Prediction of 30-day Pediatric Unplanned Hospitalizations Using the Johns Hopkins Adjusted Clinical Groups Risk Adjustment System

Background: The Johns Hopkins ACG System is widely used to predict patient healthcare service use and costs. Most applications have focused on adult populations. In this study, we evaluated the use of the ACG software to predict pediatric unplanned hospital admission in a given month, based on the past year's clinical information captured by electronic health records (EHRs).

Methods And Findings: EHR data from a multi-state pediatric integrated delivery system were obtained for 920,051 patients with at least one physician visit during January 2009 to December 2016. Over this interval an average of 0.36% of patients each month had an unplanned hospitalization. In a 70% training sample, we used the generalized linear mixed model (GLMM) to generate regression coefficients for demographic, clinical predictors derived from the ACG system, and prior year hospitalizations. Applying these coefficients to a 30% test sample to generate risk scores, we found that the area under the receiver operator characteristic curve (AUC) was 0.82. Omitting prior hospitalizations decreased the AUC from 0.82 to 0.80, and increased under-estimation of hospitalizations at the greater risk levels. Patients in the top 5% of risk scores accounted for 43% and the top 1% of risk scores accounted for 20% of all unplanned hospitalizations.

Conclusions: A predictive model based on 12-months of demographic and clinical data using the ACG system has excellent predictive performance for 30-day pediatric unplanned hospitalization. This model may be useful in population health and care management applications targeting patients likely to be hospitalized. External validation at other institutions should be done to confirm our results.