ArticleThe journal of allergy and clinical immunology. Global2026
Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.
Article in The journal of allergy and clinical immunology. Global, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Hypersensitivity reactions to pertuzumab/trastuzumab fixed-dose combination (PHESGO®): successful management with a simplified 9-step subcutaneous desensitization protocol.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
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Authors and funding
15 authors.
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Abstract
Background: Hymenoptera venom immunotherapy is an established treatment for severe allergic reactions, aiming to modulate the immune response and reduce allergen sensitivity. However, traditional methods such as skin tests and specific IgE quantification often lack precision and fail to capture the multidimensional nature of clinical data. Objective: We investigated the relationships between wheal surface area, specific IgE levels, and patient age in allergic reactions to Hymenoptera venom, assessing immunotherapy effects before and after treatment. Methods: Data were retrospectively collected from 30 patients who underwent intradermal testing before and after immunotherapy. Wheal surface areas were measured using the semiautomated method (SAM), and specific IgE levels via immunoassays. Results: A positive correlation between wheal surface area and specific IgE was observed before and after treatment, both showing reductions after immunotherapy. Age showed no significant influence. Clustering revealed two consistent profiles: response and partial response. The Müller scale confirmed clinical improvement with reduced reaction severity. Conclusion: Immunotherapy reduces allergic response, as shown by decreased wheal size and IgE level. The integration of SAM and machine learning enables robust analysis of clinical data, supporting personalized allergy management.
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