Development of a low‐dimensional model to predict admissions from triage at a pediatric emergency department. Issue 4 (15th July 2022)

• Record Type: Journal Article
• Authors: Leonard, Fiona; Gilligan, John; Barrett, Michael J.
• Is Part Of: JACEP open. Volume 3:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-15
• Subjects: Medical emergencies -- Periodicals; 616.025
• DOI: 10.1002/emp2.12779
• Languages: English
• ISSNs: 0361-1124

Abstract

Abstract: Objectives: This study aims to develop and internally validate a low‐dimensional model to predict outcomes (admission or discharge) using commonly entered data up to the post‐triage process to improve patient flow in the pediatric emergency department (ED). In hospital settings where electronic data are limited, a low‐dimensional model with fewer variables may be easier to implement. Methods: This prognostic study included ED attendances in 2017 and 2018. The Cross Industry Standard Process for Data Mining methodology was followed. Eligibility criteria was applied to the data set, splitting into 70% train and 30% test. Sampling techniques were compared. Gradient boosting machine (GBM), logistic regression, and naïve Bayes models were created. Variables of importance were obtained from the model with the highest area under the curve (AUC) and used to create a low‐dimensional model. Results: Eligible attendances totaled 72, 229 (15% admission rate). The AUC was 0.853 (95% confidence interval [CI], 0.846–0.859) for GBM, 0.845 (95% CI, 0.838–0.852) for logistic regression and 0.813 (95% CI, 0.806–0.821) for naïve Bayes. Important predictors in the GBM model used to create a low‐dimensional model were presenting complaint, triage category, referral source, registration month, location type (resuscitation/other), distance traveled, admission history, and weekday (AUC 0.835 [95% CI, 0.829‐0.842]). Conclusions: Admission and discharge probability can be predicted early in a pediatric ED using 8 variables. Future work could analyze the false positives and false negatives to gain an understanding of the implementation of these predictions.