A scoring algorithm including fasting plasma glucose measurement and a risk estimation model is an accurate strategy for detecting GDM
Aims: It is currently not clear how to construct a time and cost-effective screening strategy for gestational diabetes (GDM). Thus, we elaborated a simple screening algorithm combining i) fasting plasma glucose (FPG) measurement and ii) a risk prediction model focused on subjects with normal FPG levels to decide if a further oral glucose tolerance test (OGTT) is indicated. Methods: 1336 women were prospectively screened for several risk factors for GDM within a multicenter study conducted in Austria. Of 714 (53.4%) women, who developed GDM using the most recent diagnostic guidelines, 461 were sufficiently screened with FPG. A risk prediction score was finally developed on the remaining 253 GDMs and 622 healthy females. The screening algorithm was validated on further 258 pregnant women. Results: A risk estimation model including history of GDM, glucosuria, family history of diabetes, age, pre-conceptional dyslipidemia, and ethnic origin in addition to FPG was accurate for detecting GDM in subjects with normal FPG. Including a FPG pretest, the area under the receiver operating characteristic curve was 0.90 (95% CI: 0.88–0.91). A cut-off value of 0.25 was able to decide between low and intermediate risk for GDM with a high sensitivity. The validation cohort revealed comparable results. Moreover, we demonstrated a strong and independent association between values derived from the risk estimation and macrosomia in offspring (OR: 3.29, 95% CI: 1.87–5.87, p<0.001). Conclusion: This study demonstrated a new concept for accurate but cheap GDM screening. This approach should be further evaluated in different populations to ensure an optimized diagnostic algorithm.