# Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis

**Authors:** Stefano Palazzo, Marcello Albanesi, Mattia Cristallo, Francesco Pugliese, Nada Chaoul, Giulia Caffarella, Alessandro Cinquantasei, Cataldo Piarulli, Marco Cinquantasei, Marco Aprigliano, Roberto Caldelli, Claudio Loconsole, Silvio Tafuri, Eustachio Nettis, Attilio Di Girolamo

PMC · DOI: 10.1016/j.jacig.2026.100651 · The Journal of Allergy and Clinical Immunology: Global · 2026-01-29

## TL;DR

This study uses machine learning to analyze how patients respond to venom immunotherapy, finding two distinct response patterns and showing that immunotherapy reduces allergic reactions.

## Contribution

The novel use of machine learning clustering to identify patient subgroups in venom immunotherapy response.

## Key findings

- A positive correlation between wheal surface area and specific IgE levels was observed before and after treatment.
- Two consistent patient profiles—response and partial response—were identified through clustering.
- Immunotherapy led to reduced wheal size and IgE levels, indicating decreased allergic response.

## Abstract

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.

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.

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. K-means clustering, an unsupervised machine learning technique, was applied to identify patient subgroups on the basis of an integrated analysis of the 3 variables. Data normalization ensured comparability across different units.

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.

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.

## Full-text entities

- **Genes:** IGHE (immunoglobulin heavy constant epsilon) [NCBI Gene 3497] {aka IgE}
- **Diseases:** anaphylaxis (MESH:D000707), allergic (MESH:D004342), breathing difficulties (MESH:D004417), allergic symptoms (MESH:D063926), Asthma (MESH:D001249), swelling (MESH:D004487), Hymenoptera venom allergy (MESH:D000092422), skin allergies (MESH:D012871)
- **Chemicals:** SAM (-), histamine (MESH:D006632)
- **Species:** Apis mellifera (bee, species) [taxon 7460], Vespa crabro (European hornet, species) [taxon 7445], Polistes dominula (European paper wasp, species) [taxon 743375], Hymenoptera (hymenopterans, order) [taxon 7399], Vespidae (wasps, family) [taxon 7438], Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12950403/full.md

## References

64 references — full list in the complete paper: https://tomesphere.com/paper/PMC12950403/full.md

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Source: https://tomesphere.com/paper/PMC12950403