Criticality index conducted in pediatric emergency department triage.

OBJECTIVE

To develop and analyze the performance of a machine learning model capable of predicting the disposition of patients presenting to a pediatric emergency department (ED) based on triage assessment and historical information mined from electronic health records.

METHODS

We retrospectively reviewed data from 585,142 ED visits at a pediatric quaternary care institution between 2013 and 2020. An extreme gradient boosting machine learning model was trained on a randomly selected training data set (50%) to stratify patients into 3 classes: (1) high criticality (patients requiring intensive care unit [ICU] care within 4 h of hospital admission, patients who died within 4 h of admission, and patients who died in the ED); (2) moderate criticality (patients requiring hospitalization without the need for ICU care); and (3) low criticality (patients discharged home). Variables considered during model development included triage vital signs, aspects of triage nursing assessment, demographics, and historical information (diagnoses, medication use, and healthcare utilization). Historical factors were limited to the 6 months preceding the index ED visit. The model was tested on a previously withheld test data set (40%), and its performance analyzed.

RESULTS

The distribution of criticality among high, moderate, and low was 1.5%, 7.1%, and 91.4%, respectively. The one-versus-all area under the receiver operating characteristic (AUROC) curve for high and moderate criticality was 0.982 (95% CI 0.980, 0.983) and 0.968 (0.967, 0.969). The multi-class macro average AUROC and area under the receiver operating characteristic curve were 0.976 and 0.754. The features most integral to model performance included history of intravenous medications, capillary refill, emergency severity index level, history of hospitalization, use of a supplemental oxygen device, age, and history of admission to the ICU.

CONCLUSION

Pediatric ED disposition can be accurately predicted using information available at triage, providing an opportunity to improve quality of care and patient outcomes.