Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults with Diabetes using Electronic Health Records in the DiCAYA Network

Objective: The Diabetes in Children, Adolescents, and Young Adults (DiCAYA) network seeks to create a nationwide electronic health record (EHR)-based diabetes surveillance system. This study aimed to develop a DiCAYA-wide EHR-based CP to identify prevalent cases of diabetes.

Research Design and Methods: We conducted network-wide chart reviews on 2,134 youth (age < 18y) and 2,466 young adults (age 18 – <45 y) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype.

Results: The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive value (PPV and NPV) in finding diabetes cases were above 90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved over 90% sensitivity, specificity, PPV and NPV in classifying T1D, while demonstrating lower but robust performance in identifying T2D, consistently maintaining above 80% across metrics.

Conclusions: The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinements. Its simplicity enables broad deployment across diverse EHR systems for diabetes surveillance.