Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Women and Children Medical and Healthcare Center of Wuhan, Wuhan, Hubei, People's Republic of China.
Sci Rep. 2019 Jul 25;9(1):10834. doi: 10.1038/s41598-019-47056-0.
The study aims to develop new birth weight prediction models for different gestational age stages using 2-dimensional (2D) ultrasound measurements in a Chinese population. 2D ultrasound was examined in pregnant women with normal singleton within 3 days prior to delivery (28-42 weeks' gestation). A total of 19,310 fetuses were included in the study and randomly split into the training group and the validation group. Gestational age was divided into five stages: 28-30, 31-33, 34-36, 37-39 and 40-42 weeks. Multiple linear regression (MLR), fractional polynomial regression (FPR) and volume-based model (VM) were used to develop birth weight prediction model. New staged prediction models (VM for 28-36 weeks, MLR for 37-39 weeks, and FPR for 40-42 weeks) provided lower systematic errors and random errors than previously published models for each gestational age stage in the training group. The similar results were observed in the validation group. Compared to the previously published models, new staged models had the lowest aggregate systematic error (0.31%) and at least a 19.35% decrease; at least a 4.67% decrease for the root-mean-square error (RMSE). The prediction rates within 5% and 10% of birth weight for new staged models were higher than those for previously published models, which were 54.47% and 85.10%, respectively. New staged birth weight prediction models could improve the accuracy of birth weight estimation for different gestational age stages in a Chinese population.
本研究旨在利用二维（2D）超声测量值为中国人群开发不同孕龄阶段的新出生体重预测模型。对在分娩前 3 天内（28-42 周妊娠）正常单胎孕妇进行 2D 超声检查。共有 19310 例胎儿纳入本研究，并随机分为训练组和验证组。孕龄分为五个阶段：28-30 周、31-33 周、34-36 周、37-39 周和 40-42 周。采用多元线性回归（MLR）、分数多项式回归（FPR）和基于体积的模型（VM）建立出生体重预测模型。新的分期预测模型（28-36 周的 VM、37-39 周的 MLR 和 40-42 周的 FPR）在训练组的每个孕龄阶段提供的系统误差和随机误差均低于之前发表的模型。在验证组也观察到了类似的结果。与之前发表的模型相比，新的分期模型具有最低的综合系统误差（0.31%），至少降低了 19.35%；均方根误差（RMSE）至少降低了 4.67%。新的分期模型的预测值在出生体重的 5%和 10%以内的比例高于之前发表的模型，分别为 54.47%和 85.10%。新的分期出生体重预测模型可以提高中国人群不同孕龄阶段出生体重估计的准确性。
