Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study - Li, Luman; Zhu, Quan; Wang, Zihan; Tao, Yun; Liu, Huanyu; Tang, Fei; Liu, Song-Mei; Zhang, Yuanzhen (Frontiers in Endocrinology, 2023)
Objectives: This study aims to develop and evaluate a predictive nomogram for early assessment risk factors of gestational diabetes mellitus (GDM) during early pregnancy term, so as to help early clinical management and intervention. Methods: 824 pregnant women were enrolled in a retrospective observational study as a training dataset in Zhongnan Hospital of Wuhan University and Maternal and Child Health Hospital of Hubei Province from Feb. 1st, 2020 to April 30th, 2020. Routine clinical and laboratory information were collected, we applied least absolute shrinkage and selection operator (Lasso) logistic regression and multivariate ROC risk analysis to determine significant predictors and establish the nomogram, and the early pregnancy files (gestational weeks 12-16, n=392) at the same hospital were collected as a validation dataset. We evaluate the nomogram by receiver operating characteristic curve (ROC), C-index, calibration curve and decision curve analysis (DCA). Results: We conduct Lasso analysis and multivariate regression to establish GDM nomogram during early pregnancy term, the five selected risk predictor are as follows: age, blood urea nitrogen (BUN), Fibrinogen to albumin ratio (FAR), blood urea nitrogen to creatinine ratio (BUN/Cr), blood urea nitrogen to albumin (BUN/ALB). Calibration curve and DCA present optimal predictive power. DCA demonstrates the nomogram could be applied clinically. Conclusion: establish an effective nomogram to predict GDM, in order to help clinical management and intervention at the early gestational stage.