Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults With Diabetes Using Electronic Health Records in the DiCAYA Network
Authors: Hui Shao, Lorna E. Thorpe, Shahidul Islam, Jiang Bian, Yi Guo, Piaopiao Li, Sarah Bost, Dana Dabelea, Rebecca Conway, Tessa Crume, Brian S. Schwartz, Annemarie G. Hirsch, Katie S. Allen, Brian E. Dixon, Shaun J. Grannis, Eva Lustigova, Kristi Reynolds, Marc Rosenman, Victor W. Zhong, Anthony Wong, Pedro Rivera, Thuy Le, Meredith Akerman, Sarah Conderino, Anand Rajan, Angela D. Liese, Caroline Rudisill, Jihad S. Obeid, Joseph A. Ewing, Charles Bailey, Eneida A. Mendonca, Ibrahim Zaganjor, Deborah Rolka, Giuseppina Imperatore, Meda E. Pavkov, Jasmin Divers, Charles Bailey, Christopher Forrest, Levon Utidjian, Mitch Maltenfort, Amy Shah, Eneida A. Mendonca, G. Todd Alonso, Sara Deakyne-Davies, Tim Bunnell, Anne Kazak, Melody Kitzmiller, Manmohan Kamboj, Dimitri Christakis, Daksha Ranade, Annemarie G. Hirsch, Joseph J. DeWalle, H. Lester Kirchner, Meredith Lewis, Dione G. Mercer, Cara M. Nordberg, Amy Poissant, Brian S. Schwartz, Brian E. Dixon, Shaun Grannis, Seho Park, Katie Allen, Anna Roberts, Nimish Valvi, Jeff Warvel, Ashley Wiensch, Tamara Hannon, Kristi Reynolds, John Chang, Eva Lustigova, Don McCarthy, Matthew T. Mefford, Rong Wei, Hui Zhou, Marc Rosenman, Lu Zhang, George Lales, Anthony Wong, Allison Zelinski, Yuan Luo, Mark Weiner, Pedro Rivera, Thomas Carton, Elizabeth Nauman, Harold P. Lehmann, Victor W. Zhong, Jasmin Divers, Lorna E. Thorpe, Meredith Akerman, Rebecca Anthopolos, Stefanie Bendik, Sarah Conderino, Andrew Fair, Jessica Guillaume, Shahidul Islam, Alan Jacobson, David C. Lee, Chinyere Okpara, Anand Rajan, Andrea Titus, Tessa Crume, Dana Dabelea, Theresa Anderson, Anna Bellatorre, Rebecca Conway, Toan Ong, Jack Pattee, Shawna Burgett, Bethelhem Betty Shiferaw, Jiang Bian, Yi Guo, Hui Shao, Elizabeth A. Shenkman, Sarah J. Bost, William T. Donahoo, William R. Hogan, Piaopiao Li, Tianchen Lyu, Mattia Prosperi, Yonghui Wu, Angela D. Liese, Bo Cai, Lisa Knight, Caroline Rudisill, Jessica Stucker, Deborah Bowlby, Jihad S. Obeid, Elaine Apperson, Alex Ewing, Giuseppina Imperatore, Meda Pavkov, Deborah B. Rolka.
Journal: Diabetes Care (2025)

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 computable phenotype (CP) to identify prevalent cases of diabetes. RESEARCH DESIGN AND METHODS We conducted network-wide chart reviews of 2,134 youth (aged &amp;lt;18 years) and 2,466 young adults (aged 18 to &amp;lt;45 years) 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 values (PPV and NPV, respectively) in finding diabetes cases were &amp;gt;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 &amp;gt;90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining &amp;gt;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 refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.