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Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model
Mandana Rezaeiahari1, Clare C Brown1, Arina Eyimina1
1College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Insights
This study developed a predictive model for childhood asthma exacerbations. While the model showed moderate accuracy, factors like race, ethnicity, and social determinants of health did not improve its performance.
Area of Science:
- Pediatric pulmonology
- Health informatics
- Machine learning in healthcare
Background:
- Childhood asthma exacerbations pose a significant health burden.
- Existing predictive models often overlook crucial social determinants of health (SDOH) and demographic factors.
- There is a need for models that incorporate race, ethnicity, and SDOH to predict asthma outcomes.
Purpose of the Study:
- To develop and evaluate a predictive model for childhood asthma exacerbations.
- To explore the predictive value of race, ethnicity, rural-urban commuting area (RUCA) codes, and Child Opportunity Index (COI) for asthma outcomes.
- To assess the importance of various SDOH in predicting asthma-related hospitalizations and emergency department (ED) visits.
Main Methods:
- Utilized insurance claims data from the Arkansas All-Payer Claims Database.
- Identified a cohort of 22,631 children aged 5-18 with asthma and continuous Medicaid enrollment.
- Employed conditional random forest models to predict asthma-related hospitalizations and ED visits.
Main Results:
- The predictive model achieved an area under the curve (AUC) of approximately 72-73%.
- Previous asthma-related hospitalizations or ED visits were the strongest predictors of future events.
- Medication use (reliever and controller) also contributed to predictive accuracy.
Conclusions:
- The developed model demonstrated moderate accuracy in predicting childhood asthma exacerbations.
- Race, ethnicity, RUCA codes, COI, and ICD-10 SDOH measures did not significantly enhance the model's predictive power.
- Future research may need to explore alternative or refined SDOH metrics for improved prediction.
Objective:
Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes.
Methods:
Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors. We identified a cohort of 22,631 children with asthma aged 5-18 years with 2 years of continuous Medicaid enrollment and at least one asthma diagnosis in 2018. The goal was to predict asthma-related hospitalizations and asthma-related emergency department (ED) visits in 2019. The analytic sample was 59% age 5-11 years, 39% White, 33% Black, and 6% Hispanic. Conditional random forest models were used to train the model.
Results:
The model yielded an area under the curve (AUC) of 72%, sensitivity of 55% and specificity of 78% in the OOB samples and AUC of 73%, sensitivity of 58% and specificity of 77% in the training samples. Consistent with previous literature, asthma-related hospitalization or ED visits in the previous year (2018) were the two most important variables in predicting hospital or ED use in the following year (2019), followed by the total number of reliever and controller medications.
Conclusions:
Predictive models for asthma-related exacerbation achieved moderate accuracy, but race and ethnicity, ICD-10 SDOH, RUCA codes, and COI measures were not important in improving model accuracy.
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