Predicting Appropriate Hospital Admission of Emergency Department Patients with Bronchiolitis: Secondary Analysis

Abstract

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.

Keywords: appropriate hospital admission; bronchiolitis; emergency department; machine learning; predictive model.

Figure 1

The operational definition of appropriate hospital admission for emergency department patients with bronchiolitis.

Figure 2

The receiver operating characteristic curve of our model.

Figure 3

The calibration plot of our model by decile of predicted probability of appropriate admission.

Figure 4

The degree of missing values of each feature with missing values. SpO₂: peripheral capillary oxygen saturation.

Figure 5

The probability mass function of the number of features with missing values in each data instance.