Predictive value of mid-trimester cervical measurement data combined
with maternal demographic characteristics for twin preterm birth at
< 32 weeks: a retrospective analysis and multicentre
validation study
Abstract
Objective: The purpose of this study was to develop a dynamic nomogram
model to predict the risk of spontaneous preterm birth at <32
weeks in twin pregnancy. Design: A retrospective analysis and
multicentre validation study Setting and Population: Women with twin
pregnancies followed up in two tertiary medical centres from January
2017 to March 2019. Methods: Data on maternal demographic
characteristics, transvaginal cervical length and funneling were
extracted. The prediction model was constructed with independent
variables determined by logistic regression analyses. The risk score was
calculated according to the dynamic nomogram model. Main outcome
measures: The risk of spontaneous preterm birth at <32 weeks
in twin pregnancy. Results: In total, 1065 twin pregnancies were
eligible for the study, of which the data of 764 cases (92 twin preterm
cases (<32 weeks) and 672 control cases) were obtained from a
tertiary medical centre as the training group and those of 301 cases (36
twin preterm cases (<32 weeks) and 265 control cases) from the
other tertiary medical centre as the external validation group. Based on
logistic regression analyses, we built a dynamic nomogram model with
satisfactory discrimination in both the training group(C-index: 0.856,
95% CI: 0.813-0.899) and external validation group(C-index: 0.808, 95%
CI: 0.751-0.865). The restricted cubic splines and ROC curve supported
the performance of the prediction model. Conclusions: We developed and
validated a dynamic nomogram model to predict the individual probability
of preterm birth in twin pregnancy at <32 weeks.