Surveillance of DM status, type, and date of diagnosis requires chart review. We previously showed that ≥ 2 international classification of diseases (ICD) DM codes predicts DM status well. This project aimed to derive an EHR-based algorithm to predict diagnosis date. Youth (< 20 yrs) with potential DM evidence in 2017 (ICD DM code, elevated glucose or HbA1c, or DM medication) were identified from the inpatient and outpatient EHR data of 3 Children’s Hospitals participating in the SEARCH for Diabetes in Youth Study. Potential cases were chart reviewed to determine true DM status, DM type, and diagnosis date. Cases were restricted to those with diagnosis date and data post-2008 due to EHR limitations. We compared 2 algorithms for predicting diagnosis date: (1) first occurrence of an ICD DM code, and (2) first occurrence of any of the following: ICD DM code, elevated glucose or HbA1c, or DM medication. Among cases identified by the ICD status algorithm (n=3,678), the ICD code and multi-criteria algorithms classified diagnosis year correctly 88.9% and 88.4% of the time, respectively. Classification accuracy improved over time (Figure). Performance was poorer for type 2 than type 1. An ICD code model to predict date of diagnosis can accurately identify diagnosis date within these pediatric hospital systems. Improvement over time is likely due to increases in the amount of EHR data. Manual review of type 2 cases may be necessary.


K.M. Lenoir: None. L.E. Wagenknecht: None. J. Divers: None. R. Casanova: None. J.M. Lawrence: None. D. Dabelea: None. C. Pihoker: None. S. Saydah: None. A.D. Liese: None. D. Standiford: None. B.J. Wells: None.


Centers for Disease Control and Prevention (5U18DP006131-05-00)

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