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Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in Sweden.

预测既往剖宫产后阴道分娩: 在瑞典使用机器学习模型和基于人群的队列。

  • 影响因子:2.21
  • DOI:10.1111/aogs.14020
  • 作者列表:"Lindblad Wollmann C","Hart KD","Liu C","Caughey AB","Stephansson O","Snowden JM
  • 发表时间:2021-03-01
Abstract

INTRODUCTION:Predicting a woman's probability of vaginal birth after cesarean could facilitate the antenatal decision-making process. Having a previous vaginal birth strongly predicts vaginal birth after cesarean. Delivery outcome in women with only a cesarean delivery is more unpredictable. Therefore, to better predict vaginal birth in women with only one prior cesarean delivery and no vaginal deliveries would greatly benefit clinical practice and fill a key evidence gap in research. Our aim was to predict vaginal birth in women with one prior cesarean and no vaginal deliveries using machine-learning methods, and compare with a US prediction model and its further developed model for a Swedish setting. MATERIAL AND METHODS:A population-based cohort study with a cohort of 3116 women with only one prior birth, a cesarean, and a subsequent trial of labor during 2008-2014 in the Stockholm-Gotland region, Sweden. Three machine-learning methods (conditional inference tree, conditional random forest and lasso binary regression) were used to predict vaginal birth after cesarean among women with one previous birth. Performance of the new models was compared with two existing models developed by Grobman et al (USA) and Fagerberg et al (Sweden). Our main outcome measures were area under the receiver-operating curve (AUROC), overall accuracy, sensitivity and specificity of prediction of vaginal birth after previous cesarean delivery. RESULTS:The AUROC ranged from 0.61 to 0.69 for all models, sensitivity was above 91% and specificity below 22%. The majority of women with an unplanned repeat cesarean had a predicted probability of vaginal birth after cesarean >60%. CONCLUSIONS:Both classical regression models and machine-learning models had a high sensitivity in predicting vaginal birth after cesarean in women without a previous vaginal delivery. The majority of women with an unplanned repeat cesarean delivery were predicted to succeed with a vaginal birth (ie specificity was low). Additional covariates combined with machine-learning techniques did not outperform classical regression models in this study.

摘要

引言: 预测妇女剖宫产后阴道分娩的概率可以促进产前决策过程。既往阴道分娩强烈预测剖宫产后阴道分娩。只有剖宫产分娩的妇女的分娩结果更不可预测。因此,更好地预测只有一次剖宫产且没有阴道分娩的女性的阴道分娩将极大地有益于临床实践并填补研究中的关键证据空白。我们的目的是使用机器学习方法预测既往剖宫产且无阴道分娩的女性的阴道分娩,并与美国预测模型及其进一步开发的瑞典模型进行比较. 材料和方法: 一项以人群为基础的队列研究,研究对象为瑞典斯德哥尔摩-哥特兰地区3116-2008期间仅有1次先前出生、1次剖宫产和随后的分娩试验的2014名妇女。使用三种机器学习方法 (条件推理树、条件随机森林和lasso二元回归) 预测既往分娩1例的妇女剖宫产后阴道分娩.新模型的性能与Grobman等人 (美国) 和Fagerberg等人 (瑞典) 开发的两个现有模型进行了比较。我们的主要结果指标是受试者工作曲线下面积 (AUROC) 、预测剖宫产后阴道分娩的总体准确性、敏感性和特异性。 结果: 所有模型的AUROC范围为0.61 ~ 0.69,敏感性高于91%,特异性低于22%。大多数非计划再次剖宫产的妇女在剖宫产后阴道分娩的预测概率> 60%。 结论: 经典回归模型和机器学习模型在预测没有阴道分娩的妇女剖宫产后阴道分娩方面具有较高的敏感性。大多数计划外重复剖宫产的妇女预测阴道分娩成功 (即特异性低)。在这项研究中,额外的协变量与机器学习技术相结合并没有优于经典回归模型。

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