An Automated Risk Index for Diabetic Ketoacidosis in Pediatric Patients With Type 1 Diabetes: The RI-DKA
David D. Schwartz, Rosa Banuelos, Serife Uysal, Mili Vakharia, Kristen R. Hendrix, Kelly Fegan‐Bohm, Sarah K. Lyons, Rona Sonabend, Sheila Gunn, Selorm Dei-Tutu
Abstract
Identifying patients at high risk for diabetic ketoacidosis (DKA) is crucial for informing efforts at preventive intervention. This study sought to develop and validate an electronic medical record (EMR)-based tool for predicting DKA risk in pediatric patients with type 1 diabetes. Based on analysis of data from 1,864 patients with type 1 diabetes, three factors emerged as significant predictors of DKA: most recent A1C, type of health insurance (public vs. private), and prior DKA. A prediction model was developed based on these factors and tested to identify and categorize patients at low, moderate, and high risk for experiencing DKA within the next year. This work demonstrates that risk for DKA can be predicted using a simple model that can be automatically derived from variables in the EMR.