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Prediction Model for Failed Vacuum Assisted Delivery: A Retrospective Cohort Study.

Itamar Gilboa¹, Daniel Gabbai¹, Lee Reicher¹

¹Lis Hospital for Women's Health, Tel Aviv Sourasky Medical Center, Gray Faculty of Medicine, Tel Aviv University, Tel Aviv 6927846, Israel.

Journal of Clinical Medicine

|April 14, 2026

Summary

This summary is machine-generated.

This study identified key risk factors for failed vacuum-assisted delivery (VAD), developing a predictive model to aid obstetricians in decision-making and identifying high-risk pregnancies for complicated deliveries.

Keywords:

cesarean delivery failure labor operative vaginal delivery prediction model vacuum assisted delivery

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Area of Science:

Obstetrics and Gynecology
Maternal-Fetal Medicine
Clinical Prediction Modeling

Background:

Vacuum-assisted delivery (VAD) is a common obstetric procedure.
Failed VAD can lead to urgent cesarean delivery (CD) and increased maternal and neonatal morbidity.
Predictive tools are needed to identify pregnancies at risk for VAD failure.

Purpose of the Study:

To determine risk factors associated with failed vacuum-assisted delivery (VAD).
To develop and validate a clinically based predictive model for VAD failure.
To aid obstetricians in VAD decision-making and patient counseling.

Main Methods:

Retrospective cohort study (2011-2023) at a tertiary medical center.
Included singleton pregnancies with a VAD attempt.
Defined VAD failure by cup detachments, extraction duration, or conversion to urgent CD.

Main Results:

1.9% of VAD attempts (172/8885) failed, resulting in urgent CD.
Identified risk factors: labor induction, deep fetal head station, prolonged second stage, preeclampsia, high birthweight (>3750g), and male fetus.
Developed prediction score with good discriminatory performance (AUC 0.723), validated internally (AUC 0.764).

Conclusions:

A validated predictive model for VAD failure was developed.
The model can assist obstetricians in VAD decision-making and risk stratification.
Identifies parturients at higher risk for complicated deliveries, enabling proactive management.