Development and validation of a risk prediction model for gestational diabetes mellitus in women of advanced maternal age during the first trimester.

With the global rise in advanced maternal age (AMA) pregnancies, the risk of gestational diabetes mellitus (GDM) increases. However, few GDM prediction models are tailored for AMA women. This study aims to develop a practical risk prediction model for GDM in AMA women. Data were obtained from a prospective observational cohort of AMA pregnant women from the Obstetrics and Gynecology Hospital in Shanghai, China. Singleton pregnancies with complete OGTT results at 24-28 weeks were selected and divided into training (70%) and validation (30%) sets. First-trimester predictors, including demographic, metabolic parameters, and clinical history, were evaluated for statistical significance. A multivariate logistic regression model was developed, with performance evaluated using receiver operating characteristic (ROC) curves and calibration plots. Predictors were primarily incorporated as categorical variables in a nomogram to enhance model convenience. A model using continuous predictors was also tested for comparison. A total of 1904 AMA women were included, with GDM incidence rates of 18.3% (243/1333) in the training set and 19.3% (110/571) in the validation set. Significant predictors for GDM diagnosis at 24-28 weeks included maternal age, GDM history, first-trimester fasting plasma glucose, mean arterial pressure, and triglyceride levels. The categorical model achieved an area under the ROC curve of 0.717 (95% CI: 0.682-0.753) in the training set and 0.702 (95% CI: 0.645-0.758) in the validation set. The Hosmer-Lemeshow test indicated good calibration (p = .97 in the training set; p = .66 in the validation set). The model with category and continuous predictors exhibited similar performance. This study developed and validated a practical early risk prediction nomogram for GDM in AMA women, using commonly available clinical data. The model shows good predictive performance and is resource-efficient, making it suitable for real-world clinical implementation.