1294-P: A Novel Electronic Medical Record (EMR) Search Method to Identify Atypical Diabetes Patients

• Published in: Diabetes (New York, N.Y.) Vol. 73; p. 1
• Main Authors: Ahmed, Maaz, Kubota-Mishra, Elizabeth A, Siller, Alejandro F, Davis, Ansley E, Migacz, Iliana, Sisley, Stephanie, Faruqi, Jordana, Saeed, Zeb I, Ahmed, Sarah, Philipson, Louis H, Redondo, Maria J, Balasubramanyam, Ashok, Tosur, Mustafa
• Format: Journal Article
• Language: English
• Published: New York American Diabetes Association 01.06.2024
• Subjects: Autoantibodies; Beta cells; Diabetes; Diabetes mellitus; Diagnosis; Electronic medical records; Ketoacidosis
• ISSN: 0012-1797; 1939-327X
• DOI: 10.2337/db24-1294-P

Introduction & Objectives: Efficient automated EMR review methods to identify people with atypical diabetes (DM) for research are lacking. We aimed to develop a novel Python-based Expeditious Program for EMR Review (PEPPER) and assess its efficiency. Methods: We extracted the list of 1660 youth (<19 yo) with type 2 DM (T2D) seen between 2019-2022 from EMR to identify candidates with A-β+ (autoantibody negative, preserved β-cell function) Ketosis-prone DM (KPD) for enrollment into Rare and Atypical Diabetes Network (RADIANT). We developed PEPPER to identify diabetic ketoacidosis (DKA) occurrence within 6 months (mo) of diagnosis to prioritize individuals for detailed manual chart review for RADIANT eligibility. We also manually reviewed EMR of 100 youth with T2D to identify DKA occurrence within 6 mo of diagnosis without PEPPER for comparison. Results: PEPPER identified 110 youth with T2D who had DKA within 6 mo of diagnosis. Twenty-one met the RADIANT A-β+ KPD criteria. The time spent to identify those with T2D and DKA was significantly shorter with PEPPER compared to manual review (13.4 ± 3.9 vs. 26.6 ± 9.4 seconds, p<0.001), translating to 6.2 vs. 12.3 hours to review 1660 charts with and without PEPPER. Both methods yielded identical results, confirming PEPPER's accuracy. Conclusion: We developed a novel, efficient and reliable EMR review method that could be used on large cohorts to identify research candidates.