Nomogram model for predicting spontaneous preterm birth in twin pregnancies: a case-control study

Background This study aimed to analyze the correlation factors of spontaneous preterm birth in twin pregnancies and construct a predictive model, with the hope of providing clinical value for the prediction of spontaneous preterm delivery in twin pregnancies. Methods A total of 218 pregnant women with twin pregnancies at Jiaxing Women and Children's Hospital, Wenzhou Medical University between June 2021 and May 2024 were enrolled. One-way Analysis of Variance (One-way ANOVA) and multivariate logistic regression analysis were used to analyze the correlation factors, and a prediction model was constructed. The nomogram model was established using R, and evaluated by the area under the ROC curve, C-index, and decision curve analysis (DCA). Results One-way ANOVA showed that body mass index (BMI), length of cervical canal in the second trimester, presence or absence of cervical funnel, vaginitis during pregnancy, gestational diabetes mellitus (GDM) and intrauterine hemoglobin levels were associated with spontaneous preterm birth in twin pregnancies (P < 0.05). Multivariable logistic regression analysis showed that BMI, cervical length in the second trimester and gestational vaginitis are independent correlation factors for spontaneous preterm birth in twin pregnancies. After validation, the AUC value of the combined prediction of 3 correlation factors was the largest (0.852). Overall, a nomogram model with C-index of 0.838 was successfully constructed for predicting preterm birth in twin pregnancies. Conclusion BMI combined with cervical canal length in the second trimester and gestation vaginitis can improve the predictive value for the delivery outcome of spontaneous preterm birth in twin pregnancies.