Construction of a risk prediction model for adverse pregnancy outcomes in primipara with gestational diabetes mellitus combined with pregnancy-induced hypertension syndrome.

Abstract

Objective: This study aims to identify risk factors for adverse pregnancy outcomes in primipara with gestational diabetes mellitus (GDM) combined with pregnancy-induced hypertension syndrome (PIH) and to develop a predictive model for such outcomes.

Methods: A total of 120 primipara with GDM and PIH, admitted from January 2019 to May 2023, were divided into two groups: the adverse group (n = 57) and the good group (n = 63), based on pregnancy outcomes. Multivariate logistic regression analysis was used to identify independent risk factors for adverse outcomes. A nomogram was constructed based on these factors, and its efficacy was validated through internal evaluation.

Results: The adverse group had higher proportions of elderly parturients, higher pre-pregnancy BMI, and more weight gain during pregnancy. Additionally, the adverse group showed a higher incidence of family history of diabetes, and more severe types of PIH. Biochemical markers such as HbA1c and total cholesterol (TC) were higher in the adverse group, while high-density lipoprotein cholesterol (HDL-C) was lower (p < .01, p < .05). Multivariate logistic regression revealed that advanced maternal age, pre-pregnancy BMI, family history of diabetes, preeclampsia/chronic hypertension complicated by preeclampsia, and elevated HbA1c were independent risk factors for adverse pregnancy outcomes (p < .01). A nomogram prediction model was developed, with an AUC of 0.821. Bootstrap internal validation confirmed the model's robust discriminative ability.

Conclusion: Advanced maternal age, pre-pregnancy BMI, family history of diabetes, preeclampsia, and elevated HbA1c are significant risk factors for adverse pregnancy outcomes in GDM combined with PIH. The nomogram model provides an effective tool for predicting such outcomes.