Central Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, China.
Department of Pathology, Shenzhen Longgang People's Hospital, Shenzhen, China.
Sci Prog. 2021 Oct;104(4):368504211052359. doi: 10.1177/00368504211052359.
To investigate factors associated with fetal fraction and to develop a new predictive method for low fetal fraction before noninvasive prenatal testing.
The study was a retrospective cohort analysis based on the results of noninvasive prenatal testing, complete blood count, thyroxin test, and Down's syndrome screening during the first or second trimester in 14,043 pregnant women. Random forests algorithm was applied to predict the low fetal fraction status (fetal fraction < 4%) through individual information and laboratory records. The performance of the model was evaluated and compared to predictions using maternal weight.
Of 14,043 cases, maternal weight, red blood cell, hemoglobin, and free T3 were significantly negatively correlated with fetal fraction while gestation age, free T4, pregnancy-associated plasma protein-A, alpha-fetoprotein, unconjugated estriol, and β-human chorionic gonadotropin were significantly positively correlated with fetal fraction. Compared to predictions using maternal weight as an isolated parameter, the model had a higher area under the curve of receiver operating characteristic and overall accuracy.
The comprehensive predictive method based on combined multiple factors was more effective than a single-factor model in low fetal fraction status prediction. This method can provide more pretest quality control for noninvasive prenatal testing.
探讨与胎儿分数相关的因素，并为非侵入性产前检测前的低胎儿分数开发一种新的预测方法。
本研究基于 14043 名孕妇在第一或第二孕期进行的非侵入性产前检测、全血细胞计数、甲状腺素检测和唐氏综合征筛查的结果，进行了回顾性队列分析。通过个体信息和实验室记录，应用随机森林算法预测低胎儿分数状态（胎儿分数<4%）。评估并比较了该模型与使用母体体重进行预测的性能。
在 14043 例中，母体体重、红细胞、血红蛋白和游离 T3 与胎儿分数显著负相关，而孕龄、游离 T4、妊娠相关血浆蛋白-A、甲胎蛋白、未结合雌三醇和β-人绒毛膜促性腺激素与胎儿分数显著正相关。与使用母体体重作为孤立参数进行预测相比，该模型的接受者操作特征曲线下面积和总准确性更高。
基于多个因素综合的综合预测方法在预测低胎儿分数状态方面比单因素模型更有效。该方法可为非侵入性产前检测提供更多的预测试质量控制。
