Adherence rates to a prediction tool identifying women with an increased gestational diabetes risk: An implementation study

P. Montfort*, H.C.J. Scheepers, I.M.A. Dooren, L.J.E. Meertens, L. Wynants, M. Zelis, I.M. Zwaan, M.E.A. Spaanderman, L.J.M. Smits

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Objective The best screening strategy for gestational diabetes mellitus (GDM) remains a topic of debate. Several organizations made a statement in favor of universal screening, but the volume of oral glucose tolerance tests (OGTT) required may burden healthcare systems. As a result, many countries still rely on selective screening using a checklist of risk factors, but reported diagnostic characteristics vary. Moreover, women's discomfort due to an OGTT is often neglected. Since 2017, obstetric healthcare professionals in a Dutch region assessed women's GDM risk with a prediction model and counseled those with an increased risk regarding an OGTT.Methods From 2017 to 2018, 865 women were recruited in a multicenter prospective cohort.Results In total, 385 women (48%) had an increased predicted GDM risk. Of all women, 78% reported that their healthcare professional discussed their GDM risk. Predicted GDM risks were positively correlated with conducting an OGTT.Conclusion Implementation of a GDM prediction model resulted in moderate rates of OGTTs performed in general, but high rates in high-risk women. As 25% of women experienced discomfort from the OGTT, a selective screening strategy based on a prediction model with a high detection rate may be an interesting alternative to universal screening.Study cohort registration Netherlands Trial Register: NTR4143; .
Original languageEnglish
Pages (from-to)85-91
Number of pages7
JournalInternational Journal of Gynecology & Obstetrics
Issue number1
Publication statusPublished - Jul 2021


  • care
  • gestational diabetes mellitus
  • health
  • hyperglycemia
  • mellitus
  • models
  • oral glucose tolerance test
  • prediction
  • pregnancy
  • screening
  • CARE

Cite this