Abstract：
OBJECTIVE: To evaluate the accuracy of combined models of maternal biophysical factors, ultrasound, and biochemical markers for predicting stillbirths.
METHODS: A retrospective cohort study of pregnant women undergoing first-trimester pre-eclampsia screening at 11-13 gestational weeks was conducted. Maternal characteristics and history, mean arterial pressure (MAP) measurement, uterine artery pulsatility index (UtA-PI) ultrasound, maternal ophthalmic peak ratio Doppler, and placental growth factor (PlGF) serum were collected during the visit. Stillbirth was classified as placental dysfunction-related when it occurred with pre-eclampsia or birth weight <10th percentile. Combined prediction models were developed from significant variables in stillbirths, placental dysfunction-related, and controls. We used the area under the receiver-operating-characteristics curve (AUC), sensitivity, and specificity based on a specific cutoff to evaluate the model\'s predictive performance by measuring the capacity to distinguish between stillbirths and live births.
RESULTS: There were 13 (0.79%) cases of stillbirth in 1643 women included in the analysis. The combination of maternal factors, MAP, UtA-PI, and PlGF, significantly contributed to the prediction of stillbirth. This model was a good predictor for all (including controls) types of stillbirth (AUC 0.879, 95% CI: 0.799-0.959, sensitivity of 99.3%, specificity of 38.5%), and an excellent predictor for placental dysfunction-related stillbirth (AUC 0.984, 95% CI: 0.960-1.000, sensitivity of 98.5, specificity of 85.7).
CONCLUSIONS: Screening at 11-13 weeks\' gestation by combining maternal factors, MAP, UtA-PI, and PlGF, can predict a high proportion of stillbirths. Our model has good accuracy for predicting stillbirths, predominantly placental dysfunction-related stillbirths.
METHODS: A retrospective cohort study of pregnant women undergoing first-trimester pre-eclampsia screening at 11-13 gestational weeks was conducted. Maternal characteristics and history, mean arterial pressure (MAP) measurement, uterine artery pulsatility index (UtA-PI) ultrasound, maternal ophthalmic peak ratio Doppler, and placental growth factor (PlGF) serum were collected during the visit. Stillbirth was classified as placental dysfunction-related when it occurred with pre-eclampsia or birth weight <10th percentile. Combined prediction models were developed from significant variables in stillbirths, placental dysfunction-related, and controls. We used the area under the receiver-operating-characteristics curve (AUC), sensitivity, and specificity based on a specific cutoff to evaluate the model\'s predictive performance by measuring the capacity to distinguish between stillbirths and live births.
RESULTS: There were 13 (0.79%) cases of stillbirth in 1643 women included in the analysis. The combination of maternal factors, MAP, UtA-PI, and PlGF, significantly contributed to the prediction of stillbirth. This model was a good predictor for all (including controls) types of stillbirth (AUC 0.879, 95% CI: 0.799-0.959, sensitivity of 99.3%, specificity of 38.5%), and an excellent predictor for placental dysfunction-related stillbirth (AUC 0.984, 95% CI: 0.960-1.000, sensitivity of 98.5, specificity of 85.7).
CONCLUSIONS: Screening at 11-13 weeks\' gestation by combining maternal factors, MAP, UtA-PI, and PlGF, can predict a high proportion of stillbirths. Our model has good accuracy for predicting stillbirths, predominantly placental dysfunction-related stillbirths.
摘要：
目的：评估母体生物物理因素组合模型的准确性，超声，和预测死胎的生化标志物。
方法：对在11-13孕周接受早孕期先兆子痫筛查的孕妇进行回顾性队列研究。产妇的特点和历史，平均动脉压（MAP）测量，子宫动脉搏动指数(UtA-PI)超声,产妇眼科峰值比率多普勒，随访期间收集胎盘生长因子(PlGF)血清。当发生先兆子痫或出生体重<10百分位数时，死胎被归类为胎盘功能障碍相关。组合预测模型是根据死产的重要变量开发的，胎盘功能障碍相关，和控制。我们使用了接受者工作特征曲线(AUC)下的面积，灵敏度,以及基于特定截止值的特异性，通过测量区分死产和活产的能力来评估模型的预测性能。
结果：分析中包括1643名妇女中13例（0.79％）死产。母性因素的结合，MAP,UtA-PI，和PlGF,显著有助于预测死产。该模型是所有（包括对照）死产类型的良好预测因子（AUC0.879，95％CI：0.799-0.959，敏感性99.3％，38.5％的特异性），和胎盘功能障碍相关死胎的良好预测指标（AUC0.984，95％CI：0.960-1.000，敏感性98.5，特异性85.7）。
结论：妊娠11-13周结合母体因素进行筛查，MAP,UtA-PI，和PlGF,可以预测死产的比例很高。我们的模型对预测死胎有很好的准确性，主要是胎盘功能障碍相关的死胎。
方法：对在11-13孕周接受早孕期先兆子痫筛查的孕妇进行回顾性队列研究。产妇的特点和历史，平均动脉压（MAP）测量，子宫动脉搏动指数(UtA-PI)超声,产妇眼科峰值比率多普勒，随访期间收集胎盘生长因子(PlGF)血清。当发生先兆子痫或出生体重<10百分位数时，死胎被归类为胎盘功能障碍相关。组合预测模型是根据死产的重要变量开发的，胎盘功能障碍相关，和控制。我们使用了接受者工作特征曲线(AUC)下的面积，灵敏度,以及基于特定截止值的特异性，通过测量区分死产和活产的能力来评估模型的预测性能。
结果：分析中包括1643名妇女中13例（0.79％）死产。母性因素的结合，MAP,UtA-PI，和PlGF,显著有助于预测死产。该模型是所有（包括对照）死产类型的良好预测因子（AUC0.879，95％CI：0.799-0.959，敏感性99.3％，38.5％的特异性），和胎盘功能障碍相关死胎的良好预测指标（AUC0.984，95％CI：0.960-1.000，敏感性98.5，特异性85.7）。
结论：妊娠11-13周结合母体因素进行筛查，MAP,UtA-PI，和PlGF,可以预测死产的比例很高。我们的模型对预测死胎有很好的准确性，主要是胎盘功能障碍相关的死胎。
