ArticleMetabolites2022
Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods.
Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Prediction model developed on the basis of meta-analysis in the field of medicine: a systematic survey and methodological summaries.BMC medical research methodology · 2025Pooled it
- Development and feasibility of a theory-guided and evidence-based physical activity intervention in pregnant women with high risk for gestational diabetes mellitus: a pilot clinical trial.BMC pregnancy and childbirth · 2023Trial
- The need for pharmacotherapy among patients with one or more elevations on glucose tolerance test.The Journal of clinical endocrinology and metabolism · 2026Article
- Lipid metabolism-based machine learning models for predicting large for gestational age in non-diabetic pregnancies.Frontiers in endocrinology · 2026Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Machine learning based model for the early detection of Gestational Diabetes Mellitus.BMC medical informatics and decision making · 2025Article
- Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus.Endocrine connections · 2024Article
- Establishment of gestational diabetes risk prediction model and clinical verification.Journal of endocrinological investigation · 2024Article
- A machine learning-based clinical decision support system for effective stratification of gestational diabetes mellitus and management through Ayurveda.Journal of Ayurveda and integrative medicineArticle
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
Gestational diabetes mellitus (GDM), a common perinatal disease, is related to increased risks of maternal and neonatal adverse perinatal outcomes. We aimed to establish GDM risk prediction models that can be widely used in the first trimester using four different methods, including a score-scaled model derived from a meta-analysis using 42 studies, a logistic regression model, and two machine learning models (decision tree and random forest algorithms). The score-scaled model (seven variables) was established via a meta-analysis and a stratified cohort of 1075 Chinese pregnant women from the Northwest Women's and Children's Hospital (NWCH) and showed an area under the curve (AUC) of 0.772. The logistic regression model (seven variables) was established and validated using the above cohort and showed AUCs of 0.799 and 0.834 for the training and validation sets, respectively. Another two models were established using the decision tree (DT) and random forest (RF) algorithms and showed corresponding AUCs of 0.825 and 0.823 for the training set, and 0.816 and 0.827 for the validation set. The validation of the developed models suggested good performance in a cohort derived from another period. The score-scaled GDM prediction model, the logistic regression GDM prediction model, and the two machine learning GDM prediction models could be employed to identify pregnant women with a high risk of GDM using common clinical indicators, and interventions can be sought promptly.
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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 OpenQuestion graph.