Background: Repeat cesareans pose significant maternal risks, whereas vaginal birth after cesarean (VBAC) offers better outcomes, with a complication rate of 2.4% compared with 3.6% for elective repeat cesareans and 14.1% for failed trial of labor after cesarean (TOLAC). TOLAC rates are low in China, and success rates vary between 60% and 80%. This study aims to develop a nomogram-based predictive model for VBAC in China and improve existing, less rigorous scoring models. Methods: This retrospective cohort study was conducted at Hangzhou Women's Hospital from February 2015 to March 2020, and included 159 parturient attempts at labor after one prior low transverse cesarean section. The participants were divided into two groups based on their mode of delivery for comparison: the VBAC and the TOLAC failure group. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors for VBAC and develop a nomogram predictive model to estimate the success rate of TOLAC. The bootstrap method was used for internal validation of the models. Three different VBAC prediction models were evaluated by plotting receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: A total of 127 women had VBAC and 32 women failed TOLAC, with a success rate of 79.9%. Three independent factors affecting the VBAC were identified: gestational age at delivery, Bishop score and newborn birth weight. A predictive nomogram model for the VBAC was constructed that incorporates these three factors. The model showed a good fit (χ2 = 11.94, p = 0.154) with an overall prediction accuracy of 81.1%. The area under the ROC curve was 0.83 (95% CI (confidence interval), 0.76–0.90) (p < 0.001) and the optimal cut-off value was 83.4%. The bootstrap internal validation showed that our predictive model maintained high overall accuracy and specificity, but exhibited low sensitivity and a low Kappa coefficient. Compared to the Grobman model and Jiaming Rao et al. model, our developed prediction model possesses the strongest discriminatory ability and the highest net benefit, followed by the model by Jiaming Rao et al. All three models demonstrate a high degree of fit. Conclusions: Shorter gestational age at delivery, lower newborn birth weight and higher cervical Bishop score are favorable factors for VBAC. The predictive nomogram model for the VBAC after a single cesarean section, constructed with these three factors, has good predictive efficacy. The model is simple to calculate and has practical value in the clinical selection of suitable candidates for TOLAC after a single cesarean section.
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