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Machine learning prediction of asthma and allergic rhinitis in children with early-onset atopic dermatitis
Wansu Chen1, Botao Zhou1, Michael Schatz2
1Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, Calif.
Insights
Machine learning models can predict which children with early-onset atopic dermatitis (AD) will develop asthma and allergic rhinitis. These tools enable proactive, individualized care for at-risk children.
Area of Science:
- Computational biology
- Pediatric allergy and immunology
- Health informatics
Background:
- Early-onset atopic dermatitis (AD) is a known precursor to respiratory conditions.
- Identifying children at risk for persistent, moderate-to-severe asthma and allergic rhinitis is challenging.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting individualized risk of moderate-to-severe persistent asthma and allergic rhinitis.
- To assess risk in children diagnosed with AD before age 3.
Main Methods:
- Retrospective birth cohort study utilizing longitudinal electronic health record (EHR) data.
- Developed two prediction models (Comprehensive EHR and Simplified Clinical) for asthma and allergic rhinitis in children aged 5-11.
- Evaluated model performance using area under the curve (AUC), sensitivity, positive predictive value (PPV), and calibration.
Main Results:
- Asthma models showed strong discrimination (AUC: 0.893 Comprehensive; 0.892 Simplified).
- Rhinitis models demonstrated moderate performance (AUC: 0.793 Comprehensive; 0.773 Simplified).
- Models accurately stratified risk, particularly in highest-risk groups, supporting individualized care.
Conclusions:
- ML models utilizing early-life clinical data can effectively stratify risk for asthma and allergic rhinitis by school age.
- These models support proactive and personalized healthcare strategies for children with early-onset AD.
- The findings highlight the potential of EHR data for predicting long-term respiratory health outcomes.
Background:
Early-onset atopic dermatitis (AD) is a known precursor to respiratory atopic diseases, but identifying which children will develop persistent moderate-to-severe asthma and allergic rhinitis at school age remains difficult.
Objective:
We sought to develop and validate machine learning models that predict individualized risk for moderate-to-severe persistent asthma and allergic rhinitis in children diagnosed with AD before age 3.
Methods:
We conducted a retrospective birth cohort study using longitudinal electronic health record data from Kaiser Permanente Southern California. Two prediction models were developed for each outcome (asthma and rhinitis) among children aged 5-11: a comprehensive electronic health records model using detailed, structured clinical variables; and a simplified clinical model that was based on fewer, routinely available clinical features. Model performance was evaluated by area under the curve (AUC), sensitivity, positive predictive value (PPV), and calibration across risk strata.
Results:
Among 10,688 eligible children, asthma models demonstrated strong discrimination (AUC = 0.893 comprehensive; AUC = 0.892 simplified). At 95% specificity, the comprehensive model achieved 40.4% sensitivity and 39.3% PPV; the simplified model reached 36.2% sensitivity and 33.8% PPV. Rhinitis models showed moderate performance (AUC = 0.793 and AUC = 0.773); at 90% specificity, the comprehensive model achieved 35.5% sensitivity and 72.7% PPV, while the simplified model reached 34.0% sensitivity and 69.2% PPV. Calibration was acceptable, with strong agreement in the highest-risk groups.
Conclusion:
Machine-learning models using early-life clinical data can accurately stratify risk for moderate-to-severe persistent asthma and allergic rhinitis by school age, supporting proactive, individualized care.
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