Vbac success probability Assessment Tool — Free obstetrics Risk Score Calculator

How it Works

Scoring Variables (2021 Race-Neutral Model)

Maternal Age	Continuous variable
BMI	Calculated from height & pre-pregnancy weight
Prior Vaginal Delivery	None vs. Pre-CS vs. Post-CS (VBAC)
Prior Arrest Disorder	Yes/No (Indication for prior CS)
Chronic Hypertension	Yes/No

Physiological Rationale

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 "Race-Neutral" Update

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.

Clinical Pearls

Critical Insights

Prior VBAC is the single best predictor of future success (rates often >90%).

The calculator is most accurate for patients who enter labor spontaneously.

A success probability of >60-70% is generally considered "favorable" for a trial of labor.

Limitations & Caveats

Does not account for induction of labor — induction reduces success rates by ~10-15% compared to spontaneous labor.

Inter-pregnancy interval < 18 months significantly increases risk of uterine rupture.

Estimated fetal weight (EFW) > 4000g decreases probability of success but is not a variable in this specific model.

Next Steps

Favorable Probability (>70%)

01

Counsel patient on the high likelihood of successful vaginal delivery.

02

Discuss the benefit-risk ratio: Lower maternal morbidity compared to repeat CS if successful.

03

Document shared decision-making for TOLAC.

04

Ensure facility allows for "immediate" emergency CS should rupture occur.

Uterine Rupture Risk (TOLAC vs. ERCD)

TOLAC (Success)	Risk of rupture: ~0.5% (1 in 200)
TOLAC (Failure)	Maternal morbidity increases significantly
Induction of Labor	Increases rupture risk by 2x to 3x
Spontaneous Labor	Lowest risk profile for TOLAC

The "Danger Zone"

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.

Unfavorable/Low Probability (<60%)

01

Counsel patient that while VBAC is still possible, the risk of "failed" TOLAC is higher.

02

Explain that a failed TOLAC (maternal morbidity) is riskier than a scheduled repeat CS.

03

Consider Elective Repeat Cesarean Delivery (ERCD) if the patient prioritizes avoiding labor or failed trial.

Related Tools

Bishop Score

Pregnancy Weight Gain Calculator

Estimated Date of Delivery (EDD)

The Evidence

Foundational Model

Development of a nomogram for prediction of vaginal birth after cesarean delivery.

Grobman WA et al. • Obstet Gynecol.. 2007;Initial MFMU (Maternal-Fetal Medicine Units Network) derivation study. n = 15,515 patients. Established the core predictors for VBAC success.

Race-Neutral Validation

Prediction of Vaginal Birth After Cesarean Delivery in North America: A Race-Neutral Algorithm.

Grobman WA et al. • Obstet Gynecol.. 2021;Updated the algorithm to remove race/ethnicity, showing that the model maintained predictive performance while eliminating bias.

Guideline Reference

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.

Origins & History

The MFMU Network

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.

Dr. William Grobman

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.

VBAC Success Probability

Clinical Metrics

When to Use

When to Use

Patient Selection

Absolute Contraindications to TOLAC

How it Works

Scoring Variables (2021 Race-Neutral Model)

Physiological Rationale

The "Race-Neutral" Update

Clinical Pearls

Critical Insights

Limitations & Caveats

Next Steps

Favorable Probability (>70%)

Uterine Rupture Risk (TOLAC vs. ERCD)

The "Danger Zone"

Unfavorable/Low Probability (<60%)

Related Tools

The Evidence

Foundational Model

Development of a nomogram for prediction of vaginal birth after cesarean delivery.

Race-Neutral Validation

Prediction of Vaginal Birth After Cesarean Delivery in North America: A Race-Neutral Algorithm.

Guideline Reference

Origins & History

The MFMU Network

Dr. William Grobman

In Recent Clinical News

VBAC Success Probability

Clinical Metrics

When to Use

When to Use

Patient Selection

Absolute Contraindications to TOLAC

How it Works

Scoring Variables (2021 Race-Neutral Model)

Physiological Rationale

The "Race-Neutral" Update

Clinical Pearls

Critical Insights

Limitations & Caveats

Next Steps

Favorable Probability (>70%)

Uterine Rupture Risk (TOLAC vs. ERCD)

The "Danger Zone"

Unfavorable/Low Probability (<60%)

Related Tools

The Evidence

Foundational Model

Development of a nomogram for prediction of vaginal birth after cesarean delivery.

Race-Neutral Validation

Prediction of Vaginal Birth After Cesarean Delivery in North America: A Race-Neutral Algorithm.

Guideline Reference

Origins & History

The MFMU Network

Dr. William Grobman

In Recent Clinical News