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Trajectory Analysis of Pediatric Longitudinal Allergen Sensitization Patterns From Real World Data
Sungyun Kim1, Minjeong Lee2, Ji Soo Park3
1Interdisciplinary Program of Medical Informatics, Seoul National University College of Medicine, Seoul, Korea.
Insights
Early house dust mite sensitization predicts asthma development in children. Machine learning models can identify high-risk individuals for personalized allergy prevention strategies.
Area of Science:
- Allergy and Immunology
- Computational Biology
- Pediatric Medicine
Background:
- Allergen sensitization patterns are crucial for understanding allergic disease development.
- Longitudinal tracking of sensitization offers insights into disease trajectories.
Purpose of the Study:
- To investigate the link between allergen sensitization patterns and allergic disease diagnoses.
- To identify static and dynamic sensitization clusters using machine learning.
- To predict future allergic diseases based on early sensitization profiles.
Main Methods:
- Retrospective cohort study of 1,656 pediatric and young adult patients.
- Analysis of electronic health records and allergy testing data (2002-2021).
- Unsupervised learning (Latent Class Analysis, Gaussian Mixture Models) for cluster and trajectory identification; Categorical Boosting for prediction.
Main Results:
- Four cross-sectional sensitization clusters identified: HDM-dominant, HDM+food, polysensitization, multiple inhalant.
- Nine longitudinal trajectories revealed a shift from food to aeroallergen sensitization.
- Early house dust mite (HDM) sensitization strongly predicted asthma; eczema and food allergies linked to food/polysensitization.
Conclusions:
- Longitudinal sensitization trajectories, especially early HDM dominance, predict asthma.
- Machine learning modeling of sensitization history aids early identification of high-risk children.
- Personalized allergy prevention strategies can be informed by these findings.
Purpose:
To explore the relationship between allergen sensitization patterns and allergic disease diagnosis, with a particular focus on longitudinal sensitization trajectories. Using a large pediatric and young adult cohort from a common data model (CDM)-formatted clinical database, we applied unsupervised learning techniques to identify both static and dynamic sensitization clusters. We developed a predictive model to assess how early sensitization profiles contribute to developing allergic disease later in life.
Methods:
We conducted a single-center, retrospective cohort study using electronic health records from the Seoul National University Hospital CDM. Patients who underwent a skin prick test, multiple allergen simultaneous test, or ImmunoCAP between 2002 and 2021 were included if they were tested in 2 or more age groups. Seventeen allergen categories were analyzed. Latent class analysis identified cross-sectional sensitization clusters, and Gaussian mixture models were used to derive longitudinal trajectories. A Categorical Boosting multilabel classification model was used to predict allergic diagnoses with interpretability enhanced by SHapley Additive exPlanations.
Results:
A total of 1,656 patients were analyzed. Four cross-sectional clusters were identified (house dust mite [HDM]-dominant, HDM + food, polysensitization, and multiple inhalant clusters). Nine longitudinal trajectories showed a developmental shift from food sensitization to aeroallergen sensitization. Early HDM sensitization was strongly associated with subsequent asthma, whereas eczema and food allergies are linked to food or polysensitization trajectories. Rhinitis was common across aeroallergen clusters. The prediction performance was robust for asthma (F1 score, 0.73; area under the curve, 0.80), moderate for eczema and food allergies, and limited for rhinitis.
Conclusions:
Longitudinal sensitization trajectories, particularly early HDM dominance, were predictive of asthma development, whereas the predictive values for eczema, food allergies, and rhinitis were weaker. Modeling sensitization history using machine learning may enable the early identification of high-risk children and inform personalized strategies for allergy prevention.
