Department of Obstetrics and Gynaecology, Randers Regional Hospital, Randers, Denmark.
Department of Obstetrics and Gynaecology, Aarhus University Hospital, Aarhus, Denmark.
BMC Pregnancy Childbirth. 2024 Apr 19;24(1):291. doi: 10.1186/s12884-024-06461-8.
Current guidelines regarding oxytocin stimulation are not tailored to individuals as they are based on randomised controlled trials. The objective of the study was to develop an artificial intelligence (AI) model for individual prediction of the risk of caesarean delivery (CD) in women with a cervical dilatation of 6 cm after oxytocin stimulation for induced labour. The model included not only variables known when labour induction was initiated but also variables describing the course of the labour induction.
Secondary analysis of data from the CONDISOX randomised controlled trial of discontinued vs. continued oxytocin infusion in the active phase of induced labour. Extreme gradient boosting (XGBoost) software was used to build the prediction model. To explain the impact of the predictors, we calculated Shapley additive explanation (SHAP) values and present a summary SHAP plot. A force plot was used to explain specifics about an individual's predictors that result in a change of the individual's risk output value from the population-based risk.
Among 1060 included women, 160 (15.1%) were delivered by CD. The XGBoost model found women who delivered vaginally were more likely to be parous, taller, to have a lower estimated birth weight, and to be stimulated with a lower amount of oxytocin. In 108 women (10% of 1060) the model favoured either continuation or discontinuation of oxytocin. For the remaining 90% of the women, the model found that continuation or discontinuation of oxytocin stimulation affected the risk difference of CD by less than 5% points.
In women undergoing labour induction, this AI model based on a secondary analysis of data from the CONDISOX trial may help predict the risk of CD and assist the mother and clinician in individual tailored management of oxytocin stimulation after reaching 6 cm of cervical dilation.
目前关于催产素刺激的指南不是针对个体的，因为它们是基于随机对照试验的。本研究的目的是开发一种人工智能（AI）模型，用于预测在催产素刺激诱导分娩时宫颈扩张 6cm 后发生剖宫产的个体风险。该模型不仅包括分娩开始时已知的变量，还包括描述分娩过程的变量。
对 CONDISOX 随机对照试验中停止与继续使用催产素输注在诱导分娩活跃期的数据分析进行二次分析。使用极端梯度提升（XGBoost）软件构建预测模型。为了解释预测因子的影响，我们计算了 Shapley 加性解释（SHAP）值，并呈现了一个汇总的 SHAP 图。力图用于解释个体预测因子的具体细节，这些细节导致个体的风险输出值从基于人群的风险发生变化。
在纳入的 1060 名妇女中，有 160 名（15.1%）经剖宫产分娩。XGBoost 模型发现阴道分娩的妇女更有可能是经产妇、更高、估计出生体重更低，并且接受的催产素刺激量更低。在 108 名妇女（1060 名妇女的 10%）中，该模型倾向于继续或停止使用催产素。对于其余 90%的妇女，该模型发现继续或停止催产素刺激对剖宫产风险差异的影响小于 5%。
在接受分娩诱导的妇女中，该基于 CONDISOX 试验数据的二次分析的 AI 模型可能有助于预测剖宫产风险，并在宫颈扩张达到 6cm 后帮助母亲和临床医生对催产素刺激进行个体化管理。
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