A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women - Duo, Yanbei; Song, Shuoning; Qiao, Xiaolin; Zhang, Yuemei; Xu, Jiyu; Zhang, Jing; Peng, Zhenyao; Chen, Yan; Nie, Xiaorui; Sun, Qiujin; Yang, Xianchun; Wang, Ailing; et al. (Diabetes therapy, 2023)
Introduction This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester. Methods This prospective study included 1289 pregnant women in their first trimester (6–12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples). Results The prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer–Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797–0.853, P < 0.001) and 0.784 (95% CI 0.750–0.818, P < 0.001), respectively. The performance of our prediction model was superior to that of three other published models. Conclusion We developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations. Trial Registration ClinicalTrials.gov identifier: NCT03246295.