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孕10至14周期间颈部半透明厚度中位数的估算。

The estimation of median nuchal translucency values between 10 and 14 weeks of pregnancy.

作者信息

Bestwick Jonathan P, Huttly Wayne J, Wald Nicholas J

机构信息

Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ

Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ.

出版信息

J Med Screen. 2014 Jun;21(2):110-2. doi: 10.1177/0969141314536890.

Abstract

Nuchal translucency (NT) is a useful marker in antenatal screening for Down's syndrome in the late first trimester of pregnancy. NT measurements increase with increasing crown rump length (CRL) so multiple of the median (MoM) values are used to allow for this. Log-linear and log-quadratic regressions of NT in relation to CRL have previously been proposed to calculate MoM values. Using data on 288,079 women, these models were compared with a log-sigmoid regression. The log-linear regression overestimated the median NT above a CRL of 75 mm; for example, 1.9 mm versus 1.8 mm observed at 75-79 mm, and 2.0 mm versus 1.8 mm at 80-84 mm. The log-quadratic regression underestimated the median NT below a CRL of 45 mm at 1.03 mm versus 1.2 mm observed. The sigmoid regression provided the best overall fit to the data across the range of CRL values (40-84 mm) corresponding to gestational ages of 76 to 99 days. The differences between the three models are small. If a log-linear regression appears to be a poor fit using local data, a log-sigmoid regression could be considered.

摘要

颈部半透明厚度(NT)是孕早期晚期唐氏综合征产前筛查中的一个有用指标。NT测量值随顶臀长(CRL)增加而升高,因此采用中位数倍数(MoM)值来对此进行校正。此前有人提出用NT与CRL的对数线性回归和对数二次回归来计算MoM值。利用288,079名女性的数据,将这些模型与对数S形回归进行了比较。对数线性回归在CRL超过75 mm时高估了NT中位数;例如,在75 - 79 mm时观察值为1.8 mm,该回归模型得出的值为1.9 mm;在80 - 84 mm时观察值为1.8 mm,该回归模型得出的值为2.0 mm。对数二次回归在CRL低于45 mm时低估了NT中位数,观察值为1.2 mm,该回归模型得出的值为1.03 mm。S形回归在对应于孕76至99天的CRL值范围(40 - 84 mm)内对数据提供了总体最佳拟合。三种模型之间的差异较小。如果使用本地数据时对数线性回归似乎拟合不佳,可以考虑对数S形回归。

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