A nomogram to predict preterm birth in twin pregnancies.
Qian Gui1, Yajun Yang1, Lan Wang1
1Department of Obstetrics, Women and Children's Hospital of Chongqing Medical University, Chongqing Health Center for Women and Children Chongqing 401132, China.
American Journal of Translational Research
|November 18, 2022
Summary
This study identified key risk factors for preterm birth in twin pregnancies, including chorionic status and preeclampsia. A validated nomogram model aids in predicting preterm birth risk for improved twin pregnancy management.
Area of Science:
- Maternal-Fetal Medicine
- Perinatology
- Obstetrics
Background:
- Preterm birth in twin pregnancies poses significant risks to both maternal and neonatal health.
- Accurate prediction of preterm birth is crucial for timely intervention and improved outcomes in twin gestations.
Purpose of the Study:
- To identify risk factors associated with preterm birth in twin pregnancies.
- To develop and validate a nomogram model for predicting preterm birth risk.
- To assess the clinical utility of the predictive model.
Main Methods:
- Retrospective analysis of 266 twin pregnancies.
- Comparison of preterm (<37 weeks) and full-term (≥37 weeks) birth groups.
- Univariate and multivariate logistic regression to identify risk factors.
- Construction and validation of a nomogram using R, including ROC curve and C-index analysis.
Main Results:
- Significant predictors of preterm birth identified: chorionic status, discordant twin development, premature rupture of membranes, fetal distress, scarred uterus, and preeclampsia.
- A nomogram model was constructed with a C-index of 0.783.
- The model provides a risk score on a 0-100 scale for predicting preterm birth.
Conclusions:
- A clinical nomogram for predicting preterm birth in twin pregnancies has been developed and validated.
- Specific factors like chorionic status, twin development discordance, and preeclampsia are independent predictors.
- The nomogram offers a valuable tool for risk assessment and management in twin pregnancies.
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