Prediction of Success in External Cephalic Version for Breech Presentation at Term

• Published in: Obstetrics and gynecology (New York. 1953) Vol. 133; no. 5; pp. 857 - 866
• Main Authors: Isakov, Ofer, Reicher, Lee, Lavie, Anat, Yogev, Yariv, Maslovitz, Sharon
• Format: Journal Article
• Language: English
• Published: United States Lippincott Williams & Wilkins 01-05-2019; by The American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved
• Subjects: Adult; Amniotic Fluid; Body Mass Index; Breech Presentation; Cohort Studies; Decision Support Techniques; Female; Gestational Age; Humans; Parity; Predictive Value of Tests; Pregnancy; Prenatal Diagnosis; Retrospective Studies; Version, Fetal
• ISSN: 0029-7844; 1873-233X
• DOI: 10.1097/AOG.0000000000003196

OBJECTIVE:To design a clinically based predictive model for the likelihood of successful external cephalic version (ECV).
METHODS:This single-center retrospective study was conducted from February 2016 to July 2018 and included all candidates for ECV between 36 and 41 weeks of gestation. Variables with a potential effect on ECV success were collected. These variables includebody mass index, amniotic fluid index, gestational age, parity, location of placenta, fetal trunk posture, time in breech presentation before the procedure and the ultrasonographically measured size of the amniotic fluid preceding the fetal presenting part (fore-bag). Variablesʼ association with ECV success was evaluated using a multivariate logistic regression and a decision tree predicting ECV outcome was developed using 75% of the patients and validated on the remaining 25%.
RESULTS:Overall, 250 pregnant women were identified and opted for a trial of ECV by a single operator, with a success rate of 64.8%. Body mass index, size of fore-bag, and parity were independent determinants of the version success, whereas other variables had no statistically significant effect on the success rate. Our decision tree model divided the cohort into five subgroups according to various combinations of the three variables. When evaluated on the internal validation set, the C-Index of the tree was 0.933 (0.863–1) and the prediction accuracy was 91.9% (86.5%–97.3%).
CONCLUSION:A prediction model composed of three easily measurable variables enables accurate prediction of successful ECV at term. Fore-bag was identified as the most important discriminator. Our model holds in internal validation and it can be used to support patient counseling and decision making for ECV but should be externally validated.