Creating a cohort of women with incident diabetes and a background of gestational diabetes from UK electronic health records for retrospective cohort studies
Abstract
Abstract Introduction Electronic health records (EHRs) can provide a wealth of information about long-term complications of diabetes mellitus (DM) and gestational diabetes (GDM). Deriving the date most closely approximating that of DM diagnosis is necessary for performing time-to-event analysis. However, identifying diagnosis dates from EHRs is non-trivial, as is distinguishing GDM from chronic DM. Herein, we provide a robust methodology to answer this challenge. Methods The primary care records of women with a coding indicative of chronic DM or with a prescription for an antidiabetic medication during the period 2007–2013 were extracted from the Clinical Practice Research Datalink-Aurum database, and secondary care records for relevant subjects were then obtained. Inclusion criteria included a diagnosis of DM between 2007–2013 inclusive, and age between 18 and 65 years. Exclusion criteria included date of death before registration at a GP practice, diagnosis prior to or within the first year of registration at a GP practice, evidence pointing to a diagnosis other than diabetes (e.g. non-diabetic hyperglycaemia (NDH) or polycystic ovarian syndrome (PCOS)). Algorithms were developed to distinguish chronic DM from GDM and PCOS, and to define the date closest to the actual date of diagnosis of diabetes. Results The records of 730,353 women were obtained from the initial extract. Using the algorithms developed for this study, 83,283 women were entered into the final cohort,…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 730,353 participants or records.
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