Estimating the risk of insulin requirement in women complicated by gestational diabetes mellitus: A clinical nomogram

Abstract

Purpose: This study sought to develop a nomogram for the prediction of insulin requirement in a Chinese population with gestational diabetes mellitus (GDM). Materials and Methods: We performed a retrospective cohort study involving 626 Chinese women with GDM, of whom 188 were treated with insulin. “Least absolute shrinkage and selection operator” regression was used to optimize the independent predictors of insulin requirement during pregnancies complicated with GDM. Cox proportional hazards regression analysis was performed to establish a prediction model incorporating the selected predictors, and the nomogram was constructed to achieve individual prediction. The C-index, calibration plot and decision curve analysis were used to validate the model. Results: Maternal age, family history of type 2 diabetes mellitus in a first-degree relative, a prior GDM history, fasting plasma glucose, hemoglobin A1c, gestational age, and body mass index values at the time of GDM diagnosis were the risk factors for insulin treatment. The model displayed medium predictive power with a C-index of 0.77 (95% confidence interval: 0.73–0.81) and relatively good calibration accuracies. The decision curve demonstrated a positive net benefit with a threshold between 0.09 and 0.70. Conclusion: The findings suggest that our nomogram, incorporating seven indicators, is useful in predicting individualized survival probabilities of insulin requirement.