1262-P: Developing a Computable Phenotype Algorithm for Identification of Children and Adolescents with Diabetes Using Electronic Health Records

The Assessing the Burden of Diabetes by Type in Children, Adolescents, and Young Adults (DiCAYA) Network, a CDC/NIDDK-funded collaborative, aims to create a multi-site electronic health record (EHR) -based diabetes surveillance system. Foundational to the network's efforts is the development of a computable phenotype (CP) algorithm that can identify cases of diabetes. To advance the mission of the DiCAYA network, University of Florida (UF) Health system researchers developed a pilot CP algorithm for identifying diabetes cases in youth. The CP algorithm was iteratively derived based on structured data from EHRs (UF Health system 2012-2020) . We randomly selected 500 presumed cases among individuals < 18 years old who has (1) HbA1c ≥ 6.5%; or (2) fasting glucose ≥ 126 mg/dL; or (3) random plasma glucose ≥ 200 mg/dL; or (4) diabetes-related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes-related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type. Presumed cases without type 1 (T1D) or type 2 (T2D) diabetes diagnosis codes were categorized as nondiabetes or other types. The rest were categorized as T1D if the ratio of T1D codes to the sum of T1D and T2D codes was ≥ 0.5, or otherwise categorized as T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from step 1. Among the 500 reviewed cases, 159 and 64 had T1D and T2D. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98%, and 96% for T1D; 95%, 95%, and 73% for T2D. We developed a highly accurate EHR-based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods. A DiCAYA-wide validation and algorithm refinement process will be conducted. Disclosure P.Li: None. M.Prosperi: None. B.E.Dixon: Advisory Panel; Merck Sharp & Dohme Corp. D.Dabelea: None. L.H.Utidjian: None. T.L.Crume: None. L.Thorpe: None. A.D.Liese: None. D.Schatz: Advisory Panel; Abbott Diabetes, Medtronic. M.A.Atkinson: None. M.J.Haller: Advisory Panel; SAB Biotherapeutics , Consultant; MannKind Corporation, Sanofi. E.Spector: None. E.Shenkman: None. J.Bian: None. Y.Guo: None. H.Shao: Board Member; BRAVO4HEALTH, LLC. M.A.Atkinson: None. K.Alkhuzam: None. R.S.Patel: None. W.T.Donahoo: None. S.Bost: None. T.Lyu: None. Y.Wu: None. W.Hogan: None. Funding CDC/NIDDK U18DP006512