Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network

Abstract

Aim: To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System.

Materials and methods: The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.5%; or (2) fasting glucose levels ≥126 mg/dL; or (3) random plasma glucose levels ≥200 mg/dL; or (4) a 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.

Results: Presumed cases without type 1 (T1D) or type 2 diabetes (T2D) diagnosis codes were categorized as non-diabetes/other types of diabetes. The rest were categorized as T1D if the most recent diagnosis was T1D, or otherwise categorized as T2D if the most recent diagnosis was 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 536 reviewed cases, 159 and 64 had T1D and T2D, respectively. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98% and 96%, respectively, for T1D and 95%, 95% and 73% for T2D.

Conclusion: 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.

Keywords: database research; real‐world evidence; type 1 diabetes; type 2 diabetes.

Figure 1.

Illustration of decision-tree-based algorithm Note: Dx refers to diagnosis; T1D refers to type 1 diabetes; T2D refers to type 2 diabetes; NON-D refers to non-diabetes; Other-D refers to other type of diabetes; EDM refers that the provider’s specialty is “Endocrinology, Diabetes and Metabolism”; Ab refers to autoantibodies that are markers of type 1 diabetes, including include glutamic acid decarboxylase autoantibodies (GADA or Anti-GAD), islet cell autoantibodies (ICA), insulin autoantibodies (IAA), insulinoma-associated-2 autoantibodies (IA-2A or Anti-IA-2), or zinc transporter 8 autoantibodies (ZnT8A or Anti-ZnT8). Other types of diabetes include secondary diabetes mellitus, diabetes mellitus due to underlying condition, drug or chemical induced diabetes mellitus, other specified diabetes mellitus.

Figure 2.

Illustration of rule-based algorithm Note: Dx refers to diagnosis; T1D refers to type 1 diabetes; T2D refers to type 2 diabetes. Other types of diabetes include secondary diabetes mellitus, diabetes mellitus due to underlying condition, drug or chemical induced diabetes mellitus, other specified diabetes mellitus. T1D antibodies include glutamic acid decarboxylase autoantibodies (GADA or Anti-GAD), islet cell autoantibodies (ICA), insulin autoantibodies (IAA), insulinoma-associated-2 autoantibodies (IA-2A or Anti-IA-2), or zinc transporter 8 autoantibodies (ZnT8A or Anti-ZnT8).