Risk prediction models of gestational diabetes mellitus before 16 gestational weeks

Background: Gestational diabetes mellitus (GDM) can lead to adverse maternal and fetal outcomes, and early prevention is particularly important for their health, but there is no widely accepted approach to predict it in the early pregnancy. The aim of the present study is to build and evaluate predictive models for GDM using routine indexes, including maternal clinical characteristics and laboratory biomarkers, before 16 gestational weeks. Methods: A total of 2895 pregnant women were recruited and maternal clinical characteristics and laboratory biomarkers before 16 weeks of gestation were collected from two hospitals. All participants were randomly stratified into the training cohort and the internal validation cohort by the ratio of 7:3. Using multivariable logistic regression analysis, two nomogram models, including a basic model and an extended model, were built. The discrimination, calibration, and clinical validity were used to evaluate the models in the internal validation cohort.Results: The area under the receiver operating characteristic curve of the basic and the extended model was 0.736 and 0.756 in the training cohort, and was 0.736 and 0.763 in the validation cohort, respectively. The calibration curve analysis showed that the predicted values of the two models were not significantly different from the actual observations (p = 0.289 and 0.636 in the training cohort, p = 0.684 and 0.635 in the internal validation cohort, respectively). The decision-curve analysis showed a good clinical application value of the models.Conclusions: The present study built simple and effective models, indicating that routine clinical and laboratory parameters can be used to predict the risk of GDM in the early pregnancy, and providing a novel reference for study-ing the prediction of GDM.

Automated summary

This study aimed to build and evaluate models for predicting gestational diabetes mellitus (GDM) using routine indexes. The results showed that the models had good discrimination and calibration in both the training and validation cohorts, indicating their potential use in predicting the risk of GDM in early pregnancy.