Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study

• Main Authors: Kristin M. Lenoir, Lynne E. Wagenknecht, Jasmin Divers, Ramon Casanova, Dana Dabelea, Sharon Saydah, Catherine Pihoker, Angela D. Liese, Debra Standiford, Richard Hamman, Brian J. Wells, the SEARCH for Diabetes in Youth Study Group
• Format: Article
• Language: English
• Published: BMC 2021-10-01
• Series: BMC Medical Research Methodology
• Subjects: Electronic health records; Diabetes mellitus; Algorithms; Infants; Children; Adolescents
• Online Access: https://doi.org/10.1186/s12874-021-01394-8

Abstract Background Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. Methods A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, Cincinnati, OH, Seattle Children’s Hospital, Seattle, WA, and Children’s Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. Results Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. Conclusions Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.