ArticleBMC medical informatics and decision making2025
Prediction of Gestational Diabetes Mellitus (GDM) risk in early pregnancy based on clinical data and ultrasound information: a nomogram.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Placenta in Gestational Diabetes: An Integrated Review on Metabolic Pathways, Genetic, Epigenetic and Ultrasound Biomarkers for Clinical Perspectives.International journal of molecular sciences · 2026Review
- Predictive value of routine ultrasound indicators in the second trimester for gestational diabetes mellitus: a retrospective cohort study.Frontiers in endocrinology · 2026Article
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5 authors.
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Abstract
backgroundGestational diabetes mellitus (GDM) is one of the most common complications during pregnancy and has been on a continuous increase in recent years. This study aimed to establish a combined prediction model for the risk of GDM and to provide more reliable reference information for non-invasive assessment of GDM in clinical practice.
methodsThis study retrospectively collected clinical data and ultrasound information of 122 pregnant women who underwent fetal nuchal translucency screening, which divided into 36 cases of the GDM group and 86 cases of the non-gestational diabetes mellitus(NGDM) group. The collected clinical data and ultrasound information were analyzed using Student's t-test and Wilcoxon W test for univariate analysis. Independent risk factors for patients with GDM were screened through binary logistic regression analysis. A model was established based on the screened results, and the diagnostic performance of different models was evaluated by drawing the receiver operating characteristic curve(ROC). The optimal prediction model was selected, and the calibration curve and clinical decision curve were drawn to evaluate the goodness of fit and clinical application efficiency of the model.
resultsUnivariate results showed that age, body mass index(BMI), number of abortions, gravidity, placental volume(PV), vascularization index(VI), flow index(FI), and vascularization flow index(VFI) all had statistically significant differences between the GDM and NGDM groups(p < 0.05). Binary logistic regression analysis showed that BMI, number of abortions, PV, VI, and FI were independent risk factors for the development of GDM in pregnant women (p < 0.05). Based on these results, five prediction models were established in this study. Their area under the ROC curve(AUC) were 0.67, 0.80, 0.80, 0.87, and 0.85, respectively. The model combining clinical data with 30° ultrasound data had the highest AUC, so we constructed a nomogram for this model. The results of its calibration curve showed that the model had a good fit, and the results of the clinical decision curve showed that the model had good clinical application efficiency.
conclusionThe nomogram model combining clinical data with 30° ultrasound data has good accuracy and clinical application value for predicting the risk of GDM.
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