First trimester prediction of gestational diabetes mellitus: A clinical model based on maternal demographic parameters

Abstract

Aim: Develop a first trimester risk prediction model for GDM based on maternal clinical characteristics in a large metropolitan multi-ethnic population and compare its performance to that of other recently published GDM prediction models and clinical risk scoring systems.

Methods: A retrospective case control study of 248 women who developed GDM and 732 controls who did not. Maternal clinical parameters were prospectively obtained at 11-13⁺⁶ weeks' gestation. A predictive multivariate regression model for GDM was developed, evaluated using areas under the receiver-operating characteristic (AUC) curve. The performance of this model was then compared with other published GDM prediction models applied to our cohort and our existing clinical risk scoring system.

Results: Previous GDM, family history of diabetes, age, south/east Asian ethnicity, parity and body mass index (BMI) were significant predictors for GDM. The AUC of our multivariate regression model was 0.88 (95% Confidence Interval 0.85-0.92). This performed better than other predictive models applied to our cohort (AUCs 0.77-0.82).

Conclusion: A multivariate model based on weighted maternal clinical risk factors accurately predicts GDM in early pregnancy and performs better than other proposed multivariate and clinical risk scoring models in a multiethnic cohort.

Keywords: Demographic risk factors; Gestational diabetes; Screening.