PDF(Pubmed)
Abstract：
BACKGROUND: The birth of large-for-gestational-age (LGA) infants is associated with many short-term adverse pregnancy outcomes. It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus (GDM) is significantly higher than that born to healthy pregnant women. However, traditional methods for the diagnosis of LGA have limitations. Therefore, this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants.
OBJECTIVE: To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM, and provide strategies for the effective prevention and timely intervention of LGA.
METHODS: The multivariable prediction model was developed by carrying out the following steps. First, the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses, for which the P value was < 0.10. Subsequently, Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations, and the optimal combination factors were selected by choosing lambda 1se as the criterion. The final predictors were determined by multiple backward stepwise logistic regression analysis, in which only the independent variables were associated with LGA risk, with a P value < 0.05. Finally, a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve, calibration curve and decision curve analyses.
RESULTS: After using a multistep screening method, we establish a predictive model. Several risk factors for delivering an LGA infant were identified (P < 0.01), including weight gain during pregnancy, parity, triglyceride-glucose index, free tetraiodothyronine level, abdominal circumference, alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks. The nomogram\'s prediction ability was supported by the area under the curve (0.703, 0.709, and 0.699 for the training cohort, validation cohort, and test cohort, respectively). The calibration curves of the three cohorts displayed good agreement. The decision curve showed that the use of the 10%-60% threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.
CONCLUSIONS: Our nomogram incorporated easily accessible risk factors, facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.
OBJECTIVE: To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM, and provide strategies for the effective prevention and timely intervention of LGA.
METHODS: The multivariable prediction model was developed by carrying out the following steps. First, the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses, for which the P value was < 0.10. Subsequently, Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations, and the optimal combination factors were selected by choosing lambda 1se as the criterion. The final predictors were determined by multiple backward stepwise logistic regression analysis, in which only the independent variables were associated with LGA risk, with a P value < 0.05. Finally, a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve, calibration curve and decision curve analyses.
RESULTS: After using a multistep screening method, we establish a predictive model. Several risk factors for delivering an LGA infant were identified (P < 0.01), including weight gain during pregnancy, parity, triglyceride-glucose index, free tetraiodothyronine level, abdominal circumference, alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks. The nomogram\'s prediction ability was supported by the area under the curve (0.703, 0.709, and 0.699 for the training cohort, validation cohort, and test cohort, respectively). The calibration curves of the three cohorts displayed good agreement. The decision curve showed that the use of the 10%-60% threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.
CONCLUSIONS: Our nomogram incorporated easily accessible risk factors, facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.
摘要：
背景：大胎龄（LGA）婴儿的出生与许多短期不良妊娠结局有关。已经观察到，患有妊娠期糖尿病（GDM）的孕妇所生的LGA婴儿的比例明显高于健康孕妇所生的LGA婴儿的比例。然而,传统的LGA诊断方法具有局限性。因此,本研究旨在建立一个预测模型,该模型可以有效地识别有分娩LGA婴儿风险的GDM女性.
目的：建立并验证GDM孕妇分娩LGA的列线图预测模型，并为LGA的有效预防和及时干预提供策略。
方法：通过执行以下步骤建立多变量预测模型。首先,通过单变量分析筛选GDM孕妇中与LGA风险相关的变量,P值<0.10。随后，最小绝对收缩和选择算子回归使用十个交叉验证进行拟合，并以λ1se为标准选择最优组合因子。最终的预测因子由多个反向逐步逻辑回归分析确定，其中只有独立变量与LGA风险相关，P值<0.05。最后,建立了风险预测模型，随后利用接受者工作特征曲线下面积进行了评估，校准曲线和决策曲线分析。
结果：使用多步筛选方法后，我们建立了一个预测模型。确定了分娩LGA婴儿的几个危险因素(P<0.01)，包括怀孕期间体重增加，奇偶校验，甘油三酯-葡萄糖指数，游离四碘甲状腺原氨酸水平，腹围,丙氨酸转氨酶-天冬氨酸转氨酶比率和24孕周体重。列线图的预测能力得到曲线下面积的支持（训练队列的0.703、0.709和0.699，验证队列，和测试队列，分别)。三个队列的校准曲线显示出良好的一致性。决策曲线表明，使用10％-60％的阈值来识别具有分娩LGA婴儿风险的GDM孕妇将导致积极的净收益。
结论：我们的列线图包含了容易获得的风险因素，促进对可能分娩LGA婴儿的GDM孕妇的个性化预测。
目的：建立并验证GDM孕妇分娩LGA的列线图预测模型，并为LGA的有效预防和及时干预提供策略。
方法：通过执行以下步骤建立多变量预测模型。首先,通过单变量分析筛选GDM孕妇中与LGA风险相关的变量,P值<0.10。随后，最小绝对收缩和选择算子回归使用十个交叉验证进行拟合，并以λ1se为标准选择最优组合因子。最终的预测因子由多个反向逐步逻辑回归分析确定，其中只有独立变量与LGA风险相关，P值<0.05。最后,建立了风险预测模型，随后利用接受者工作特征曲线下面积进行了评估，校准曲线和决策曲线分析。
结果：使用多步筛选方法后，我们建立了一个预测模型。确定了分娩LGA婴儿的几个危险因素(P<0.01)，包括怀孕期间体重增加，奇偶校验，甘油三酯-葡萄糖指数，游离四碘甲状腺原氨酸水平，腹围,丙氨酸转氨酶-天冬氨酸转氨酶比率和24孕周体重。列线图的预测能力得到曲线下面积的支持（训练队列的0.703、0.709和0.699，验证队列，和测试队列，分别)。三个队列的校准曲线显示出良好的一致性。决策曲线表明，使用10％-60％的阈值来识别具有分娩LGA婴儿风险的GDM孕妇将导致积极的净收益。
结论：我们的列线图包含了容易获得的风险因素，促进对可能分娩LGA婴儿的GDM孕妇的个性化预测。
