Vaginal birth after a caesarean section: the development of a Western European population-based prediction model for deliveries at term
Article first published online: 26 DEC 2013
© 2013 Royal College of Obstetricians and Gynaecologists
BJOG: An International Journal of Obstetrics & Gynaecology
Special Issue: Management of pregnancy after caesarean section
Volume 121, Issue 2, pages 194–201, January 2014
How to Cite
Vaginal birth after a caesarean section: the development of a Western European population-based prediction model for deliveries at term. BJOG 2014;121:194–201., , , , , , , , , , , , , , , , , , , , , , .
- Issue published online: 26 DEC 2013
- Article first published online: 26 DEC 2013
- Manuscript Accepted: 25 SEP 2013
- the Netherlands Organisation for Health Research and Development (ZonMw). Grant Number: 17100.3006
- Personalised decision-making;
- prediction model;
- vaginal birth after caesarean;
To develop and internally validate a model that predicts the outcome of an intended vaginal birth after caesarean (VBAC) for a Western European population that can be used to personalise counselling for deliveries at term.
Registration-based retrospective cohort study.
Five university teaching hospitals, seven non-university teaching hospitals, and five non-university non-teaching hospitals in the Netherlands.
A cohort of 515 women with a history of one caesarean section and a viable singleton pregnancy, without a contraindication for intended VBAC, who delivered at term.
Potential predictors for a vaginal delivery after caesarean section were chosen based on literature and expert opinions. We internally validated the prediction model using bootstrapping techniques.
Main outcome measures
Predictors for VBAC. For model validation, the area under the receiver operating characteristic curve (AUC) for discriminative capacity and calibration-per-risk-quantile for accuracy were calculated.
A total of 371 out of 515 women had a VBAC (72%). Variables included in the model were: estimated fetal weight greater than the 90th percentile in the third trimester; previous non-progressive labour; previous vaginal delivery; induction of labour; pre-pregnancy body mass index; and ethnicity. The AUC was 71% (95% confidence interval, 95% CI = 69–73%), indicating a good discriminative ability. The calibration plot shows that the predicted probabilities are well calibrated, especially from 65% up, which accounts for 77% of the total study population.
We developed an appropriate Western European population-based prediction model that is aimed to personalise counselling for term deliveries.