Self-reported pain scores as a predictor of preterm birth in symptomatic twin pregnancy: a retrospective study

Development and validation of a prediction model on spontaneous preterm birth in twin pregnancy: a retrospective cohort study

BACKGROUND

This study was conducted to develop and validate an individualized prediction model for spontaneous preterm birth (sPTB) in twin pregnancies.

METHODS

This a retrospective cohort study included 3845 patients who gave birth at the Chongqing Maternal and Child Health Hospital from January 2017 to December 2022. Both univariable and multivariable logistic regression analyses were performed to find factors associated with sPTB. The associations were estimated using the odds ratio (OR) and the 95% confidence interval (CI). Model performance was estimated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

RESULTS

A total of 1313 and 564 cases were included in the training and testing sets, respectively. In the training set, univariate and multivariate logistic regression analysis indicated that age ≥ 35 years (OR, 2.28; 95% CI 1.67-3.13), pre-pregnancy underweight (OR, 2.36; 95% CI 1.60-3.47), pre-pregnancy overweight (OR, 1.67; 95% CI 1.09-2.56), and obesity (OR, 10.45; 95% CI, 3.91-27.87), nulliparity (OR, 0.58; 95% CI 0.41-0.82), pre-pregnancy diabetes (OR, 5.81; 95% CI 3.24-10.39), pre-pregnancy hypertension (OR, 2.79; 95% CI 1.44-5.41), and cervical incompetence (OR, 5.12; 95% CI 3.08-8.48) are independent risk factors for sPTB in twin pregnancies. The AUC of the training and validation set was 0.71 (95% CI 0.68-0.74) and 0.68 (95% CI 0.64-0.73), respectively. And then we integrated those risk factors to construct the nomogram.

CONCLUSIONS

The nomogram developed for predicting the risk of sPTB in pregnant women with twins demonstrated good performance. The prediction nomogram serves as a practical tool by including all necessary predictors that are readily accessible to practitioners.

Clinical analysis on the influencing factors related to preterm twin pregnancy

OBJECTIVE

The aim of this study was to explore the influencing factors of preterm twin pregnancy.

METHODS

In total, 602 twin-pregnant women delivered from February 2016 to February 2020 were analyzed retrospectively. According to whether the pregnant women were preterm or not, they were divided into preterm group (n=363) and term group (n=239). Baseline information, such as maternal age, address, and education level of the pregnant women, were collected. The clinical information of pregnant women, such as chorionic, preeclampsia, gestational diabetes, premature rupture of membranes, abnormal fetal position, and fetal weight, were analyzed. Logistic regression analysis was used to analyze the risk factors. p-value <0.05 was considered statistically significant.

RESULTS

In the preterm group, monochorionic diamniotic comorbidities were significantly higher compared with the control group (p<0.05). Higher risks of preterm group have lower education (p<0.05). Multiple logistic regression analysis demonstrated that education, preeclampsia, and premature rupture of membranes were risk factors for preterm twin pregnancy.

CONCLUSIONS

Preterm birth in twin pregnancy is associated with many risk factors, such as education, preeclampsia, and premature rupture of membranes. Pregnancy supervision and prenatal guidance for twin pregnancy should be strengthened. Furthermore, early detection and diagnosis of comorbidities can improve maternal and neonatal outcomes.