Fetal Medicine Research Institute, King's College Hospital, London, UK.
Institute of Health Research, University of Exeter, Exeter, UK.
Ultrasound Obstet Gynecol. 2021 Jun;57(6):917-924. doi: 10.1002/uog.23593. Epub 2021 May 5.
To develop further a new competing-risks model for the prediction of a small-for-gestational-age (SGA) neonate, by including second-trimester ultrasonographic estimated fetal weight (EFW).
This was a prospective observational study in 96 678 women with singleton pregnancy undergoing routine ultrasound examination at 19-24 weeks' gestation. All pregnancies had ultrasound biometry assessment, and EFW was calculated according to the Hadlock formula. We refitted in this large dataset a previously described competing-risks model for the joint distribution of gestational age (GA) at delivery and birth-weight Z-score, according to maternal demographic characteristics and medical history, to obtain the prior distribution. The continuous likelihood of the EFW was fitted conditionally to GA at delivery and birth-weight Z-score and modified the prior distribution, according to Bayes' theorem, to obtain individualized distributions for GA at delivery and birth-weight Z-score and therefore patient-specific risks for any cut-offs for GA at delivery and birth-weight Z-score. We assessed the discriminative ability of the model for predicting SGA with, without or independently of pre-eclampsia occurrence. A calibration study was carried out. Performance of screening was evaluated for SGA defined according to the Fetal Medicine Foundation birth-weight charts.
The distribution of EFW, conditional to both GA at delivery and birth-weight Z-score, was best described by a regression model. For earlier gestations, the association between EFW and birth weight was steeper. The prediction of SGA by maternal factors and EFW improved for increasing degree of prematurity and greater severity of smallness but not for coexistence of pre-eclampsia. Screening by maternal factors predicted 31%, 34% and 39% of SGA neonates with birth weight < 10 percentile delivered at ≥ 37, < 37 and < 30 weeks' gestation, respectively, at a 10% false-positive rate, and, after addition of EFW, these rates increased to 38%, 43% and 59%, respectively; the respective rates for birth weight < 3 percentile were 43%, 50% and 64%. The addition of EFW improved the calibration of the model.
In the competing-risks model for prediction of SGA, the performance of screening by maternal characteristics and medical history is improved by the addition of second-trimester EFW. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.
通过纳入中孕期超声估计胎儿体重（EFW），进一步开发一种预测小于胎龄儿（SGA）的新竞争风险模型。
这是一项前瞻性观察性研究，共纳入 96678 名单胎妊娠妇女，在妊娠 19-24 周时进行常规超声检查。所有妊娠均进行超声生物测量评估，并根据 Hadlock 公式计算 EFW。我们在这个大型数据集上重新拟合了之前描述的用于分娩时胎龄（GA）和出生体重 Z 评分联合分布的竞争风险模型，根据母亲的人口统计学特征和病史，获得先验分布。根据贝叶斯定理，EFW 的连续似然条件拟合至分娩时 GA 和出生体重 Z 评分，并修改先验分布，以获得分娩时 GA 和出生体重 Z 评分的个体化分布，从而获得任何分娩时 GA 和出生体重 Z 评分截断值的患者特定风险。我们评估了该模型在预测 SGA 时的判别能力，无论是否发生子痫前期。进行了校准研究。根据胎儿医学基金会的出生体重图表，评估了根据 SGA 定义进行筛查的性能。
EFW 的分布，条件是分娩时 GA 和出生体重 Z 评分，最好用回归模型来描述。对于更早的妊娠，EFW 与出生体重之间的关系更为陡峭。随着早产程度的增加和小胎龄程度的加重，母体因素和 EFW 对 SGA 的预测得到改善，但子痫前期的共存则没有。通过母体因素进行筛查，在假阳性率为 10%的情况下，预测在≥37、<37 和<30 周时出生体重<第 10 百分位数的 SGA 新生儿的比例分别为 31%、34%和 39%，在添加 EFW 后，这些比例分别增加至 38%、43%和 59%；出生体重<第 3 百分位的相应比例分别为 43%、50%和 64%。添加 EFW 提高了模型的校准能力。
在预测 SGA 的竞争风险模型中，通过添加中孕期 EFW，母体特征和病史的筛查性能得到改善。
