ArticleBMC pregnancy and childbirth2022
Nomogram-based risk prediction of macrosomia: a case-control study.
Article in BMC pregnancy and childbirth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 16 citations in OpenAlex.
- Mediating effect of gestational weight gain on the preventive effect of exercise during pregnancy on macrosomia: a randomized clinical trial.BMC pregnancy and childbirth · 2024Trial
- Machine Learning-Based Prediction of Fetal Macrosomia Using Maternal: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Vaginal versus caesarean delivery in fetal macrosomia-a retrospective cohort study.Wiener medizinische Wochenschrift (1946) · 2026Article
- A Machine Learning Model Based on First-Trimester Lipidomic Signatures for Predicting Metabolic Pregnancy Complications.International journal of molecular sciences · 2025Article
- Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China.BMC pregnancy and childbirth · 2025Article
- Neonatal Birthweight Spectrum: Maternal Risk Factors and Pregnancy Outcomes in Saudi Arabia.Medicina (Kaunas, Lithuania) · 2024Article
- Chromosome 1p36 candidate gene ZNF436 predicts the prognosis of neuroblastoma: a bioinformatic analysis.Italian journal of pediatrics · 2023Article
- Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia.BMC pregnancy and childbirth · 2023Article
- Maternal isolated hypothyroxinemia in the first trimester is not associated with adverse pregnancy outcomes, except for macrosomia: a prospective cohort study in China.Frontiers in endocrinology · 2023Article
- Analytical Comparison of Risk Prediction Models for the Onset of Macrosomia Based on Three Statistical Methods.Disease markers · 2022Article
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8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMacrosomia is closely associated with poor maternal and fetal outcome. But there is short of studies on the risk of macrosomia in early pregnancy. The purpose of this study is to establish a nomogram for predicting macrosomia in the first trimester.
methodsA case-control study involving 1549 pregnant women was performed. According to the birth weight of newborn, the subjects were divided into macrosomia group and non-macrosomia group. The risk factors for macrosomia in early pregnancy were analyzed by multivariate logistic regression. A nomogram was used to predict the risk of macrosomia.
resultsThe prevalence of macrosomia was 6.13% (95/1549) in our hospital. Multivariate logistic regression analysis showed that prepregnancy overweight (OR: 2.13 95% CI: 1.18-3.83)/obesity (OR: 3.54, 95% CI: 1.56-8.04), multiparity (OR:1.88, 95% CI: 1.16-3.04), the history of macrosomia (OR: 36.97, 95% CI: 19.90-68.67), the history of GDM/DM (OR: 2.29, 95% CI: 1.31-3.98), the high levels of HbA1c (OR: 1.76, 95% CI: 1.00-3.10) and TC (OR: 1.36, 95% CI: 1.00-1.84) in the first trimester were the risk factors of macrosomia. The area under ROC (the receiver operating characteristic) curve of the nomogram model was 0.807 (95% CI: 0.755-0.859). The sensitivity and specificity of the model were 0.716 and 0.777, respectively.
conclusionThe nomogram model provides an effective mothed for clinicians to predict macrosomia in the first trimester.
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