Commentary on ‘Vaginal birth after a caesarean section: the development of a Western European population-based prediction model for deliveries at term
The decision to pursue a vaginal birth after a caesarean delivery (VBAC) for many women rests on balancing the risks and benefits of an attempted vaginal delivery versus those of a planned repeat caesarean delivery. Some factors related to the decision – such as the personal value a patient places on attempting a vaginal delivery – are unrelated to quantifiable risks and benefits. Other factors, such as planned family size and anticipated number of future deliveries, are unrelated to a current pregnancy. Counselling women appropriately on this decision, however, must involve a clear discussion of the risks associated with both options and the probability for success with an attempted vaginal delivery.
A number of prediction models for success in achieving a VBAC have been developed and validated. These models provide patients and providers with data on probability of success in individual cases. Additionally, they highlight clinical risk factors for failure (such as the absence of spontaneous labour) that may alter the balance of risks and benefits, and guide decision-making. The degree to which prediction models are useful across diverse patient populations is unclear, and the creation and validation of models for specific populations to optimise the quality of information for counselling is a laudable goal.
In the above article, Schoorel and colleagues present an analysis that incorporates prior vaginal delivery, labour induction, body mass index, ethnicity, indication for prior caesarean delivery, and estimated fetal weight in a prediction model for a primarily white Dutch population. The inclusion of these parameters adds to a growing literature on the importance of several of these parameters in predicting the success of VBAC (Grobman et al. Obstet Gynecol 2007;109:806–12).
Are the differences in probability of success for individual patients predicted by this model clinically significant? We simulated data on covariates for two patients: the first, a non-white woman with a pre-pregnancy BMI of 40 kg/m2, with previous non-progressive labour, no previous vaginal delivery, induced labour, and with a fetus that is large for gestational age; and the second, a white woman with a pre-pregnancy BMI of 20 kg/m2, without previous non-progressive labour, with previous vaginal delivery, with no labour induction, and with a fetus that is not large for gestational age (the two scenarios are in stark contrast with one another). The predicted VBAC success probabilities are 23.6 and 91.1%, respectively. Is this model successful in discriminating between high and low probability for success? We believe so!
The success of a prediction algorithm depends on several statistical aspects. First, the model needs to incorporate important risk factors while maintaining parsimony. Second, validating the derived model in both an internal population (i.e. on the same patient base as was used to derive the model) and an external population is crucial. Third, the model needs to be refined and improved to increase the efficiency and accuracy of predictions. Lastly – and arguably the most important characteristic of the model – is that the model should be easy to implement, widely applicable to different patient populations, and universally acceptable. Time alone will determine whether the model developed by Schoorel and colleagues meet these criteria. At the moment, their algorithm for predicting the success of VBAC seems solid, and one that is likely to be successful, at least for a European population.
Disclosure of interests
C.V.A. is the Editor-in-Chief of Paediatric and Perinatal Epidemiology, an international journal that is also published by Wiley-Blackwell. C.V.A and A.M.F. have no interests to disclose.
CV Ananth a,b & AM Friedman a
aDepartment of Obstetrics and Gynecology, College of Physicians and Surgeons, Columbia University, NY, USA
bDepartment of Epidemiology, Mailman School of Public Health, Columbia University, NY, USA