ArticleInternational journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics2025
Development of a spontaneous preterm birth predictive model using a panel of serum protein biomarkers for early pregnant women: A nested case-control study.
Article in International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A machine learning-based risk prediction model for early preterm birth: development and prospective validation.Frontiers in medicine · 2026Article
- Development and Validation of an Early-Pregnancy Prediction Model for Indicated Preterm Birth Using Three-Dimensional Placental Ultrasound and Maternal Clinical Characteristics.International journal of women's health · 2026Article
- Development of a spontaneous preterm birth predictive model using a panel of serum protein biomarkers for early pregnant women: A nested case-control study.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Article
- Blood-based predictive biomarkers for preterm birth: Redefining risk stratification in modern perinatal care.Tzu chi medical journalReview
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a model based on maternal serum liquid chromatography tandem mass spectrometry (LC-MS/MS) proteins to predict spontaneous preterm birth (sPTB).
methodsThis nested case-control study used the data from a cohort of 2053 women in China from July 1, 2018, to January 31, 2019. In total, 110 singleton pregnancies at 11-13
resultsA novel predictive panel with four proteins, including soluble fms-like tyrosine kinase-1, matrix metalloproteinase 8, ceruloplasmin, and sex-hormone-binding globulin, was developed. The optimal model of logistic regression had an AUC of 0.934, with additional prediction of sPTB in second and third trimester (AUC = 0.868).
conclusionFirst-trimester modeling based on maternal serum LC-MS/MS identifies pregnant women at risk of sPTB, which may provide utility in identifying women at risk at an early stage of pregnancy before clinical presentation to allow for earlier intervention.
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