Establishment of an antepartum predictive scoring model to identify candidates for vaginal birth after cesarean

Abstract

Background Evidence-based medicine has shown that successful vaginal birth after cesarean (VBAC) is associated with fewer complications than an elective repeat cesarean. Although spontaneous vaginal births and reductions in cesarean delivery (CD) rates have been advocated, the risk factors for VBAC complications remain unclear and failed trials of labor (TOL) can lead to adverse pregnancy outcomes. Methods To construct an antepartum predictive scoring model for VBAC. Retrospective analysis of charts from 1062 women who underwent TOL at no less than 28 gestational weeks with vertex singletons and no more than one prior CD. Results We constructed our scoring model based on the following variables: maternal age, previous vaginal delivery, interdelivery interval (time between prior cesarean and the following delivery), presence of prior cesarean TOL, dystocia as prior CD indication, intertuberous diameter, maternal predelivery body mass index, gestational age at delivery, estimated fetal weight, and hypertensive disorders. Previous vaginal delivery was the most influential variable. The nomogram showed an area under the curve of 77.7% (95% confidence interval, 73.8–81.5%; sensitivity, 78%; specificity, 70%; cut-off, 13 points). The Kappa value to judge the consistency of the results between the predictive model and the actual results was 0.71(95% confidence interval 0.65–0.77) indicating strong consistency. We used the cut-off to divide the VBAC women into two groups according to the success of the TOL. The maternal and neonatal outcomes such as labor time, number of deliveries by midwives, postpartum hemorrhage, uterine rupture, neonatal asphyxia, puerperal infection were significantly different between the two groups. Conclusions Our predictive scoring model incorporates easily ascertainable variables and can be used to personalize antepartum counselling for successful TOLs after cesareans.

Predictive Models for Estimating the Probability of Successful Vaginal Birth After Cesarean Delivery

“…Despite this, eight (8/17, 47%) of these unvalidated models were stated to be suitable for clinical use by their authors. 13–20…”

“…Despite this, eight (8/17, 47%) of these unvalidated models were stated to be suitable for clinical use by their authors. [13][14][15][16][17][18][19][20] Five of the externally validated Grobman 2007 models altered the ethnicity predictor to reflect their population (eg, Annessi et al replaced Latina with Asian). These models are highlighted as Grobman 2007* in Table 1.…”

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

“… 33 However, our research uncovered that the VBAC prediction model trained with SHAP consistently upheld a coherent hierarchy of variable importance, closely mirroring the factors that influence clinical decision-making, similar to previous investigations. 36 , 37 In the context of the SHAP analysis for the CatBoost model, the significance of gravidity as the most influential factor can be illuminated from multiple perspectives. Gravidity may intricately correlate with physiological elements, such as uterine status, uterine musculature elasticity, and ligamentous tension, all of which collectively influence the likelihood of a successful vaginal birth.…”