BMC pregnancy and childbirth · 2026
Nomogram model for predicting spontaneous preterm birth in twin pregnancies: a case-control study.
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Xu Wei-Na, Ai Ling, Zhang Xiao-Yan, Wang Jian-Guo, Huang Yi-Min
Аннотация
BACKGROUND: This study aimed to identify independent factors and develop a nomogram for spontaneous preterm birth in twin pregnancies.
METHODS: In this retrospective study, a total of 218 women with twin pregnancies from Jiaxing Women and Children's Hospital, Wenzhou Medical University between June 2021 and May 2024 were enrolled. Univariate analysis and subsequent multivariate logistic regression analysis were used to identify independent factors. A nomogram prediction model was constructed using R software and evaluated by the area under the ROC curve (AUC), concordance index (C-index), and decision curve analysis (DCA).
RESULTS: Univariate analysis identified body mass index (BMI) at delivery, cervical length during the second trimester, cervical funnel, gestational vaginitis, gestational diabetes mellitus and prenatal hemoglobin levels as factors associated with spontaneous preterm birth in twin pregnancies (P < 0.05). Multivariable logistic regression analysis confirmed BMI at delivery (OR = 0.887), cervical length during the second trimester (OR = 0.886), and gestational vaginitis (OR = 2.909) as independent predictors. The prediction model demonstrated good performance, with a C-index of 0.838 for the nomogram and an AUC of 0.838 from the ROC curve. DCA indicated the model provided net clinical benefit across a wide range of threshold probabilities.
CONCLUSION: A nomogram incorporating BMI at delivery, cervical length during the second trimester, and gestation vaginitis status effectively predicts spontaneous preterm birth in twin pregnancies. This practical tool may aid in individualized risk assessment and guide clinical management.
METHODS: In this retrospective study, a total of 218 women with twin pregnancies from Jiaxing Women and Children's Hospital, Wenzhou Medical University between June 2021 and May 2024 were enrolled. Univariate analysis and subsequent multivariate logistic regression analysis were used to identify independent factors. A nomogram prediction model was constructed using R software and evaluated by the area under the ROC curve (AUC), concordance index (C-index), and decision curve analysis (DCA).
RESULTS: Univariate analysis identified body mass index (BMI) at delivery, cervical length during the second trimester, cervical funnel, gestational vaginitis, gestational diabetes mellitus and prenatal hemoglobin levels as factors associated with spontaneous preterm birth in twin pregnancies (P < 0.05). Multivariable logistic regression analysis confirmed BMI at delivery (OR = 0.887), cervical length during the second trimester (OR = 0.886), and gestational vaginitis (OR = 2.909) as independent predictors. The prediction model demonstrated good performance, with a C-index of 0.838 for the nomogram and an AUC of 0.838 from the ROC curve. DCA indicated the model provided net clinical benefit across a wide range of threshold probabilities.
CONCLUSION: A nomogram incorporating BMI at delivery, cervical length during the second trimester, and gestation vaginitis status effectively predicts spontaneous preterm birth in twin pregnancies. This practical tool may aid in individualized risk assessment and guide clinical management.