Validation of Danish registry‐cases of type 1 diabetes in women giving live birth using a clinical cohort as gold standard. Issue 1 (22nd November 2022)

• Record Type: Journal Article
• Authors: Gundersen, Tina Wullum; Ebbehoj, Andreas; Knorr, Sine; Jensen, Dorte Møller; Damm, Peter; Løkkegaard, Ellen Christine Leth; Mathiesen, Elisabeth R.; Thomsen, Reimar W.; Clausen, Tine Dalsgaard
• Is Part Of: Endocrinology, diabetes & metabolism. Volume 6:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-22
• Subjects: case‐identification -- diabetes mellitus -- pregnancy; Endocrinology -- Periodicals; Diabetes -- Periodicals; Metabolism -- Periodicals; 616.4
• DOI: 10.1002/edm2.374
• Languages: English
• ISSNs: 2398-9238

Abstract

Abstract: Introduction: The aim of this study was to validate type 1 diabetes in women giving live birth in the Danish national registries against a clinical cohort of confirmed cases (the Danish Diabetes Birth Registry [DDBR] cohort). Methods: National registries including diagnosis codes, redeemed prescriptions and background data were combined. Three main algorithms were constructed to define type 1 diabetes in women giving live birth: (1) Any diabetes diagnosis registered before delivery and before age of 30, (2) a specific type 1 diabetes diagnosis registered before delivery regardless of maternal age and (3) a 'preexisting type 1 diabetes in pregnancy' diagnosis registered before delivery. In additional sub‐algorithms, we added information on anti‐diabetic medicine and gestational diabetes diagnosis. We calculated positive predictive value (PPV) and completeness using the DDBR cohort as gold standard. Since DDBR included between 75 and 93% of women with confirmed type 1 diabetes giving live birth, we used quantitative bias analysis to assess the potential impact of missing data on PPV and completeness. Results: Main algorithm 2 had the highest PPV (77.4%) and shared the highest completeness (92.4%) with main algorithm 1. Information on anti‐diabetic medicine and gestational diabetes increased PPV, on expense of completeness. All algorithms varied with PPV between 65.7 and 87.6% and completeness between 73.6 and 92.4%. The quantitative bias analysis indicated that PPV was underestimated, and completeness overestimated for all algorithms. For algorithm 2, corrected PPV was between 82.1 and 94.6% and corrected completeness between 84.7 and 91.2%. Conclusions: The Danish national registries can identify type 1 diabetes in women giving live birth with a reasonably high accuracy. The registries are a valuable source for future comparative outcome studies and may also be suitable for monitoring prevalence and incidence of type 1 diabetes in women giving live birth. Abstract : The aim of this study was to validate type 1 diabetes live births in the Danish national registries against a clinical cohort of confirmed cases.The study concludes that algorithms can reliably identify type 1 diabetes in pregnancy in health registries, with a PPV above 90% and completeness around 80%. The algorithms are well‐suited for future comparative outcome studies.