Ferreira Iolanda, Simões Joana, Correia João, Areia Ana Luísa
Obstetrics Department, University and Hospitalar Centre of Coimbra, Coimbra, Portugal.
Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
Acta Obstet Gynecol Scand. 2025 Jan;104(1):164-173. doi: 10.1111/aogs.14953. Epub 2024 Nov 27.
INTRODUCTION: Induction of labor, often used for pregnancy termination, has globally rising rates, especially in high-income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean section (CS) rates after induction of labor (IOL) demand for improved counseling on delivery mode within this context. MATERIAL AND METHODS: We aim to develop a prognostic model for predicting vaginal delivery after labor induction using computational learning. Secondary aims include elaborating a prognostic model for CS due to abnormal fetal heart rate and labor dystocia, and evaluation of these models' feature importance, using maternal clinical predictors at IOL admission. The best performing model was assessed in an independent validation data using the area under the receiver operating curve (AUROC). Internal model validation was performed using 10-fold cross-validation. Feature importance was calculated using SHAP (SHapley Additive exPlanation) values to interpret the importance of influential features. Our main outcome measures were mode of delivery after induction of labor, dichotomized as vaginal or cesarean delivery and CS indications, dichotomized as abnormal fetal heart rate and labor dystocia. RESULTS: Our sample comprised singleton term pregnant women (n = 2434) referred for IOL to a tertiary Obstetrics center between January 2018 and December 2021. Prediction of vaginal delivery obtained good discrimination in the independent validation data (AUROC = 0.794, 95% CI 0.783-0.805), showing high positive and negative predictive values (PPV and NPV) of 0.752 and 0.793, respectively, high specificity (0.910) and sensitivity (0.766). The CS model showed an AUROC of 0.590 (95% CI 0.565-0.615) and high specificity (0.893). Sensitivity, PPV and NVP values were 0.665, 0.617, and 0.7, respectively. Labor features associated with vaginal delivery were by order of importance: Bishop score, number of previous term deliveries, maternal height, interpregnancy time interval, and previous eutocic delivery. CONCLUSIONS: This prognostic model produced a 0.794 AUROC for predicting vaginal delivery. This, coupled with knowing the features influencing this outcome, may aid providers in assessing an individual's risk of CS after IOL and provide personalized counseling.
引言：引产常用于终止妊娠，其全球发生率呈上升趋势，尤其是在高收入国家，那里的孕妇合并症更多。因此，人们担心引产（IOL）后剖宫产（CS）率可能上升，这就需要在这种情况下改进关于分娩方式的咨询。 材料与方法：我们旨在使用计算学习开发一种预测引产术后阴道分娩的预后模型。次要目标包括阐述因胎儿心率异常和产程难产导致剖宫产的预后模型，并使用引产入院时的母亲临床预测指标评估这些模型的特征重要性。在独立验证数据中使用受试者操作特征曲线下面积（AUROC）评估表现最佳的模型。使用10折交叉验证进行内部模型验证。使用SHAP（SHapley加性解释）值计算特征重要性，以解释有影响特征的重要性。我们的主要结局指标是引产术后的分娩方式，分为阴道分娩或剖宫产，以及剖宫产指征，分为胎儿心率异常和产程难产。 结果：我们的样本包括2018年1月至2021年12月期间转诊至三级产科中心进行引产的单胎足月孕妇（n = 2434）。阴道分娩预测在独立验证数据中具有良好的区分度（AUROC = 0.794，95%CI 0.783 - 0.805），显示出较高的阳性和阴性预测值（PPV和NPV），分别为0.752和0.793，高特异性（0.910）和敏感性（0.766）。剖宫产模型的AUROC为0.590（95%CI 0.565 - 0.615），特异性高（0.893）。敏感性、PPV和NVP值分别为0.665、0.617和0.7。与阴道分娩相关的产程特征按重要性排序为：Bishop评分、既往足月分娩次数、母亲身高、两次妊娠间隔时间和既往顺产。 结论：该预后模型预测阴道分娩的AUROC为0.794。这一点，再加上了解影响这一结局的特征，可能有助于医疗服务提供者评估个体引产术后剖宫产的风险，并提供个性化咨询。
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