ArticleBMC pregnancy and childbirth2019
A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women.
Article in BMC pregnancy and childbirth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Progress of the application clinical prediction model in polycystic ovary syndrome.Journal of ovarian research · 2023Pooled it
- Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis.Journal of medical Internet research · 2022Pooled it
- A three-metabolite microbiota-associated signature for early risk stratification of gestational diabetes mellitus.Cardiovascular diabetology · 2026Article
- Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- Metabolic and endometrial ultrasonographic factors associated with clinical pregnancy in PCOS patients undergoing FET: a nomogram development study with internal validation.Frontiers in endocrinology · 2026Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- 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
- Mathematical Modelling for Community Based Intervention for Managing Diabetes: A Systematic Literature Review.Journal of multidisciplinary healthcare · 2025Review
- Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus.Endocrine connections · 2024Article
- Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation.BMC pregnancy and childbirth · 2024Article
- Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach.Journal of endocrinological investigation · 2024Article
- Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Modified Delphi Study.Healthcare (Basel, Switzerland) · 2024Article
- A nested case-control study on the association of gut virome in early pregnancy and gestational diabetes mellitus.Frontiers in microbiology · 2024Article
- A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2023Article
- Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support.Diagnostics (Basel, Switzerland) · 2023Article
- Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy.BMC pregnancy and childbirth · 2023Article
- MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women.Scientific reports · 2023Article
- Prediction of recurrent gestational diabetes mellitus: a retrospective cohort study.Archives of gynecology and obstetrics · 2023Article
- Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea.Endocrinology and metabolism (Seoul, Korea) · 2023Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundGestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Screening for GDM and applying adequate interventions may reduce the risk of adverse outcomes. However, the diagnosis of GDM depends largely on tests performed in late second trimester. The aim of the present study was to bulid a simple model to predict GDM in early pregnancy in Chinese women using biochemical markers and machine learning algorithm.
methodsData on a total of 4771 pregnant women in early gestation were used to fit the GDM risk-prediction model. Predictive maternal factors were selected through Bayesian adaptive sampling. Selected maternal factors were incorporated into a multivariate Bayesian logistic regression using Markov Chain Monte Carlo simulation. The area under receiver operating characteristic curve (AUC) was used to assess discrimination.
resultsThe prevalence of GDM was 12.8%. From 8th to 20th week of gestation fasting plasma glucose (FPG) levels decreased slightly and triglyceride (TG) levels increased slightly. These levels were correlated with those of other lipid metabolites. The risk of GDM could be predicted with maternal age, prepregnancy body mass index (BMI), FPG and TG with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801).
conclusionsThis GDM prediction model is simple and potentially applicable in Chinese women. Further validation is necessary.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.