Emily B Schroeder1,2,3, William T Donahoo2,3, Glenn K Goodrich1, Marsha A Raebel1,4. 1. Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA. 2. Division of Endocrinology, Colorado Permanente Medical Group, Denver, CO, USA. 3. Division of Endocrinology, Metabolism and Diabetes, University of Colorado Denver, Aurora, CO, USA. 4. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Denver, Aurora, CO, USA.
Abstract
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.
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.
Authors: Gregory A Nichols; Emily B Schroeder; Andrew J Karter; Edward W Gregg; Jay Desai; Jean M Lawrence; Patrick J O'Connor; Stanley Xu; Katherine M Newton; Marsha A Raebel; Ram D Pathak; Beth Waitzfelder; Jodi Segal; Jennifer Elston Lafata; Melissa G Butler; H Lester Kirchner; Abraham Thomas; John F Steiner Journal: Am J Epidemiol Date: 2014-12-16 Impact factor: 4.897
Authors: Leif I Solberg; Karen I Engebretson; Joann M Sperl-Hillen; Mary C Hroscikoski; Patrick J O'Connor Journal: Am J Med Qual Date: 2006 Jul-Aug Impact factor: 1.852
Authors: Michael Klompas; Emma Eggleston; Jason McVetta; Ross Lazarus; Lingling Li; Richard Platt Journal: Diabetes Care Date: 2012-11-27 Impact factor: 19.112
Authors: Anjali Gopalan; Pranita Mishra; Stacey E Alexeeff; Maruta A Blatchins; Eileen Kim; Alan Man; Andrew J Karter; Richard W Grant Journal: Diabetes Care Date: 2020-03-04 Impact factor: 19.112
Authors: Rozalina G McCoy; Hayley J Dykhoff; Lindsey Sangaralingham; Joseph S Ross; Pinar Karaca-Mandic; Victor M Montori; Nilay D Shah Journal: Diabetes Technol Ther Date: 2019-10-09 Impact factor: 6.118
Authors: Amelia S Wallace; Alex R Chang; Jung-Im Shin; Jodie Reider; Justin B Echouffo-Tcheugui; Morgan E Grams; Elizabeth Selvin Journal: J Clin Endocrinol Metab Date: 2022-04-19 Impact factor: 6.134
Authors: Jason M Glanz; Christina L Clarke; Stanley Xu; Matthew F Daley; Jo Ann Shoup; Emily B Schroeder; Bruno J Lewin; David L McClure; Elyse Kharbanda; Nicola P Klein; Frank DeStefano Journal: JAMA Pediatr Date: 2020-05-01 Impact factor: 16.193
Authors: Emily B Schroeder; John L Adams; Michel Chonchol; Gregory A Nichols; Patrick J O'Connor; J David Powers; Julie A Schmittdiel; Susan M Shetterly; John F Steiner Journal: J Gen Intern Med Date: 2020-04-16 Impact factor: 5.128
Authors: Mary Ellen Vajravelu; Talia A Hitt; Sandra Amaral; Lorraine E Levitt Katz; Joyce M Lee; Andrea Kelly Journal: Pediatr Diabetes Date: 2021-06-30 Impact factor: 3.409
Authors: Alanna Weisman; Karen Tu; Jacqueline Young; Matthew Kumar; Peter C Austin; Liisa Jaakkimainen; Lorraine Lipscombe; Ronnie Aronson; Gillian L Booth Journal: BMJ Open Diabetes Res Care Date: 2020-06