A modified prediction model for VBAC, in a European population

Abstract

Objective: The first aim of the study is to validate the Grobman's Nomogram on Italian population, and then to include other variables with the purpose to increase the accuracy of the Nomogram.

Methods: This is a multicenter study in which eligible subjects were pregnant women reaching term having one prior cesarean section (CS) and then choosing for a trial of labor. Multivariate logistic regression model have been performed, and then the predicted percentages of vaginal delivery (VD) success were divided into 10 groups and compared with the observed ones.

Results: Among 1161 women, 1100 were enrolled, of which 857 (77.9%) delivered vaginally. At the multivariate logistic regression, the variables predicting vaginal birth after cesarean (VBAC) in the validation were maternal age (p < 0.001), maternal body mass index (p = 0.007), having had a VD (p = 0.008) and recurring indication for CS (p < 0.001). By adding the two new variables in the proposed model, was reached the significance of "African ethnicity" (p = 0.037) and especially "years of education" (p = 0.032).

Conclusions: The Grobman's Nomogram seems to be applicable to Italian population too, even if with less accuracy than in the US population. The addition of the level of maternal education increases the accuracy of the model, underlining the importance of the social context in the choice of VBAC.

Keywords: Cesarean section; nomogram prediction; trial of labor; vaginal birth.