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Evaluation of Risk Scores to Predict Pediatric Severe Asthma Exacerbations
Chao Niu1, Yuanfang Xu2, Christine L Schuler3
1Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, Chongqing, China.
Insights
Identifying children at high risk for asthma exacerbations is crucial for proactive management. A new prediction model uses electronic health data to identify at-risk children, enabling targeted interventions and reducing healthcare costs.
Area of Science:
- Pediatric Pulmonology
- Clinical Informatics
- Health Services Research
Background:
- Asthma exacerbations in children result in significant healthcare utilization and costs.
- Early identification of high-risk children is essential for proactive asthma management strategies.
Purpose of the Study:
- To evaluate common asthma risk factors for predicting exacerbation probability in children aged 0-21 years.
- To develop and validate a predictive model using electronic medical record (EMR) data.
Main Methods:
- Longitudinal EMR data from over 3000 children with asthma were analyzed over 7 years.
- The study population was stratified into age groups (0-4, 5-11, 12-21 years) with derivation and validation cohorts.
- A risk score model was developed to predict exacerbation risk within the next 12 months.
Main Results:
- The prediction model was validated across age groups and calendar years.
- Key risk factors varied by age: race, allergic sensitization, and smoke exposure for 0-4 years; abnormal spirometry and obesity for >12 years.
- Higher risk scores correlated with increased probability of asthma exacerbation.
Conclusions:
- An asthma exacerbation prediction model and clinical tool can aid clinicians in identifying at-risk children.
- This tool supports targeted, aggressive management and risk mitigation for pediatric asthma exacerbations.
Background:
Asthma exacerbations commonly lead to unplanned health care utilization and are costly. Early identification of children at increased risk of asthma exacerbations would allow a proactive management approach.
Objective:
We evaluated common asthma risk factors to predict the probability of exacerbation for individual children aged 0-21 years using data from the electronic medical record (EMR).
Methods:
We analyzed longitudinal EMR data for over 3000 participants with asthma seen at Cincinnati Children's Hospital Medical Center over a 7-year period. The study population was divided into 3 age groups: 0-4, 5-11, and 12-21 years. Each age group was divided into a derivation cohort and a validation cohort, which were used to build a risk score model. We predicted risk of exacerbation in the next 12 months, validated the scores by risk stratum, and developed a clinical tool to determine the risk level based on this model.
Results:
Risk model results were confirmed with validation cohorts by calendar year and age groups. Race, allergic sensitization, and smoke exposure were each important risk factors in the 0-4 age group. Abnormal spirometry and obesity were more sensitive predictors of exacerbation in children >12 years. For each age group, a higher expanded score was associated with a higher predicted probability of an asthma exacerbation in the subsequent year.
Conclusion:
This asthma exacerbation prediction model, and the associated clinical tool, may assist clinicians in identifying children at high risk for exacerbation that may benefit from more aggressive management and targeted risk mitigation.
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