Development and Validation of a Novel Algorithm for Classifying Diabetes Type in Adolescents and Young Adults Using...

Accurate classification of diabetes type in early-onset populations is essential for epidemiologic and health services research but remains challenging when using electronic health record (EHR) data alone. Our objective was to develop and validate a diabetes typing algorithm distinguishing type 1 from type 2 diabetes in adolescents and young adults (AYAs).

We developed and validated an EHR-based algorithm to identify and classify diabetes type among individuals aged 10-30 years enrolled in the Kaiser Permanente Northern California network (2007-2024). Identification of AYAs with diabetes incorporated diagnosis codes, laboratory results, and medications. Diabetes type was classified using a hierarchical algorithm that leveraged pancreatic autoantibody results and longitudinal diagnostic patterns. Algorithm performance was assessed via blinded medical record review in a random subset of 100 AYAs with diabetes, representing 0.14% of the 71,385 AYAs with algorithm-identified diabetes.

Among 71,385 AYAs with diabetes, the disease was classified as type 1 diabetes in 16.6%, as type 2 diabetes in 75.2%, as other diabetes in 3.0%, and as indeterminate in 5.2%. When validated against blinded medical record review in a subset of patients, sensitivity and specificity were 100% and 98%, respectively, for type 1 diabetes and 98% and 95%, respectively, for type 2 diabetes. Inclusion of pancreatic autoantibody data prevented potential misclassification of type 1 diabetes in 609 patients, representing ∼6% of those with algorithm-classified type 1 diabetes.

This validated diabetes typing algorithm supports scalable early-onset diabetes research using EHR data.