• Personalised decision-making;
  • prediction model;
  • vaginal birth after caesarean;
  • VBAC


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.