Mollie E Wood1, Szu-Ta Chen, Krista F Huybrechts, Brian T Bateman, Kathryn J Gray, Ellen W Seely, Yanmin Zhu, Helen Mogun, Elisabetta Patorno, Sonia Hernández-Díaz. 1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital and Harvard Medical School, Boston, USA. Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA. Division of Maternal-Fetal Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA. Endocrinology, Diabetes and Hypertension Division, Brigham and Women's Hospital and Harvard Medical School.
Abstract
BACKGROUND: Identifying pregestational diabetes in pregnant women using administrative claims databases is important for studies of the safety of antidiabetic treatment in pregnancy, but limited data are available on the validity of case-identifying algorithms. The purpose of this study was to evaluate the validity of an administrative claims-based algorithm to identify pregestational diabetes. METHODS: Using a cohort of pregnant women nested within the Medicaid Analytic Extract (MAX) database, we developed an algorithm to identify pregestational type 1 and type 2 diabetes mellitus, distinct from gestational diabetes. Within a single large healthcare system in the Boston area, we identified women who delivered an infant between 2000 and 2010 and were covered by Medicaid, and linked their electronic health records to their Medicaid claims within MAX. Medical records were reviewed by two physicians blinded to the algorithm classification to confirm or rule out pregestational diabetes, with disagreements resolved by discussion. We calculated positive predictive values with 95% confidence intervals using the medical record as the reference standard. RESULTS: We identified 49 pregnancies classified by the claims-based algorithm as pregestational diabetes that were linked to the electronic health records and had records available for review. The PPV for any pregestational diabetes was 92% (95% CI 82 to 97%), type 2 diabetes 87% (68 to 95%), and type 1 diabetes 57% (37 to 75%). CONCLUSIONS: The claims-based algorithm for pregestational diabetes and type 2 diabetes performed well; however, the PPV was low for type 1 diabetes.
BACKGROUND: Identifying pregestational diabetes in pregnant women using administrative claims databases is important for studies of the safety of antidiabetic treatment in pregnancy, but limited data are available on the validity of case-identifying algorithms. The purpose of this study was to evaluate the validity of an administrative claims-based algorithm to identify pregestational diabetes. METHODS: Using a cohort of pregnant women nested within the Medicaid Analytic Extract (MAX) database, we developed an algorithm to identify pregestational type 1 and type 2 diabetes mellitus, distinct from gestational diabetes. Within a single large healthcare system in the Boston area, we identified women who delivered an infant between 2000 and 2010 and were covered by Medicaid, and linked their electronic health records to their Medicaid claims within MAX. Medical records were reviewed by two physicians blinded to the algorithm classification to confirm or rule out pregestational diabetes, with disagreements resolved by discussion. We calculated positive predictive values with 95% confidence intervals using the medical record as the reference standard. RESULTS: We identified 49 pregnancies classified by the claims-based algorithm as pregestational diabetes that were linked to the electronic health records and had records available for review. The PPV for any pregestational diabetes was 92% (95% CI 82 to 97%), type 2 diabetes 87% (68 to 95%), and type 1 diabetes 57% (37 to 75%). CONCLUSIONS: The claims-based algorithm for pregestational diabetes and type 2 diabetes performed well; however, the PPV was low for type 1 diabetes.
Authors: Eric Rubenstein; Deborah B Ehrenthal; Jenna Nobles; David C Mallinson; Lauren Bishop; Marina C Jenkins; Hsiang-Hui Kuo; Maureen S Durkin Journal: Disabil Health J Date: 2022-03-24 Impact factor: 4.615
Authors: Mollie E Wood; Elisabetta Patorno; Krista F Huybrechts; Brian T Bateman; Kathryn J Gray; Ellen W Seely; Seanna Vine; Sonia Hernández-Díaz Journal: Endocrinol Diabetes Metab Date: 2021-12-24