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Predicting Appropriate Hospital Admission of Emergency Department Patients with Bronchiolitis: Secondary Analysis
Gang Luo1, Bryan L Stone2, Flory L Nkoy2
1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.
Insights
A new machine learning model accurately predicts appropriate hospital admission for bronchiolitis in children, improving emergency department care. This tool can reduce practice variation and enhance patient outcomes.
Area of Science:
- Pediatric Emergency Medicine
- Clinical Decision Support Systems
- Machine Learning in Healthcare
Background:
- Bronchiolitis is a leading cause of hospitalization in children under two years old.
- Current management of bronchiolitis in emergency departments lacks objective criteria, leading to significant practice variation.
- A previous study defined appropriate hospital admission, revealing 6.08% of disposition decisions were inappropriate.
Purpose of the Study:
- To develop a highly accurate predictive model for appropriate hospital admission in emergency department patients with bronchiolitis.
- To provide clinicians with an objective tool to guide disposition decisions, reducing subjective variability.
Main Methods:
- Development of the first machine learning classification model using Intermountain Healthcare data (2011-2014).
- The model was designed to predict appropriate hospital admission for pediatric patients diagnosed with bronchiolitis.
Main Results:
- The model achieved 90.66% accuracy, with a sensitivity of 92.09% and specificity of 89.96%.
- The area under the receiver operating characteristic curve was 0.960, indicating strong predictive performance.
- Potential areas for model enhancement were identified for future research.
Conclusions:
- The developed model demonstrates high accuracy in predicting appropriate hospital admission for bronchiolitis.
- Further refinement of this model can form the basis for clinical decision-support tools.
- These tools can standardize and improve disposition decisions for children with bronchiolitis in emergency settings.
Background:
In children below the age of 2 years, bronchiolitis is the most common reason for hospitalization. Each year in the United States, bronchiolitis causes 287,000 emergency department visits, 32%-40% of which result in hospitalization. Due to a lack of evidence and objective criteria for managing bronchiolitis, clinicians often make emergency department disposition decisions on hospitalization or discharge to home subjectively, leading to large practice variation. Our recent study provided the first operational definition of appropriate hospital admission for emergency department patients with bronchiolitis and showed that 6.08% of emergency department disposition decisions for bronchiolitis were inappropriate. An accurate model for predicting appropriate hospital admission can guide emergency department disposition decisions for bronchiolitis and improve outcomes, but has not been developed thus far.
Objective:
The objective of this study was to develop a reasonably accurate model for predicting appropriate hospital admission.
Methods:
Using Intermountain Healthcare data from 2011-2014, we developed the first machine learning classification model to predict appropriate hospital admission for emergency department patients with bronchiolitis.
Results:
Our model achieved an accuracy of 90.66% (3242/3576, 95% CI: 89.68-91.64), a sensitivity of 92.09% (1083/1176, 95% CI: 90.33-93.56), a specificity of 89.96% (2159/2400, 95% CI: 88.69-91.17), and an area under the receiver operating characteristic curve of 0.960 (95% CI: 0.954-0.966). We identified possible improvements to the model to guide future research on this topic.
Conclusions:
Our model has good accuracy for predicting appropriate hospital admission for emergency department patients with bronchiolitis. With further improvement, our model could serve as a foundation for building decision-support tools to guide disposition decisions for children with bronchiolitis presenting to emergency departments.
International Registered Report Identifier (Irrid):
RR2-10.2196/resprot.5155.
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