Citation
Grobman, William A.; Sandoval, Grecio; Rice, Madeline Murguia; Bailit, Jennifer L.; Chauhan, Suneet P.; Costantine, Maged M.; Gyamfi-Bannerman, Cynthia; Metz, Torri D.; Parry, Samuel; & Rouse, Dwight J., et al. (2021). Prediction of Vaginal Birth after Cesarean in Term Gestations: A Calculator without Race and Ethnicity. American Journal of Obstetrics & Gynecology, 225(6), 664e.1-7. PMCID: PMC8611105Abstract
BACKGROUND: Investigators have attempted to derive tools that could provide clinicians with an easily-obtainable estimate of the chance of vaginal birth after cesarean (VBAC) for those who undertake trial of labor after cesarean (TOLAC). One tool that subsequently was validated externally was derived from data from the Maternal-Fetal Medicine Units (MFMU) Cesarean Registry. Concern has been raised, however, that this tool includes the socially-constructed variables of race and ethnicity.OBJECTIVE: To develop an accurate tool to predict VBAC, using data easily obtainable early in pregnancy, without the inclusion of race/ethnicity.
STUDY DESIGN: This is a secondary analysis of the Cesarean Registry of the MFMU Network. The approach to the present analysis is similar to that of the analysis in which the prior VBAC 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 TOLAC, and had history of one prior 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 VBAC 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<0.05). The accuracy of the model to predict VBAC was assessed using the c-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 analytic population.
RESULTS: Of the 11,687 individuals who met inclusion criteria for this secondary analysis, VBAC occurred in 8636 (74%). The backward-elimination variable selection yielded a model from the training set that included maternal age, pre-pregnancy weight, height, indication for prior cesarean, obstetric history, and chronic hypertension. VBAC was significantly more likely for those who were taller and had a prior vaginal birth, particularly if that vaginal birth had occurred after the prior cesarean. Conversely, VBAC was significantly less likely among those whose age was older, whose weight was heavier, whose indication for prior cesarean 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 analytic population, an AUC of 0.75 (95% CI: 0.74 - 0.77), which is similar to the AUC of the previous model (0.75) that included race/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 estimation of VBAC probability.
URL
http://dx.doi.org/10.1016/j.ajog.2021.05.021Reference Type
Journal ArticleYear Published
2021Journal Title
American Journal of Obstetrics & GynecologyAuthor(s)
Grobman, William A.Sandoval, Grecio
Rice, Madeline Murguia
Bailit, Jennifer L.
Chauhan, Suneet P.
Costantine, Maged M.
Gyamfi-Bannerman, Cynthia
Metz, Torri D.
Parry, Samuel
Rouse, Dwight J.
Saade, George R.
Simhan, Hyagriv N.
Thorp, John M., Jr.
Tita, Alan T. N.
Longo, Monica
Landon, Mark B., for the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Maternal-Fetal Medicine Units (MFMU) Network