Ayele Mulat, Lake Eyob Shitie, Tilahun Befkad Derese, Yilak Gizachew, Alamrew Abebaw, Kumie Getinet, Kitaw Tegene Atamenta, Abate Biruk Beletew, Azeze Getnet Gedefaw, Yimer Nigus Bililign
Department of Midwifery, College of Health Sciences, Woldia University, Woldia, Ethiopia.
Department of Nursing, College of Health Sciences, Woldia University, Woldia, Ethiopia.
BMC Pregnancy Childbirth. 2025 Jul 2;25(1):701. doi: 10.1186/s12884-025-07822-7.
Despite its benefit in improving outcomes, emergency cesarean delivery is not without maternal and neonatal risks. Early identification of a laboring woman’s risk of cesarean delivery is crucial for reducing complications associated with cesarean delivery. Therefore, this study aimed to develop and validate a risk prediction model for predicting caesarean delivery risk using maternal demographic and obstetric variables. A cross-sectional study was conducted involving a total of 702 laboring mothers. Data were entered into EpiData, Version 4.6, and exported into STATA version 17 and R version 4.2.2 for data management and analysis. A stepwise backward elimination technique was employed to select predictors for the final model. Model was developed using multivariable logistic regression analysis. The final prediction model included three modifiable predictors (place of antenatal follow-up, onset of labor, partograph use) and six non modifiable predictors (age, previous cesarean delivery, number of fetus,, fetal presentation, parity, and gestational age). The model’s performance was assessed using discrimination and calibration plots. Internal validation of the model was conducted using bootstrapping technique. The net benefit of the model was evaluated using decision curve analysis, and a nomogram was developed to calculate the individualized risk of laboring mothers. The model exhibited a discriminatory value of 85.1% (95% CI: 82.1–88.1%) with a good calibration performance. Internal validation showed low over-optimism coefficient (0.003), indicating low risk of over fitting. These findings suggest that clinicians can utilize this model to personalize patient management strategies, focusing on interventions that target modifiable risks while considering the inherent limitations of non-modifiable factors. By integrating this model into clinical practice, healthcare providers can enhance decision-making processes and potentially improve patient outcomes.
尽管急诊剖宫产在改善分娩结局方面有益，但它并非没有母婴风险。尽早识别分娩妇女的剖宫产风险对于减少与剖宫产相关的并发症至关重要。因此，本研究旨在开发并验证一种使用产妇人口统计学和产科变量预测剖宫产风险的风险预测模型。进行了一项横断面研究，共纳入702名分娩母亲。数据录入EpiData 4.6版，然后导出到STATA 17版和R 4.2.2版进行数据管理和分析。采用逐步向后排除技术为最终模型选择预测因素。使用多变量逻辑回归分析建立模型。最终的预测模型包括三个可改变的预测因素（产前随访地点、临产开始、产程图使用情况）和六个不可改变的预测因素（年龄、既往剖宫产史、胎儿数量、胎位、产次和孕周）。使用区分度和校准图评估模型的性能。使用自抽样技术对模型进行内部验证。使用决策曲线分析评估模型的净效益，并绘制列线图以计算分娩母亲的个体化风险。该模型的区分度值为85.1%（95%置信区间：82.1 - 88.1%），校准性能良好。内部验证显示过度乐观系数较低（0.003），表明过度拟合风险较低。这些发现表明，临床医生可以利用该模型个性化患者管理策略，重点关注针对可改变风险的干预措施，同时考虑不可改变因素的固有局限性。通过将该模型整合到临床实践中，医疗服务提供者可以加强决策过程并可能改善患者结局。
