A prospective study using an individualized nomogram to predict the success rate of external cephalic version

Abstract

To establish a clinical-based nomogram for predicting the success rate of external cephalic version (ECV) through a prospective study. This was a single-center prospective study that collected eligible breech pregnant women. 152 participants were enrolled in the training cohort, who received ECV procedures performed by a single operator. We used the training cohort to establish regression equations and prediction models. These variables include maternal factors (age, operation gestational age, pre-pregnancy BMI (Body Mass Index), operation BMI, BMI increase, multipara), ultrasound factors (fetal weight estimation, amniotic fluid index, placental location, type of breech presentation, spinal position), and anesthesia. Univariate and multivariable analyses were used to screen the factors affecting the success of ECV. A nomogram scoring model was established based on these factors. And C-index, DCA (Decision Curve Analysis) and calibration curve, Hosmer–Lemeshow test was used to verify the prediction effect of the model. Finally, 33 participants were enrolled in the testing cohort who received ECV with an unrestricted operator. We used C-index, DCA (decision curve analysis), and Hosmer–Lemeshow to verify the application value of the prediction model. The calibration curves and ROC curves of both the training cohort and testing cohort are plotted for internal and external validation of the model. The ECV success rate of the training cohort was 62.5%. Univariate analysis showed that the predictors related to the success rate of ECV were age, BMI increase value, AFI (amniotic fluid index), breech type, placental location, spinal position, anesthesia, and multipara. The prediction thresholds of the corresponding indexes were calculated according to the Youden index. Multivariate logistic regression analysis showed that BMI increase ≥ 3.85 kg/m2, AFI ≥ 10.6 cm, anesthesia, multipara, and non-anterior placenta were independent predictors of ECV success. Through the internal and external validation, it is confirmed that the model has a good calibration and prediction ability. Our nomogram has a good ability to predict the success rate of ECV.

Prediction Models for Successful External Cephalic Version: An Updated Systematic Review

“…While the large majority of articles studied maternal weight or BMI, only a few indicated whether this was collected or intended to be a pregravid, peri-ECV, or peripartum BMI. Of the 8 models using some form of maternal weight or BMI, one model used BMI at ECV 46 , one model used the BMI increase from prepregnancy to ECV 39 , two models from the same article used pre-pregnancy BMI 36 , and 4 models were not clear on when BMI was evaluated 30,31,41,44 .…”

“…The remaining 13 new articles used datasets, proposed models, and were performed by researchers that were entirely distinct from the 8 original articles. Of these articles, two were from the same team of authors and used the same dataset; Anand 2019 used the dataset to create a model predicting cephalic position after the ECV, while the Palepu 2021 created a model to predict vaginal delivery 30,31 The remaining 11 articles were completely independent from each other; two of these articles proposed two models each 32,33 , while the remaining 9 proposed a single model each [34][35][36][37][38][39][40][41][42] (Figure 2). Two of the articles had an erratum: one was trivial 36,43 and only modi ed a sentence in the abstract, while the other was a correction of a gure describing the decision tree model 37,44 .…”

“…The results of the study quality assessment based on the signaling questions are shown in Table 4. Out of 17 new articles, 10 studies were identi ed as having low overall risk of bias and high study quality 22,[29][30][31][32]35,37,[40][41][42] , 6 studies were identi ed as having moderate bias and moderate study quality 25,33,34,38,39,43 , and 1 was considered to have high bias and low quality 26 . This is in addition to Velzel et al's assessment of the 8 prior articles of which 4 were low risk of bias and high study quality and 4 were moderate risk of bias and study quality 10 .…”

Prediction Models for Successful External Cephalic Version: An Updated Systematic Review

“…Of these articles, two were from the same team of authors and used the same dataset; Anand et al used the dataset to create a model predicting cephalic position after the ECV, while the Palepu et al created a model to predict vaginal delivery 34,35 . The remaining 11 articles were completely independent from each other; two of these articles proposed two models each 36,37 , while the remaining 9 proposed a single model each [38][39][40][41][42][43][44][45][46] (Figure 2). Two of the articles had an erratum: one was trivial 40,47 and only modified a sentence in the abstract, while the other was a correction of a figure describing the decision tree model 41,48 .…”

“…The results of the study quality assessment based on the signaling questions are shown in Table 4. Out of 17 new articles, 10 studies were identified as having low overall risk of bias and high study quality 22,[33][34][35][36]39,41,[44][45][46] , 6 studies were identified as having moderate bias and moderate study quality 29,37,38,42,43,47 , and 1 was considered to have high bias and low quality 30 . This is in addition to Velzel et al's assessment of the 8 prior articles of which 4 were low risk of bias and high study quality and 4 were moderate risk of bias and study quality 10 .…”