Prediction model of success for external cephalic version. Complications and perinatal outcomes after a successful version

López-Pérez, Rocío; Lorente-Fernández, Mónica; Velasco-Martínez, María; Martínez-Cendán, Juan Pedro

doi:10.1111/jog.14385

Cited by 11 publications

(20 citation statements)

References 28 publications

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“…We believe that the three pillars of information about ECV are the high probability of success, the safety of the technique, and the rate of vaginal delivery after successful ECV. Having a successful predictive model can optimize information in a personalized way 43 . To improve the practicability of clinical practice, we propose a scoring system that allows individualized calculation of the success rate of ECV.…”

Section: Discussionmentioning

confidence: 99%

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

Lin

Liu

et al. 2022

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

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

Lin

Liu

et al. 2022

Sci Rep

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“…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 .…”

Section: Relationships Between Articlesmentioning

confidence: 99%

“…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 .…”

Section: Quality and Biasmentioning

confidence: 99%

“…The model types, level development, and model performance are shown in Table 6, 13 models (from 12 articles) of the 25 models were only at the model derivation stage and had no internal or external validation 24,26,28,31,33,34,36,[38][39][40][41]45 . 7 models (from six articles) had internal validation by either random split, bootstrapping, and/or cross-validation, but had no form of external validation 24,29,30,32,37,42 .…”

Section: Phases Of Model Developmentmentioning

confidence: 99%

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

Yerrabelli

Lee

Palsgaard

et al. 2023

Preprint

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PURPOSE: To review the decision aids currently available or being developed to predict a patient’s odds that their external cephalic version (ECV) will be successful. METHODS: We searched PubMed/MEDLINE, Cochrane Central, and ClinicalTrials.gov from 2015-2022. Articles from a pre-2015 systematic review were also included. We selected English-language articles describing or evaluating models (prediction rules) designed to predict an outcome of ECV for an individual patient. Acceptable model outcomes included cephalic presentation after the ECV attempt and whether the ECV ultimately resulted in a vaginal delivery. Two authors independently performed article selection following PRISMA 2020 guidelines. Since 2015, 380 unique records underwent title and abstract screening, and 49 reports underwent full-text review. Ultimately, 17 new articles and 8 from the prior review were included. Of the 25 articles, 22 proposed 1-2 models each for a total of 25 models, while the remaining 3 articles validated prior models without proposing new ones. RESULTS: Of the 17 new articles, 10 were low, 6 moderate, and 1 high risk of bias. Almost all articles were from Europe (11/25) or Asia (10/25); only one study in the last 20 years was from the USA. The models found had diverse presentations including score charts, decision trees (flowcharts), and equations. The majority (13/25) had no form of validation and only 5/25 reached external validation. Only the Newman-Peacock model (USA, 1993) was repeatedly externally validated (Pakistan, 2012 and Portugal, 2018). Most models (14/25) were published in the last 5 years. In general, newer models were designed more robustly, used larger sample sizes, and were more mathematically rigorous. Thus, although they await further validation, there is great potential for these models to be more predictive than the Newman-Peacock model. CONCLUSION: Only the Newman-Peacock model is ready for regular clinical use. Many newer models are promising but require further validation.

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“…External cephalic version has been shown to reduce the frequency of malpresentation at term, thereby also reduc-ing the number of cesarean deliveries performed for this indication. 1 The success frequency of ECV across populations ranges from 28%-72%, [5][6][7][8][9][10][11][12][13][14][15][16][17][18] with an average of 53% in a meta-analysis of 10,149 women. 19 Multiple studies have demonstrated patient factors that are associated with ECV success, including parity, 5,[7][8][9][11][12][13][14][15][16][17][18][20][21][22][23] maternal body mass index (BMI, calculated as weight in kilograms divided by height in meters squared), 5,13,16,17,20 estimated fetal weight, 7,12,17 amniotic fluid volume, 5,7,9,11,12,15,17,18,…”

mentioning

confidence: 99%

A Multivariable Predictive Model for Success of External Cephalic Version

Dahl

Zhang

Ong

et al. 2021

Obstetrics &Amp; Gynecology

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OBJECTIVE: To create a prediction model for external cephalic version (ECV) success using objective patient characteristics. METHODS: This retrospective study included pregnant individuals of at least 18 years of age with a nonanomalous, singleton gestation who underwent an ECV attempt between 2006 and 2016 at a single quaternary care hospital. Variables assessed included maternal age, height, weight, body mass index (BMI), parity, fetal sex, gestational age, estimated fetal weight, type of fetal malpresentation, placental location, and amniotic fluid volume. Univariable and multivariable logistic regression models were used to determine the association of patient characteristics with ECV success. Estimated odds ratios and corresponding 95% CIs were calculated for each variable, and backward elimination and bootstrapping were used to find a parsimonious model for ECV success with the highest discriminatory capacity (as determined by the area under the receiver operating characteristic curve [AUC]). This model was evaluated with a calibration curve across deciles of success. RESULTS: A total of 1,138 individuals underwent an ECV attempt and were included in this analysis. The overall ECV success frequency was 40.6%. Factors significantly associated with ECV success were maternal age, parity, placental location, estimated fetal weight, and type of fetal malpresentation. A final model with BMI, parity, placental location, and type of fetal malpresentation had the highest AUC (0.667 [95% CI 0.634–0.701]), resulted in good calibration, and is represented by the following equation: 1/[1+e-x] where x=1.1726–0.0314 (BMI)−0.9299 (nulliparity)+1.0218 (transverse or oblique presentation at ECV)−0.5113 (anterior placenta). An interactive version of this equation was created and can be accessed at www.ecvcalculator.com. CONCLUSION: A prediction model that estimates the probability of ECV success was created and internally validated. This model incorporates easily obtainable and objective patient factors known before ECV and may be used in decision making and patient counseling about ECV.

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Prediction model of success for external cephalic version. Complications and perinatal outcomes after a successful version

Cited by 11 publications

References 28 publications

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

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

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

A Multivariable Predictive Model for Success of External Cephalic Version

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