Development and external validation of a nomogram for predicting preterm birth at\u2009

Abstract

The purpose of this study was to develop a dynamic model to predict the risk of spontaneous preterm birth at < 32 weeks in twin pregnancy. A retrospective clinical study of consecutively asymptomatic women with twin pregnancies from January 2017 to December 2019 in two tertiary medical centres was performed. Data from one centre were used to construct the model, and data from the other were used to evaluate the model. Data on maternal demographic characteristics, transvaginal cervical length and funnelling during 20–24 weeks were extracted. The prediction model was constructed with independent variables determined by multivariate logistic regression analyses. After applying specified exclusion criteria, an algorithm with maternal and biophysical factors was developed based on 88 twin pregnancies with a preterm birth < 32 weeks and 639 twin pregnancies with a delivery ≥ 32 weeks. It was then evaluated among 34 pregnancies with a preterm birth < 32 weeks and 252 pregnancies with a delivery ≥ 32 weeks in a second tertiary centre without specific training. The model reached a sensitivity of 80.00%, specificity of 88.17%, positive predictive value of 50.33% and negative predictive value of 96.71%; ROC characteristics proved that the model was superior to any single parameter with an AUC of 0.848 (all P < 0.005). We developed and validated a dynamic nomogram model to predict the individual probability of early preterm birth to better represent the complex aetiology of twin pregnancies and hopefully improve the prediction and indication of interventions.