Implementation of a first-trimester prognostic model to improve screening for gestational diabetes mellitus

Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

BACKGROUND

Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM.

METHODS

Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times.

RESULTS

We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I² ranged from 0% to 97%).

CONCLUSION

Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.

Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders

Cardiovascular disease, especially coronary heart disease and cerebrovascular disease, is a leading cause of mortality and morbidity in women globally. The development of cardiometabolic conditions in pregnancy, such as gestational diabetes mellitus and hypertensive disorders of pregnancy, portend an increased risk of future cardiovascular disease in women. Pregnancy therefore represents a unique opportunity to detect and manage risk factors, prior to the development of cardiovascular sequelae. Risk prediction models for gestational diabetes mellitus and hypertensive disorders of pregnancy can help identify at-risk women in early pregnancy, allowing timely intervention to mitigate both short- and long-term adverse outcomes. In this narrative review, we outline the shared pathophysiological pathways for gestational diabetes mellitus and hypertensive disorders of pregnancy, summarise contemporary risk prediction models and candidate predictors for these conditions, and discuss the utility of these models in clinical application.