Prognostic models versus single risk factor approach in first-trimester selective screening for gestational diabetes mellitus: a prospective population-based multicentre cohort study.

Abstract

To evaluate whether (1) first-trimester prognostic models for gestational diabetes mellitus (GDM) outperform the currently used single risk factor approach, and (2) a first-trimester random venous glucose measurement improves model performance. Prospective population-based multicentre cohort. Thirty-one independent midwifery practices and six hospitals in the Netherlands. Women recruited before 14 weeks of gestation without pre-existing diabetes. The single risk factor approach (presence of at least one risk factor: BMI ≥30 kg/m² , previous macrosomia, history of GDM, positive first-degree family history of diabetes, non-western ethnicity) was compared with the four best performing models in our previously published external validation study (Gabbay-Benziv 2014, Nanda 2011, Teede 2011, van Leeuwen 2010) with and without the addition of glucose. Discrimination was assessed by c-statistics, calibration by calibration plots, added value of glucose by the likelihood ratio chi-square test, net benefit by decision curve analysis and reclassification by reclassification plots. Of the 3723 women included, a total of 181 (4.9%) developed GDM. The c-statistics of the prognostic models were higher, ranging from 0.74 to 0.78 without glucose and from 0.78 to 0.80 with glucose, compared with the single risk factor approach (0.72). Models showed adequate calibration, and yielded a higher net benefit than the single risk factor approach for most threshold probabilities. Teede 2011 performed best in the reclassification analysis. First-trimester prognostic models seem to outperform the currently used single risk factor approach in screening for GDM, particularly when glucose was added as a predictor. Prognostic models seem to outperform the currently used single risk factor approach in screening for gestational diabetes.