A Novel and Modern Calculator to Predict Vaginal Birth after Cesarean Delivery

Abstract

Objective

Counseling patients who are considering a trial of labor after cesarean (TOLAC) is a challenging task given the risks and benefits of either approach. While calculators exist to give patients an idea of their likelihood of having a successful vaginal birth after cesarean (VBAC), their validity is limited by outdated mathematical methods used to develop them. Most importantly, current VBAC calculators only offer insight into the chance of successful VBAC, without any ability to predict the risk of adverse outcomes relevant to both the patient and neonate. The objective of this study is to develop a prediction model for individualized risks and benefits of a TOLAC using modern mathematical techniques.

Study Design

This was a secondary analysis of the Cesarean Registry database, the same database used in developing the Maternal–Fetal Medicine Units (MFMU) VBAC calculator. The primary outcome was the prediction of the success of VBAC. Secondary outcomes were the prediction of uterine rupture, maternal complications, and neonatal complications. Inclusion criteria were term, singleton gestation, and cephalic presentation pregnancies with one prior low transverse cesarean delivery (CD). Exclusion criteria included intrauterine fetal demise, planned cesarean, and prior myomectomy. Univariate comparisons identified variables that were independently associated with VBAC. An optimal decision tree was used to create a prediction model. A test set was withheld for validation. A risk calculator tool was developed for the prediction of successful VBAC and adverse perinatal outcomes. Adverse maternal outcomes: uterine dehiscence, hysterectomy, postpartum hemorrhage, endometritis, intensive care unit admission, thromboembolic event, readmission, and organ injury. Adverse neonatal outcomes: hypoxic-ischemic encephalopathy, respiratory distress, seizures, apnea, respirator use, death, and cord blood pH < 7.1.

Results

The study population included 73,262 deliveries of which 12,942 patients met inclusion and exclusion criteria. After removing cases for the test set, the included patients were 8,078 patients, of which 5,970 people had a successful VBAC (73.9%). Parity, number of years since prior CD, prepregnancy body mass index (BMI), delivery BMI, maternal age, and previous VBAC were associated with successful VBAC. A risk predictor calculator was created, and a receiver operator characteristic curve was developed with an area under the curve of 0.72. The tool was also developed to identify a person's risk of uterine rupture, composite maternal morbidity, and neonatal morbidity.

Conclusion

VBAC for patients with term, cephalic, singleton gestation was associated with several variables. This advanced calculator tool will facilitate shared decision-making about the value of a TOLAC regarding the personalized risks of maternal and neonatal morbidity. By using more advanced mathematical models, this tool allows providers to predict not only the likelihood of successful VBAC but also the risk of maternal and neonatal morbidity involved in attempting VBAC.

Key Points

• Current VBAC calculators are limited by the mathematical methods used to make them.

• This novel calculator uses more advanced machine-learning methods than previous calculators.

• This VBAC calculator predicts both the chance of success VBAC and the risk of morbidity.

• The more modern VBAC calculator gives providers more information to use when counseling patients.

Keywords

cesarean delivery - machine learning - risk prediction - VBAC

Publication History

Received: 20 February 2025

Accepted: 11 May 2025

Accepted Manuscript online:
12 May 2025

Article published online:
29 May 2025

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