Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data

PURPOSE: Algorithms using information from electronic health records to identify adults with type 1 diabetes have not been well studied. Such algorithms would have applications in pharmacoepidemiology, drug safety research, clinical trials, surveillance, and quality improvement. Our main objectives were to determine the positive predictive value for identifying type 1 diabetes in adults using a published algorithm (developed by Klompas et al) and to compare it to a simple requirement that the majority of diabetes diagnosis codes be type 1. METHODS: We applied the Klompas algorithm and the diagnosis code criterion to a cohort of 66 690 adult Kaiser Permanente Colorado members with diabetes. We reviewed 220 charts of those identified as having type 1 diabetes and calculated positive predictive values. RESULTS: The Klompas algorithm identified 3286 (4.9% of 66 690) adults with diabetes as having type 1 diabetes. Based on chart reviews, the overall positive predictive value was 94.5%. The requirement that the majority of diabetes diagnosis codes be type 1 identified 3000 (4.5%) as having type 1 diabetes and had a positive predictive value of 96.4%. However, the algorithm criterion involving dispensing of urine acetone test strips performed poorly, with a positive predictive value of 20.0%. CONCLUSIONS: Data from electronic health records can be used to accurately identify adults with type 1 diabetes. When identifying adults with type 1 diabetes, we recommend either a modified version of the Klompas algorithm without the urine acetone test strips criterion or the requirement that the majority of diabetes diagnosis codes be type 1 codes.