Rahul S Yerrabelli, Peggy K Palsgaard, Priya Shankarappa, Valerie Jennings
{"title":"头外侧翻成功的最佳预测模型。","authors":"Rahul S Yerrabelli, Peggy K Palsgaard, Priya Shankarappa, Valerie Jennings","doi":"10.1055/a-2419-9146","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong> 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.</p><p><strong>Study design: </strong> 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).</p><p><strong>Results: </strong> 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.</p><p><strong>Conclusion: </strong> Multiple prediction tools have now been externally validated for ECV success. Dahl 2021 is the most promising prediction tool.</p><p><strong>Key points: </strong>· 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..</p>","PeriodicalId":7584,"journal":{"name":"American journal of perinatology","volume":" ","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Optimal Prediction Model for Successful External Cephalic Version.\",\"authors\":\"Rahul S Yerrabelli, Peggy K Palsgaard, Priya Shankarappa, Valerie Jennings\",\"doi\":\"10.1055/a-2419-9146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong> 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.</p><p><strong>Study design: </strong> 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).</p><p><strong>Results: </strong> 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.</p><p><strong>Conclusion: </strong> Multiple prediction tools have now been externally validated for ECV success. Dahl 2021 is the most promising prediction tool.</p><p><strong>Key points: </strong>· 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..</p>\",\"PeriodicalId\":7584,\"journal\":{\"name\":\"American journal of perinatology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American journal of perinatology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1055/a-2419-9146\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of perinatology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/a-2419-9146","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
The Optimal Prediction Model for Successful External Cephalic Version.
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..
期刊介绍:
The American Journal of Perinatology is an international, peer-reviewed, and indexed journal publishing 14 issues a year dealing with original research and topical reviews. It is the definitive forum for specialists in obstetrics, neonatology, perinatology, and maternal/fetal medicine, with emphasis on bridging the different fields.
The focus is primarily on clinical and translational research, clinical and technical advances in diagnosis, monitoring, and treatment as well as evidence-based reviews. Topics of interest include epidemiology, diagnosis, prevention, and management of maternal, fetal, and neonatal diseases. Manuscripts on new technology, NICU set-ups, and nursing topics are published to provide a broad survey of important issues in this field.
All articles undergo rigorous peer review, with web-based submission, expedited turn-around, and availability of electronic publication.
The American Journal of Perinatology is accompanied by AJP Reports - an Open Access journal for case reports in neonatology and maternal/fetal medicine.