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Am J Perinatol 2025; 42(06): 751-757
DOI: 10.1055/a-2419-9146
Original Article

The Optimal Prediction Model for Successful External Cephalic Version

Rahul S. Yerrabelli 
1   Carle Illinois College of Medicine, The University of Illinois at Urbana-Champaign, Champaign, Illinois
2   Department of Obstetrics and Gynecology, Reading Hospital, Tower Health, West Reading, Pennsylvania
,
Peggy K. Palsgaard 
1   Carle Illinois College of Medicine, The University of Illinois at Urbana-Champaign, Champaign, Illinois
3   Department of Obstetrics and Gynecology, Western Michigan University, Kalamazoo, Michigan
,
Priya Shankarappa
1   Carle Illinois College of Medicine, The University of Illinois at Urbana-Champaign, Champaign, Illinois
4   Department of Internal Medicine, Brown University, Providence, Rhode Island
,
Valerie Jennings
1   Carle Illinois College of Medicine, The University of Illinois at Urbana-Champaign, Champaign, Illinois
5   Department of Obstetrics and Gynecology, Carle Foundation Hospital, Urbana, Illinois
› Author Affiliations
Funding None.
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Abstract

Objective

The majority of breech fetuses are delivered by cesarean birth as few physicians are trained in vaginal breech birth. An external cephalic version (ECV) can prevent cesarean delivery and the associated morbidity in these patients. Current guidelines recommend that all patients with breech presentation be offered an ECV attempt. Not all attempts are successful, and an attempt does carry some risks, so shared decision-making is necessary. To aid in patient counseling, over a dozen prediction models to predict ECV success have been proposed in the last few years. However, very few models have been externally validated, and thus, none have been adopted into clinical practice. This study aims to use data from a U.S. hospital to provide further data on ECV prediction models.

Study Design

This study retrospectively gathered data from Carle Foundation Hospital and used it to test six models previously proposed to predict ECV success. These models were Dahl 2021, Bilgory 2023, López Pérez 2020, Kok 2011, Burgos 2010, and Tasnim 2012 (GNK-PIMS score).

Results

A total of 125 patients undergoing 132 ECV attempts were included. A total of 69 attempts were successful (52.2%). Dahl 2021 had the greatest predictive value (area under the curve [AUC]: 0.779), whereas Tasnim 2012 performed the worst (AUC: 0.626). The remaining models had similar predictive values as each other (AUC: 0.68–0.71). Bootstrapping confirmed that all models except Tasnim 2012 had confidence intervals not including 0.5. The bootstrapped 95% AUC confidence interval for Dahl 2021 was 0.71 to 0.84. In terms of calibration, Dahl 2021 was well calibrated with predicted probabilities matching observed probabilities. Bilgory 2023 and López Pérez were poorly calibrated.

Conclusion

Multiple prediction tools have now been externally validated for ECV success. Dahl 2021 is the most promising prediction tool.

Key Points

  • Prediction models can be powerful tools for patient counseling.

  • The odds of ECV success can estimated based on patient factors and clinical findings.

  • Of the six tested models, only Dahl 2021 appears to have good predictive value and calibration.

Keywords

breech presentation - cesarean section - clinical prediction rule - logistic regression - machine learning - nomogram - obstetrics - prognostic score

Availability of Data and Materials

The data are published in the supplemental data. The code and data are also hosted in a public GitHub repository and are available at https://github.com/ryerrabelli/ECV-Analysis-Public or https://doi.org/10.5281/zenodo.13917274 .


Supplementary Material



Publication History

Received: 30 June 2024

Accepted: 23 September 2024

Accepted Manuscript online:
24 September 2024

Article published online:
21 October 2024

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