超声波因素在预测引产后剖宫产中的价值。

IF 3.1 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Frontiers in Medicine Pub Date : 2024-10-31 eCollection Date: 2024-01-01 DOI:10.3389/fmed.2024.1430815
Guangpu Liu, Chaofan Zhou, Zhifen Yang, Jingya Zhang
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引用次数: 0

摘要

本研究旨在开发并验证引产(IOL)后剖宫产的预测模型。利用一家中国医院在 2017 年 7 月至 2023 年 12 月期间收集的患者数据,以回顾性方式比较了超声和非超声因素的组合,创建了单胎头位足月引产后剖宫产预测提名图。使用接收者操作特征曲线下面积(AUROC)和校准曲线对模型的区分度和校准进行了评估。随后,进行了决策曲线分析(DCA),以确定最佳概率阈值,使预测模型在临床决策中显示出实际意义。共纳入了 738 名妇女。将超声波因素与非超声波因素结合后,得出的 AUC 值更高。在分析的三个超声因素中,最能预测人工晶体植入术后剖宫产的因素是胎儿头围。在生成包含产妇年龄、胎龄、身高、既往剖宫产史、既往阴道分娩史、改良毕夏普评分、分娩时体重指数和超声检查胎儿头围等八个有效因素的提名图后,训练和验证的AUC值分别为0.826(95%置信区间0.786-0.867)和0.883(95%置信区间0.839-0.926)。决策曲线分析表明,该模型的净收益在概率阈值的 0% 到 80% 之间,这表明使用该模型对概率阈值范围内的患者进行决策是有益的。我们的提名图基于产科因素和超声获得的胎儿头围,可用于为考虑接受人工晶体植入术的妇女提供咨询。该模型在 0% 到 80% 的概率阈值范围内显示了良好的净效益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The value of ultrasonographic factors in predicting cesarean following induction.

This study aimed to develop and validate a prediction model of cesarean following induction of labor (IOL). A nomogram for the prediction of cesarean following IOL for singleton, cephalic term deliveries was created by comparing combinations of ultrasonographic and nonultrasonographic factors in a retrospective manner using patient data collected from a Chinese hospital between July, 2017 and December, 2023. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUROC) and a calibration curve. Subsequently, decision curve analysis (DCA) was conducted to pinpoint the optimal probability threshold for the predictive model to exhibit practical significance for clinical decision-making. A total of 738 women were included. The inclusion of ultrasound factors yielded a higher AUC when combined with nonultrasonographic factors. Of the three ultrasonographic factors analyzed, the most predictive factor for cesarean following IOL was fetal head circumference. After generating a nomogram with eight validated factors, including maternal age, gestational age, height, prior caesarean delivery, previous vaginal delivery, modified Bishop score, body mass index at delivery, and fetal head circumference by ultrasound, the trained and validated AUC values were 0.826 (95% confidence interval 0.786-0.867) and 0.883 (95% confidence interval 0.839-0.926), respectively. Decision curve analysis indicated that the model provided net benefits of between 0% and 80% of the probability threshold, indicating the benefits of using the model to make decisions concerning patients who fall within the identified range of the probability threshold. Our nomogram based on obstetric factors and fetal head circumference as obtained by ultrasound could be used to help counsel women who are considering IOL. The model demonstrates favorable net benefits within a probability threshold range of 0 to 80%.

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来源期刊
Frontiers in Medicine
Frontiers in Medicine Medicine-General Medicine
CiteScore
5.10
自引率
5.10%
发文量
3710
审稿时长
12 weeks
期刊介绍: Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world
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