Abstract：
UNASSIGNED: Maternal glycemia is associated with the risk of cesarean delivery (CD); therefore, our study aims to developed a prediction model based on glucose indicators in the second trimester to earlier identify the risk of CD.
UNASSIGNED: This was a nested case-control study, and data were collected from the 5th Central Hospital of Tianjin (training set) and Changzhou Second People\'s Hospital (testing set) from 2020 to 2021. Variables with significant difference in training set were incorporated to develop the random forest model. Model performance was assessed by calculating the area under the curve (AUC) and Komogorov-Smirnoff (KS), as well as accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
UNASSIGNED: A total of 504 eligible women were enrolled; of these, 169 underwent CD. Pre-pregnancy body mass index (BMI), first pregnancy, history of full-term birth, history of livebirth, 1 h plasma glucose (1hPG), glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), and 2 h plasma glucose (2hPG) were used to develop the model. The model showed a good performance, with an AUC of 0.852 [95% confidence interval (CI): 0.809-0.895]. The pre-pregnancy BMI, 1hPG, 2hPG, HbA1c, and FPG were identifies as the more significant predictors. External validation confirmed the good performance of our model, with an AUC of 0.734 (95%CI: 0.664-0.804).
UNASSIGNED: Our model based on glucose indicators in the second trimester performed well to predict the risk of CD, which may reach the earlier identification of CD risk and may be beneficial to make interventions in time to decrease the risk of CD.
UNASSIGNED: This was a nested case-control study, and data were collected from the 5th Central Hospital of Tianjin (training set) and Changzhou Second People\'s Hospital (testing set) from 2020 to 2021. Variables with significant difference in training set were incorporated to develop the random forest model. Model performance was assessed by calculating the area under the curve (AUC) and Komogorov-Smirnoff (KS), as well as accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
UNASSIGNED: A total of 504 eligible women were enrolled; of these, 169 underwent CD. Pre-pregnancy body mass index (BMI), first pregnancy, history of full-term birth, history of livebirth, 1 h plasma glucose (1hPG), glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), and 2 h plasma glucose (2hPG) were used to develop the model. The model showed a good performance, with an AUC of 0.852 [95% confidence interval (CI): 0.809-0.895]. The pre-pregnancy BMI, 1hPG, 2hPG, HbA1c, and FPG were identifies as the more significant predictors. External validation confirmed the good performance of our model, with an AUC of 0.734 (95%CI: 0.664-0.804).
UNASSIGNED: Our model based on glucose indicators in the second trimester performed well to predict the risk of CD, which may reach the earlier identification of CD risk and may be beneficial to make interventions in time to decrease the risk of CD.
摘要：
■产妇血糖与剖宫产（CD）的风险有关；因此，我们的研究旨在建立基于妊娠中期血糖指标的预测模型,以更早地识别CD的风险.
■这是一项嵌套病例对照研究，数据收集自2020年至2021年天津市第五中心医院（培训集）和常州市第二人民医院（检测集）。结合训练集中具有显著差异的变量来开发随机森林模型。通过计算曲线下面积（AUC）和科莫戈罗夫-斯米尔诺夫（KS）评估模型性能，以及准确性，灵敏度,特异性，阳性预测值(PPV)，和阴性预测值(NPV)。
■总共有504名符合条件的女性被注册；其中，169接受了CD。孕前体重指数(BMI),第一次怀孕,足月分娩史,生活的历史，1h血浆葡萄糖（1hPG），糖化血红蛋白（HbA1c），空腹血糖（FPG），2h血浆葡萄糖（2hPG）用于建立模型。该模型表现出良好的性能，AUC为0.852[95%置信区间（CI）：0.809-0.895]。孕前BMI,1hPG,2hPG，HbA1c,和FPG被确定为更显著的预测因子。外部验证证实了我们模型的良好性能，AUC为0.734（95CI：0.664-0.804）。
■我们基于妊娠中期葡萄糖指标的模型在预测CD的风险方面表现良好，这可能会达到CD风险的早期识别，并可能有利于及时进行干预以降低CD的风险。
■这是一项嵌套病例对照研究，数据收集自2020年至2021年天津市第五中心医院（培训集）和常州市第二人民医院（检测集）。结合训练集中具有显著差异的变量来开发随机森林模型。通过计算曲线下面积（AUC）和科莫戈罗夫-斯米尔诺夫（KS）评估模型性能，以及准确性，灵敏度,特异性，阳性预测值(PPV)，和阴性预测值(NPV)。
■总共有504名符合条件的女性被注册；其中，169接受了CD。孕前体重指数(BMI),第一次怀孕,足月分娩史,生活的历史，1h血浆葡萄糖（1hPG），糖化血红蛋白（HbA1c），空腹血糖（FPG），2h血浆葡萄糖（2hPG）用于建立模型。该模型表现出良好的性能，AUC为0.852[95%置信区间（CI）：0.809-0.895]。孕前BMI,1hPG,2hPG，HbA1c,和FPG被确定为更显著的预测因子。外部验证证实了我们模型的良好性能，AUC为0.734（95CI：0.664-0.804）。
■我们基于妊娠中期葡萄糖指标的模型在预测CD的风险方面表现良好，这可能会达到CD风险的早期识别，并可能有利于及时进行干预以降低CD的风险。
