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Development and validation of a predictive risk nomogram for atopic dermatitis
Jiayue Cui1, Yahui Song1, Qin Rui1
1The Fourth Affiliated Hospital of Soochow University, Suzhou Dushu Lake Hospital, Medical Centre of Soochow University/Centre of Clinical Laboratory and Translational Medicine, Suzhou, China.
Postepy Dermatologii I Alergologii
|February 5, 2026
Summary
Frequent hospital visits, allergic disease history, family allergy history, and infantile onset are key predictors for atopic dermatitis (AD). A new predictive model utilizing these factors demonstrates significant value for early AD risk assessment.
Area of Science:
- Dermatology
- Epidemiology
- Clinical Prediction Modeling
Background:
- Atopic dermatitis (AD) represents a significant global health burden.
- Understanding factors influencing AD incidence is crucial for effective management.
Purpose of the Study:
- To identify key factors associated with atopic dermatitis (AD) incidence.
- To develop and validate a predictive risk nomogram model for AD.
Main Methods:
- Retrospective analysis of 469 dermatitis/eczema patients (216 AD, 253 non-AD).
- Univariate and multivariate logistic regression to identify risk factors.
- Model validation using calibration curves, Hosmer-Lemeshow tests, ROC curves, and DCA.
Main Results:
- Significant differences between AD and non-AD groups included age, allergy history, family history, infantile onset, medical visits, eosinophil count, and IgE levels.
- Key predictors for AD incidence identified: number of medical visits (OR=1.16), history of allergic diseases (OR=2.68), family history of allergy (OR=2.30), and infantile onset history (OR=23.80).
- The predictive model showed good fit and accuracy (AUC training=0.808, validation=0.812) with clinical utility.
Conclusions:
- Frequent hospital visits, personal/family history of allergies, and infantile onset are strongly linked to AD development.
- The developed nomogram model serves as a valuable tool for predicting AD occurrence.
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