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

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.