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Development of a Clinical Prediction Model for Recurrent Anaphylaxis
Suwannee Uthaisangsook1, Sagoontee Inkate2, Susita Wangchiraniran1
1Department of Pediatrics, Faculty of Medicine, Naresuan University, 99 Tha Pho, Mueang District, Phitsanulok 65000, Thailand.
Journal of Clinical Medicine
|June 12, 2026
Summary
A new model identifies key factors for recurrent anaphylaxis, including allergies and asthma, to predict patient risk. This tool aids in developing targeted prevention strategies for better patient safety.
Area of Science:
- Allergy and Immunology
- Clinical Prediction Modeling
- Public Health
Background:
- Recurrent anaphylaxis poses a significant patient safety risk.
- Effective prevention strategies require accurate risk identification.
- Targeted interventions depend on understanding recurrence predictors.
Purpose of the Study:
- To identify factors predicting anaphylaxis recurrence.
- To develop and validate a clinical prediction model for anaphylaxis recurrence risk.
- To enhance targeted preventive strategies for recurrent anaphylaxis.
Main Methods:
- Retrospective observational cohort study using an anaphylaxis registry (2011-2021).
- Multivariable Cox proportional hazards regression for model development.
- Model performance assessed via Area Under the Receiver Operating Characteristic Curve (AuROC) and calibration.
Main Results:
- Six predictors identified: food, insect, drug allergies, asthma, chest discomfort, severe anaphylaxis.
- A risk score model was developed, categorizing patients into low, moderate, and high-risk groups.
- The model showed acceptable discrimination (AuROC 0.773) and good calibration, with clinical utility.
Conclusions:
- A simple, clinically applicable prediction model for recurrent anaphylaxis risk has been developed.
- This tool can aid clinicians in identifying high-risk individuals.
- Improved risk stratification supports enhanced prevention and management of anaphylaxis.
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