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Gestational diabetes mellitus (GDM) is a common disease in pregnancy causing maternal and fetal complications. To prevent these adverse outcomes, optimal screening and diagnostic criteria must be adequate, timely, and efficient. This study suggests a novel approach that is practical, efficient, and patient- and clinician-friendly in predicting adverse outcomes of GDM. The authors conducted a retrospective cohort study via medical record review of patients admitted between March 2001 and April 2013 at the Severance Hospital, Seoul, South Korea. Patients diagnosed by a conventional 2-step method were evaluated according to the presence of adverse outcomes (neonatal hypoglycemia, hyperbilirubinemia, and hyperinsulinemia; admission to the neonatal intensive care unit; large for gestational age; gestational insulin therapy; and gestational hypertension). Of 802 women who had an abnormal 50-g, 1-hour glucose challenge test, 306 were diagnosed with GDM and 496 did not have GDM (false-positive group). In the GDM group, 218 women (71.2%) had adverse outcomes. In contrast, 240 women (48.4%) in the false-positive group had adverse outcomes. Women with adverse outcomes had a significantly higher body mass index (BMI) at entry (P = 0.03) and fasting blood glucose (FBG) (P = 0.03). Our logistic regression model derived from 2 variables, BMI at entry and FBG, predicted GDM adverse outcome with an area under the curve of 0.642, accuracy of 61.3%, sensitivity of 57.2%, and specificity of 66.9% compared with the conventional 2-step method with an area under the curve of 0.610, accuracy of 59.1%, sensitivity of 47.6%, and specificity of 74.4%. Our model performed better in predicting GDM adverse outcomes than the conventional 2-step method using only BMI at entry and FBG. Moreover, our model represents a practical, inexpensive, efficient, reproducible, easy, and patient- and clinician-friendly approach.

Development of a Screening Tool for Predicting Adverse Outcomes of Gestational Diabetes Mellitus