Cervical length and obstetric history predict spontaneous preterm birth: development and validation of a model to provide individualized risk assessment

Abstract

To evaluate the ability of combinations of cervical length and maternal history to assess the risk of spontaneous preterm birth, and to provide a simple procedure for the optimal estimation of risk. This prospective observational study was carried out between January 1998 and May 2006. Transvaginal sonographic measurement of cervical length at 20 + 0 to 24 + 6 weeks of gestation was carried out in 58 807 singleton pregnancies as part of routine antenatal care. The outcome measure was spontaneous extreme (< 28 weeks), early (28–30 weeks), moderate (31–33 weeks) and mild (34–36 weeks) preterm birth. Logistic regression analysis was used to derive models for the prediction of spontaneous preterm birth from the maternal obstetric history, demographic characteristics and cervical length. The rates of extreme, early, moderate and mild spontaneous preterm birth were 0.23%, 0.24%, 0.57% and 2.93%, respectively. The best prediction of spontaneous preterm birth was provided by cervical length (area under the receiver–operating characteristics curve (AUC), extreme 0.903, early 0.816, moderate 0.784 and mild 0.617) and this was improved by adding obstetric history (AUC, extreme 0.919, early 0.836, moderate 0.819 and mild 0.650). Addition of other parameters was without material effect. For a 10% screen‐positive rate, models using cervical length and obstetric history had a sensitivity of 80.6%, 58.5%, 53.0% and 28.6% for extreme, early, moderate and mild spontaneous preterm birth, respectively. These models were expressed as tables of adjusted likelihood ratios to allow simple estimation of the risk of spontaneous preterm birth. A model combining cervical length and obstetric history provides a better prediction of spontaneous preterm birth than either factor alone, and the sensitivity of screening improves for increasing degrees of prematurity. Copyright © 2008 ISUOG. Published by John Wiley & Sons, Ltd.