Fetal Medicine Research Institute, King's College Hospital, London, United Kingdom.
Institute of Health Research, University of Exeter, Exeter, United Kingdom.
Am J Obstet Gynecol. 2021 Nov;225(5):530.e1-530.e19. doi: 10.1016/j.ajog.2021.04.247. Epub 2021 Apr 24.
Antenatal identification of women at high risk to deliver small-for-gestational-age neonates may improve the management of the condition. The traditional but ineffective methods for small-for-gestational-age screening are the use of risk scoring systems based on maternal demographic characteristics and medical history and the measurement of the symphysial-fundal height. Another approach is to use logistic regression models that have higher performance and provide patient-specific risks for different prespecified cutoffs of birthweight percentile and gestational age at delivery. However, such models have led to an arbitrary dichotomization of the condition; different models for different small-for-gestational-age definitions are required and adding new biomarkers or examining other cutoffs requires refitting of the whole model. An alternative approach for the prediction of small-for-gestational-age neonates is to consider small for gestational age as a spectrum disorder whose severity is continuously reflected in both the gestational age at delivery and z score in birthweight for gestational age.
This study aimed to develop a new competing risks model for the prediction of small-for-gestational-age neonates based on a combination of maternal demographic characteristics and medical history with sonographic estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure at 19 to 24 weeks' gestation.
This was a prospective observational study of 96,678 women with singleton pregnancies undergoing routine ultrasound examination at 19 to 24 weeks' gestation, which included recording of estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure. The competing risks model for small for gestational age is based on a previous joint distribution of gestational age at delivery and birthweight z score, according to maternal demographic characteristics and medical history. The likelihoods of the estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure were fitted conditionally to both gestational age at delivery and birthweight z score and modified the previous distribution, according to the Bayes theorem, to obtain an individualized posterior distribution for gestational age at delivery and birthweight z score and therefore patient-specific risks for any desired cutoffs for birthweight z score and gestational age at delivery. The model was internally validated by randomly dividing the data into a training data set, to obtain the parameters of the model, and a test data set, to evaluate the model. The discrimination and calibration of the model were also examined.
The estimated fetal weight was described using a regression model with an interaction term between gestational age at delivery and birthweight z score. Folded plane regression models were fitted for uterine artery pulsatility index and mean arterial pressure. The prediction of small for gestational age by maternal factors was improved by adding biomarkers for increasing degree of prematurity, higher severity of smallness, and coexistence of preeclampsia. Screening by maternal factors with estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure, predicted 41%, 56%, and 70% of small-for-gestational-age neonates with birthweights of <10th percentile delivered at ≥37, <37, and <32 weeks' gestation, at a 10% false-positive rate. The respective rates for a birthweight of <3rd percentile were 47%, 65%, and 77%. The rates in the presence of preeclampsia were 41%, 72%, and 91% for small-for-gestational-age neonates with birthweights of <10th percentile and 50%, 75%, and 92% for small-for-gestational-age neonates with birthweights of <3rd percentile. Overall, the model was well calibrated. The detection rates and calibration indices were similar in the training and test data sets, demonstrating the internal validity of the model.
The performance of screening for small-for-gestational-age neonates by a competing risks model that combines maternal factors with estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure was superior to that of screening by maternal characteristics and medical history alone.
对可能分娩小于胎龄儿的高危孕妇进行产前筛查，可以改善这种情况的管理。传统的但无效的小胎龄儿筛查方法是使用基于产妇人口统计学特征和病史的风险评分系统以及测量耻骨联合宫底高度。另一种方法是使用逻辑回归模型，这些模型具有更高的性能，并针对不同预定的出生体重百分位数和分娩时的孕龄提供特定于患者的风险。然而，这种模型导致了对该情况的任意二分法；需要针对不同的小胎龄儿定义制定不同的模型，并且添加新的生物标志物或检查其他截止值需要重新拟合整个模型。预测小于胎龄儿的另一种方法是将小于胎龄儿视为一种连续反映在分娩时的孕龄和出生体重与胎龄的 Z 分数中的频谱障碍。
本研究旨在开发一种新的竞争风险模型，用于预测小于胎龄儿，该模型基于产妇人口统计学特征和病史与超声估计胎儿体重、子宫动脉搏动指数和 19 至 24 周妊娠时的平均动脉压相结合。
这是一项前瞻性观察性研究，共纳入 96678 名单胎妊娠妇女，在 19 至 24 周妊娠时进行常规超声检查，包括记录估计胎儿体重、子宫动脉搏动指数和平均动脉压。基于产妇人口统计学特征和病史，小胎龄儿的竞争风险模型是基于分娩时的孕龄和出生体重 Z 分数的联合分布。根据Bayes 定理，将估计胎儿体重、子宫动脉搏动指数和平均动脉压的可能性条件拟合到分娩时的孕龄和出生体重 Z 分数上，并修改之前的分布，以获得分娩时的孕龄和出生体重 Z 分数的个体化后验分布，从而获得任何所需的出生体重 Z 分数和分娩时的孕龄的特定于患者的风险。通过将数据随机分为训练数据集和测试数据集，对模型进行内部验证，以获得模型的参数，并评估模型。还检查了模型的区分度和校准度。
使用包含分娩时的孕龄和出生体重 Z 分数交互项的回归模型描述估计胎儿体重。为子宫动脉搏动指数和平均动脉压拟合了折叠平面回归模型。通过添加生物标志物来增加早产的严重程度、增加小胎龄的严重程度和同时存在子痫前期，可以改善产妇因素对小胎龄儿的预测。使用产妇因素、估计胎儿体重、子宫动脉搏动指数和平均动脉压进行筛查，可以预测出生体重<10%分位数且分娩时孕龄≥37 周、<37 周和<32 周的小于胎龄儿的 41%、56%和 70%，假阳性率为 10%。对于出生体重<3%分位数的相应比率分别为 47%、65%和 77%。在子痫前期存在的情况下，出生体重<10%分位数的小于胎龄儿的检出率分别为 41%、72%和 91%，出生体重<3%分位数的小于胎龄儿的检出率分别为 50%、75%和 92%。总的来说，该模型校准良好。在训练数据集中和测试数据集中，该模型的检测率和校准指数相似，表明该模型具有内部有效性。
使用将产妇因素与估计胎儿体重、子宫动脉搏动指数和平均动脉压相结合的竞争风险模型进行小于胎龄儿的筛查，其性能优于仅使用产妇特征和病史进行筛查。
