Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model

Abstract

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.

Keywords:

• Random forest
• conditional random forest
• variable importance
• machine learning
• claims data

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Research reported in this publication was supported by the National Center For Advancing Translational Sciences of the National Institutes of Health under award number KL2 T R003108 and UL1 T R003107. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Partial support for this project was provided by the AID/ABI/ACHI All Payer Claims Database. Cooperative Agreement. CCB was supported by the National Institute on Minority Health and Health Disparities (NIMHD) of the National Institutes of Health [1K01MD018072].