A MULTIVARIATE RISK STRATIFICATION MODEL FOR PREDICTING SUCCESSFUL VAGINAL BIRTH AFTER CESAREAN SECTION: A PROSPECTIVE MULTI-CENTER ANALYISIS

Prof. Lars Nielsen; MD; Dr. Ingrid Hansen; PhD

Abstract

The rising global rate of primary cesarean delivery has necessitated robust clinical frameworks for identifying candidates for vaginal birth after cesarean (VBAC). Despite international guidelines, clinical decision-making often relies on subjective assessment, leading to high rates of repeat surgeries or failed trials of labor after cesarean (TOLAC), which carry significant morbidity. This study presents the development and validation of a multifaceted risk stratification model designed to predict successful VBAC with high precision. By integrating maternal morphometric data, obstetric history, and real-time fetal biometry, the 'VBAC-RS' model categorizes patients into low, intermediate, and high-risk cohorts. Our analysis of a multi-center cohort demonstrates that standardized risk scoring significantly outperforms traditional clinical intuition. The findings suggest that utilizing comprehensive scoring systems can reduce the incidence of uterine rupture and emergency repeat sections by optimizing patient selection. This research provides a data-driven foundation for antenatal counseling and labor management, aiming to improve maternal outcomes and reproductive health equity in secondary and tertiary care settings.

Keywords: Vaginal Birth After Cesarean, Trial of Labor After Cesarean (TOLAC), Risk Stratification, Uterine Rupture, Obstetric Outcomes, Predictive Modeling, Maternal Health

With primary cesarean rates exceeding 30% in many regions, the management of subsequent pregnancies is a critical public health priority. Failed trials of labor are associated with increased risks of hysterectomy, bladder injury, and neonatal hypoxic-ischemic encephalopathy compared to successful VBAC or planned repeat sections. Effective risk stratification is the cornerstone of modern reproductive health, ensuring that surgical interventions are reserved for high-risk scenarios while promoting physiological birth for low-risk candidates. This research addresses a vital gap in personalized obstetric care, shifting the focus from generalized protocols to individualized, evidence-based management. By reducing unnecessary surgical morbidity and optimizing resource allocation in labor wards, this model contributes to the overarching goal of improving long-term maternal and neonatal health outcomes in the post-cesarean population.

To develop and validate a multivariate risk stratification model (VBAC-RS) to predict the probability of successful vaginal delivery and minimize maternal-fetal complications in women undergoing a trial of labor after one prior cesarean.

A prospective observational study was conducted across three tertiary maternity centers over a 36-month period, enrolling 840 participants with one previous low-transverse cesarean section. Data points harvested included maternal age, pre-pregnancy BMI, inter-pregnancy interval, prior vaginal delivery, and cervical effacement at admission. Fetal parameters included estimated fetal weight (EFW) via ultrasound and head circumference. The primary outcome was successful VBAC, defined as a spontaneous or operative vaginal delivery. Logistic regression analysis was employed to identify independent predictors of success, which were then weighted to create the 100-point VBAC-RS score. Model performance was evaluated using the Area Under the Receiver Operating Characteristic (AUROC) curve. Secondary outcomes included uterine rupture, neonatal Apgar scores

80), the VBAC rate was 91.4%. Conversely, in the 'Low-Success' cohort (score

4000g decreased the probability of success by 45% (OR 0.55), especially in the absence of a prior vaginal delivery.

The VBAC-RS model represents a significant advancement over binary prediction tools by offering nuanced risk categories that align with clinical reality. Our data suggest that for patients in the high-risk tier (score <40%), elective repeat cesarean delivery should be prioritized to avoid the nearly fourfold increase in maternal morbidity associated with failed TOLAC. Conversely, the model provides robust reassurance for the low-risk cohort, potentially increasing VBAC uptake and reducing the global cesarean burden. Implementation of this tool could standardize obstetric decision-making, mitigate the risks of uterine rupture, and provide a standardized framework for patient counseling. Future longitudinal studies should focus on integrating this model into electronic health records for real-time clinical decision support.