The Optimal Prediction Model for Successful External Cephalic Version.

Area of Science:

• Obstetrics and Gynecology
• Maternal-Fetal Medicine
• Clinical Prediction Models

Background:

• External cephalic version (ECV) is recommended to reduce cesarean deliveries for breech presentation.
• Physician training in vaginal breech birth is limited, increasing reliance on ECV.
• Accurate prediction of ECV success is crucial for shared decision-making and patient counseling.

Purpose of the Study:

• To externally validate and compare the performance of six existing prediction models for ECV success.
• To identify the most reliable model for predicting ECV outcomes in a U.S. hospital population.

Main Methods:

• Retrospective analysis of 132 ECV attempts from 125 patients at Carle Foundation Hospital.
• Evaluation of six prediction models: Dahl 2021, Bilgory 2023, López Pérez 2020, Kok 2011, Burgos 2010, and Tasnim 2012.
• Assessment of predictive value using area under the curve (AUC) and calibration analysis.

Main Results:

• Overall ECV success rate was 52.2% (69 out of 132 attempts).
• Dahl 2021 demonstrated the highest predictive value (AUC: 0.779) and good calibration.
• Tasnim 2012 performed poorly (AUC: 0.626), while other models showed moderate predictive values (AUC: 0.68-0.71).

Conclusions:

• The Dahl 2021 model is the most promising tool for predicting ECV success among the validated models.
• External validation of prediction models is essential for clinical adoption.
• Accurate prediction models can significantly improve patient counseling regarding ECV attempts.