Original Article
Allergy algorithm to increase pre-test probability of allergic disease
Abstract
Background: Since the introduction of molecular allergology, a big challenge of allergy diagnostics is to connect clinical symptoms with optimal test use and correct interpretation of results. The aim of the study was to (I) develop an algorithm that would meet that need; and (II) to evaluate the effect of the introduction of the algorithm into clinical practice.
Methods: The algorithm which was developed groups clinical symptoms into six categories: rhinoconjunctivitis/asthma, oral allergy syndrome (OAS), acute urticaria/angioedema, acute eczema /atopic dermatitis, anaphylaxis, and a combination of symptoms and associates them with the knowledge of possible allergen specificity. This information is combined with two basic allergen mixtures (panels), reflex testing of relevant food molecular components (MCs) and accompanied by interpretative comments. The effect of the introduction of this algorithm was evaluated, based on comparison of allergy diagnostics (request pattern) before introduction to after the introduction of the algorithm.
Results: The designed algorithm has led to a more problem-oriented approach in allergy diagnostics, resulting in less inhalation screenings, unchanged food screenings and an increase in the requested MCs. The OAS was seldom recognized or used as a symptom by specialists. The reduction in costs, by using the possibility that the disease presentation may be a consequence of a relatively not dangerous OAS, was therefore not achieved. All PR-10 positive proteins in various allergen sources showed also positivity for Birch antigen suggesting that allergy diagnostics may be more efficient if PR-10 of Birch is included in early screening. The awareness of the link between PR-10 and OAS, may also help in early recognition of OAS.
Conclusions: The screening based on this algorithm has potential to enable clinicians/general practitioners with a tool to increase the pre-test probability of allergy for the most frequently occurring allergens.
Methods: The algorithm which was developed groups clinical symptoms into six categories: rhinoconjunctivitis/asthma, oral allergy syndrome (OAS), acute urticaria/angioedema, acute eczema /atopic dermatitis, anaphylaxis, and a combination of symptoms and associates them with the knowledge of possible allergen specificity. This information is combined with two basic allergen mixtures (panels), reflex testing of relevant food molecular components (MCs) and accompanied by interpretative comments. The effect of the introduction of this algorithm was evaluated, based on comparison of allergy diagnostics (request pattern) before introduction to after the introduction of the algorithm.
Results: The designed algorithm has led to a more problem-oriented approach in allergy diagnostics, resulting in less inhalation screenings, unchanged food screenings and an increase in the requested MCs. The OAS was seldom recognized or used as a symptom by specialists. The reduction in costs, by using the possibility that the disease presentation may be a consequence of a relatively not dangerous OAS, was therefore not achieved. All PR-10 positive proteins in various allergen sources showed also positivity for Birch antigen suggesting that allergy diagnostics may be more efficient if PR-10 of Birch is included in early screening. The awareness of the link between PR-10 and OAS, may also help in early recognition of OAS.
Conclusions: The screening based on this algorithm has potential to enable clinicians/general practitioners with a tool to increase the pre-test probability of allergy for the most frequently occurring allergens.
