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基于群组轨迹建模的方法在 LIFECODES 胎儿生长研究中小龄小样儿出生中定义胎儿生长表型的应用。

An application of group-based trajectory modeling to define fetal growth phenotypes among small-for-gestational-age births in the LIFECODES Fetal Growth Study.

机构信息

Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, NC.

Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

出版信息

Am J Obstet Gynecol. 2023 Mar;228(3):334.e1-334.e21. doi: 10.1016/j.ajog.2022.08.041. Epub 2022 Aug 24.

Abstract

BACKGROUND

Reductions in fetal growth are associated with adverse outcomes at birth and later in life. However, identifying fetuses with pathologically small growth remains challenging. Definitions of small-for-gestational age are often used as a proxy to identify those experiencing pathologic growth (ie, fetal growth restriction). However, this approach is subject to limitation as most newborns labeled small-for-gestational age are constitutionally, not pathologically, small. Incorporating repeated ultrasound measures to examine fetal growth trajectories may help distinguish pathologic deviations in growth from normal variability, beyond a simple definition of small-for-gestational age.

OBJECTIVE

This study aimed to characterize phenotypes of growth using ultrasound trajectories of fetal growth among small-for-gestational-age births.

STUDY DESIGN

This study identified and described trajectories of fetal growth among small-for-gestational-age births (<10th percentile weight for gestational age; n=245) in the LIFECODES Fetal Growth Study using univariate and multivariate trajectory modeling approaches. Available ultrasound measures of fetal growth (estimated fetal weight, head circumference, abdominal circumference, and femur length) from health records were abstracted. First, univariate group-based trajectory modeling was used to define trajectories of estimated fetal weight z scores during gestation. Second, group-based multi-trajectory modeling was used to identify trajectories based on concurrent measures of head circumference, abdominal circumference, and femur length z scores. Last, how these trajectories were related to patient demographics, pregnancy characteristics, and birth outcomes compared with those observed among appropriate-for-gestational-age controls was described.

RESULTS

Of note, 3 univariate trajectories of estimated fetal weight and 4 multivariate trajectories of fetal growth among small-for-gestational-age births were identified. In our univariate approach, infants with the smallest estimated fetal weight trajectory throughout pregnancy had poorer outcomes, including the highest risk of neonatal intensive care unit admission. The remaining univariate trajectory groups did not have an elevated risk of adverse birth outcomes relative to appropriate-for-gestational-age controls. In our multivariate approach, 2 groups at increased or moderately increased risk of neonatal intensive care unit admission were identified, including infants that remained extremely small for all parameters throughout pregnancy and those who had disproportionately smaller femur length and abdominal circumference compared with head circumference. The remaining multivariate trajectory groups did not have an elevated risk of adverse birth outcome relative to appropriate-for-gestational-age controls.

CONCLUSION

Latent class group-based trajectory modeling applied to ultrasound measures of fetal growth may help distinguish pathologic vs constitutional growth profiles among newborns born small-for-gestational age. Although trajectories cannot be fully characterized until delivery, limiting the direct clinical application of these methods, they may still contribute to the development of approaches for separating growth restriction from constitutional smallness.

摘要

背景

胎儿生长受限与出生时和生命后期的不良结局有关。然而,识别病理性生长受限的胎儿仍然具有挑战性。小胎龄儿的定义通常被用作识别经历病理性生长(即胎儿生长受限)的替代指标。然而,这种方法存在局限性,因为大多数被标记为小胎龄儿的新生儿在体型上是正常的,而不是病理性的。结合重复超声测量来检查胎儿生长轨迹可能有助于区分病理性生长偏差与正常变异性,而不仅仅是小胎龄儿的简单定义。

目的

本研究旨在通过超声测量胎儿生长轨迹来描述小胎龄儿出生时的生长表型。

研究设计

本研究使用 LIFECODES 胎儿生长研究中的小胎龄儿(<10 百分位体重的胎龄;n=245)的健康记录中提取的胎儿生长的单变量和多变量轨迹建模方法,识别和描述小胎龄儿的生长轨迹。首先,使用单变量基于群组的轨迹建模来定义妊娠期估计胎儿体重 z 分数的轨迹。其次,使用基于群组的多轨迹建模来根据头围、腹围和股骨长 z 分数的同时测量来识别轨迹。最后,描述这些轨迹与合适胎龄儿对照组相比,与患者人口统计学、妊娠特征和分娩结局的关系。

结果

值得注意的是,确定了 3 种小胎龄儿的估计胎儿体重的单变量轨迹和 4 种小胎龄儿的多变量生长轨迹。在我们的单变量方法中,整个孕期估计胎儿体重最小的轨迹的婴儿结局最差,包括新生儿重症监护病房(NICU)入院的风险最高。其余的单变量轨迹组与合适胎龄儿对照组相比,没有增加不良分娩结局的风险。在我们的多变量方法中,确定了 2 个具有较高或中度增加 NICU 入院风险的组,包括整个孕期一直非常小的胎儿和股骨长和腹围与头围相比明显较小的胎儿。其余的多变量轨迹组与合适胎龄儿对照组相比,没有增加不良分娩结局的风险。

结论

应用于胎儿生长超声测量的潜在类别基于群组的轨迹建模可以帮助区分小胎龄儿出生时的病理性与正常性生长特征。尽管直到分娩才能完全描述轨迹,但限制了这些方法的直接临床应用,它们仍可能有助于制定区分生长受限与正常小的方法。

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