title: Development of a Screening Tool for Predicting Adverse Outcomes of Gestational Diabetes Mellitus: A Retrospective Cohort Study.
abstract: 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.
authors.affiliation: From the Department of Medicine (JSP); Department of Medical Engineering (DWK); Graduate Program in Biomedical Engineering (DWK); and Department of Obstetrics and Gynecology, Institute of Women's Medical Life Science, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea (YHK, JK, YWP, HYC).
authors.forename: Jee Soo
authors.initials: JS
authors.lastname: Park
authors.forename: Deok Won
authors.initials: DW
authors.lastname: Kim
authors.forename: Ja-Young
authors.initials: JY
authors.lastname: Kwon
authors.forename: Yong Won
authors.initials: YW
authors.lastname: Park
authors.forename: Young Han
authors.initials: YH
authors.lastname: Kim
authors.forename: Hee Young
authors.initials: HY
authors.lastname: Cho
mesh_terms: D000328:Adult; D001786:Blood Glucose; D015992:Body Mass Index; D016640:Diabetes, Gestational / Q000453:epidemiology*; D005189:False Positive Reactions; D005260:Female; D005951:Glucose Tolerance Test; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000453:epidemiology*; D008403:Mass Screening / Q000379:methods*; D011237:Predictive Value of Tests; D011247:Pregnancy; D056910:Republic of Korea; D012189:Retrospective Studies; D012307:Risk Factors
chemical_list: D001786:Blood Glucose