PDF(Pubmed)
Abstract：
OBJECTIVE: To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.
METHODS: To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.
RESULTS: An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).
METHODS: To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.
RESULTS: An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).
摘要：
目的：建立并验证基于孕早期电子病历的妊娠期糖尿病（GDM）早期风险预测模型。
方法：为了开发和验证GDM预测模型，本回顾性研究使用了两个数据集.其中包括来自荷兰马西玛医疗中心（MMC）的14015名孕妇的数据。另一个来自开源数据库nuMoM2b，其中包括10,038名未分娩孕妇的数据，收集在美国。广泛使用的孕产妇人口统计学和临床危险因素被考虑用于建模。从MMC数据的子集训练基于弹性净逻辑回归的GDM预测模型。对剩余的MMC数据进行内部验证以评估模型性能。对于外部验证，预测模型在nuMoM2b数据集的外部测试集上进行了测试。
结果：在MMC测试数据上早期预测GDM的接收器工作特征曲线（AUC）下面积为0.81，与以前研究报告的性能相当。虽然在外部nuMoM2b测试数据上测试基于MMC的模型时，性能显着降低至AUC为0.69，接近仅在nuMoM2b数据集上训练和测试的性能(AUC=0.70)。
方法：为了开发和验证GDM预测模型，本回顾性研究使用了两个数据集.其中包括来自荷兰马西玛医疗中心（MMC）的14015名孕妇的数据。另一个来自开源数据库nuMoM2b，其中包括10,038名未分娩孕妇的数据，收集在美国。广泛使用的孕产妇人口统计学和临床危险因素被考虑用于建模。从MMC数据的子集训练基于弹性净逻辑回归的GDM预测模型。对剩余的MMC数据进行内部验证以评估模型性能。对于外部验证，预测模型在nuMoM2b数据集的外部测试集上进行了测试。
结果：在MMC测试数据上早期预测GDM的接收器工作特征曲线（AUC）下面积为0.81，与以前研究报告的性能相当。虽然在外部nuMoM2b测试数据上测试基于MMC的模型时，性能显着降低至AUC为0.69，接近仅在nuMoM2b数据集上训练和测试的性能(AUC=0.70)。
