Curated insights • How it Works • Practical Pearls • Evidence Base
Do not use this calculator or attempt TOLAC if: Prior classical or T-shaped uterine incision, prior uterine rupture, extensive transfundal uterine surgery (e.g., myomectomy), or any standard contraindication to vaginal delivery (e.g., placenta previa).
| Maternal Age |
| BMI |
| Prior Vaginal Delivery |
| Prior Arrest Disorder |
| Chronic Hypertension |
The model utilizes a logistic regression equation. Prior vaginal delivery is the strongest positive predictor of success, reflecting a "proven" pelvis. Conversely, a prior CS performed for an arrest disorder (failure to progress or fail to descend) suggests a recurring mechanical or physiological barrier, reducing the likelihood of success in subsequent trials.
The 2021 revision (Grobman et al.) removed race and ethnicity as variables. The updated model provides similar predictive accuracy (AUC 0.71) without codifying social constructs as biological determinants, addressing historical disparities where Black and Hispanic patients were assigned lower success probabilities.
| TOLAC (Success) |
| TOLAC (Failure) |
| Induction of Labor |
| Spontaneous Labor |
The risk of uterine rupture is significantly higher if the prior cesarean was performed less than 18 months ago, or if prostaglandins (misoprostol) are used for induction.
Development of a nomogram for prediction of vaginal birth after cesarean delivery.
Prediction of Vaginal Birth After Cesarean Delivery in North America: A Race-Neutral Algorithm.
ACOG Practice Bulletin No. 205 (Vaginal Birth After Cesarean Delivery) recommends the use of validated scoring systems like this MFMU model to assist in counseling and shared decision-making.
The Maternal-Fetal Medicine Units (MFMU) Network was established by the NICHD to conduct large-scale clinical trials in obstetrics. This calculator is the result of massive multi-center data collection aimed at reducing the rising primary cesarean rate in the US.
A leading figure in MFM and obstetric health services research. Dr. Grobman championed the removal of race from clinical algorithms to ensure that evidence-based tools do not inadvertently reinforce health inequities.