Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study

Abstract

Objective: 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: A total of 824 pregnant women at Zhongnan Hospital of Wuhan University and Maternal and Child Health Hospital of Hubei Province from 1 February 2020 to 30 April 2020 were enrolled in a retrospective observational study and comprised the training dataset. Routine clinical and laboratory information was 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 evaluated the nomogram via the receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis (DCA).

Results: We conducted LASSO analysis and multivariate regression to establish a GDM nomogram during the early pregnancy term; the five selected risk predictors are as follows: age, blood urea nitrogen (BUN), fibrinogen-to-albumin ratio (FAR), blood urea nitrogen-to-creatinine ratio (BUN/Cr), and blood urea nitrogen-to-albumin ratio (BUN/ALB). The calibration curve and DCA present optimal predictive power. DCA demonstrates that the nomogram could be applied clinically.

Conclusion: An effective nomogram that predicts GDM should be established in order to help clinical management and intervention at the early gestational stage.

Keywords: GDM; early pregnancy term; nomogram; prediction model; validation.