/261, 74%) were non-reactive. The algorithm failed to correctly predict 32 OFCs with a potentially detrimental consequence but among these only three children had severe symptoms. One hundred eighty-four of the 212 “low-risk” OFCs, (88%) were correctly predicted with a high positive predictive value (87%) and low negative predictive value (44%). These results were confirmed with a validation cohort giving a specificity of 98% and negative predictive value of 100%. Conclusion: This study suggests that the algorithm we present here can predict “low-risk” OFCs in children with MFA which could be safely conducted in an office-based setting. Our results must be confirmed with an algorithm-based machine-learning approach.