Aims: We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria.
Methods: A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede).
Results: A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69-0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64-0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72-0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66-0.78), compared to an AUC of 0.67 (95% CI 0.63-0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62-0.70) using the Teede model.
Conclusions: A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
Keywords: 2013 WHO criteria; Gestational diabetes mellitus; Prediction; Risk factors.