ArticleAmerican journal of translational research2024
Development and validation of a risk prediction model for spontaneous preterm birth.
Article in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Predicting the risk of preterm birth with machine learning and electronic health records in China.BMC medical informatics and decision making · 2025Article
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3 authors.
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Abstract
objectivesTo identify the factors influencing spontaneous preterm birth (SPTB) and develop a prediction model for clinical practice.
methodsThis retrospective study included a total of 130 pregnant women with spontaneous preterm birth or full-term delivery at Fujian Maternity and Child Health Hospital between January 2020 and December 2023. The SPTB group consisted of 50 women with spontaneous preterm birth, while the full-term group included 70 women with full-term deliveries. Logistic regression analysis was performed to explore the factors associated with clinical prognosis, and a nomogram prediction model for SPTB risk was constructed and validated.
resultsMultivariate logistic regression analysis identified multiple pregnancies (95% CI: 1.415-8.926, P=0.006), abnormal fetal position (95% CI: 1.124-2.331, P=0.008), gestational diabetes (95% CI: 4.918-19.164, P=0.002), mode of conception (95% CI: 1.765-4.285,P=0.002), lower genital tract infection (95% CI: 1.076-2.867, P=0.032), and second trimester cervical length (95% CI: 1.071-2.991, P=0.031) as independent risk factors of SPTB. Using these six variables, a nomogram was developed to predict the incidence of SPTB, with an AUC value of 0.833 (95% CI: 0.665-0.847), demonstrating acceptable agreement between predicted and observed outcomes. Decision curve analysis (DCA) showed a good positive net benefit of the model.
conclusionsMultiple pregnancies, abnormal fetal position, gestational diabetes, mode of conception, lower genital tract infection, and second-trimester cervical length are independent risk factors for the onset of SPTB. In addition, the nomogram prediction model demonstrated good predictive performance, high accuracy, and clinical applicability.
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