Prediction of vaginal birth after cesarean delivery in term gestations: a calculator without race and ethnicity

Abstract

Background: Investigators have attempted to derive tools that could provide clinicians with an easily obtainable estimate of the chance of vaginal birth after cesarean delivery for those who undertake trial of labor after cesarean delivery. One tool that has been validated externally was derived from data from the Maternal-Fetal Medicine Units Cesarean Registry. However, concern has been raised that this tool includes the socially constructed variables of race and ethnicity.

Objective: This study aimed to develop an accurate tool to predict vaginal birth after cesarean delivery, using data easily obtainable early in pregnancy, without the inclusion of race and ethnicity.

Study design: This was a secondary analysis of the Cesarean Registry of the Maternal-Fetal Medicine Units Network. The approach to the current analysis is similar to that of the analysis in which the previous vaginal birth after cesarean delivery prediction tool was derived. Specifically, individuals were included in this analysis if they were delivered on or after 37 0/7 weeks' gestation with a live singleton cephalic fetus at the time of labor and delivery admission, had a trial of labor after cesarean delivery, and had a history of 1 previous low-transverse cesarean delivery. Information was only considered for inclusion in the model if it was ascertainable at an initial prenatal visit. Model selection and internal validation were performed using a cross-validation procedure, with the dataset randomly and equally divided into a training set and a test set. The training set was used to identify factors associated with vaginal birth after cesarean delivery and build the logistic regression predictive model using stepwise backward elimination. A final model was generated that included all variables found to be significant (P<.05). The accuracy of the model to predict vaginal birth after cesarean delivery was assessed using the concordance index. The independent test set was used to estimate classification errors and validate the model that had been developed from the training set, and calibration was assessed. The final model was then applied to the overall analytical population.

Results: Of the 11,687 individuals who met the inclusion criteria for this secondary analysis, 8636 (74%) experienced vaginal birth after cesarean delivery. The backward elimination variable selection yielded a model from the training set that included maternal age, prepregnancy weight, height, indication for previous cesarean delivery, obstetrical history, and chronic hypertension. Vaginal birth after cesarean delivery was significantly more likely for women who were taller and had a previous vaginal birth, particularly if that vaginal birth had occurred after a previous cesarean delivery. Conversely, vaginal birth after cesarean delivery was significantly less likely for women whose age was older, whose weight was heavier, whose indication for previous cesarean delivery was arrest of dilation or descent, and who had a history of medication-treated chronic hypertension. The model had excellent calibration between predicted and empirical probabilities and, when applied to the overall analytical population, an area under the receiver operating characteristic curve of 0.75 (95% confidence interval, 0.74-0.77), which is similar to the area under the receiver operating characteristic curve of the previous model (0.75) that included race and ethnicity.

Conclusion: We successfully derived an accurate model (available at https://mfmunetwork.bsc.gwu.edu/web/mfmunetwork/vaginal-birth-after-cesarean-calculator), which did not include race or ethnicity, for the estimation of the probability of vaginal birth after cesarean delivery.

Keywords: calculator; calibration; personalized; prediction; trial of labor after cesarean delivery; vaginal birth after cesarean delivery; validation.

Figure 1.

Flow chart of eligibility for inclusion in this secondary analysis

Figure 2.

Calibration curve (with 95% confidence interval) of model validation in the test set The dashed straight line is the line of perfect calibration. The solid curve is the calibration curve generated by the prediction model, which is surrounded by dashed curves that represent the 95% confidence band of the calibration curve.

Figure 3.

Receiver-operating characteristic curve of the prediction model for vaginal birth after cesarean based on the overall analytic population