ArticleAmerican journal of translational research2023
Construction and validation of risk prediction model for gestational diabetes based on a nomogram.
Article in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 5 citations in OpenAlex.
- Development and validation of a multidimensional indicator-based risk prediction model for gestational diabetes mellitus: a nested case-control study.BMC endocrine disorders · 2026Article
- Early-pregnancy trunk phase angle derived from bioelectrical impedance analysis for the prediction of gestational diabetes mellitus.Frontiers in endocrinology · 2026Article
- Review
- Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China.BMC pregnancy and childbirth · 2025Article
- Establishment and evaluation of a risk prediction model for gestational diabetes mellitus.World journal of diabetes · 2023Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo construct a model to predict the risk of gestational diabetes mellitus (GDM) based on a nomogram and verify it.
methodsData from 182 patients with GDM treated in Xi'an International Medical Center Hospital from January 2018 to May 2021 were retrospectively analyzed. A total of 491 normal parturients who underwent physical examination in Xi'an International Medical Center Hospital during the same period were selected as controls. With a ratio of 7:3, patients with GDM were divided into a training group (n=128) and a verification (n=54) group, and 491 normal parturients were divided into a training control group (n=344) and a verification control group (n=147). Clinical data were collected, and risk factors for GDM were analyzed by logistic regression. R language was used to construct a prognostic prediction nomogram model for GDM, and a receiver operating characteristic curve was employed to evaluate the accuracy of this nomogram model in predicting the prognosis of GDM.
resultsUnivariate analysis revealed that age, body mass index (BMI), family history of diabetes, hemoglobin, triglycerides, serum ferritin, and fasting blood glucose in the first trimester were different between the training group and the training control group (P<0.05). Multivariate analysis revealed that age, BMI, hemoglobin, triglycerides, serum ferritin, and fasting blood glucose in the first trimester were independent risk factors for GDM (P<0.05). Based on a logistic regression equation, the risk formula was -5.971 + 1.054 * age + 1.133 * BMI + 1.763 * hemoglobin + 1.260 * triglycerides + 3.041 * serum ferritin + 1.756 * fasting blood glucose in the first trimester. The area under the curve for predicting the risk of GDM in the training group was 0.920, and that of the validation group was 0.753.
conclusionAge, BMI, hemoglobin, serum ferritin, and fasting blood glucose in the first trimester are risk factors for GDM.
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36915791PMC10006798W4324128853What OpenQuestion holds
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