title: A two-step screening algorithm including fasting plasma glucose measurement and a risk estimation model is an accurate strategy for detecting gestational diabetes mellitus.
abstract: OBJECTIVE
  It is currently not clear how to construct a time- and cost-effective screening strategy for gestational diabetes mellitus (GDM). Thus, we elaborated a simple screening algorithm combining (1) fasting plasma glucose (FPG) measurement; and (2) a multivariable risk estimation model focused on individuals with normal FPG levels to decide if a further OGTT is indicated.
  
  
  METHODS
  A total of 1,336 women were prospectively screened for several risk factors for GDM within a multicentre study conducted in Austria. Of 714 women (53.4%) who developed GDM using recent diagnostic guidelines, 461 were sufficiently screened with FPG. A risk prediction score was finally developed using data from the remaining 253 women with GDM and 622 healthy women. The screening algorithm was validated with a further 258 pregnant women.
  
  
  RESULTS
  A risk estimation model including history of GDM, glycosuria, family history of diabetes, age, preconception dyslipidaemia and ethnic origin, in addition to FPG, was accurate for detecting GDM in participants with normal FPG. Including an FPG pretest, the receiver operating characteristic AUC of the screening algorithm was 0.90 (95% CI 0.88, 0.91). A cut-off value of 0.20 was able to differentiate between low and intermediate risk for GDM with a high sensitivity. Comparable results were seen with the validation cohort. Moreover, we demonstrated an independent association between values derived from the risk estimation and macrosomia in offspring (OR 3.03, 95% CI 1.79, 5.19, p < 0.001).
  
  
  CONCLUSIONS
  This study demonstrates a new concept for accurate but cheap GDM screening. This approach should be further evaluated in different populations to ensure an optimised diagnostic algorithm.
authors.affiliation: Department of Gynecology and Obstetrics, Division of Feto-Maternal Medicine, Medical University of Vienna, Vienna, Austria.
authors.forename: C S
authors.initials: CS
authors.lastname: Göbl
authors.forename: L
authors.initials: L
authors.lastname: Bozkurt
authors.forename: P
authors.initials: P
authors.lastname: Rivic
authors.forename: G
authors.initials: G
authors.lastname: Schernthaner
authors.forename: R
authors.initials: R
authors.lastname: Weitgasser
authors.forename: G
authors.initials: G
authors.lastname: Pacini
authors.forename: M
authors.initials: M
authors.lastname: Mittlböck
authors.forename: D
authors.initials: D
authors.lastname: Bancher-Todesca
authors.forename: M
authors.initials: M
authors.lastname: Lechleitner
authors.forename: A
authors.initials: A
authors.lastname: Kautzky-Willer
mesh_terms: D000328:Adult; D000465:Algorithms; D001317:Austria / Q000453:epidemiology; D001786:Blood Glucose / Q000378:metabolism*; D016640:Diabetes, Gestational / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D005215:Fasting / Q000097:blood*; D005260:Female; D005320:Fetal Macrosomia / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D005951:Glucose Tolerance Test; D006801:Humans; D007231:Infant, Newborn; D008403:Mass Screening / Q000379:methods*; D011247:Pregnancy; D011336:Probability; D012372:ROC Curve; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose