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Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in

Charlotte Lindblad Wollmann^1,2, Kyle D Hart³, Can Liu¹

¹Clinical Epidemiology Division, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.

Acta Obstetricia Et Gynecologica Scandinavica

|October 8, 2020

Summary

Predicting vaginal birth after cesarean is challenging for women without prior vaginal births. Machine-learning models showed high sensitivity but low specificity, indicating they often predicted success for women who ultimately had repeat cesareans.

Keywords:

Cesarean delivery machine-learning prediction random forest trial of labor vaginal birth after cesarean

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Area of Science:

Obstetrics and Gynecology
Reproductive Health
Clinical Decision-Making

Background:

Predicting vaginal birth after cesarean (VBAC) is crucial for antenatal decision-making.
Women with a prior vaginal birth have a high VBAC success rate, but outcomes are less predictable for those with only a prior cesarean delivery.
A significant evidence gap exists in predicting VBAC for women with one prior cesarean and no prior vaginal births.

Purpose of the Study:

To predict the probability of vaginal birth after cesarean in women with one prior cesarean delivery and no prior vaginal births.
To compare the performance of machine-learning models against existing US and Swedish prediction models.
To improve clinical practice and fill an evidence gap in VBAC prediction.

Main Methods:

A population-based cohort study of 3116 women in Sweden (2008-2014) with one prior cesarean and a subsequent trial of labor.
Application of three machine-learning methods: conditional inference tree, conditional random forest, and lasso binary regression.
Comparison of model performance using Area Under the Receiver-Operating Curve (AUROC), accuracy, sensitivity, and specificity against established models.

Main Results:

All models, including machine-learning and classical regression, demonstrated high sensitivity (above 91%) for predicting VBAC.
Specificities were low (below 22%), meaning many women who had an unplanned repeat cesarean were predicted to have a successful vaginal birth.
The AUROC values ranged from 0.61 to 0.69 across all evaluated models.

Conclusions:

Both classical regression and machine-learning models exhibit high sensitivity but low specificity in predicting VBAC for women without prior vaginal deliveries.
Machine-learning models, even with additional covariates, did not significantly outperform classical regression models in this specific population.
The low specificity highlights a challenge in accurately identifying women who will undergo an unplanned repeat cesarean delivery.