Ofer Isakov1, Lee Reicher, Anat Lavie, Yariv Yogev, Sharon Maslovitz. 1. Department of Internal Medicine "T," Sourasky Medical Center, and the Department of Obstetrics and Gynecology, Lis Maternity Hospital, Sourasky Medical Center, Tel Aviv, Israel.
Abstract
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 include: body 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). Variables' 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.
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 include: body 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). Variables' 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.
Authors: Javier Sánchez-Romero; Jesús López-Pérez; Ana Belén Flores-Muñoz; María Josefa Méndez-Martínez; Fernando Araico-Rodríguez; Jaime Mendiola-Olivares; José Eliseo Blanco-Carnero; Luis Falcón-Araña; Aníbal Nieto-Díaz; María Luisa Sánchez-Ferrer Journal: J Clin Med Date: 2022-01-19 Impact factor: 4.241