Language
English
Publication Date
1-1-2022
Journal
Clinical Diabetes
DOI
10.2337/cd21-0070
PMID
35669298
PMCID
PMC9160557
PubMedCentral® Posted Date
4-15-2022
PubMedCentral® Full Text Version
Post-print
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.
Published Open-Access
yes
Recommended Citation
Schwartz, David D; Banuelos, Rosa; Uysal, Serife; et al., "An Automated Risk Index for Diabetic Ketoacidosis in Pediatric Patients With Type 1 Diabetes: The RI-DKA" (2022). Faculty and Staff Publications. 3102.
https://digitalcommons.library.tmc.edu/baylor_docs/3102
Included in
Endocrine System Diseases Commons, Endocrinology, Diabetes, and Metabolism Commons, Internal Medicine Commons, Medical Sciences Commons, Pediatrics Commons