[Application of seven prediction models of vaginal birth after cesarean in a Chinese hospital].

OBJECTIVE

To evaluate the seven existing vaginal birth after cesarean (VBAC) screening tools and to identify additional factors that may predict VBAC or failed trial of labor in China.

METHODS

In the study, 53 patients with 1 previous cesarean delivery who then delivered between January 1, 2007 and Novenber 31, 2014 were recruited. The average age of the patients was (32.1±3.5) years,the average gestational age was (38.0±2.3) weeks. There was no significant difference of the successful group and the failed group in the maternal/neonatal mortality and morbidity, also in the incidence of the postpartum hemorrhage and the postpartum infection. The probability of VBAC was calculated for each participant using 7 prediction models created by Weinstein, Flamm, Grobman, Gonen, Troyer, Smith and Torri. The data were analyzed using t test, rank-sum test, and receiver operating curve analysis.

RESULTS

44 trial of labor patients had a vaginal birth after cesarean delivery, and the successful rate was 83%. The scores between the successful group and the failed group had significant difference when evaluated by Weinstein and Grobman scoring models only. After recalculating the successful rate of VBAC in different score levels according to the references, there was significant difference between the rates of different score levels when evaluated by the Weinstein model. The successful rates of different score levels were higher compared to the references (<50%) when evaluated by the Troyer (70%), Gonen (60%), Torri (85.7%) models. The area under the receiver operating characteristic curve of Weinstein prediction model (0.746) and Flamm prediction model (0.723) were more than 0.7, and there was no significant difference between the seven models.

CONCLUSION

Among the seven scoring models, the Weinstein model is more applicable to the population of our country, but a new model more applying to Chinese women still needs to be created.