The Interdisciplinary Unit of Women's, Children's and Families' Health, the Juliane Marie Centre, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
Department of Obstetrics, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
PLoS One. 2024 Sep 6;19(9):e0308018. doi: 10.1371/journal.pone.0308018. eCollection 2024.
Obstetrics research has predominantly focused on the management and identification of factors associated with labor dystocia. Despite these efforts, clinicians currently lack the necessary tools to effectively predict a woman's risk of experiencing labor dystocia. Therefore, the objective of this study was to create a predictive model for labor dystocia.
The study population included nulliparous women with a single baby in the cephalic presentation in spontaneous labor at term. With a cohort-based registry design utilizing data from the Copenhagen Pregnancy Cohort and the Danish Medical Birth Registry, we included women who had given birth from 2014 to 2020 at Copenhagen University Hospital-Rigshospitalet, Denmark. Logistic regression analysis, augmented by a super learner algorithm, was employed to construct the prediction model with candidate predictors pre-selected based on clinical reasoning and existing evidence. These predictors included maternal age, pre-pregnancy body mass index, height, gestational age, physical activity, self-reported medical condition, WHO-5 score, and fertility treatment. Model performance was evaluated using the area under the receiver operating characteristics curve (AUC) for discriminative capacity and Brier score for model calibration.
A total of 12,445 women involving 5,525 events of labor dystocia (44%) were included. All candidate predictors were retained in the final model, which demonstrated discriminative ability with an AUC of 62.3% (95% CI:60.7-64.0) and Brier score of 0.24.
Our model represents an initial advancement in the prediction of labor dystocia utilizing readily available information obtainable upon admission in active labor. As a next step further model development and external testing across other populations is warranted. With time a well-performing model may be a step towards facilitating risk stratification and the development of a user-friendly online tool for clinicians.
产科研究主要集中在管理和识别与分娩困难相关的因素上。尽管已经做了这些努力，临床医生目前仍然缺乏有效预测女性分娩困难风险的必要工具。因此，本研究的目的是建立一种预测分娩困难的模型。
研究人群包括在足月自然分娩时处于头位的初产妇。本研究采用基于队列的登记设计，利用哥本哈根妊娠队列和丹麦医学出生登记处的数据，纳入了 2014 年至 2020 年期间在丹麦哥本哈根大学医院 - 里格希姆医院分娩的女性。使用逻辑回归分析，并结合超级学习者算法，构建预测模型，候选预测因子是基于临床推理和现有证据预先选择的。这些预测因子包括母亲年龄、孕前体重指数、身高、孕龄、体力活动、自我报告的健康状况、WHO-5 评分和生育治疗。使用接受者操作特征曲线（ROC）下的面积（AUC）评估模型的判别能力，使用 Brier 评分评估模型的校准。
共纳入 12445 名女性，其中 5525 名发生分娩困难（44%）。所有候选预测因子均保留在最终模型中，该模型具有鉴别能力，AUC 为 62.3%（95%CI：60.7-64.0），Brier 评分 0.24。
我们的模型代表了利用主动分娩时可获得的现有信息预测分娩困难的初步进展。下一步是在其他人群中进一步开发和外部测试该模型。随着时间的推移，一个表现良好的模型可能是朝着为临床医生提供风险分层和开发用户友好的在线工具的方向迈出的一步。
