ArticleEClinicalMedicine2022
Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes: The PeRSonal GDM model.
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction Models for Maternal and Offspring Short- and Long-Term Outcomes Following Gestational Diabetes: A Systematic Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- Potential for primary prevention of low birth weight in pregnancies with and without gestational diabetes mellitus: a prospective cohort study in Central China.BMC pregnancy and childbirth · 2026Article
- Enhancing Early Prediction of Gestational Diabetes Mellitus Through Data Augmentation and Feature Guidance: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Construction and validation of a multicenter predictive model for neonatal hypoglycemia risk in infants of mothers with gestational diabetes mellitus.American journal of translational research · 2026Article
- Demethylation of leptin promoter in gestational diabetes mellitus: evidence from a mouse model.Frontiers in cell and developmental biology · 2026Article
- Distinct modifiable risk factors and preventable burdens of preterm birth: a risk-stratified analysis of pregnancies with and without gestational diabetes mellitus.Frontiers in pediatrics · 2026Article
- Variation in gestational diabetes diagnosis and care practices in maternity services in three high-income countries; a cross-sectional survey.BMC pregnancy and childbirth · 2025Article
- Predicting gestational diabetes before conception for personalized interpregnancy weight management.Scientific reports · 2025Article
- Prediction of high-risk pregnancy based on machine learning algorithms.Scientific reports · 2025Article
- Effect of Galactooligosaccharide on PPARs/PI3K/Akt Pathway and Gut Microbiota in High-Fat and High-Sugar Diet Combined with STZ-Induced GDM Rat Model.Probiotics and antimicrobial proteins · 2025Article
- Analyzing electronic medical records to extract prepregnancy morbidities and pregnancy complications: Toward a learning health system.Learning health systems · 2025Article
- Development, validation, and clinical application of a machine learning model for risk stratification and management of cervical cancer screening based on full-genotyping hrHPV test (SMART-HPV): a modelling study.The Lancet regional health. Western Pacific · 2025Article
- Optimizing gestational diabetes diagnostic criteria to predict adverse perinatal outcomes in the United Arab Emirates: The Mutaba'ah Study.Frontiers in endocrinology · 2025Article
- Pre-pregnancy body mass index and risk of macrosomia: glycemic status-specific thresholds and subgroup interactions in a prospective cohort.Frontiers in nutrition · 2025Article
- Article
- Advancement in predictive biomarkers for gestational diabetes mellitus diagnosis and related outcomes: a scoping review.BMJ open · 2024Article
- Geographical validation of the Smart Triage Model by age group.PLOS digital health · 2024Article
- Metabolic profiles in gestational diabetes mellitus can reveal novel biomarkers for prediction of adverse neonatal outcomes.Frontiers in pediatrics · 2024Article
- Precision gestational diabetes treatment: a systematic review and meta-analyses.Communications medicine · 2023Article
- Diagnosis of neurosyphilis in HIV-negative patients with syphilis: development, validation, and clinical utility of a suite of machine learning models.EClinicalMedicine · 2023Article
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9 authors.
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Abstract
Background: The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. Methods: A prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis. Findings: The final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination ( Interpretation: The PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria. Funding: This work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.
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